From 77f7bab2c6f9ee5a78efc469f6c6ef2ca87a7d1c Mon Sep 17 00:00:00 2001 From: Martin van Rongen Date: Wed, 23 Oct 2024 16:20:57 +0100 Subject: [PATCH] 2310 fixes #33 --- .../courseformat/_extension 2.yml | 10 - .../courseformat/_extension 3.yml | 10 - .../courseformat/_extension 4.yml | 12 - .../courseformat/_extension.yml | 7 +- .../courseformat/callout_exercise.lua | 129 + .../courseformat/citation_cff.lua | 159 + .../cambiotraining/courseformat/footer 2.html | 36 - .../img/university-of-cambridge-favicon 2.ico | Bin 811 -> 0 bytes .../img/university-of-cambridge-favicon 3.ico | Bin 811 -> 0 bytes .../img/university-of-cambridge-favicon 4.ico | Bin 811 -> 0 bytes .../img/university_crest_reversed 2.png | Bin 214457 -> 0 bytes .../cambiotraining/courseformat/theme 2.scss | 65 - .../cambiotraining/courseformat/theme.scss | 43 +- .../fontawesome/_extension 2.yml | 6 - .../fontawesome/_extension 3.yml | 6 - .../fontawesome/_extension 4.yml | 6 - .../fontawesome/assets/css/all 2.css | 7831 ----------------- .../fontawesome/assets/css/all 3.css | 7831 ----------------- .../fontawesome/assets/css/all 4.css | 7831 ----------------- .../assets/css/latex-fontsize 2.css | 30 - .../assets/css/latex-fontsize 3.css | 30 - .../assets/css/latex-fontsize 4.css | 30 - .../assets/webfonts/fa-brands-400 2.ttf | Bin 181852 -> 0 bytes .../assets/webfonts/fa-brands-400 2.woff2 | Bin 105536 -> 0 bytes .../assets/webfonts/fa-regular-400 2.ttf | Bin 60520 -> 0 bytes .../assets/webfonts/fa-regular-400 2.woff2 | Bin 23940 -> 0 bytes .../assets/webfonts/fa-regular-400 3.woff2 | Bin 23940 -> 0 bytes .../assets/webfonts/fa-solid-900 2.ttf | Bin 388460 -> 0 bytes .../assets/webfonts/fa-solid-900 2.woff2 | Bin 154228 -> 0 bytes .../assets/webfonts/fa-solid-900 3.ttf | Bin 388460 -> 0 bytes .../assets/webfonts/fa-solid-900 4.ttf | Bin 388460 -> 0 bytes .../assets/webfonts/fa-v4compatibility 2.ttf | Bin 10556 -> 0 bytes .../webfonts/fa-v4compatibility 2.woff2 | Bin 4960 -> 0 bytes .../fontawesome/fontawesome 2.lua | 77 - .../fontawesome/fontawesome 3.lua | 77 - .../fontawesome/fontawesome 4.lua | 77 - .../execute-results/html.json | 5 +- .../figure-html/unnamed-chunk-12-1.png | Bin 17344 -> 17122 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 17029 -> 16725 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 40115 -> 40484 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 170602 -> 170883 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 146443 -> 146593 bytes .../figure-html/unnamed-chunk-29-1.png | Bin 18280 -> 17825 bytes .../figure-html/unnamed-chunk-29-2.png | Bin 17010 -> 17280 bytes .../figure-html/unnamed-chunk-35-1.png | Bin 19858 -> 19453 bytes .../figure-html/unnamed-chunk-36-3.png | Bin 23119 -> 23249 bytes .../figure-html/unnamed-chunk-40-1.png | Bin 45184 -> 45399 bytes .../execute-results/html.json | 5 +- .../figure-html/unnamed-chunk-13-1.png | Bin 27294 -> 26028 bytes .../figure-html/unnamed-chunk-14-3.png | Bin 156771 -> 160063 bytes .../figure-html/unnamed-chunk-22-1.png | Bin 47019 -> 45312 bytes .../figure-html/unnamed-chunk-36-1.png | Bin 18767 -> 18873 bytes .../figure-html/unnamed-chunk-37-3.png | Bin 91936 -> 94382 bytes .../figure-html/unnamed-chunk-41-1.png | Bin 38249 -> 39670 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 59963 -> 59617 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 40564 -> 38792 bytes .../execute-results/html.json | 5 +- .../figure-html/unnamed-chunk-13-1.png | Bin 20844 -> 20441 bytes .../figure-html/unnamed-chunk-30-1.png | Bin 55436 -> 54072 bytes .../figure-html/unnamed-chunk-35-1.png | Bin 21881 -> 21467 bytes .../figure-html/unnamed-chunk-39-1.png | Bin 53042 -> 52085 bytes _quarto.yml | 1 + materials/cs1_practical_one-sample.qmd | 67 +- .../cs1_practical_two-samples-paired.qmd | 26 +- materials/cs1_practical_two-samples.qmd | 50 +- 65 files changed, 405 insertions(+), 24057 deletions(-) delete mode 100644 _extensions/cambiotraining/courseformat/_extension 2.yml delete mode 100644 _extensions/cambiotraining/courseformat/_extension 3.yml delete mode 100644 _extensions/cambiotraining/courseformat/_extension 4.yml create mode 100644 _extensions/cambiotraining/courseformat/callout_exercise.lua create mode 100644 _extensions/cambiotraining/courseformat/citation_cff.lua delete mode 100644 _extensions/cambiotraining/courseformat/footer 2.html delete mode 100644 _extensions/cambiotraining/courseformat/img/university-of-cambridge-favicon 2.ico delete mode 100644 _extensions/cambiotraining/courseformat/img/university-of-cambridge-favicon 3.ico delete mode 100644 _extensions/cambiotraining/courseformat/img/university-of-cambridge-favicon 4.ico delete mode 100644 _extensions/cambiotraining/courseformat/img/university_crest_reversed 2.png delete mode 100644 _extensions/cambiotraining/courseformat/theme 2.scss delete mode 100644 _extensions/cambiotraining/fontawesome/_extension 2.yml delete mode 100644 _extensions/cambiotraining/fontawesome/_extension 3.yml delete mode 100644 _extensions/cambiotraining/fontawesome/_extension 4.yml delete mode 100644 _extensions/cambiotraining/fontawesome/assets/css/all 2.css delete mode 100644 _extensions/cambiotraining/fontawesome/assets/css/all 3.css delete mode 100644 _extensions/cambiotraining/fontawesome/assets/css/all 4.css delete mode 100644 _extensions/cambiotraining/fontawesome/assets/css/latex-fontsize 2.css delete mode 100644 _extensions/cambiotraining/fontawesome/assets/css/latex-fontsize 3.css delete mode 100644 _extensions/cambiotraining/fontawesome/assets/css/latex-fontsize 4.css delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-brands-400 2.ttf delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-brands-400 2.woff2 delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-regular-400 2.ttf delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-regular-400 2.woff2 delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-regular-400 3.woff2 delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-solid-900 2.ttf delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-solid-900 2.woff2 delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-solid-900 3.ttf delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-solid-900 4.ttf delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-v4compatibility 2.ttf delete mode 100644 _extensions/cambiotraining/fontawesome/assets/webfonts/fa-v4compatibility 2.woff2 delete mode 100644 _extensions/cambiotraining/fontawesome/fontawesome 2.lua delete mode 100644 _extensions/cambiotraining/fontawesome/fontawesome 3.lua delete mode 100644 _extensions/cambiotraining/fontawesome/fontawesome 4.lua diff --git a/_extensions/cambiotraining/courseformat/_extension 2.yml b/_extensions/cambiotraining/courseformat/_extension 2.yml deleted file mode 100644 index f33afe2..0000000 --- a/_extensions/cambiotraining/courseformat/_extension 2.yml +++ /dev/null @@ -1,10 +0,0 @@ -title: Course Page Format -author: Cambridge Informatics Training -version: 1.0.2 -contributes: - formats: - html: - theme: [default, theme.scss] - toc: true - code-link: true - code-copy: true \ No newline at end of file diff --git a/_extensions/cambiotraining/courseformat/_extension 3.yml b/_extensions/cambiotraining/courseformat/_extension 3.yml deleted file mode 100644 index 8057f08..0000000 --- a/_extensions/cambiotraining/courseformat/_extension 3.yml +++ /dev/null @@ -1,10 +0,0 @@ -title: Course Page Format -author: Cambridge Informatics Training -version: 1.0.3 -contributes: - formats: - html: - theme: [default, theme.scss] - toc: true - code-link: true - code-copy: true \ No newline at end of file diff --git a/_extensions/cambiotraining/courseformat/_extension 4.yml b/_extensions/cambiotraining/courseformat/_extension 4.yml deleted file mode 100644 index c05fea7..0000000 --- a/_extensions/cambiotraining/courseformat/_extension 4.yml +++ /dev/null @@ -1,12 +0,0 @@ -title: Course Page Format -author: Cambridge Informatics Training -version: 1.0.4 -contributes: - formats: - html: - theme: [default, theme.scss] - toc: true - number-sections: true - number-depth: 3 - code-link: true - code-copy: true \ No newline at end of file diff --git a/_extensions/cambiotraining/courseformat/_extension.yml b/_extensions/cambiotraining/courseformat/_extension.yml index 8492fbb..5600a71 100644 --- a/_extensions/cambiotraining/courseformat/_extension.yml +++ b/_extensions/cambiotraining/courseformat/_extension.yml @@ -1,6 +1,6 @@ title: Course Page Format author: Cambridge Informatics Training -version: 1.0.5 +version: 1.2.1 contributes: formats: html: @@ -11,4 +11,7 @@ contributes: code-link: true code-copy: true shortcodes: - - star_levels.lua \ No newline at end of file + - star_levels.lua + - citation_cff.lua + filters: + - callout_exercise.lua \ No newline at end of file diff --git a/_extensions/cambiotraining/courseformat/callout_exercise.lua b/_extensions/cambiotraining/courseformat/callout_exercise.lua new file mode 100644 index 0000000..60d97ec --- /dev/null +++ b/_extensions/cambiotraining/courseformat/callout_exercise.lua @@ -0,0 +1,129 @@ +local number_exercises = true +local hide_answers = false +local exercises = {} -- Table to store exercise IDs and titles +local exercise_counter = 0 -- Global counter for exercises +local seen_ids = {} -- Table to track seen IDs and check for duplicates + +-- function to throw a yellow warning +function warn(message) + local yellow = "\27[33m" + local reset = "\27[0m" + io.stderr:write(yellow .. "WARN: " .. message .. reset .. "\n") +end + +-- Meta function to read yml metadata +function Meta(meta) + if meta["exercises"] and meta["exercises"]["number"] == true then + number_exercises = true + end + + if meta["exercises"] and meta["exercises"]["hide-answers"] == true then + hide_answers = true + end +end + +-- Div function to process exercises and answers +function Div(div) + -- Process callout-exercise divs + if div.classes:includes("callout-exercise") then + exercise_counter = exercise_counter + 1 + local exercise_number = "Exercise " .. exercise_counter -- Always generate the number + + -- Store exercise ID and title if it has an ID starting with "ex-" + if div.identifier and div.identifier:match("^ex%-") then + -- Check for duplicate IDs + if seen_ids[div.identifier] then + warn("Duplicate exercise ID found: #" .. div.identifier) + end + + seen_ids[div.identifier] = true + exercises[div.identifier] = exercise_number + end + + -- Process the header within the exercise + if div.content[1] ~= nil and div.content[1].t == "Header" then + local title = pandoc.utils.stringify(div.content[1]) + div.content:remove(1) -- Remove the original header from content + local final_title = number_exercises and exercise_number .. " - " .. title or title + return quarto.Callout({ + type = "exercise", + content = { div }, + title = final_title, + icon = false, + collapse = false + }) + else + -- If no header, use just the exercise number (if numbering is on) + local final_title = number_exercises and exercise_number or nil + return quarto.Callout({ + type = "exercise", + content = { div }, + title = final_title, + icon = false, + collapse = false + }) + end + end + + -- Process callout-answer divs + if div.classes:includes("callout-answer") then + if (hide_answers and div.attributes["hide"] ~= "false") or div.attributes["hide"] == "true" then + return pandoc.RawBlock('html', '') + else + return quarto.Callout({ + type = "answer", + content = { div }, + title = "Answer", + icon = false, + collapse = true + }) + end + end + + -- Process callout-hint divs + if div.classes:includes("callout-hint") then + return quarto.Callout({ + type = "hint", + content = { div }, + title = "Hint", + icon = false, + collapse = true + }) + end +end + +-- Replace cross-references in Pandoc elements +function Pandoc(doc) + -- Walk through the document and replace any Str element that matches @ex-* + local function replace_xref(elem) + if elem.t == "Str" and elem.text:match("^@ex%-") then + local id = elem.text:sub(2) -- Remove the "@" prefix + local exercise_title = exercises[id] + if exercise_title then + -- Return the custom HTML link + local link_html = string.format( + '%s', + id, + exercise_title + ) + return pandoc.RawInline('html', link_html) + else + -- If the ID is not found, throw a warning + warn("Undefined exercise cross-reference: @" .. id) + end + end + return elem + end + + -- Apply the replacement to all inline elements + return doc:walk({ + Str = replace_xref + }) +end + +-- Return the filter +return { + { Meta = Meta }, + { Div = Div }, + { Pandoc = Pandoc } +} diff --git a/_extensions/cambiotraining/courseformat/citation_cff.lua b/_extensions/cambiotraining/courseformat/citation_cff.lua new file mode 100644 index 0000000..581fdbb --- /dev/null +++ b/_extensions/cambiotraining/courseformat/citation_cff.lua @@ -0,0 +1,159 @@ +-- Function to parse the CFF file +function parse_cff(file_path) + -- Initialize tables to store extracted information + local data = { + title = "", + year = "", + month = "", + url = "", + authors = {} + } + + -- Function to trim whitespace from the beginning and end of a string + local function trim(s) + return (s:gsub("^%s*(.-)%s*$", "%1")) + end + + -- Read the YAML content from the file + local file = io.open(file_path, "r") + local yaml_content = file:read("*all") + file:close() + + -- Parse the YAML content line by line + local current_author = nil + local in_authors_section = false + for line in yaml_content:gmatch("[^\r\n]+") do + line = trim(line) + + -- Detect the start of the authors section + if line:match("^authors:") then + in_authors_section = true + end + + -- Parse the title + if line:match("^title:") then + data.title = trim(line:gsub("title:", "")) + end + + -- Parse the URL + if line:match("^url:") then + data.url = trim(line:gsub("url:", ""):gsub("'", "")) + end + + -- Parse the release date and extract year and month + if line:match("^date%-released:") then + local date = trim(line:gsub("date%-released:", ""):gsub("'", "")) + data.year, data.month = date:match("(%d%d%d%d)%-(%d%d)") + end + + -- Start processing authors once in the authors section + if in_authors_section then + -- Detect a new author block by a line starting with "-" + if line:match("^%- ") then + -- Add the previous author to the authors list if it's not nil + if current_author then + table.insert(data.authors, current_author) + end + -- Start a new author + current_author = {} + end + + -- Populate author fields + if line:match("given%-names:") then + current_author["given-names"] = trim(line:gsub("%-?%s*given%-names:", ""):gsub("'", "")) + elseif line:match("family%-names:") then + current_author["family-names"] = trim(line:gsub("%-?%s*family%-names:", ""):gsub("'", "")) + elseif line:match("affiliation:") then + current_author["affiliation"] = trim(line:gsub("%-?%s*affiliation:", ""):gsub("'", "")) + elseif line:match("website:") then + current_author["website"] = trim(line:gsub("%-?%s*website:", ""):gsub("'", "")) + elseif line:match("orcid:") then + current_author["orcid"] = trim(line:gsub("%-?%s*orcid:", ""):gsub("'", "")) + elseif line:match("alias:") then + current_author["alias"] = trim(line:gsub("%-?%s*alias:", ""):gsub("'", "")) + end + end + end + + -- Add the last author if it exists + if current_author then + table.insert(data.authors, current_author) + end + + -- Return the extracted data + return data +end + +return { + ["citation"] = function(args, kwargs) + -- Read the CFF file into a table + local cff_data = parse_cff("CITATION.cff") + + -- Extract relevant data from the parsed CFF + local title = cff_data.title + local year = cff_data.year + local month = cff_data.month + local url = cff_data.url + local authors = cff_data.authors + + -- Generate APA-style author list + local apa_authors = {} + for _, author in ipairs(authors) do + local name = author["family-names"] .. ", " .. string.sub(author["given-names"], 1, 1) .. "." + table.insert(apa_authors, name) + end + local apa_author_str = table.concat(apa_authors, ", ") + + -- Generate APA citation + local apa_citation = apa_author_str .. " (" .. year .. "). " .. title .. ". " .. url + + -- Generate BibTeX citation + local bibtex_authors = {} + for _, author in ipairs(authors) do + local name = author["family-names"] .. ", " .. author["given-names"] + table.insert(bibtex_authors, name) + end + local bibtex_author_str = table.concat(bibtex_authors, " and ") + + local bibtex_citation = string.format([[ +@misc{YourReferenceHere, + author = {%s}, + month = {%d}, + title = {%s}, + url = {%s}, + year = {%s} +}]], bibtex_author_str, tonumber(month), title, url, year) + + -- Generate the author information HTML + local author_info = "" + + -- Generate the final output in HTML format + local output = [[ +

You can cite these materials as:

+
+

]] .. apa_citation .. [[

+
+

Or in BibTeX format:

+
]] .. bibtex_citation .. [[
+

About the authors:

+]] .. author_info .. [[ +]] + + -- Return the output as raw HTML + return pandoc.RawBlock("html", output) + end +} diff --git a/_extensions/cambiotraining/courseformat/footer 2.html b/_extensions/cambiotraining/courseformat/footer 2.html deleted file mode 100644 index 670c0a9..0000000 --- a/_extensions/cambiotraining/courseformat/footer 2.html +++ /dev/null @@ -1,36 +0,0 @@ - - - - - - - -
-
-
- UoC Bioinformatics Training Facility -

Licensed CC BY 4.0

-
-
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z1?K(~aOE6j{QnBvP^6;XehP6+`YYdmYx!RSCNuvj-~(|~A5sP)`TyMdpI^|F{Fqou z6+Bbwzr*>T>;G2vm@VKr3SSfK#Ap0jHvD(9|CVv00=ix|k03RjkL1rt{J%v^2Cn6C Y-ttcA9zxbVaDb1Ril%b8qGkC10Ic|uSpWb4 diff --git a/_extensions/cambiotraining/courseformat/theme 2.scss b/_extensions/cambiotraining/courseformat/theme 2.scss deleted file mode 100644 index be5e622..0000000 --- a/_extensions/cambiotraining/courseformat/theme 2.scss +++ /dev/null @@ -1,65 +0,0 @@ -/*-- Importing fa icons --*/ -@import url("https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css"); - - -/*-- scss:defaults --*/ - -// UoC green colour scheme (from dark to light) -// #003a41 -// #0c5963 -// #106470 -// #28828A -$navbar-bg: #106470; -$navbar-fg: white; -$body-color: black; -$link-color: #003a41; - -//make font size a bit larger than default -$font-size-root: 1.2rem; -$toc-font-size: 0.9rem; -$sidebar-font-size: 0.9rem; -$sidebar-font-size-section: 0.9rem !default; - -//tweak callout colours to match our scheme -//based on https://coolors.co/28828a-106470-003a41-d62828-f77f00 -$callout-color-note: #C6C5B9; -$callout-color-tip: #28828A; -$callout-color-caution: #D62828; -$callout-color-warning: #D62828; -$callout-color-important: #28828A; - - -/*-- scss:rules --*/ - -// headings colour -title, h1, h2, h3, h4, h5, h6 { - color: #003a41; -} - -// active section heading - affects content navigation bar on the left -div.sidebar-item-container .active { - font-weight: bold; -} - -// active links - affects header and TOC navigation bar -.nav-link.active { - font-weight: bold; -} - -// active links on the header specifically -.navbar-dark .navbar-nav .nav-link.active { - font-weight: bold; - color: white; - text-decoration: underline; -} - -// links on the header upon hover or after visited -.navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-nav .nav-link:visited { - text-decoration: underline; - color: white; -} - -// font size on footer - make larger than default -.nav-footer, .nav-footer-left, .nav-footer-right, .nav-footer-center { - font-size: 1.2rem; -} \ No newline at end of file diff --git a/_extensions/cambiotraining/courseformat/theme.scss b/_extensions/cambiotraining/courseformat/theme.scss index 9c0da77..4f6aabe 100644 --- a/_extensions/cambiotraining/courseformat/theme.scss +++ b/_extensions/cambiotraining/courseformat/theme.scss @@ -41,13 +41,6 @@ div.sidebar-item-container .active { font-weight: bold; } -// part titles text bold -div.sidebar-item-container .text-start { - color: #003a41; - font-weight: bold; - font-size: 0.8rem; -} - // active links - affects header and TOC navigation bar .sidebar .nav-link.active { font-weight: bold; @@ -100,6 +93,40 @@ div.sidebar-item-container .text-start { } +// Callout styling // + +// /* General style for the callout toggle */ +// .callout-header .callout-toggle { +// display: inline-block; +// transition: transform 0.3s ease; /* Smooth animation */ +// font-size: 1.2rem; +// margin-right: 8px; +// } + +// /* Right-pointing triangle (initial state) */ +// .callout-header .callout-toggle::before { +// content: '\25B6'; /* Unicode for right-pointing triangle */ +// } + +// /* When the parent is not collapsed: rotate the triangle */ +// .callout-header:not(.collapsed) .callout-toggle::before { +// content: '\25BC'; /* Unicode for down-pointing triangle */ +// } + + +// this is here due to a styling bug where a callout within a callout adds an unecessary padding at the bottom by default +// in our case this would affect answer callout within an exercise callout +.callout.callout-titled .callout-body > :last-child:not(.sourceCode), .callout.callout-titled .callout-body > div > :last-child:not(.sourceCode) { + padding-bottom: 0; + margin-bottom: 0; +} + +// this is here so there's some padding for a callout within a callout +// again this mostly affects our answer callout within an exercise callout +.callout.callout-style-default .callout-body { + padding-bottom: 0.5rem; +} + // Exercise callout styling div.callout-exercise.callout { border-left-color: #c6c5b9; @@ -143,4 +170,4 @@ div.callout-hint.callout { div.callout-hint.callout-style-default > .callout-header { background-color: #f9f9f8; -} +} \ No newline at end of file diff --git a/_extensions/cambiotraining/fontawesome/_extension 2.yml b/_extensions/cambiotraining/fontawesome/_extension 2.yml deleted file mode 100644 index cdd0a27..0000000 --- a/_extensions/cambiotraining/fontawesome/_extension 2.yml +++ /dev/null @@ -1,6 +0,0 @@ -title: Font Awesome support -author: Carlos Scheidegger -version: 0.0.1 -contributes: - shortcodes: - - fontawesome.lua diff --git a/_extensions/cambiotraining/fontawesome/_extension 3.yml b/_extensions/cambiotraining/fontawesome/_extension 3.yml deleted file mode 100644 index cdd0a27..0000000 --- a/_extensions/cambiotraining/fontawesome/_extension 3.yml +++ /dev/null @@ -1,6 +0,0 @@ -title: Font Awesome support -author: Carlos Scheidegger -version: 0.0.1 -contributes: - shortcodes: - - fontawesome.lua diff --git a/_extensions/cambiotraining/fontawesome/_extension 4.yml b/_extensions/cambiotraining/fontawesome/_extension 4.yml deleted file mode 100644 index cdd0a27..0000000 --- a/_extensions/cambiotraining/fontawesome/_extension 4.yml +++ /dev/null @@ -1,6 +0,0 @@ -title: Font Awesome support -author: Carlos Scheidegger -version: 0.0.1 -contributes: - shortcodes: - - fontawesome.lua diff --git a/_extensions/cambiotraining/fontawesome/assets/css/all 2.css b/_extensions/cambiotraining/fontawesome/assets/css/all 2.css deleted file mode 100644 index 9c2adee..0000000 --- a/_extensions/cambiotraining/fontawesome/assets/css/all 2.css +++ /dev/null @@ -1,7831 +0,0 @@ -/*! - * Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com - * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) - * Copyright 2022 Fonticons, Inc. - */ -.fa { - font-family: var(--fa-style-family, "Font Awesome 6 Free"); - font-weight: var(--fa-style, 900); } - -.fa, -.fas, -.fa-solid, -.far, -.fa-regular, -.fal, -.fa-light, -.fat, -.fa-thin, -.fad, -.fa-duotone, -.fab, -.fa-brands { - -moz-osx-font-smoothing: grayscale; - -webkit-font-smoothing: antialiased; - display: var(--fa-display, inline-block); - font-style: normal; - font-variant: normal; - line-height: 1; - text-rendering: auto; } - -.fa-1x { - font-size: 1em; } - -.fa-2x { - font-size: 2em; } - -.fa-3x { - font-size: 3em; } - -.fa-4x { - font-size: 4em; } - -.fa-5x { - font-size: 5em; } - -.fa-6x { - font-size: 6em; } - -.fa-7x { - font-size: 7em; } - -.fa-8x { - font-size: 8em; } - -.fa-9x { - font-size: 9em; } - -.fa-10x { - font-size: 10em; } - -.fa-2xs { - font-size: 0.625em; - line-height: 0.1em; - vertical-align: 0.225em; } - -.fa-xs { - font-size: 0.75em; - line-height: 0.08333em; - vertical-align: 0.125em; } - -.fa-sm { - font-size: 0.875em; - line-height: 0.07143em; - vertical-align: 0.05357em; } - -.fa-lg { - font-size: 1.25em; - line-height: 0.05em; - vertical-align: -0.075em; } - -.fa-xl { - font-size: 1.5em; - line-height: 0.04167em; - vertical-align: -0.125em; } - -.fa-2xl { - font-size: 2em; - line-height: 0.03125em; - vertical-align: -0.1875em; } - -.fa-fw { - text-align: center; - width: 1.25em; } - -.fa-ul { - list-style-type: none; - margin-left: var(--fa-li-margin, 2.5em); - padding-left: 0; } - .fa-ul > li { - position: relative; } - -.fa-li { - left: calc(var(--fa-li-width, 2em) * -1); - position: absolute; - text-align: center; - width: var(--fa-li-width, 2em); - line-height: inherit; } - -.fa-border { - border-color: var(--fa-border-color, #eee); - border-radius: var(--fa-border-radius, 0.1em); - border-style: var(--fa-border-style, solid); - border-width: var(--fa-border-width, 0.08em); - padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } - -.fa-pull-left { - float: left; - margin-right: var(--fa-pull-margin, 0.3em); } - -.fa-pull-right { - float: right; - margin-left: var(--fa-pull-margin, 0.3em); } - -.fa-beat { - -webkit-animation-name: fa-beat; - animation-name: fa-beat; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); - animation-timing-function: var(--fa-animation-timing, ease-in-out); } - -.fa-bounce { - -webkit-animation-name: fa-bounce; - animation-name: fa-bounce; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } - -.fa-fade { - -webkit-animation-name: fa-fade; - animation-name: fa-fade; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } - -.fa-beat-fade { - -webkit-animation-name: fa-beat-fade; - animation-name: fa-beat-fade; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } - -.fa-flip { - -webkit-animation-name: fa-flip; - animation-name: fa-flip; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); - animation-timing-function: var(--fa-animation-timing, ease-in-out); } - -.fa-shake { - -webkit-animation-name: fa-shake; - animation-name: fa-shake; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 2s); - animation-duration: var(--fa-animation-duration, 2s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin-reverse { - --fa-animation-direction: reverse; } - -.fa-pulse, -.fa-spin-pulse { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); - animation-timing-function: var(--fa-animation-timing, steps(8)); } - -@media (prefers-reduced-motion: reduce) { - .fa-beat, - .fa-bounce, - .fa-fade, - .fa-beat-fade, - .fa-flip, - .fa-pulse, - .fa-shake, - .fa-spin, - .fa-spin-pulse { - -webkit-animation-delay: -1ms; - animation-delay: -1ms; - -webkit-animation-duration: 1ms; - animation-duration: 1ms; - -webkit-animation-iteration-count: 1; - animation-iteration-count: 1; - transition-delay: 0s; - transition-duration: 0s; } } - -@-webkit-keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@-webkit-keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@-webkit-keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@-webkit-keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@-webkit-keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@-webkit-keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@-webkit-keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -@keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -.fa-rotate-90 { - -webkit-transform: rotate(90deg); - transform: rotate(90deg); } - -.fa-rotate-180 { - -webkit-transform: rotate(180deg); - transform: rotate(180deg); } - -.fa-rotate-270 { - -webkit-transform: rotate(270deg); - transform: rotate(270deg); } - -.fa-flip-horizontal { - -webkit-transform: scale(-1, 1); - transform: scale(-1, 1); } - -.fa-flip-vertical { - -webkit-transform: scale(1, -1); - transform: scale(1, -1); } - -.fa-flip-both, -.fa-flip-horizontal.fa-flip-vertical { - -webkit-transform: scale(-1, -1); - transform: scale(-1, -1); } - -.fa-rotate-by { - -webkit-transform: rotate(var(--fa-rotate-angle, none)); - transform: rotate(var(--fa-rotate-angle, none)); } - -.fa-stack { - display: inline-block; - height: 2em; - line-height: 2em; - position: relative; - vertical-align: middle; - width: 2.5em; } - -.fa-stack-1x, -.fa-stack-2x { - left: 0; - position: absolute; - text-align: center; - width: 100%; - z-index: var(--fa-stack-z-index, auto); } - -.fa-stack-1x { - line-height: inherit; } - -.fa-stack-2x { - font-size: 2em; } - -.fa-inverse { - color: var(--fa-inverse, #fff); } - -/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen -readers do not read off random characters that represent icons */ -.fa-0::before { - content: "\30"; } - -.fa-1::before { - content: "\31"; } - -.fa-2::before { - content: "\32"; } - -.fa-3::before { - content: "\33"; } - -.fa-4::before { - content: "\34"; } - -.fa-5::before { - content: "\35"; } - -.fa-6::before { - content: "\36"; } - -.fa-7::before { - content: "\37"; } - -.fa-8::before { - content: "\38"; } - -.fa-9::before { - content: "\39"; } - -.fa-a::before { - content: "\41"; } - -.fa-address-book::before { - content: "\f2b9"; } - -.fa-contact-book::before { - content: "\f2b9"; } - -.fa-address-card::before { - content: "\f2bb"; } - -.fa-contact-card::before { - content: "\f2bb"; } - -.fa-vcard::before { - content: "\f2bb"; } - -.fa-align-center::before { - content: "\f037"; } - -.fa-align-justify::before { - content: "\f039"; } - -.fa-align-left::before { - content: "\f036"; } - -.fa-align-right::before { - content: "\f038"; } - -.fa-anchor::before { - content: "\f13d"; } - -.fa-anchor-circle-check::before { - content: "\e4aa"; } - -.fa-anchor-circle-exclamation::before { - content: "\e4ab"; } - -.fa-anchor-circle-xmark::before { - content: "\e4ac"; } - -.fa-anchor-lock::before { - content: "\e4ad"; } - -.fa-angle-down::before { - content: "\f107"; } - -.fa-angle-left::before { - content: "\f104"; } - -.fa-angle-right::before { - content: "\f105"; } - -.fa-angle-up::before { - content: "\f106"; } - -.fa-angles-down::before { - content: "\f103"; } - -.fa-angle-double-down::before { - content: "\f103"; } - -.fa-angles-left::before { - content: "\f100"; } - -.fa-angle-double-left::before { - content: "\f100"; } - -.fa-angles-right::before { - content: "\f101"; } - -.fa-angle-double-right::before { - content: "\f101"; } - -.fa-angles-up::before { - content: "\f102"; } - -.fa-angle-double-up::before { - content: "\f102"; } - -.fa-ankh::before { - content: "\f644"; } - -.fa-apple-whole::before { - content: "\f5d1"; } - -.fa-apple-alt::before { - content: "\f5d1"; } - -.fa-archway::before { - content: "\f557"; } - -.fa-arrow-down::before { - content: "\f063"; } - -.fa-arrow-down-1-9::before { - content: "\f162"; } - -.fa-sort-numeric-asc::before { - content: "\f162"; } - -.fa-sort-numeric-down::before { - content: "\f162"; } - -.fa-arrow-down-9-1::before { - content: "\f886"; } - -.fa-sort-numeric-desc::before { - content: "\f886"; } - -.fa-sort-numeric-down-alt::before { - content: "\f886"; } - -.fa-arrow-down-a-z::before { - content: "\f15d"; } - -.fa-sort-alpha-asc::before { - content: "\f15d"; } - -.fa-sort-alpha-down::before { - content: "\f15d"; } - -.fa-arrow-down-long::before { - content: "\f175"; } - -.fa-long-arrow-down::before { - content: "\f175"; } - -.fa-arrow-down-short-wide::before { - content: "\f884"; } - -.fa-sort-amount-desc::before { - content: "\f884"; } - -.fa-sort-amount-down-alt::before { - content: "\f884"; } - -.fa-arrow-down-up-across-line::before { - content: "\e4af"; } - -.fa-arrow-down-up-lock::before { - content: "\e4b0"; } - -.fa-arrow-down-wide-short::before { - content: "\f160"; } - -.fa-sort-amount-asc::before { - content: "\f160"; } - -.fa-sort-amount-down::before { - content: "\f160"; } - -.fa-arrow-down-z-a::before { - content: "\f881"; } - -.fa-sort-alpha-desc::before { - content: "\f881"; } - -.fa-sort-alpha-down-alt::before { - content: "\f881"; } - -.fa-arrow-left::before { - content: "\f060"; } - -.fa-arrow-left-long::before { - content: "\f177"; } - -.fa-long-arrow-left::before { - content: "\f177"; } - -.fa-arrow-pointer::before { - content: "\f245"; } - -.fa-mouse-pointer::before { - content: "\f245"; } - -.fa-arrow-right::before { - content: "\f061"; } - -.fa-arrow-right-arrow-left::before { - content: "\f0ec"; } - -.fa-exchange::before { - content: "\f0ec"; } - -.fa-arrow-right-from-bracket::before { - content: "\f08b"; } - -.fa-sign-out::before { - content: "\f08b"; } - -.fa-arrow-right-long::before { - content: "\f178"; } - -.fa-long-arrow-right::before { - content: "\f178"; } - -.fa-arrow-right-to-bracket::before { - content: "\f090"; } - -.fa-sign-in::before { - content: "\f090"; } - -.fa-arrow-right-to-city::before { - content: "\e4b3"; } - -.fa-arrow-rotate-left::before { - content: "\f0e2"; } - -.fa-arrow-left-rotate::before { - content: "\f0e2"; } - -.fa-arrow-rotate-back::before { - content: "\f0e2"; } - -.fa-arrow-rotate-backward::before { - content: "\f0e2"; } - -.fa-undo::before { - content: "\f0e2"; } - -.fa-arrow-rotate-right::before { - content: "\f01e"; } - -.fa-arrow-right-rotate::before { - content: "\f01e"; } - -.fa-arrow-rotate-forward::before { - content: "\f01e"; } - -.fa-redo::before { - content: "\f01e"; } - -.fa-arrow-trend-down::before { - content: "\e097"; } - -.fa-arrow-trend-up::before { - content: "\e098"; } - -.fa-arrow-turn-down::before { - content: "\f149"; } - -.fa-level-down::before { - content: "\f149"; } - -.fa-arrow-turn-up::before { - content: "\f148"; } - -.fa-level-up::before { - content: "\f148"; } - -.fa-arrow-up::before { - content: "\f062"; } - -.fa-arrow-up-1-9::before { - content: "\f163"; } - -.fa-sort-numeric-up::before { - content: "\f163"; } - -.fa-arrow-up-9-1::before { - content: "\f887"; } - -.fa-sort-numeric-up-alt::before { - content: "\f887"; } - -.fa-arrow-up-a-z::before { - content: "\f15e"; } - -.fa-sort-alpha-up::before { - content: "\f15e"; } - -.fa-arrow-up-from-bracket::before { - content: "\e09a"; } - -.fa-arrow-up-from-ground-water::before { - content: "\e4b5"; } - -.fa-arrow-up-from-water-pump::before { - content: "\e4b6"; } - -.fa-arrow-up-long::before { - content: "\f176"; } - -.fa-long-arrow-up::before { - content: "\f176"; } - -.fa-arrow-up-right-dots::before { - content: "\e4b7"; } - -.fa-arrow-up-right-from-square::before { - content: "\f08e"; } - -.fa-external-link::before { - content: "\f08e"; } - -.fa-arrow-up-short-wide::before { - content: "\f885"; } - -.fa-sort-amount-up-alt::before { - content: "\f885"; } - -.fa-arrow-up-wide-short::before { - content: "\f161"; } - -.fa-sort-amount-up::before { - content: "\f161"; } - -.fa-arrow-up-z-a::before { - content: "\f882"; } - -.fa-sort-alpha-up-alt::before { - content: "\f882"; } - -.fa-arrows-down-to-line::before { - content: "\e4b8"; } - -.fa-arrows-down-to-people::before { - content: "\e4b9"; } - -.fa-arrows-left-right::before { - content: "\f07e"; } - -.fa-arrows-h::before { - content: "\f07e"; } - -.fa-arrows-left-right-to-line::before { - content: "\e4ba"; } - -.fa-arrows-rotate::before { - content: "\f021"; } - -.fa-refresh::before { - content: "\f021"; } - -.fa-sync::before { - content: "\f021"; } - -.fa-arrows-spin::before { - content: "\e4bb"; } - -.fa-arrows-split-up-and-left::before { - content: "\e4bc"; } - -.fa-arrows-to-circle::before { - content: "\e4bd"; } - -.fa-arrows-to-dot::before { - content: "\e4be"; } - -.fa-arrows-to-eye::before { - content: "\e4bf"; } - -.fa-arrows-turn-right::before { - content: "\e4c0"; } - -.fa-arrows-turn-to-dots::before { - content: "\e4c1"; } - -.fa-arrows-up-down::before { - content: "\f07d"; } - -.fa-arrows-v::before { - content: "\f07d"; } - -.fa-arrows-up-down-left-right::before { - content: "\f047"; } - -.fa-arrows::before { - content: "\f047"; } - -.fa-arrows-up-to-line::before { - content: "\e4c2"; } - -.fa-asterisk::before { - content: "\2a"; } - -.fa-at::before { - content: "\40"; } - -.fa-atom::before { - content: "\f5d2"; } - -.fa-audio-description::before { - content: "\f29e"; } - -.fa-austral-sign::before { - content: "\e0a9"; } - -.fa-award::before { - content: "\f559"; } - -.fa-b::before { - content: "\42"; } - -.fa-baby::before { - content: "\f77c"; } - -.fa-baby-carriage::before { - content: "\f77d"; } - -.fa-carriage-baby::before { - content: "\f77d"; } - -.fa-backward::before { - content: "\f04a"; } - -.fa-backward-fast::before { - content: "\f049"; } - -.fa-fast-backward::before { - content: "\f049"; } - -.fa-backward-step::before { - content: "\f048"; } - -.fa-step-backward::before { - content: "\f048"; } - -.fa-bacon::before { - content: "\f7e5"; } - -.fa-bacteria::before { - content: "\e059"; } - -.fa-bacterium::before { - content: "\e05a"; } - -.fa-bag-shopping::before { - content: "\f290"; } - -.fa-shopping-bag::before { - content: "\f290"; } - -.fa-bahai::before { - content: "\f666"; } - -.fa-baht-sign::before { - content: "\e0ac"; } - -.fa-ban::before { - content: "\f05e"; } - -.fa-cancel::before { - content: "\f05e"; } - -.fa-ban-smoking::before { - content: "\f54d"; } - -.fa-smoking-ban::before { - content: "\f54d"; } - -.fa-bandage::before { - content: "\f462"; } - -.fa-band-aid::before { - content: "\f462"; } - -.fa-barcode::before { - content: "\f02a"; } - -.fa-bars::before { - content: "\f0c9"; } - -.fa-navicon::before { - content: "\f0c9"; } - -.fa-bars-progress::before { - content: "\f828"; } - -.fa-tasks-alt::before { - content: "\f828"; } - -.fa-bars-staggered::before { - content: "\f550"; } - -.fa-reorder::before { - content: "\f550"; } - -.fa-stream::before { - content: "\f550"; } - -.fa-baseball::before { - content: "\f433"; } - -.fa-baseball-ball::before { - content: "\f433"; } - -.fa-baseball-bat-ball::before { - content: "\f432"; } - -.fa-basket-shopping::before { - content: "\f291"; } - -.fa-shopping-basket::before { - content: "\f291"; } - -.fa-basketball::before { - content: "\f434"; } - -.fa-basketball-ball::before { - content: "\f434"; } - -.fa-bath::before { - content: "\f2cd"; } - -.fa-bathtub::before { - content: "\f2cd"; } - -.fa-battery-empty::before { - content: "\f244"; } - -.fa-battery-0::before { - content: "\f244"; } - -.fa-battery-full::before { - content: "\f240"; } - -.fa-battery::before { - content: "\f240"; } - -.fa-battery-5::before { - content: "\f240"; } - -.fa-battery-half::before { - content: "\f242"; } - -.fa-battery-3::before { - content: "\f242"; } - -.fa-battery-quarter::before { - content: "\f243"; } - -.fa-battery-2::before { - content: "\f243"; } - -.fa-battery-three-quarters::before { - content: "\f241"; } - -.fa-battery-4::before { - content: "\f241"; } - -.fa-bed::before { - content: "\f236"; } - -.fa-bed-pulse::before { - content: "\f487"; } - -.fa-procedures::before { - content: "\f487"; } - -.fa-beer-mug-empty::before { - content: "\f0fc"; } - -.fa-beer::before { - content: "\f0fc"; } - -.fa-bell::before { - content: "\f0f3"; } - -.fa-bell-concierge::before { - content: "\f562"; } - -.fa-concierge-bell::before { - content: "\f562"; } - -.fa-bell-slash::before { - content: "\f1f6"; } - -.fa-bezier-curve::before { - content: "\f55b"; } - -.fa-bicycle::before { - content: "\f206"; } - -.fa-binoculars::before { - content: "\f1e5"; } - -.fa-biohazard::before { - content: "\f780"; } - -.fa-bitcoin-sign::before { - content: "\e0b4"; } - -.fa-blender::before { - content: "\f517"; } - -.fa-blender-phone::before { - content: "\f6b6"; } - -.fa-blog::before { - content: "\f781"; } - -.fa-bold::before { - content: "\f032"; } - -.fa-bolt::before { - content: "\f0e7"; } - -.fa-zap::before { - content: "\f0e7"; } - -.fa-bolt-lightning::before { - content: "\e0b7"; } - -.fa-bomb::before { - content: "\f1e2"; } - -.fa-bone::before { - content: "\f5d7"; } - -.fa-bong::before { - content: "\f55c"; } - -.fa-book::before { - content: "\f02d"; } - -.fa-book-atlas::before { - content: "\f558"; } - -.fa-atlas::before { - content: "\f558"; } - -.fa-book-bible::before { - content: "\f647"; } - -.fa-bible::before { - content: "\f647"; } - -.fa-book-bookmark::before { - content: "\e0bb"; } - -.fa-book-journal-whills::before { - content: "\f66a"; } - -.fa-journal-whills::before { - content: "\f66a"; } - -.fa-book-medical::before { - content: "\f7e6"; } - -.fa-book-open::before { - content: "\f518"; } - -.fa-book-open-reader::before { - content: "\f5da"; } - -.fa-book-reader::before { - content: "\f5da"; } - -.fa-book-quran::before { - content: "\f687"; } - -.fa-quran::before { - content: "\f687"; } - -.fa-book-skull::before { - content: "\f6b7"; } - -.fa-book-dead::before { - content: "\f6b7"; } - -.fa-bookmark::before { - content: "\f02e"; } - -.fa-border-all::before { - content: "\f84c"; } - -.fa-border-none::before { - content: "\f850"; } - -.fa-border-top-left::before { - content: "\f853"; } - -.fa-border-style::before { - content: "\f853"; } - -.fa-bore-hole::before { - content: "\e4c3"; } - -.fa-bottle-droplet::before { - content: "\e4c4"; } - -.fa-bottle-water::before { - content: "\e4c5"; } - -.fa-bowl-food::before { - content: "\e4c6"; } - -.fa-bowl-rice::before { - content: "\e2eb"; } - -.fa-bowling-ball::before { - content: "\f436"; } - -.fa-box::before { - content: "\f466"; } - -.fa-box-archive::before { - content: "\f187"; } - -.fa-archive::before { - content: "\f187"; } - -.fa-box-open::before { - content: "\f49e"; } - -.fa-box-tissue::before { - content: "\e05b"; } - -.fa-boxes-packing::before { - content: "\e4c7"; } - -.fa-boxes-stacked::before { - content: "\f468"; } - -.fa-boxes::before { - content: "\f468"; } - -.fa-boxes-alt::before { - content: "\f468"; } - -.fa-braille::before { - content: "\f2a1"; } - -.fa-brain::before { - content: "\f5dc"; } - -.fa-brazilian-real-sign::before { - content: "\e46c"; } - -.fa-bread-slice::before { - content: "\f7ec"; } - -.fa-bridge::before { - content: "\e4c8"; } - -.fa-bridge-circle-check::before { - content: "\e4c9"; } - -.fa-bridge-circle-exclamation::before { - content: "\e4ca"; } - -.fa-bridge-circle-xmark::before { - content: "\e4cb"; } - -.fa-bridge-lock::before { - content: "\e4cc"; } - -.fa-bridge-water::before { - content: "\e4ce"; } - -.fa-briefcase::before { - content: "\f0b1"; } - -.fa-briefcase-medical::before { - content: "\f469"; } - -.fa-broom::before { - content: "\f51a"; } - -.fa-broom-ball::before { - content: "\f458"; } - -.fa-quidditch::before { - content: "\f458"; } - -.fa-quidditch-broom-ball::before { - content: "\f458"; } - -.fa-brush::before { - content: "\f55d"; } - -.fa-bucket::before { - content: "\e4cf"; } - -.fa-bug::before { - content: "\f188"; } - -.fa-bug-slash::before { - content: "\e490"; } - -.fa-bugs::before { - content: "\e4d0"; } - -.fa-building::before { - content: "\f1ad"; } - -.fa-building-circle-arrow-right::before { - content: "\e4d1"; } - -.fa-building-circle-check::before { - content: "\e4d2"; } - -.fa-building-circle-exclamation::before { - content: "\e4d3"; } - -.fa-building-circle-xmark::before { - content: "\e4d4"; } - -.fa-building-columns::before { - content: "\f19c"; } - -.fa-bank::before { - content: "\f19c"; } - -.fa-institution::before { - content: "\f19c"; } - -.fa-museum::before { - content: "\f19c"; } - -.fa-university::before { - content: "\f19c"; } - -.fa-building-flag::before { - content: "\e4d5"; } - -.fa-building-lock::before { - content: "\e4d6"; } - -.fa-building-ngo::before { - content: "\e4d7"; } - -.fa-building-shield::before { - content: "\e4d8"; } - -.fa-building-un::before { - content: "\e4d9"; } - -.fa-building-user::before { - content: "\e4da"; } - -.fa-building-wheat::before { - content: "\e4db"; } - -.fa-bullhorn::before { - content: "\f0a1"; } - -.fa-bullseye::before { - content: "\f140"; } - -.fa-burger::before { - content: "\f805"; } - -.fa-hamburger::before { - content: "\f805"; } - -.fa-burst::before { - content: "\e4dc"; } - -.fa-bus::before { - content: "\f207"; } - -.fa-bus-simple::before { - content: "\f55e"; } - -.fa-bus-alt::before { - content: "\f55e"; } - -.fa-business-time::before { - content: "\f64a"; } - -.fa-briefcase-clock::before { - content: "\f64a"; } - -.fa-c::before { - content: "\43"; } - -.fa-cake-candles::before { - content: "\f1fd"; } - -.fa-birthday-cake::before { - content: "\f1fd"; } - -.fa-cake::before { - content: "\f1fd"; } - -.fa-calculator::before { - content: "\f1ec"; } - -.fa-calendar::before { - content: "\f133"; } - -.fa-calendar-check::before { - content: "\f274"; } - -.fa-calendar-day::before { - content: "\f783"; } - -.fa-calendar-days::before { - content: "\f073"; } - -.fa-calendar-alt::before { - content: "\f073"; } - -.fa-calendar-minus::before { - content: "\f272"; } - -.fa-calendar-plus::before { - content: "\f271"; } - -.fa-calendar-week::before { - content: "\f784"; } - -.fa-calendar-xmark::before { - content: "\f273"; } - -.fa-calendar-times::before { - content: "\f273"; } - -.fa-camera::before { - content: "\f030"; } - -.fa-camera-alt::before { - content: "\f030"; } - -.fa-camera-retro::before { - content: "\f083"; } - -.fa-camera-rotate::before { - content: "\e0d8"; } - -.fa-campground::before { - content: "\f6bb"; } - -.fa-candy-cane::before { - content: "\f786"; } - -.fa-cannabis::before { - content: "\f55f"; } - -.fa-capsules::before { - content: "\f46b"; } - -.fa-car::before { - content: "\f1b9"; } - -.fa-automobile::before { - content: "\f1b9"; } - -.fa-car-battery::before { - content: "\f5df"; } - -.fa-battery-car::before { - content: "\f5df"; } - -.fa-car-burst::before { - content: "\f5e1"; } - -.fa-car-crash::before { - content: "\f5e1"; } - -.fa-car-on::before { - content: "\e4dd"; } - -.fa-car-rear::before { - content: "\f5de"; } - -.fa-car-alt::before { - content: "\f5de"; } - -.fa-car-side::before { - content: "\f5e4"; } - -.fa-car-tunnel::before { - content: "\e4de"; } - -.fa-caravan::before { - content: "\f8ff"; } - -.fa-caret-down::before { - content: "\f0d7"; } - -.fa-caret-left::before { - content: "\f0d9"; } - -.fa-caret-right::before { - content: "\f0da"; } - -.fa-caret-up::before { - content: "\f0d8"; } - -.fa-carrot::before { - content: "\f787"; } - -.fa-cart-arrow-down::before { - content: "\f218"; } - -.fa-cart-flatbed::before { - content: "\f474"; } - -.fa-dolly-flatbed::before { - content: "\f474"; } - -.fa-cart-flatbed-suitcase::before { - content: "\f59d"; } - -.fa-luggage-cart::before { - content: "\f59d"; } - -.fa-cart-plus::before { - content: "\f217"; } - -.fa-cart-shopping::before { - content: "\f07a"; } - -.fa-shopping-cart::before { - content: "\f07a"; } - -.fa-cash-register::before { - content: "\f788"; } - -.fa-cat::before { - content: "\f6be"; } - -.fa-cedi-sign::before { - content: "\e0df"; } - -.fa-cent-sign::before { - content: "\e3f5"; } - -.fa-certificate::before { - content: "\f0a3"; } - -.fa-chair::before { - content: "\f6c0"; } - -.fa-chalkboard::before { - content: "\f51b"; } - -.fa-blackboard::before { - content: "\f51b"; } - -.fa-chalkboard-user::before { - content: "\f51c"; } - -.fa-chalkboard-teacher::before { - content: "\f51c"; } - -.fa-champagne-glasses::before { - content: "\f79f"; } - -.fa-glass-cheers::before { - content: "\f79f"; } - -.fa-charging-station::before { - content: "\f5e7"; } - -.fa-chart-area::before { - content: "\f1fe"; } - -.fa-area-chart::before { - content: "\f1fe"; } - -.fa-chart-bar::before { - content: "\f080"; } - -.fa-bar-chart::before { - content: "\f080"; } - -.fa-chart-column::before { - content: "\e0e3"; } - -.fa-chart-gantt::before { - content: "\e0e4"; } - -.fa-chart-line::before { - content: "\f201"; } - -.fa-line-chart::before { - content: "\f201"; } - -.fa-chart-pie::before { - content: "\f200"; } - -.fa-pie-chart::before { - content: "\f200"; } - -.fa-chart-simple::before { - content: "\e473"; } - -.fa-check::before { - content: "\f00c"; } - -.fa-check-double::before { - content: "\f560"; } - -.fa-check-to-slot::before { - content: "\f772"; } - -.fa-vote-yea::before { - content: "\f772"; } - -.fa-cheese::before { - content: "\f7ef"; } - -.fa-chess::before { - content: "\f439"; } - -.fa-chess-bishop::before { - content: "\f43a"; } - -.fa-chess-board::before { - content: "\f43c"; } - -.fa-chess-king::before { - content: "\f43f"; } - -.fa-chess-knight::before { - content: "\f441"; } - -.fa-chess-pawn::before { - content: "\f443"; } - -.fa-chess-queen::before { - content: "\f445"; } - -.fa-chess-rook::before { - content: "\f447"; } - -.fa-chevron-down::before { - content: "\f078"; } - -.fa-chevron-left::before { - content: "\f053"; } - -.fa-chevron-right::before { - content: "\f054"; } - -.fa-chevron-up::before { - content: "\f077"; } - -.fa-child::before { - content: "\f1ae"; } - -.fa-child-dress::before { - content: "\e59c"; } - -.fa-child-reaching::before { - content: "\e59d"; } - -.fa-child-rifle::before { - content: "\e4e0"; } - -.fa-children::before { - content: "\e4e1"; } - -.fa-church::before { - content: "\f51d"; } - -.fa-circle::before { - content: "\f111"; } - -.fa-circle-arrow-down::before { - content: "\f0ab"; } - -.fa-arrow-circle-down::before { - content: "\f0ab"; } - -.fa-circle-arrow-left::before { - content: "\f0a8"; } - -.fa-arrow-circle-left::before { - content: "\f0a8"; } - -.fa-circle-arrow-right::before { - content: "\f0a9"; } - -.fa-arrow-circle-right::before { - content: "\f0a9"; } - -.fa-circle-arrow-up::before { - content: "\f0aa"; } - -.fa-arrow-circle-up::before { - content: "\f0aa"; } - -.fa-circle-check::before { - content: "\f058"; } - -.fa-check-circle::before { - content: "\f058"; } - -.fa-circle-chevron-down::before { - content: "\f13a"; } - -.fa-chevron-circle-down::before { - content: "\f13a"; } - -.fa-circle-chevron-left::before { - content: "\f137"; } - -.fa-chevron-circle-left::before { - content: "\f137"; } - -.fa-circle-chevron-right::before { - content: "\f138"; } - -.fa-chevron-circle-right::before { - content: "\f138"; } - -.fa-circle-chevron-up::before { - content: "\f139"; } - -.fa-chevron-circle-up::before { - content: "\f139"; } - -.fa-circle-dollar-to-slot::before { - content: "\f4b9"; } - -.fa-donate::before { - content: "\f4b9"; } - -.fa-circle-dot::before { - content: "\f192"; } - -.fa-dot-circle::before { - content: "\f192"; } - -.fa-circle-down::before { - content: "\f358"; } - -.fa-arrow-alt-circle-down::before { - content: "\f358"; } - -.fa-circle-exclamation::before { - content: "\f06a"; } - -.fa-exclamation-circle::before { - content: "\f06a"; } - -.fa-circle-h::before { - content: "\f47e"; } - -.fa-hospital-symbol::before { - content: "\f47e"; } - -.fa-circle-half-stroke::before { - content: "\f042"; } - -.fa-adjust::before { - content: "\f042"; } - -.fa-circle-info::before { - content: "\f05a"; } - -.fa-info-circle::before { - content: "\f05a"; } - -.fa-circle-left::before { - content: "\f359"; } - -.fa-arrow-alt-circle-left::before { - content: "\f359"; } - -.fa-circle-minus::before { - content: "\f056"; } - -.fa-minus-circle::before { - content: "\f056"; } - -.fa-circle-nodes::before { - content: "\e4e2"; } - -.fa-circle-notch::before { - content: "\f1ce"; } - -.fa-circle-pause::before { - content: "\f28b"; } - -.fa-pause-circle::before { - content: "\f28b"; } - -.fa-circle-play::before { - content: "\f144"; } - -.fa-play-circle::before { - content: "\f144"; } - -.fa-circle-plus::before { - content: "\f055"; } - -.fa-plus-circle::before { - content: "\f055"; } - -.fa-circle-question::before { - content: "\f059"; } - -.fa-question-circle::before { - content: "\f059"; } - -.fa-circle-radiation::before { - content: "\f7ba"; } - -.fa-radiation-alt::before { - content: "\f7ba"; } - -.fa-circle-right::before { - content: "\f35a"; } - -.fa-arrow-alt-circle-right::before { - content: "\f35a"; } - -.fa-circle-stop::before { - content: "\f28d"; } - -.fa-stop-circle::before { - content: "\f28d"; } - -.fa-circle-up::before { - content: "\f35b"; } - -.fa-arrow-alt-circle-up::before { - content: "\f35b"; } - -.fa-circle-user::before { - content: "\f2bd"; } - -.fa-user-circle::before { - content: "\f2bd"; } - -.fa-circle-xmark::before { - content: "\f057"; } - -.fa-times-circle::before { - content: "\f057"; } - -.fa-xmark-circle::before { - content: "\f057"; } - -.fa-city::before { - content: "\f64f"; } - -.fa-clapperboard::before { - content: "\e131"; } - -.fa-clipboard::before { - content: "\f328"; } - -.fa-clipboard-check::before { - content: "\f46c"; } - -.fa-clipboard-list::before { - content: "\f46d"; } - -.fa-clipboard-question::before { - content: "\e4e3"; } - -.fa-clipboard-user::before { - content: "\f7f3"; } - -.fa-clock::before { - content: "\f017"; } - -.fa-clock-four::before { - content: "\f017"; } - -.fa-clock-rotate-left::before { - content: "\f1da"; } - -.fa-history::before { - content: "\f1da"; } - -.fa-clone::before { - content: "\f24d"; } - -.fa-closed-captioning::before { - content: "\f20a"; } - -.fa-cloud::before { - content: "\f0c2"; } - -.fa-cloud-arrow-down::before { - content: "\f0ed"; } - -.fa-cloud-download::before { - content: "\f0ed"; } - -.fa-cloud-download-alt::before { - content: "\f0ed"; } - -.fa-cloud-arrow-up::before { - content: "\f0ee"; } - -.fa-cloud-upload::before { - content: "\f0ee"; } - -.fa-cloud-upload-alt::before { - content: "\f0ee"; } - -.fa-cloud-bolt::before { - content: "\f76c"; } - -.fa-thunderstorm::before { - content: "\f76c"; } - -.fa-cloud-meatball::before { - content: "\f73b"; } - -.fa-cloud-moon::before { - content: "\f6c3"; } - -.fa-cloud-moon-rain::before { - content: "\f73c"; } - -.fa-cloud-rain::before { - content: "\f73d"; } - -.fa-cloud-showers-heavy::before { - content: "\f740"; } - -.fa-cloud-showers-water::before { - content: "\e4e4"; } - -.fa-cloud-sun::before { - content: "\f6c4"; } - -.fa-cloud-sun-rain::before { - content: "\f743"; } - -.fa-clover::before { - content: "\e139"; } - -.fa-code::before { - content: "\f121"; } - -.fa-code-branch::before { - content: "\f126"; } - -.fa-code-commit::before { - content: "\f386"; } - -.fa-code-compare::before { - content: "\e13a"; } - -.fa-code-fork::before { - content: "\e13b"; } - -.fa-code-merge::before { - content: "\f387"; } - -.fa-code-pull-request::before { - content: "\e13c"; } - -.fa-coins::before { - content: "\f51e"; } - -.fa-colon-sign::before { - content: "\e140"; } - -.fa-comment::before { - content: "\f075"; } - -.fa-comment-dollar::before { - content: "\f651"; } - -.fa-comment-dots::before { - content: "\f4ad"; } - -.fa-commenting::before { - content: "\f4ad"; } - -.fa-comment-medical::before { - content: "\f7f5"; } - -.fa-comment-slash::before { - content: "\f4b3"; } - -.fa-comment-sms::before { - content: "\f7cd"; } - -.fa-sms::before { - content: "\f7cd"; } - -.fa-comments::before { - content: "\f086"; } - -.fa-comments-dollar::before { - content: "\f653"; } - -.fa-compact-disc::before { - content: "\f51f"; } - -.fa-compass::before { - content: "\f14e"; } - -.fa-compass-drafting::before { - content: "\f568"; } - -.fa-drafting-compass::before { - content: "\f568"; } - -.fa-compress::before { - content: "\f066"; } - -.fa-computer::before { - content: "\e4e5"; } - -.fa-computer-mouse::before { - content: "\f8cc"; } - -.fa-mouse::before { - content: "\f8cc"; } - -.fa-cookie::before { - content: "\f563"; } - -.fa-cookie-bite::before { - content: "\f564"; } - -.fa-copy::before { - content: "\f0c5"; } - -.fa-copyright::before { - content: "\f1f9"; } - -.fa-couch::before { - content: "\f4b8"; } - -.fa-cow::before { - content: "\f6c8"; } - -.fa-credit-card::before { - content: "\f09d"; } - -.fa-credit-card-alt::before { - content: "\f09d"; } - -.fa-crop::before { - content: "\f125"; } - -.fa-crop-simple::before { - content: "\f565"; } - -.fa-crop-alt::before { - content: "\f565"; } - -.fa-cross::before { - content: "\f654"; } - -.fa-crosshairs::before { - content: "\f05b"; } - -.fa-crow::before { - content: "\f520"; } - -.fa-crown::before { - content: "\f521"; } - -.fa-crutch::before { - content: "\f7f7"; } - -.fa-cruzeiro-sign::before { - content: "\e152"; } - -.fa-cube::before { - content: "\f1b2"; } - -.fa-cubes::before { - content: "\f1b3"; } - -.fa-cubes-stacked::before { - content: "\e4e6"; } - -.fa-d::before { - content: "\44"; } - -.fa-database::before { - content: "\f1c0"; } - -.fa-delete-left::before { - content: "\f55a"; } - -.fa-backspace::before { - content: "\f55a"; } - -.fa-democrat::before { - content: "\f747"; } - -.fa-desktop::before { - content: "\f390"; } - -.fa-desktop-alt::before { - content: "\f390"; } - -.fa-dharmachakra::before { - content: "\f655"; } - -.fa-diagram-next::before { - content: "\e476"; } - -.fa-diagram-predecessor::before { - content: "\e477"; } - -.fa-diagram-project::before { - content: "\f542"; } - -.fa-project-diagram::before { - content: "\f542"; } - -.fa-diagram-successor::before { - content: "\e47a"; } - -.fa-diamond::before { - content: "\f219"; } - -.fa-diamond-turn-right::before { - content: "\f5eb"; } - -.fa-directions::before { - content: "\f5eb"; } - -.fa-dice::before { - content: "\f522"; } - -.fa-dice-d20::before { - content: "\f6cf"; } - -.fa-dice-d6::before { - content: "\f6d1"; } - -.fa-dice-five::before { - content: "\f523"; } - -.fa-dice-four::before { - content: "\f524"; } - -.fa-dice-one::before { - content: "\f525"; } - -.fa-dice-six::before { - content: "\f526"; } - -.fa-dice-three::before { - content: "\f527"; } - -.fa-dice-two::before { - content: "\f528"; } - -.fa-disease::before { - content: "\f7fa"; } - -.fa-display::before { - content: "\e163"; } - -.fa-divide::before { - content: "\f529"; } - -.fa-dna::before { - content: "\f471"; } - -.fa-dog::before { - content: "\f6d3"; } - -.fa-dollar-sign::before { - content: "\24"; } - -.fa-dollar::before { - content: "\24"; } - -.fa-usd::before { - content: "\24"; } - -.fa-dolly::before { - content: "\f472"; } - -.fa-dolly-box::before { - content: "\f472"; } - -.fa-dong-sign::before { - content: "\e169"; } - -.fa-door-closed::before { - content: "\f52a"; } - -.fa-door-open::before { - content: "\f52b"; } - -.fa-dove::before { - content: "\f4ba"; } - -.fa-down-left-and-up-right-to-center::before { - content: "\f422"; } - -.fa-compress-alt::before { - content: "\f422"; } - -.fa-down-long::before { - content: "\f309"; } - -.fa-long-arrow-alt-down::before { - content: "\f309"; } - -.fa-download::before { - content: "\f019"; } - -.fa-dragon::before { - content: "\f6d5"; } - -.fa-draw-polygon::before { - content: "\f5ee"; } - -.fa-droplet::before { - content: "\f043"; } - -.fa-tint::before { - content: "\f043"; } - -.fa-droplet-slash::before { - content: "\f5c7"; } - -.fa-tint-slash::before { - content: "\f5c7"; } - -.fa-drum::before { - content: "\f569"; } - -.fa-drum-steelpan::before { - content: "\f56a"; } - -.fa-drumstick-bite::before { - content: "\f6d7"; } - -.fa-dumbbell::before { - content: "\f44b"; } - -.fa-dumpster::before { - content: "\f793"; } - -.fa-dumpster-fire::before { - content: "\f794"; } - -.fa-dungeon::before { - content: "\f6d9"; } - -.fa-e::before { - content: "\45"; } - -.fa-ear-deaf::before { - content: "\f2a4"; } - -.fa-deaf::before { - content: "\f2a4"; } - -.fa-deafness::before { - content: "\f2a4"; } - -.fa-hard-of-hearing::before { - content: "\f2a4"; } - -.fa-ear-listen::before { - content: "\f2a2"; } - -.fa-assistive-listening-systems::before { - content: "\f2a2"; } - -.fa-earth-africa::before { - content: "\f57c"; } - -.fa-globe-africa::before { - content: "\f57c"; } - -.fa-earth-americas::before { - content: "\f57d"; } - -.fa-earth::before { - content: "\f57d"; } - -.fa-earth-america::before { - content: "\f57d"; } - -.fa-globe-americas::before { - content: "\f57d"; } - -.fa-earth-asia::before { - content: "\f57e"; } - -.fa-globe-asia::before { - content: "\f57e"; } - -.fa-earth-europe::before { - content: "\f7a2"; } - -.fa-globe-europe::before { - content: "\f7a2"; } - -.fa-earth-oceania::before { - content: "\e47b"; } - -.fa-globe-oceania::before { - content: "\e47b"; } - -.fa-egg::before { - content: "\f7fb"; } - -.fa-eject::before { - content: "\f052"; } - -.fa-elevator::before { - content: "\e16d"; } - -.fa-ellipsis::before { - content: "\f141"; } - -.fa-ellipsis-h::before { - content: "\f141"; } - -.fa-ellipsis-vertical::before { - content: "\f142"; } - -.fa-ellipsis-v::before { - content: "\f142"; } - -.fa-envelope::before { - content: "\f0e0"; } - -.fa-envelope-circle-check::before { - content: "\e4e8"; } - -.fa-envelope-open::before { - content: "\f2b6"; } - -.fa-envelope-open-text::before { - content: "\f658"; } - -.fa-envelopes-bulk::before { - content: "\f674"; } - -.fa-mail-bulk::before { - content: "\f674"; } - -.fa-equals::before { - content: "\3d"; } - -.fa-eraser::before { - content: "\f12d"; } - -.fa-ethernet::before { - content: "\f796"; } - -.fa-euro-sign::before { - content: "\f153"; } - -.fa-eur::before { - content: "\f153"; } - -.fa-euro::before { - content: "\f153"; } - -.fa-exclamation::before { - content: "\21"; } - -.fa-expand::before { - content: "\f065"; } - -.fa-explosion::before { - content: "\e4e9"; } - -.fa-eye::before { - content: "\f06e"; } - -.fa-eye-dropper::before { - content: "\f1fb"; } - -.fa-eye-dropper-empty::before { - content: "\f1fb"; } - -.fa-eyedropper::before { - content: "\f1fb"; } - -.fa-eye-low-vision::before { - content: "\f2a8"; } - -.fa-low-vision::before { - content: "\f2a8"; } - -.fa-eye-slash::before { - content: "\f070"; } - -.fa-f::before { - content: "\46"; } - -.fa-face-angry::before { - content: "\f556"; } - -.fa-angry::before { - content: "\f556"; } - -.fa-face-dizzy::before { - content: "\f567"; } - -.fa-dizzy::before { - content: "\f567"; } - -.fa-face-flushed::before { - content: "\f579"; } - -.fa-flushed::before { - content: "\f579"; } - -.fa-face-frown::before { - content: "\f119"; } - -.fa-frown::before { - content: "\f119"; } - -.fa-face-frown-open::before { - content: "\f57a"; } - -.fa-frown-open::before { - content: "\f57a"; } - -.fa-face-grimace::before { - content: "\f57f"; } - -.fa-grimace::before { - content: "\f57f"; } - -.fa-face-grin::before { - content: "\f580"; } - -.fa-grin::before { - content: "\f580"; } - -.fa-face-grin-beam::before { - content: "\f582"; } - -.fa-grin-beam::before { - content: "\f582"; } - -.fa-face-grin-beam-sweat::before { - content: "\f583"; } - -.fa-grin-beam-sweat::before { - content: "\f583"; } - -.fa-face-grin-hearts::before { - content: "\f584"; } - -.fa-grin-hearts::before { - content: "\f584"; } - -.fa-face-grin-squint::before { - content: "\f585"; } - -.fa-grin-squint::before { - content: "\f585"; } - -.fa-face-grin-squint-tears::before { - content: "\f586"; } - -.fa-grin-squint-tears::before { - content: "\f586"; } - -.fa-face-grin-stars::before { - content: "\f587"; } - -.fa-grin-stars::before { - content: "\f587"; } - -.fa-face-grin-tears::before { - content: "\f588"; } - -.fa-grin-tears::before { - content: "\f588"; } - -.fa-face-grin-tongue::before { - content: "\f589"; } - -.fa-grin-tongue::before { - content: "\f589"; } - -.fa-face-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-face-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-face-grin-wide::before { - content: "\f581"; } - -.fa-grin-alt::before { - content: "\f581"; } - -.fa-face-grin-wink::before { - content: "\f58c"; } - -.fa-grin-wink::before { - content: "\f58c"; } - -.fa-face-kiss::before { - content: "\f596"; } - -.fa-kiss::before { - content: "\f596"; } - -.fa-face-kiss-beam::before { - content: "\f597"; } - -.fa-kiss-beam::before { - content: "\f597"; } - -.fa-face-kiss-wink-heart::before { - content: "\f598"; } - -.fa-kiss-wink-heart::before { - content: "\f598"; } - -.fa-face-laugh::before { - content: "\f599"; } - -.fa-laugh::before { - content: "\f599"; } - -.fa-face-laugh-beam::before { - content: "\f59a"; } - -.fa-laugh-beam::before { - content: "\f59a"; } - -.fa-face-laugh-squint::before { - content: "\f59b"; } - -.fa-laugh-squint::before { - content: "\f59b"; } - -.fa-face-laugh-wink::before { - content: "\f59c"; } - -.fa-laugh-wink::before { - content: "\f59c"; } - -.fa-face-meh::before { - content: "\f11a"; } - -.fa-meh::before { - content: "\f11a"; } - -.fa-face-meh-blank::before { - content: "\f5a4"; } - -.fa-meh-blank::before { - content: "\f5a4"; } - -.fa-face-rolling-eyes::before { - content: "\f5a5"; } - -.fa-meh-rolling-eyes::before { - content: "\f5a5"; } - -.fa-face-sad-cry::before { - content: "\f5b3"; } - -.fa-sad-cry::before { - content: "\f5b3"; } - -.fa-face-sad-tear::before { - content: "\f5b4"; } - -.fa-sad-tear::before { - content: "\f5b4"; } - -.fa-face-smile::before { - content: "\f118"; } - -.fa-smile::before { - content: "\f118"; } - -.fa-face-smile-beam::before { - content: "\f5b8"; } - -.fa-smile-beam::before { - content: "\f5b8"; } - -.fa-face-smile-wink::before { - content: "\f4da"; } - -.fa-smile-wink::before { - content: "\f4da"; } - -.fa-face-surprise::before { - content: "\f5c2"; } - -.fa-surprise::before { - content: "\f5c2"; } - -.fa-face-tired::before { - content: "\f5c8"; } - -.fa-tired::before { - content: "\f5c8"; } - -.fa-fan::before { - content: "\f863"; } - -.fa-faucet::before { - content: "\e005"; } - -.fa-faucet-drip::before { - content: "\e006"; } - -.fa-fax::before { - content: "\f1ac"; } - -.fa-feather::before { - content: "\f52d"; } - -.fa-feather-pointed::before { - content: "\f56b"; } - -.fa-feather-alt::before { - content: "\f56b"; } - -.fa-ferry::before { - content: "\e4ea"; } - -.fa-file::before { - content: "\f15b"; } - -.fa-file-arrow-down::before { - content: "\f56d"; } - -.fa-file-download::before { - content: "\f56d"; } - -.fa-file-arrow-up::before { - content: "\f574"; } - -.fa-file-upload::before { - content: "\f574"; } - -.fa-file-audio::before { - content: "\f1c7"; } - -.fa-file-circle-check::before { - content: "\e493"; } - -.fa-file-circle-exclamation::before { - content: "\e4eb"; } - -.fa-file-circle-minus::before { - content: "\e4ed"; } - -.fa-file-circle-plus::before { - content: "\e4ee"; } - -.fa-file-circle-question::before { - content: "\e4ef"; } - -.fa-file-circle-xmark::before { - content: "\e494"; } - -.fa-file-code::before { - content: "\f1c9"; } - -.fa-file-contract::before { - content: "\f56c"; } - -.fa-file-csv::before { - content: "\f6dd"; } - -.fa-file-excel::before { - content: "\f1c3"; } - -.fa-file-export::before { - content: "\f56e"; } - -.fa-arrow-right-from-file::before { - content: "\f56e"; } - -.fa-file-image::before { - content: "\f1c5"; } - -.fa-file-import::before { - content: "\f56f"; } - -.fa-arrow-right-to-file::before { - content: "\f56f"; } - -.fa-file-invoice::before { - content: "\f570"; } - -.fa-file-invoice-dollar::before { - content: "\f571"; } - -.fa-file-lines::before { - content: "\f15c"; } - -.fa-file-alt::before { - content: "\f15c"; } - -.fa-file-text::before { - content: "\f15c"; } - -.fa-file-medical::before { - content: "\f477"; } - -.fa-file-pdf::before { - content: "\f1c1"; } - -.fa-file-pen::before { - content: "\f31c"; } - -.fa-file-edit::before { - content: "\f31c"; } - -.fa-file-powerpoint::before { - content: "\f1c4"; } - -.fa-file-prescription::before { - content: "\f572"; } - -.fa-file-shield::before { - content: "\e4f0"; } - -.fa-file-signature::before { - content: "\f573"; } - -.fa-file-video::before { - content: "\f1c8"; } - -.fa-file-waveform::before { - content: "\f478"; } - -.fa-file-medical-alt::before { - content: "\f478"; } - -.fa-file-word::before { - content: "\f1c2"; } - -.fa-file-zipper::before { - content: "\f1c6"; } - -.fa-file-archive::before { - content: "\f1c6"; } - -.fa-fill::before { - content: "\f575"; } - -.fa-fill-drip::before { - content: "\f576"; } - -.fa-film::before { - content: "\f008"; } - -.fa-filter::before { - content: "\f0b0"; } - -.fa-filter-circle-dollar::before { - content: "\f662"; } - -.fa-funnel-dollar::before { - content: "\f662"; } - -.fa-filter-circle-xmark::before { - content: "\e17b"; } - -.fa-fingerprint::before { - content: "\f577"; } - -.fa-fire::before { - content: "\f06d"; } - -.fa-fire-burner::before { - content: "\e4f1"; } - -.fa-fire-extinguisher::before { - content: "\f134"; } - -.fa-fire-flame-curved::before { - content: "\f7e4"; } - -.fa-fire-alt::before { - content: "\f7e4"; } - -.fa-fire-flame-simple::before { - content: "\f46a"; } - -.fa-burn::before { - content: "\f46a"; } - -.fa-fish::before { - content: "\f578"; } - -.fa-fish-fins::before { - content: "\e4f2"; } - -.fa-flag::before { - content: "\f024"; } - -.fa-flag-checkered::before { - content: "\f11e"; } - -.fa-flag-usa::before { - content: "\f74d"; } - -.fa-flask::before { - content: "\f0c3"; } - -.fa-flask-vial::before { - content: "\e4f3"; } - -.fa-floppy-disk::before { - content: "\f0c7"; } - -.fa-save::before { - content: "\f0c7"; } - -.fa-florin-sign::before { - content: "\e184"; } - -.fa-folder::before { - content: "\f07b"; } - -.fa-folder-blank::before { - content: "\f07b"; } - -.fa-folder-closed::before { - content: "\e185"; } - -.fa-folder-minus::before { - content: "\f65d"; } - -.fa-folder-open::before { - content: "\f07c"; } - -.fa-folder-plus::before { - content: "\f65e"; } - -.fa-folder-tree::before { - content: "\f802"; } - -.fa-font::before { - content: "\f031"; } - -.fa-football::before { - content: "\f44e"; } - -.fa-football-ball::before { - content: "\f44e"; } - -.fa-forward::before { - content: "\f04e"; } - -.fa-forward-fast::before { - content: "\f050"; } - -.fa-fast-forward::before { - content: "\f050"; } - -.fa-forward-step::before { - content: "\f051"; } - -.fa-step-forward::before { - content: "\f051"; } - -.fa-franc-sign::before { - content: "\e18f"; } - -.fa-frog::before { - content: "\f52e"; } - -.fa-futbol::before { - content: "\f1e3"; } - -.fa-futbol-ball::before { - content: "\f1e3"; } - -.fa-soccer-ball::before { - content: "\f1e3"; } - -.fa-g::before { - content: "\47"; } - -.fa-gamepad::before { - content: "\f11b"; } - -.fa-gas-pump::before { - content: "\f52f"; } - -.fa-gauge::before { - content: "\f624"; } - -.fa-dashboard::before { - content: "\f624"; } - -.fa-gauge-med::before { - content: "\f624"; } - -.fa-tachometer-alt-average::before { - content: "\f624"; } - -.fa-gauge-high::before { - content: "\f625"; } - -.fa-tachometer-alt::before { - content: "\f625"; } - -.fa-tachometer-alt-fast::before { - content: "\f625"; } - -.fa-gauge-simple::before { - content: "\f629"; } - -.fa-gauge-simple-med::before { - content: "\f629"; } - -.fa-tachometer-average::before { - content: "\f629"; } - -.fa-gauge-simple-high::before { - content: "\f62a"; } - -.fa-tachometer::before { - content: "\f62a"; } - -.fa-tachometer-fast::before { - content: "\f62a"; } - -.fa-gavel::before { - content: "\f0e3"; } - -.fa-legal::before { - content: "\f0e3"; } - -.fa-gear::before { - content: "\f013"; } - -.fa-cog::before { - content: "\f013"; } - -.fa-gears::before { - content: "\f085"; } - -.fa-cogs::before { - content: "\f085"; } - -.fa-gem::before { - content: "\f3a5"; } - -.fa-genderless::before { - content: "\f22d"; } - -.fa-ghost::before { - content: "\f6e2"; } - -.fa-gift::before { - content: "\f06b"; } - -.fa-gifts::before { - content: "\f79c"; } - -.fa-glass-water::before { - content: "\e4f4"; } - -.fa-glass-water-droplet::before { - content: "\e4f5"; } - -.fa-glasses::before { - content: "\f530"; } - -.fa-globe::before { - content: "\f0ac"; } - -.fa-golf-ball-tee::before { - content: "\f450"; } - -.fa-golf-ball::before { - content: "\f450"; } - -.fa-gopuram::before { - content: "\f664"; } - -.fa-graduation-cap::before { - content: "\f19d"; } - -.fa-mortar-board::before { - content: "\f19d"; } - -.fa-greater-than::before { - content: "\3e"; } - -.fa-greater-than-equal::before { - content: "\f532"; } - -.fa-grip::before { - content: "\f58d"; } - -.fa-grip-horizontal::before { - content: "\f58d"; } - -.fa-grip-lines::before { - content: "\f7a4"; } - -.fa-grip-lines-vertical::before { - content: "\f7a5"; } - -.fa-grip-vertical::before { - content: "\f58e"; } - -.fa-group-arrows-rotate::before { - content: "\e4f6"; } - -.fa-guarani-sign::before { - content: "\e19a"; } - -.fa-guitar::before { - content: "\f7a6"; } - -.fa-gun::before { - content: "\e19b"; } - -.fa-h::before { - content: "\48"; } - -.fa-hammer::before { - content: "\f6e3"; } - -.fa-hamsa::before { - content: "\f665"; } - -.fa-hand::before { - content: "\f256"; } - -.fa-hand-paper::before { - content: "\f256"; } - -.fa-hand-back-fist::before { - content: "\f255"; } - -.fa-hand-rock::before { - content: "\f255"; } - -.fa-hand-dots::before { - content: "\f461"; } - -.fa-allergies::before { - content: "\f461"; } - -.fa-hand-fist::before { - content: "\f6de"; } - -.fa-fist-raised::before { - content: "\f6de"; } - -.fa-hand-holding::before { - content: "\f4bd"; } - -.fa-hand-holding-dollar::before { - content: "\f4c0"; } - -.fa-hand-holding-usd::before { - content: "\f4c0"; } - -.fa-hand-holding-droplet::before { - content: "\f4c1"; } - -.fa-hand-holding-water::before { - content: "\f4c1"; } - -.fa-hand-holding-hand::before { - content: "\e4f7"; } - -.fa-hand-holding-heart::before { - content: "\f4be"; } - -.fa-hand-holding-medical::before { - content: "\e05c"; } - -.fa-hand-lizard::before { - content: "\f258"; } - -.fa-hand-middle-finger::before { - content: "\f806"; } - -.fa-hand-peace::before { - content: "\f25b"; } - -.fa-hand-point-down::before { - content: "\f0a7"; } - -.fa-hand-point-left::before { - content: "\f0a5"; } - -.fa-hand-point-right::before { - content: "\f0a4"; } - -.fa-hand-point-up::before { - content: "\f0a6"; } - -.fa-hand-pointer::before { - content: "\f25a"; } - -.fa-hand-scissors::before { - content: "\f257"; } - -.fa-hand-sparkles::before { - content: "\e05d"; } - -.fa-hand-spock::before { - content: "\f259"; } - -.fa-handcuffs::before { - content: "\e4f8"; } - -.fa-hands::before { - content: "\f2a7"; } - -.fa-sign-language::before { - content: "\f2a7"; } - -.fa-signing::before { - content: "\f2a7"; } - -.fa-hands-asl-interpreting::before { - content: "\f2a3"; } - -.fa-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-asl-interpreting::before { - content: "\f2a3"; } - -.fa-hands-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-hands-bound::before { - content: "\e4f9"; } - -.fa-hands-bubbles::before { - content: "\e05e"; } - -.fa-hands-wash::before { - content: "\e05e"; } - -.fa-hands-clapping::before { - content: "\e1a8"; } - -.fa-hands-holding::before { - content: "\f4c2"; } - -.fa-hands-holding-child::before { - content: "\e4fa"; } - -.fa-hands-holding-circle::before { - content: "\e4fb"; } - -.fa-hands-praying::before { - content: "\f684"; } - -.fa-praying-hands::before { - content: "\f684"; } - -.fa-handshake::before { - content: "\f2b5"; } - -.fa-handshake-angle::before { - content: "\f4c4"; } - -.fa-hands-helping::before { - content: "\f4c4"; } - -.fa-handshake-simple::before { - content: "\f4c6"; } - -.fa-handshake-alt::before { - content: "\f4c6"; } - -.fa-handshake-simple-slash::before { - content: "\e05f"; } - -.fa-handshake-alt-slash::before { - content: "\e05f"; } - -.fa-handshake-slash::before { - content: "\e060"; } - -.fa-hanukiah::before { - content: "\f6e6"; } - -.fa-hard-drive::before { - content: "\f0a0"; } - -.fa-hdd::before { - content: "\f0a0"; } - -.fa-hashtag::before { - content: "\23"; } - -.fa-hat-cowboy::before { - content: "\f8c0"; } - -.fa-hat-cowboy-side::before { - content: "\f8c1"; } - -.fa-hat-wizard::before { - content: "\f6e8"; } - -.fa-head-side-cough::before { - content: "\e061"; } - -.fa-head-side-cough-slash::before { - content: "\e062"; } - -.fa-head-side-mask::before { - content: "\e063"; } - -.fa-head-side-virus::before { - content: "\e064"; } - -.fa-heading::before { - content: "\f1dc"; } - -.fa-header::before { - content: "\f1dc"; } - -.fa-headphones::before { - content: "\f025"; } - -.fa-headphones-simple::before { - content: "\f58f"; } - -.fa-headphones-alt::before { - content: "\f58f"; } - -.fa-headset::before { - content: "\f590"; } - -.fa-heart::before { - content: "\f004"; } - -.fa-heart-circle-bolt::before { - content: "\e4fc"; } - -.fa-heart-circle-check::before { - content: "\e4fd"; } - -.fa-heart-circle-exclamation::before { - content: "\e4fe"; } - -.fa-heart-circle-minus::before { - content: "\e4ff"; } - -.fa-heart-circle-plus::before { - content: "\e500"; } - -.fa-heart-circle-xmark::before { - content: "\e501"; } - -.fa-heart-crack::before { - content: "\f7a9"; } - -.fa-heart-broken::before { - content: "\f7a9"; } - -.fa-heart-pulse::before { - content: "\f21e"; } - -.fa-heartbeat::before { - content: "\f21e"; } - -.fa-helicopter::before { - content: "\f533"; } - -.fa-helicopter-symbol::before { - content: "\e502"; } - -.fa-helmet-safety::before { - content: "\f807"; } - -.fa-hard-hat::before { - content: "\f807"; } - -.fa-hat-hard::before { - content: "\f807"; } - -.fa-helmet-un::before { - content: "\e503"; } - -.fa-highlighter::before { - content: "\f591"; } - -.fa-hill-avalanche::before { - content: "\e507"; } - -.fa-hill-rockslide::before { - content: "\e508"; } - -.fa-hippo::before { - content: "\f6ed"; } - -.fa-hockey-puck::before { - content: "\f453"; } - -.fa-holly-berry::before { - content: "\f7aa"; } - -.fa-horse::before { - content: "\f6f0"; } - -.fa-horse-head::before { - content: "\f7ab"; } - -.fa-hospital::before { - content: "\f0f8"; } - -.fa-hospital-alt::before { - content: "\f0f8"; } - -.fa-hospital-wide::before { - content: "\f0f8"; } - -.fa-hospital-user::before { - content: "\f80d"; } - -.fa-hot-tub-person::before { - content: "\f593"; } - -.fa-hot-tub::before { - content: "\f593"; } - -.fa-hotdog::before { - content: "\f80f"; } - -.fa-hotel::before { - content: "\f594"; } - -.fa-hourglass::before { - content: "\f254"; } - -.fa-hourglass-2::before { - content: "\f254"; } - -.fa-hourglass-half::before { - content: "\f254"; } - -.fa-hourglass-empty::before { - content: "\f252"; } - -.fa-hourglass-end::before { - content: "\f253"; } - -.fa-hourglass-3::before { - content: "\f253"; } - -.fa-hourglass-start::before { - content: "\f251"; } - -.fa-hourglass-1::before { - content: "\f251"; } - -.fa-house::before { - content: "\f015"; } - -.fa-home::before { - content: "\f015"; } - -.fa-home-alt::before { - content: "\f015"; } - -.fa-home-lg-alt::before { - content: "\f015"; } - -.fa-house-chimney::before { - content: "\e3af"; } - -.fa-home-lg::before { - content: "\e3af"; } - -.fa-house-chimney-crack::before { - content: "\f6f1"; } - -.fa-house-damage::before { - content: "\f6f1"; } - -.fa-house-chimney-medical::before { - content: "\f7f2"; } - -.fa-clinic-medical::before { - content: "\f7f2"; } - -.fa-house-chimney-user::before { - content: "\e065"; } - -.fa-house-chimney-window::before { - content: "\e00d"; } - -.fa-house-circle-check::before { - content: "\e509"; } - -.fa-house-circle-exclamation::before { - content: "\e50a"; } - -.fa-house-circle-xmark::before { - content: "\e50b"; } - -.fa-house-crack::before { - content: "\e3b1"; } - -.fa-house-fire::before { - content: "\e50c"; } - -.fa-house-flag::before { - content: "\e50d"; } - -.fa-house-flood-water::before { - content: "\e50e"; } - -.fa-house-flood-water-circle-arrow-right::before { - content: "\e50f"; } - -.fa-house-laptop::before { - content: "\e066"; } - -.fa-laptop-house::before { - content: "\e066"; } - -.fa-house-lock::before { - content: "\e510"; } - -.fa-house-medical::before { - content: "\e3b2"; } - -.fa-house-medical-circle-check::before { - content: "\e511"; } - -.fa-house-medical-circle-exclamation::before { - content: "\e512"; } - -.fa-house-medical-circle-xmark::before { - content: "\e513"; } - -.fa-house-medical-flag::before { - content: "\e514"; } - -.fa-house-signal::before { - content: "\e012"; } - -.fa-house-tsunami::before { - content: "\e515"; } - -.fa-house-user::before { - content: "\e1b0"; } - -.fa-home-user::before { - content: "\e1b0"; } - -.fa-hryvnia-sign::before { - content: "\f6f2"; } - -.fa-hryvnia::before { - content: "\f6f2"; } - -.fa-hurricane::before { - content: "\f751"; } - -.fa-i::before { - content: "\49"; } - -.fa-i-cursor::before { - content: "\f246"; } - -.fa-ice-cream::before { - content: "\f810"; } - -.fa-icicles::before { - content: "\f7ad"; } - -.fa-icons::before { - content: "\f86d"; } - -.fa-heart-music-camera-bolt::before { - content: "\f86d"; } - -.fa-id-badge::before { - content: "\f2c1"; } - -.fa-id-card::before { - content: "\f2c2"; } - -.fa-drivers-license::before { - content: "\f2c2"; } - -.fa-id-card-clip::before { - content: "\f47f"; } - -.fa-id-card-alt::before { - content: "\f47f"; } - -.fa-igloo::before { - content: "\f7ae"; } - -.fa-image::before { - content: "\f03e"; } - -.fa-image-portrait::before { - content: "\f3e0"; } - -.fa-portrait::before { - content: "\f3e0"; } - -.fa-images::before { - content: "\f302"; } - -.fa-inbox::before { - content: "\f01c"; } - -.fa-indent::before { - content: "\f03c"; } - -.fa-indian-rupee-sign::before { - content: "\e1bc"; } - -.fa-indian-rupee::before { - content: "\e1bc"; } - -.fa-inr::before { - content: "\e1bc"; } - -.fa-industry::before { - content: "\f275"; } - -.fa-infinity::before { - content: "\f534"; } - -.fa-info::before { - content: "\f129"; } - -.fa-italic::before { - content: "\f033"; } - -.fa-j::before { - content: "\4a"; } - -.fa-jar::before { - content: "\e516"; } - -.fa-jar-wheat::before { - content: "\e517"; } - -.fa-jedi::before { - content: "\f669"; } - -.fa-jet-fighter::before { - content: "\f0fb"; } - -.fa-fighter-jet::before { - content: "\f0fb"; } - -.fa-jet-fighter-up::before { - content: "\e518"; } - -.fa-joint::before { - content: "\f595"; } - -.fa-jug-detergent::before { - content: "\e519"; } - -.fa-k::before { - content: "\4b"; } - -.fa-kaaba::before { - content: "\f66b"; } - -.fa-key::before { - content: "\f084"; } - -.fa-keyboard::before { - content: "\f11c"; } - -.fa-khanda::before { - content: "\f66d"; } - -.fa-kip-sign::before { - content: "\e1c4"; } - -.fa-kit-medical::before { - content: "\f479"; } - -.fa-first-aid::before { - content: "\f479"; } - -.fa-kitchen-set::before { - content: "\e51a"; } - -.fa-kiwi-bird::before { - content: "\f535"; } - -.fa-l::before { - content: "\4c"; } - -.fa-land-mine-on::before { - content: "\e51b"; } - -.fa-landmark::before { - content: "\f66f"; } - -.fa-landmark-dome::before { - content: "\f752"; } - -.fa-landmark-alt::before { - content: "\f752"; } - -.fa-landmark-flag::before { - content: "\e51c"; } - -.fa-language::before { - content: "\f1ab"; } - -.fa-laptop::before { - content: "\f109"; } - -.fa-laptop-code::before { - content: "\f5fc"; } - -.fa-laptop-file::before { - content: "\e51d"; } - -.fa-laptop-medical::before { - content: "\f812"; } - -.fa-lari-sign::before { - content: "\e1c8"; } - -.fa-layer-group::before { - content: "\f5fd"; } - -.fa-leaf::before { - content: "\f06c"; } - -.fa-left-long::before { - content: "\f30a"; } - -.fa-long-arrow-alt-left::before { - content: "\f30a"; } - -.fa-left-right::before { - content: "\f337"; } - -.fa-arrows-alt-h::before { - content: "\f337"; } - -.fa-lemon::before { - content: "\f094"; } - -.fa-less-than::before { - content: "\3c"; } - -.fa-less-than-equal::before { - content: "\f537"; } - -.fa-life-ring::before { - content: "\f1cd"; } - -.fa-lightbulb::before { - content: "\f0eb"; } - -.fa-lines-leaning::before { - content: "\e51e"; } - -.fa-link::before { - content: "\f0c1"; } - -.fa-chain::before { - content: "\f0c1"; } - -.fa-link-slash::before { - content: "\f127"; } - -.fa-chain-broken::before { - content: "\f127"; } - -.fa-chain-slash::before { - content: "\f127"; } - -.fa-unlink::before { - content: "\f127"; } - -.fa-lira-sign::before { - content: "\f195"; } - -.fa-list::before { - content: "\f03a"; } - -.fa-list-squares::before { - content: "\f03a"; } - -.fa-list-check::before { - content: "\f0ae"; } - -.fa-tasks::before { - content: "\f0ae"; } - -.fa-list-ol::before { - content: "\f0cb"; } - -.fa-list-1-2::before { - content: "\f0cb"; } - -.fa-list-numeric::before { - content: "\f0cb"; } - -.fa-list-ul::before { - content: "\f0ca"; } - -.fa-list-dots::before { - content: "\f0ca"; } - -.fa-litecoin-sign::before { - content: "\e1d3"; } - -.fa-location-arrow::before { - content: "\f124"; } - -.fa-location-crosshairs::before { - content: "\f601"; } - -.fa-location::before { - content: "\f601"; } - -.fa-location-dot::before { - content: "\f3c5"; } - -.fa-map-marker-alt::before { - content: "\f3c5"; } - -.fa-location-pin::before { - content: "\f041"; } - -.fa-map-marker::before { - content: "\f041"; } - -.fa-location-pin-lock::before { - content: "\e51f"; } - -.fa-lock::before { - content: "\f023"; } - -.fa-lock-open::before { - content: "\f3c1"; } - -.fa-locust::before { - content: "\e520"; } - -.fa-lungs::before { - content: "\f604"; } - -.fa-lungs-virus::before { - content: "\e067"; } - -.fa-m::before { - content: "\4d"; } - -.fa-magnet::before { - content: "\f076"; } - -.fa-magnifying-glass::before { - content: "\f002"; } - -.fa-search::before { - content: "\f002"; } - -.fa-magnifying-glass-arrow-right::before { - content: "\e521"; } - -.fa-magnifying-glass-chart::before { - content: "\e522"; } - -.fa-magnifying-glass-dollar::before { - content: "\f688"; } - -.fa-search-dollar::before { - content: "\f688"; } - -.fa-magnifying-glass-location::before { - content: "\f689"; } - -.fa-search-location::before { - content: "\f689"; } - -.fa-magnifying-glass-minus::before { - content: "\f010"; } - -.fa-search-minus::before { - content: "\f010"; } - -.fa-magnifying-glass-plus::before { - content: "\f00e"; } - -.fa-search-plus::before { - content: "\f00e"; } - -.fa-manat-sign::before { - content: "\e1d5"; } - -.fa-map::before { - content: "\f279"; } - -.fa-map-location::before { - content: "\f59f"; } - -.fa-map-marked::before { - content: "\f59f"; } - -.fa-map-location-dot::before { - content: "\f5a0"; } - -.fa-map-marked-alt::before { - content: "\f5a0"; } - -.fa-map-pin::before { - content: "\f276"; } - -.fa-marker::before { - content: "\f5a1"; } - -.fa-mars::before { - content: "\f222"; } - -.fa-mars-and-venus::before { - content: "\f224"; } - -.fa-mars-and-venus-burst::before { - content: "\e523"; } - -.fa-mars-double::before { - content: "\f227"; } - -.fa-mars-stroke::before { - content: "\f229"; } - -.fa-mars-stroke-right::before { - content: "\f22b"; } - -.fa-mars-stroke-h::before { - content: "\f22b"; } - -.fa-mars-stroke-up::before { - content: "\f22a"; } - -.fa-mars-stroke-v::before { - content: "\f22a"; } - -.fa-martini-glass::before { - content: "\f57b"; } - -.fa-glass-martini-alt::before { - content: "\f57b"; } - -.fa-martini-glass-citrus::before { - content: "\f561"; } - -.fa-cocktail::before { - content: "\f561"; } - -.fa-martini-glass-empty::before { - content: "\f000"; } - -.fa-glass-martini::before { - content: "\f000"; } - -.fa-mask::before { - content: "\f6fa"; } - -.fa-mask-face::before { - content: "\e1d7"; } - -.fa-mask-ventilator::before { - content: "\e524"; } - -.fa-masks-theater::before { - content: "\f630"; } - -.fa-theater-masks::before { - content: "\f630"; } - -.fa-mattress-pillow::before { - content: "\e525"; } - -.fa-maximize::before { - content: "\f31e"; } - -.fa-expand-arrows-alt::before { - content: "\f31e"; } - -.fa-medal::before { - content: "\f5a2"; } - -.fa-memory::before { - content: "\f538"; } - -.fa-menorah::before { - content: "\f676"; } - -.fa-mercury::before { - content: "\f223"; } - -.fa-message::before { - content: "\f27a"; } - -.fa-comment-alt::before { - content: "\f27a"; } - -.fa-meteor::before { - content: "\f753"; } - -.fa-microchip::before { - content: "\f2db"; } - -.fa-microphone::before { - content: "\f130"; } - -.fa-microphone-lines::before { - content: "\f3c9"; } - -.fa-microphone-alt::before { - content: "\f3c9"; } - -.fa-microphone-lines-slash::before { - content: "\f539"; } - -.fa-microphone-alt-slash::before { - content: "\f539"; } - -.fa-microphone-slash::before { - content: "\f131"; } - -.fa-microscope::before { - content: "\f610"; } - -.fa-mill-sign::before { - content: "\e1ed"; } - -.fa-minimize::before { - content: "\f78c"; } - -.fa-compress-arrows-alt::before { - content: "\f78c"; } - -.fa-minus::before { - content: "\f068"; } - -.fa-subtract::before { - content: "\f068"; } - -.fa-mitten::before { - content: "\f7b5"; } - -.fa-mobile::before { - content: "\f3ce"; } - -.fa-mobile-android::before { - content: "\f3ce"; } - -.fa-mobile-phone::before { - content: "\f3ce"; } - -.fa-mobile-button::before { - content: "\f10b"; } - -.fa-mobile-retro::before { - content: "\e527"; } - -.fa-mobile-screen::before { - content: "\f3cf"; } - -.fa-mobile-android-alt::before { - content: "\f3cf"; } - -.fa-mobile-screen-button::before { - content: "\f3cd"; } - -.fa-mobile-alt::before { - content: "\f3cd"; } - -.fa-money-bill::before { - content: "\f0d6"; } - -.fa-money-bill-1::before { - content: "\f3d1"; } - -.fa-money-bill-alt::before { - content: "\f3d1"; } - -.fa-money-bill-1-wave::before { - content: "\f53b"; } - -.fa-money-bill-wave-alt::before { - content: "\f53b"; } - -.fa-money-bill-transfer::before { - content: "\e528"; } - -.fa-money-bill-trend-up::before { - content: "\e529"; } - -.fa-money-bill-wave::before { - content: "\f53a"; } - -.fa-money-bill-wheat::before { - content: "\e52a"; } - -.fa-money-bills::before { - content: "\e1f3"; } - -.fa-money-check::before { - content: "\f53c"; } - -.fa-money-check-dollar::before { - content: "\f53d"; } - -.fa-money-check-alt::before { - content: "\f53d"; } - -.fa-monument::before { - content: "\f5a6"; } - -.fa-moon::before { - content: "\f186"; } - -.fa-mortar-pestle::before { - content: "\f5a7"; } - -.fa-mosque::before { - content: "\f678"; } - -.fa-mosquito::before { - content: "\e52b"; } - -.fa-mosquito-net::before { - content: "\e52c"; } - -.fa-motorcycle::before { - content: "\f21c"; } - -.fa-mound::before { - content: "\e52d"; } - -.fa-mountain::before { - content: "\f6fc"; } - -.fa-mountain-city::before { - content: "\e52e"; } - -.fa-mountain-sun::before { - content: "\e52f"; } - -.fa-mug-hot::before { - content: "\f7b6"; } - -.fa-mug-saucer::before { - content: "\f0f4"; } - -.fa-coffee::before { - content: "\f0f4"; } - -.fa-music::before { - content: "\f001"; } - -.fa-n::before { - content: "\4e"; } - -.fa-naira-sign::before { - content: "\e1f6"; } - -.fa-network-wired::before { - content: "\f6ff"; } - -.fa-neuter::before { - content: "\f22c"; } - -.fa-newspaper::before { - content: "\f1ea"; } - -.fa-not-equal::before { - content: "\f53e"; } - -.fa-note-sticky::before { - content: "\f249"; } - -.fa-sticky-note::before { - content: "\f249"; } - -.fa-notes-medical::before { - content: "\f481"; } - -.fa-o::before { - content: "\4f"; } - -.fa-object-group::before { - content: "\f247"; } - -.fa-object-ungroup::before { - content: "\f248"; } - -.fa-oil-can::before { - content: "\f613"; } - -.fa-oil-well::before { - content: "\e532"; } - -.fa-om::before { - content: "\f679"; } - -.fa-otter::before { - content: "\f700"; } - -.fa-outdent::before { - content: "\f03b"; } - -.fa-dedent::before { - content: "\f03b"; } - -.fa-p::before { - content: "\50"; } - -.fa-pager::before { - content: "\f815"; } - -.fa-paint-roller::before { - content: "\f5aa"; } - -.fa-paintbrush::before { - content: "\f1fc"; } - -.fa-paint-brush::before { - content: "\f1fc"; } - -.fa-palette::before { - content: "\f53f"; } - -.fa-pallet::before { - content: "\f482"; } - -.fa-panorama::before { - content: "\e209"; } - -.fa-paper-plane::before { - content: "\f1d8"; } - -.fa-paperclip::before { - content: "\f0c6"; } - -.fa-parachute-box::before { - content: "\f4cd"; } - -.fa-paragraph::before { - content: "\f1dd"; } - -.fa-passport::before { - content: "\f5ab"; } - -.fa-paste::before { - content: "\f0ea"; } - -.fa-file-clipboard::before { - content: "\f0ea"; } - -.fa-pause::before { - content: "\f04c"; } - -.fa-paw::before { - content: "\f1b0"; } - -.fa-peace::before { - content: "\f67c"; } - -.fa-pen::before { - content: "\f304"; } - -.fa-pen-clip::before { - content: "\f305"; } - -.fa-pen-alt::before { - content: "\f305"; } - -.fa-pen-fancy::before { - content: "\f5ac"; } - -.fa-pen-nib::before { - content: "\f5ad"; } - -.fa-pen-ruler::before { - content: "\f5ae"; } - -.fa-pencil-ruler::before { - content: "\f5ae"; } - -.fa-pen-to-square::before { - content: "\f044"; } - -.fa-edit::before { - content: "\f044"; } - -.fa-pencil::before { - content: "\f303"; } - -.fa-pencil-alt::before { - content: "\f303"; } - -.fa-people-arrows-left-right::before { - content: "\e068"; } - -.fa-people-arrows::before { - content: "\e068"; } - -.fa-people-carry-box::before { - content: "\f4ce"; } - -.fa-people-carry::before { - content: "\f4ce"; } - -.fa-people-group::before { - content: "\e533"; } - -.fa-people-line::before { - content: "\e534"; } - -.fa-people-pulling::before { - content: "\e535"; } - -.fa-people-robbery::before { - content: "\e536"; } - -.fa-people-roof::before { - content: "\e537"; } - -.fa-pepper-hot::before { - content: "\f816"; } - -.fa-percent::before { - content: "\25"; } - -.fa-percentage::before { - content: "\25"; } - -.fa-person::before { - content: "\f183"; } - -.fa-male::before { - content: "\f183"; } - -.fa-person-arrow-down-to-line::before { - content: "\e538"; } - -.fa-person-arrow-up-from-line::before { - content: "\e539"; } - -.fa-person-biking::before { - content: "\f84a"; } - -.fa-biking::before { - content: "\f84a"; } - -.fa-person-booth::before { - content: "\f756"; } - -.fa-person-breastfeeding::before { - content: "\e53a"; } - -.fa-person-burst::before { - content: "\e53b"; } - -.fa-person-cane::before { - content: "\e53c"; } - -.fa-person-chalkboard::before { - content: "\e53d"; } - -.fa-person-circle-check::before { - content: "\e53e"; } - -.fa-person-circle-exclamation::before { - content: "\e53f"; } - -.fa-person-circle-minus::before { - content: "\e540"; } - -.fa-person-circle-plus::before { - content: "\e541"; } - -.fa-person-circle-question::before { - content: "\e542"; } - -.fa-person-circle-xmark::before { - content: "\e543"; } - -.fa-person-digging::before { - content: "\f85e"; } - -.fa-digging::before { - content: "\f85e"; } - -.fa-person-dots-from-line::before { - content: "\f470"; } - -.fa-diagnoses::before { - content: "\f470"; } - -.fa-person-dress::before { - content: "\f182"; } - -.fa-female::before { - content: "\f182"; } - -.fa-person-dress-burst::before { - content: "\e544"; } - -.fa-person-drowning::before { - content: "\e545"; } - -.fa-person-falling::before { - content: "\e546"; } - -.fa-person-falling-burst::before { - content: "\e547"; } - -.fa-person-half-dress::before { - content: "\e548"; } - -.fa-person-harassing::before { - content: "\e549"; } - -.fa-person-hiking::before { - content: "\f6ec"; } - -.fa-hiking::before { - content: "\f6ec"; } - -.fa-person-military-pointing::before { - content: "\e54a"; } - -.fa-person-military-rifle::before { - content: "\e54b"; } - -.fa-person-military-to-person::before { - content: "\e54c"; } - -.fa-person-praying::before { - content: "\f683"; } - -.fa-pray::before { - content: "\f683"; } - -.fa-person-pregnant::before { - content: "\e31e"; } - -.fa-person-rays::before { - content: "\e54d"; } - -.fa-person-rifle::before { - content: "\e54e"; } - -.fa-person-running::before { - content: "\f70c"; } - -.fa-running::before { - content: "\f70c"; } - -.fa-person-shelter::before { - content: "\e54f"; } - -.fa-person-skating::before { - content: "\f7c5"; } - -.fa-skating::before { - content: "\f7c5"; } - -.fa-person-skiing::before { - content: "\f7c9"; } - -.fa-skiing::before { - content: "\f7c9"; } - -.fa-person-skiing-nordic::before { - content: "\f7ca"; } - -.fa-skiing-nordic::before { - content: "\f7ca"; } - -.fa-person-snowboarding::before { - content: "\f7ce"; } - -.fa-snowboarding::before { - content: "\f7ce"; } - -.fa-person-swimming::before { - content: "\f5c4"; } - -.fa-swimmer::before { - content: "\f5c4"; } - -.fa-person-through-window::before { - content: "\e433"; } - -.fa-person-walking::before { - content: "\f554"; } - -.fa-walking::before { - content: "\f554"; } - -.fa-person-walking-arrow-loop-left::before { - content: "\e551"; } - -.fa-person-walking-arrow-right::before { - content: "\e552"; } - -.fa-person-walking-dashed-line-arrow-right::before { - content: "\e553"; } - -.fa-person-walking-luggage::before { - content: "\e554"; } - -.fa-person-walking-with-cane::before { - content: "\f29d"; } - -.fa-blind::before { - content: "\f29d"; } - -.fa-peseta-sign::before { - content: "\e221"; } - -.fa-peso-sign::before { - content: "\e222"; } - -.fa-phone::before { - content: "\f095"; } - -.fa-phone-flip::before { - content: "\f879"; } - -.fa-phone-alt::before { - content: "\f879"; } - -.fa-phone-slash::before { - content: "\f3dd"; } - -.fa-phone-volume::before { - content: "\f2a0"; } - -.fa-volume-control-phone::before { - content: "\f2a0"; } - -.fa-photo-film::before { - content: "\f87c"; } - -.fa-photo-video::before { - content: "\f87c"; } - -.fa-piggy-bank::before { - content: "\f4d3"; } - -.fa-pills::before { - content: "\f484"; } - -.fa-pizza-slice::before { - content: "\f818"; } - -.fa-place-of-worship::before { - content: "\f67f"; } - -.fa-plane::before { - content: "\f072"; } - -.fa-plane-arrival::before { - content: "\f5af"; } - -.fa-plane-circle-check::before { - content: "\e555"; } - -.fa-plane-circle-exclamation::before { - content: "\e556"; } - -.fa-plane-circle-xmark::before { - content: "\e557"; } - -.fa-plane-departure::before { - content: "\f5b0"; } - -.fa-plane-lock::before { - content: "\e558"; } - -.fa-plane-slash::before { - content: "\e069"; } - -.fa-plane-up::before { - content: "\e22d"; } - -.fa-plant-wilt::before { - content: "\e43b"; } - -.fa-plate-wheat::before { - content: "\e55a"; } - -.fa-play::before { - content: "\f04b"; } - -.fa-plug::before { - content: "\f1e6"; } - -.fa-plug-circle-bolt::before { - content: "\e55b"; } - -.fa-plug-circle-check::before { - content: "\e55c"; } - -.fa-plug-circle-exclamation::before { - content: "\e55d"; } - -.fa-plug-circle-minus::before { - content: "\e55e"; } - -.fa-plug-circle-plus::before { - content: "\e55f"; } - -.fa-plug-circle-xmark::before { - content: "\e560"; } - -.fa-plus::before { - content: "\2b"; } - -.fa-add::before { - content: "\2b"; } - -.fa-plus-minus::before { - content: "\e43c"; } - -.fa-podcast::before { - content: "\f2ce"; } - -.fa-poo::before { - content: "\f2fe"; } - -.fa-poo-storm::before { - content: "\f75a"; } - -.fa-poo-bolt::before { - content: "\f75a"; } - -.fa-poop::before { - content: "\f619"; } - -.fa-power-off::before { - content: "\f011"; } - -.fa-prescription::before { - content: "\f5b1"; } - -.fa-prescription-bottle::before { - content: "\f485"; } - -.fa-prescription-bottle-medical::before { - content: "\f486"; } - -.fa-prescription-bottle-alt::before { - content: "\f486"; } - -.fa-print::before { - content: "\f02f"; } - -.fa-pump-medical::before { - content: "\e06a"; } - -.fa-pump-soap::before { - content: "\e06b"; } - -.fa-puzzle-piece::before { - content: "\f12e"; } - -.fa-q::before { - content: "\51"; } - -.fa-qrcode::before { - content: "\f029"; } - -.fa-question::before { - content: "\3f"; } - -.fa-quote-left::before { - content: "\f10d"; } - -.fa-quote-left-alt::before { - content: "\f10d"; } - -.fa-quote-right::before { - content: "\f10e"; } - -.fa-quote-right-alt::before { - content: "\f10e"; } - -.fa-r::before { - content: "\52"; } - -.fa-radiation::before { - content: "\f7b9"; } - -.fa-radio::before { - content: "\f8d7"; } - -.fa-rainbow::before { - content: "\f75b"; } - -.fa-ranking-star::before { - content: "\e561"; } - -.fa-receipt::before { - content: "\f543"; } - -.fa-record-vinyl::before { - content: "\f8d9"; } - -.fa-rectangle-ad::before { - content: "\f641"; } - -.fa-ad::before { - content: "\f641"; } - -.fa-rectangle-list::before { - content: "\f022"; } - -.fa-list-alt::before { - content: "\f022"; } - -.fa-rectangle-xmark::before { - content: "\f410"; } - -.fa-rectangle-times::before { - content: "\f410"; } - -.fa-times-rectangle::before { - content: "\f410"; } - -.fa-window-close::before { - content: "\f410"; } - -.fa-recycle::before { - content: "\f1b8"; } - -.fa-registered::before { - content: "\f25d"; } - -.fa-repeat::before { - content: "\f363"; } - -.fa-reply::before { - content: "\f3e5"; } - -.fa-mail-reply::before { - content: "\f3e5"; } - -.fa-reply-all::before { - content: "\f122"; } - -.fa-mail-reply-all::before { - content: "\f122"; } - -.fa-republican::before { - content: "\f75e"; } - -.fa-restroom::before { - content: "\f7bd"; } - -.fa-retweet::before { - content: "\f079"; } - -.fa-ribbon::before { - content: "\f4d6"; } - -.fa-right-from-bracket::before { - content: "\f2f5"; } - -.fa-sign-out-alt::before { - content: "\f2f5"; } - -.fa-right-left::before { - content: "\f362"; } - -.fa-exchange-alt::before { - content: "\f362"; } - -.fa-right-long::before { - content: "\f30b"; } - -.fa-long-arrow-alt-right::before { - content: "\f30b"; } - -.fa-right-to-bracket::before { - content: "\f2f6"; } - -.fa-sign-in-alt::before { - content: "\f2f6"; } - -.fa-ring::before { - content: "\f70b"; } - -.fa-road::before { - content: "\f018"; } - -.fa-road-barrier::before { - content: "\e562"; } - -.fa-road-bridge::before { - content: "\e563"; } - -.fa-road-circle-check::before { - content: "\e564"; } - -.fa-road-circle-exclamation::before { - content: "\e565"; } - -.fa-road-circle-xmark::before { - content: "\e566"; } - -.fa-road-lock::before { - content: "\e567"; } - -.fa-road-spikes::before { - content: "\e568"; } - -.fa-robot::before { - content: "\f544"; } - -.fa-rocket::before { - content: "\f135"; } - -.fa-rotate::before { - content: "\f2f1"; } - -.fa-sync-alt::before { - content: "\f2f1"; } - -.fa-rotate-left::before { - content: "\f2ea"; } - -.fa-rotate-back::before { - content: "\f2ea"; } - -.fa-rotate-backward::before { - content: "\f2ea"; } - -.fa-undo-alt::before { - content: "\f2ea"; } - -.fa-rotate-right::before { - content: "\f2f9"; } - -.fa-redo-alt::before { - content: "\f2f9"; } - -.fa-rotate-forward::before { - content: "\f2f9"; } - -.fa-route::before { - content: "\f4d7"; } - -.fa-rss::before { - content: "\f09e"; } - -.fa-feed::before { - content: "\f09e"; } - -.fa-ruble-sign::before { - content: "\f158"; } - -.fa-rouble::before { - content: "\f158"; } - -.fa-rub::before { - content: "\f158"; } - -.fa-ruble::before { - content: "\f158"; } - -.fa-rug::before { - content: "\e569"; } - -.fa-ruler::before { - content: "\f545"; } - -.fa-ruler-combined::before { - content: "\f546"; } - -.fa-ruler-horizontal::before { - content: "\f547"; } - -.fa-ruler-vertical::before { - content: "\f548"; } - -.fa-rupee-sign::before { - content: "\f156"; } - -.fa-rupee::before { - content: "\f156"; } - -.fa-rupiah-sign::before { - content: "\e23d"; } - -.fa-s::before { - content: "\53"; } - -.fa-sack-dollar::before { - content: "\f81d"; } - -.fa-sack-xmark::before { - content: "\e56a"; } - -.fa-sailboat::before { - content: "\e445"; } - -.fa-satellite::before { - content: "\f7bf"; } - -.fa-satellite-dish::before { - content: "\f7c0"; } - -.fa-scale-balanced::before { - content: "\f24e"; } - -.fa-balance-scale::before { - content: "\f24e"; } - -.fa-scale-unbalanced::before { - content: "\f515"; } - -.fa-balance-scale-left::before { - content: "\f515"; } - -.fa-scale-unbalanced-flip::before { - content: "\f516"; } - -.fa-balance-scale-right::before { - content: "\f516"; } - -.fa-school::before { - content: "\f549"; } - -.fa-school-circle-check::before { - content: "\e56b"; } - -.fa-school-circle-exclamation::before { - content: "\e56c"; } - -.fa-school-circle-xmark::before { - content: "\e56d"; } - -.fa-school-flag::before { - content: "\e56e"; } - -.fa-school-lock::before { - content: "\e56f"; } - -.fa-scissors::before { - content: "\f0c4"; } - -.fa-cut::before { - content: "\f0c4"; } - -.fa-screwdriver::before { - content: "\f54a"; } - -.fa-screwdriver-wrench::before { - content: "\f7d9"; } - -.fa-tools::before { - content: "\f7d9"; } - -.fa-scroll::before { - content: "\f70e"; } - -.fa-scroll-torah::before { - content: "\f6a0"; } - -.fa-torah::before { - content: "\f6a0"; } - -.fa-sd-card::before { - content: "\f7c2"; } - -.fa-section::before { - content: "\e447"; } - -.fa-seedling::before { - content: "\f4d8"; } - -.fa-sprout::before { - content: "\f4d8"; } - -.fa-server::before { - content: "\f233"; } - -.fa-shapes::before { - content: "\f61f"; } - -.fa-triangle-circle-square::before { - content: "\f61f"; } - -.fa-share::before { - content: "\f064"; } - -.fa-arrow-turn-right::before { - content: "\f064"; } - -.fa-mail-forward::before { - content: "\f064"; } - -.fa-share-from-square::before { - content: "\f14d"; } - -.fa-share-square::before { - content: "\f14d"; } - -.fa-share-nodes::before { - content: "\f1e0"; } - -.fa-share-alt::before { - content: "\f1e0"; } - -.fa-sheet-plastic::before { - content: "\e571"; } - -.fa-shekel-sign::before { - content: "\f20b"; } - -.fa-ils::before { - content: "\f20b"; } - -.fa-shekel::before { - content: "\f20b"; } - -.fa-sheqel::before { - content: "\f20b"; } - -.fa-sheqel-sign::before { - content: "\f20b"; } - -.fa-shield::before { - content: "\f132"; } - -.fa-shield-blank::before { - content: "\f132"; } - -.fa-shield-cat::before { - content: "\e572"; } - -.fa-shield-dog::before { - content: "\e573"; } - -.fa-shield-halved::before { - content: "\f3ed"; } - -.fa-shield-alt::before { - content: "\f3ed"; } - -.fa-shield-heart::before { - content: "\e574"; } - -.fa-shield-virus::before { - content: "\e06c"; } - -.fa-ship::before { - content: "\f21a"; } - -.fa-shirt::before { - content: "\f553"; } - -.fa-t-shirt::before { - content: "\f553"; } - -.fa-tshirt::before { - content: "\f553"; } - -.fa-shoe-prints::before { - content: "\f54b"; } - -.fa-shop::before { - content: "\f54f"; } - -.fa-store-alt::before { - content: "\f54f"; } - -.fa-shop-lock::before { - content: "\e4a5"; } - -.fa-shop-slash::before { - content: "\e070"; } - -.fa-store-alt-slash::before { - content: "\e070"; } - -.fa-shower::before { - content: "\f2cc"; } - -.fa-shrimp::before { - content: "\e448"; } - -.fa-shuffle::before { - content: "\f074"; } - -.fa-random::before { - content: "\f074"; } - -.fa-shuttle-space::before { - content: "\f197"; } - -.fa-space-shuttle::before { - content: "\f197"; } - -.fa-sign-hanging::before { - content: "\f4d9"; } - -.fa-sign::before { - content: "\f4d9"; } - -.fa-signal::before { - content: "\f012"; } - -.fa-signal-5::before { - content: "\f012"; } - -.fa-signal-perfect::before { - content: "\f012"; } - -.fa-signature::before { - content: "\f5b7"; } - -.fa-signs-post::before { - content: "\f277"; } - -.fa-map-signs::before { - content: "\f277"; } - -.fa-sim-card::before { - content: "\f7c4"; } - -.fa-sink::before { - content: "\e06d"; } - -.fa-sitemap::before { - content: "\f0e8"; } - -.fa-skull::before { - content: "\f54c"; } - -.fa-skull-crossbones::before { - content: "\f714"; } - -.fa-slash::before { - content: "\f715"; } - -.fa-sleigh::before { - content: "\f7cc"; } - -.fa-sliders::before { - content: "\f1de"; } - -.fa-sliders-h::before { - content: "\f1de"; } - -.fa-smog::before { - content: "\f75f"; } - -.fa-smoking::before { - content: "\f48d"; } - -.fa-snowflake::before { - content: "\f2dc"; } - -.fa-snowman::before { - content: "\f7d0"; } - -.fa-snowplow::before { - content: "\f7d2"; } - -.fa-soap::before { - content: "\e06e"; } - -.fa-socks::before { - content: "\f696"; } - -.fa-solar-panel::before { - content: "\f5ba"; } - -.fa-sort::before { - content: "\f0dc"; } - -.fa-unsorted::before { - content: "\f0dc"; } - -.fa-sort-down::before { - content: "\f0dd"; } - -.fa-sort-desc::before { - content: "\f0dd"; } - -.fa-sort-up::before { - content: "\f0de"; } - -.fa-sort-asc::before { - content: "\f0de"; } - -.fa-spa::before { - content: "\f5bb"; } - -.fa-spaghetti-monster-flying::before { - content: "\f67b"; } - -.fa-pastafarianism::before { - content: "\f67b"; } - -.fa-spell-check::before { - content: "\f891"; } - -.fa-spider::before { - content: "\f717"; } - -.fa-spinner::before { - content: "\f110"; } - -.fa-splotch::before { - content: "\f5bc"; } - -.fa-spoon::before { - content: "\f2e5"; } - -.fa-utensil-spoon::before { - content: "\f2e5"; } - -.fa-spray-can::before { - content: "\f5bd"; } - -.fa-spray-can-sparkles::before { - content: "\f5d0"; } - -.fa-air-freshener::before { - content: "\f5d0"; } - -.fa-square::before { - content: "\f0c8"; } - -.fa-square-arrow-up-right::before { - content: "\f14c"; } - -.fa-external-link-square::before { - content: "\f14c"; } - -.fa-square-caret-down::before { - content: "\f150"; } - -.fa-caret-square-down::before { - content: "\f150"; } - -.fa-square-caret-left::before { - content: "\f191"; } - -.fa-caret-square-left::before { - content: "\f191"; } - -.fa-square-caret-right::before { - content: "\f152"; } - -.fa-caret-square-right::before { - content: "\f152"; } - -.fa-square-caret-up::before { - content: "\f151"; } - -.fa-caret-square-up::before { - content: "\f151"; } - -.fa-square-check::before { - content: "\f14a"; } - -.fa-check-square::before { - content: "\f14a"; } - -.fa-square-envelope::before { - content: "\f199"; } - -.fa-envelope-square::before { - content: "\f199"; } - -.fa-square-full::before { - content: "\f45c"; } - -.fa-square-h::before { - content: "\f0fd"; } - -.fa-h-square::before { - content: "\f0fd"; } - -.fa-square-minus::before { - content: "\f146"; } - -.fa-minus-square::before { - content: "\f146"; } - -.fa-square-nfi::before { - content: "\e576"; } - -.fa-square-parking::before { - content: "\f540"; } - -.fa-parking::before { - content: "\f540"; } - -.fa-square-pen::before { - content: "\f14b"; } - -.fa-pen-square::before { - content: "\f14b"; } - -.fa-pencil-square::before { - content: "\f14b"; } - -.fa-square-person-confined::before { - content: "\e577"; } - -.fa-square-phone::before { - content: "\f098"; } - -.fa-phone-square::before { - content: "\f098"; } - -.fa-square-phone-flip::before { - content: "\f87b"; } - -.fa-phone-square-alt::before { - content: "\f87b"; } - -.fa-square-plus::before { - content: "\f0fe"; } - -.fa-plus-square::before { - content: "\f0fe"; } - -.fa-square-poll-horizontal::before { - content: "\f682"; } - -.fa-poll-h::before { - content: "\f682"; } - -.fa-square-poll-vertical::before { - content: "\f681"; } - -.fa-poll::before { - content: "\f681"; } - -.fa-square-root-variable::before { - content: "\f698"; } - -.fa-square-root-alt::before { - content: "\f698"; } - -.fa-square-rss::before { - content: "\f143"; } - -.fa-rss-square::before { - content: "\f143"; } - -.fa-square-share-nodes::before { - content: "\f1e1"; } - -.fa-share-alt-square::before { - content: "\f1e1"; } - -.fa-square-up-right::before { - content: "\f360"; } - -.fa-external-link-square-alt::before { - content: "\f360"; } - -.fa-square-virus::before { - content: "\e578"; } - -.fa-square-xmark::before { - content: "\f2d3"; } - -.fa-times-square::before { - content: "\f2d3"; } - -.fa-xmark-square::before { - content: "\f2d3"; } - -.fa-staff-aesculapius::before { - content: "\e579"; } - -.fa-rod-asclepius::before { - content: "\e579"; } - -.fa-rod-snake::before { - content: "\e579"; } - -.fa-staff-snake::before { - content: "\e579"; } - -.fa-stairs::before { - content: "\e289"; } - -.fa-stamp::before { - content: "\f5bf"; } - -.fa-star::before { - content: "\f005"; } - -.fa-star-and-crescent::before { - content: "\f699"; } - -.fa-star-half::before { - content: "\f089"; } - -.fa-star-half-stroke::before { - content: "\f5c0"; } - -.fa-star-half-alt::before { - content: "\f5c0"; } - -.fa-star-of-david::before { - content: "\f69a"; } - -.fa-star-of-life::before { - content: "\f621"; } - -.fa-sterling-sign::before { - content: "\f154"; } - -.fa-gbp::before { - content: "\f154"; } - -.fa-pound-sign::before { - content: "\f154"; } - -.fa-stethoscope::before { - content: "\f0f1"; } - -.fa-stop::before { - content: "\f04d"; } - -.fa-stopwatch::before { - content: "\f2f2"; } - -.fa-stopwatch-20::before { - content: "\e06f"; } - -.fa-store::before { - content: "\f54e"; } - -.fa-store-slash::before { - content: "\e071"; } - -.fa-street-view::before { - content: "\f21d"; } - -.fa-strikethrough::before { - content: "\f0cc"; } - -.fa-stroopwafel::before { - content: "\f551"; } - -.fa-subscript::before { - content: "\f12c"; } - -.fa-suitcase::before { - content: "\f0f2"; } - -.fa-suitcase-medical::before { - content: "\f0fa"; } - -.fa-medkit::before { - content: "\f0fa"; } - -.fa-suitcase-rolling::before { - content: "\f5c1"; } - -.fa-sun::before { - content: "\f185"; } - -.fa-sun-plant-wilt::before { - content: "\e57a"; } - -.fa-superscript::before { - content: "\f12b"; } - -.fa-swatchbook::before { - content: "\f5c3"; } - -.fa-synagogue::before { - content: "\f69b"; } - -.fa-syringe::before { - content: "\f48e"; } - -.fa-t::before { - content: "\54"; } - -.fa-table::before { - content: "\f0ce"; } - -.fa-table-cells::before { - content: "\f00a"; } - -.fa-th::before { - content: "\f00a"; } - -.fa-table-cells-large::before { - content: "\f009"; } - -.fa-th-large::before { - content: "\f009"; } - -.fa-table-columns::before { - content: "\f0db"; } - -.fa-columns::before { - content: "\f0db"; } - -.fa-table-list::before { - content: "\f00b"; } - -.fa-th-list::before { - content: "\f00b"; } - -.fa-table-tennis-paddle-ball::before { - content: "\f45d"; } - -.fa-ping-pong-paddle-ball::before { - content: "\f45d"; } - -.fa-table-tennis::before { - content: "\f45d"; } - -.fa-tablet::before { - content: "\f3fb"; } - -.fa-tablet-android::before { - content: "\f3fb"; } - -.fa-tablet-button::before { - content: "\f10a"; } - -.fa-tablet-screen-button::before { - content: "\f3fa"; } - -.fa-tablet-alt::before { - content: "\f3fa"; } - -.fa-tablets::before { - content: "\f490"; } - -.fa-tachograph-digital::before { - content: "\f566"; } - -.fa-digital-tachograph::before { - content: "\f566"; } - -.fa-tag::before { - content: "\f02b"; } - -.fa-tags::before { - content: "\f02c"; } - -.fa-tape::before { - content: "\f4db"; } - -.fa-tarp::before { - content: "\e57b"; } - -.fa-tarp-droplet::before { - content: "\e57c"; } - -.fa-taxi::before { - content: "\f1ba"; } - -.fa-cab::before { - content: "\f1ba"; } - -.fa-teeth::before { - content: "\f62e"; } - -.fa-teeth-open::before { - content: "\f62f"; } - -.fa-temperature-arrow-down::before { - content: "\e03f"; } - -.fa-temperature-down::before { - content: "\e03f"; } - -.fa-temperature-arrow-up::before { - content: "\e040"; } - -.fa-temperature-up::before { - content: "\e040"; } - -.fa-temperature-empty::before { - content: "\f2cb"; } - -.fa-temperature-0::before { - content: "\f2cb"; } - -.fa-thermometer-0::before { - content: "\f2cb"; } - -.fa-thermometer-empty::before { - content: "\f2cb"; } - -.fa-temperature-full::before { - content: "\f2c7"; } - -.fa-temperature-4::before { - content: "\f2c7"; } - -.fa-thermometer-4::before { - content: "\f2c7"; } - -.fa-thermometer-full::before { - content: "\f2c7"; } - -.fa-temperature-half::before { - content: "\f2c9"; } - -.fa-temperature-2::before { - content: "\f2c9"; } - -.fa-thermometer-2::before { - content: "\f2c9"; } - -.fa-thermometer-half::before { - content: "\f2c9"; } - -.fa-temperature-high::before { - content: "\f769"; } - -.fa-temperature-low::before { - content: "\f76b"; } - -.fa-temperature-quarter::before { - content: "\f2ca"; } - -.fa-temperature-1::before { - content: "\f2ca"; } - -.fa-thermometer-1::before { - content: "\f2ca"; } - -.fa-thermometer-quarter::before { - content: "\f2ca"; } - -.fa-temperature-three-quarters::before { - content: "\f2c8"; } - -.fa-temperature-3::before { - content: "\f2c8"; } - -.fa-thermometer-3::before { - content: "\f2c8"; } - -.fa-thermometer-three-quarters::before { - content: "\f2c8"; } - -.fa-tenge-sign::before { - content: "\f7d7"; } - -.fa-tenge::before { - content: "\f7d7"; } - -.fa-tent::before { - content: "\e57d"; } - -.fa-tent-arrow-down-to-line::before { - content: "\e57e"; } - -.fa-tent-arrow-left-right::before { - content: "\e57f"; } - -.fa-tent-arrow-turn-left::before { - content: "\e580"; } - -.fa-tent-arrows-down::before { - content: "\e581"; } - -.fa-tents::before { - content: "\e582"; } - -.fa-terminal::before { - content: "\f120"; } - -.fa-text-height::before { - content: "\f034"; } - -.fa-text-slash::before { - content: "\f87d"; } - -.fa-remove-format::before { - content: "\f87d"; } - -.fa-text-width::before { - content: "\f035"; } - -.fa-thermometer::before { - content: "\f491"; } - -.fa-thumbs-down::before { - content: "\f165"; } - -.fa-thumbs-up::before { - content: "\f164"; } - -.fa-thumbtack::before { - content: "\f08d"; } - -.fa-thumb-tack::before { - content: "\f08d"; } - -.fa-ticket::before { - content: "\f145"; } - -.fa-ticket-simple::before { - content: "\f3ff"; } - -.fa-ticket-alt::before { - content: "\f3ff"; } - -.fa-timeline::before { - content: "\e29c"; } - -.fa-toggle-off::before { - content: "\f204"; } - -.fa-toggle-on::before { - content: "\f205"; } - -.fa-toilet::before { - content: "\f7d8"; } - -.fa-toilet-paper::before { - content: "\f71e"; } - -.fa-toilet-paper-slash::before { - content: "\e072"; } - -.fa-toilet-portable::before { - content: "\e583"; } - -.fa-toilets-portable::before { - content: "\e584"; } - -.fa-toolbox::before { - content: "\f552"; } - -.fa-tooth::before { - content: "\f5c9"; } - -.fa-torii-gate::before { - content: "\f6a1"; } - -.fa-tornado::before { - content: "\f76f"; } - -.fa-tower-broadcast::before { - content: "\f519"; } - -.fa-broadcast-tower::before { - content: "\f519"; } - -.fa-tower-cell::before { - content: "\e585"; } - -.fa-tower-observation::before { - content: "\e586"; } - -.fa-tractor::before { - content: "\f722"; } - -.fa-trademark::before { - content: "\f25c"; } - -.fa-traffic-light::before { - content: "\f637"; } - -.fa-trailer::before { - content: "\e041"; } - -.fa-train::before { - content: "\f238"; } - -.fa-train-subway::before { - content: "\f239"; } - -.fa-subway::before { - content: "\f239"; } - -.fa-train-tram::before { - content: "\f7da"; } - -.fa-tram::before { - content: "\f7da"; } - -.fa-transgender::before { - content: "\f225"; } - -.fa-transgender-alt::before { - content: "\f225"; } - -.fa-trash::before { - content: "\f1f8"; } - -.fa-trash-arrow-up::before { - content: "\f829"; } - -.fa-trash-restore::before { - content: "\f829"; } - -.fa-trash-can::before { - content: "\f2ed"; } - -.fa-trash-alt::before { - content: "\f2ed"; } - -.fa-trash-can-arrow-up::before { - content: "\f82a"; } - -.fa-trash-restore-alt::before { - content: "\f82a"; } - -.fa-tree::before { - content: "\f1bb"; } - -.fa-tree-city::before { - content: "\e587"; } - -.fa-triangle-exclamation::before { - content: "\f071"; } - -.fa-exclamation-triangle::before { - content: "\f071"; } - -.fa-warning::before { - content: "\f071"; } - -.fa-trophy::before { - content: "\f091"; } - -.fa-trowel::before { - content: "\e589"; } - -.fa-trowel-bricks::before { - content: "\e58a"; } - -.fa-truck::before { - content: "\f0d1"; } - -.fa-truck-arrow-right::before { - content: "\e58b"; } - -.fa-truck-droplet::before { - content: "\e58c"; } - -.fa-truck-fast::before { - content: "\f48b"; } - -.fa-shipping-fast::before { - content: "\f48b"; } - -.fa-truck-field::before { - content: "\e58d"; } - -.fa-truck-field-un::before { - content: "\e58e"; } - -.fa-truck-front::before { - content: "\e2b7"; } - -.fa-truck-medical::before { - content: "\f0f9"; } - -.fa-ambulance::before { - content: "\f0f9"; } - -.fa-truck-monster::before { - content: "\f63b"; } - -.fa-truck-moving::before { - content: "\f4df"; } - -.fa-truck-pickup::before { - content: "\f63c"; } - -.fa-truck-plane::before { - content: "\e58f"; } - -.fa-truck-ramp-box::before { - content: "\f4de"; } - -.fa-truck-loading::before { - content: "\f4de"; } - -.fa-tty::before { - content: "\f1e4"; } - -.fa-teletype::before { - content: "\f1e4"; } - -.fa-turkish-lira-sign::before { - content: "\e2bb"; } - -.fa-try::before { - content: "\e2bb"; } - -.fa-turkish-lira::before { - content: "\e2bb"; } - -.fa-turn-down::before { - content: "\f3be"; } - -.fa-level-down-alt::before { - content: "\f3be"; } - -.fa-turn-up::before { - content: "\f3bf"; } - -.fa-level-up-alt::before { - content: "\f3bf"; } - -.fa-tv::before { - content: "\f26c"; } - -.fa-television::before { - content: "\f26c"; } - -.fa-tv-alt::before { - content: "\f26c"; } - -.fa-u::before { - content: "\55"; } - -.fa-umbrella::before { - content: "\f0e9"; } - -.fa-umbrella-beach::before { - content: "\f5ca"; } - -.fa-underline::before { - content: "\f0cd"; } - -.fa-universal-access::before { - content: "\f29a"; } - -.fa-unlock::before { - content: "\f09c"; } - -.fa-unlock-keyhole::before { - content: "\f13e"; } - -.fa-unlock-alt::before { - content: "\f13e"; } - -.fa-up-down::before { - content: "\f338"; } - -.fa-arrows-alt-v::before { - content: "\f338"; } - -.fa-up-down-left-right::before { - content: "\f0b2"; } - -.fa-arrows-alt::before { - content: "\f0b2"; } - -.fa-up-long::before { - content: "\f30c"; } - -.fa-long-arrow-alt-up::before { - content: "\f30c"; } - -.fa-up-right-and-down-left-from-center::before { - content: "\f424"; } - -.fa-expand-alt::before { - content: "\f424"; } - -.fa-up-right-from-square::before { - content: "\f35d"; } - -.fa-external-link-alt::before { - content: "\f35d"; } - -.fa-upload::before { - content: "\f093"; } - -.fa-user::before { - content: "\f007"; } - -.fa-user-astronaut::before { - content: "\f4fb"; } - -.fa-user-check::before { - content: "\f4fc"; } - -.fa-user-clock::before { - content: "\f4fd"; } - -.fa-user-doctor::before { - content: "\f0f0"; } - -.fa-user-md::before { - content: "\f0f0"; } - -.fa-user-gear::before { - content: "\f4fe"; } - -.fa-user-cog::before { - content: "\f4fe"; } - -.fa-user-graduate::before { - content: "\f501"; } - -.fa-user-group::before { - content: "\f500"; } - -.fa-user-friends::before { - content: "\f500"; } - -.fa-user-injured::before { - content: "\f728"; } - -.fa-user-large::before { - content: "\f406"; } - -.fa-user-alt::before { - content: "\f406"; } - -.fa-user-large-slash::before { - content: "\f4fa"; } - -.fa-user-alt-slash::before { - content: "\f4fa"; } - -.fa-user-lock::before { - content: "\f502"; } - -.fa-user-minus::before { - content: "\f503"; } - -.fa-user-ninja::before { - content: "\f504"; } - -.fa-user-nurse::before { - content: "\f82f"; } - -.fa-user-pen::before { - content: "\f4ff"; } - -.fa-user-edit::before { - content: "\f4ff"; } - -.fa-user-plus::before { - content: "\f234"; } - -.fa-user-secret::before { - content: "\f21b"; } - -.fa-user-shield::before { - content: "\f505"; } - -.fa-user-slash::before { - content: "\f506"; } - -.fa-user-tag::before { - content: "\f507"; } - -.fa-user-tie::before { - content: "\f508"; } - -.fa-user-xmark::before { - content: "\f235"; } - -.fa-user-times::before { - content: "\f235"; } - -.fa-users::before { - content: "\f0c0"; } - -.fa-users-between-lines::before { - content: "\e591"; } - -.fa-users-gear::before { - content: "\f509"; } - -.fa-users-cog::before { - content: "\f509"; } - -.fa-users-line::before { - content: "\e592"; } - -.fa-users-rays::before { - content: "\e593"; } - -.fa-users-rectangle::before { - content: "\e594"; } - -.fa-users-slash::before { - content: "\e073"; } - -.fa-users-viewfinder::before { - content: "\e595"; } - -.fa-utensils::before { - content: "\f2e7"; } - -.fa-cutlery::before { - content: "\f2e7"; } - -.fa-v::before { - content: "\56"; } - -.fa-van-shuttle::before { - content: "\f5b6"; } - -.fa-shuttle-van::before { - content: "\f5b6"; } - -.fa-vault::before { - content: "\e2c5"; } - -.fa-vector-square::before { - content: "\f5cb"; } - -.fa-venus::before { - content: "\f221"; } - -.fa-venus-double::before { - content: "\f226"; } - -.fa-venus-mars::before { - content: "\f228"; } - -.fa-vest::before { - content: "\e085"; } - -.fa-vest-patches::before { - content: "\e086"; } - -.fa-vial::before { - content: "\f492"; } - -.fa-vial-circle-check::before { - content: "\e596"; } - -.fa-vial-virus::before { - content: "\e597"; } - -.fa-vials::before { - content: "\f493"; } - -.fa-video::before { - content: "\f03d"; } - -.fa-video-camera::before { - content: "\f03d"; } - -.fa-video-slash::before { - content: "\f4e2"; } - -.fa-vihara::before { - content: "\f6a7"; } - -.fa-virus::before { - content: "\e074"; } - -.fa-virus-covid::before { - content: "\e4a8"; } - -.fa-virus-covid-slash::before { - content: "\e4a9"; } - -.fa-virus-slash::before { - content: "\e075"; } - -.fa-viruses::before { - content: "\e076"; } - -.fa-voicemail::before { - content: "\f897"; } - -.fa-volcano::before { - content: "\f770"; } - -.fa-volleyball::before { - content: "\f45f"; } - -.fa-volleyball-ball::before { - content: "\f45f"; } - -.fa-volume-high::before { - content: "\f028"; } - -.fa-volume-up::before { - content: "\f028"; } - -.fa-volume-low::before { - content: "\f027"; } - -.fa-volume-down::before { - content: "\f027"; } - -.fa-volume-off::before { - content: "\f026"; } - -.fa-volume-xmark::before { - content: "\f6a9"; } - -.fa-volume-mute::before { - content: "\f6a9"; } - -.fa-volume-times::before { - content: "\f6a9"; } - -.fa-vr-cardboard::before { - content: "\f729"; } - -.fa-w::before { - content: "\57"; } - -.fa-walkie-talkie::before { - content: "\f8ef"; } - -.fa-wallet::before { - content: "\f555"; } - -.fa-wand-magic::before { - content: "\f0d0"; } - -.fa-magic::before { - content: "\f0d0"; } - -.fa-wand-magic-sparkles::before { - content: "\e2ca"; } - -.fa-magic-wand-sparkles::before { - content: "\e2ca"; } - -.fa-wand-sparkles::before { - content: "\f72b"; } - -.fa-warehouse::before { - content: "\f494"; } - -.fa-water::before { - content: "\f773"; } - -.fa-water-ladder::before { - content: "\f5c5"; } - -.fa-ladder-water::before { - content: "\f5c5"; } - -.fa-swimming-pool::before { - content: "\f5c5"; } - -.fa-wave-square::before { - content: "\f83e"; } - -.fa-weight-hanging::before { - content: "\f5cd"; } - -.fa-weight-scale::before { - content: "\f496"; } - -.fa-weight::before { - content: "\f496"; } - -.fa-wheat-awn::before { - content: "\e2cd"; } - -.fa-wheat-alt::before { - content: "\e2cd"; } - -.fa-wheat-awn-circle-exclamation::before { - content: "\e598"; } - -.fa-wheelchair::before { - content: "\f193"; } - -.fa-wheelchair-move::before { - content: "\e2ce"; } - -.fa-wheelchair-alt::before { - content: "\e2ce"; } - -.fa-whiskey-glass::before { - content: "\f7a0"; } - -.fa-glass-whiskey::before { - content: "\f7a0"; } - -.fa-wifi::before { - content: "\f1eb"; } - -.fa-wifi-3::before { - content: "\f1eb"; } - -.fa-wifi-strong::before { - content: "\f1eb"; } - -.fa-wind::before { - content: "\f72e"; } - -.fa-window-maximize::before { - content: "\f2d0"; } - -.fa-window-minimize::before { - content: "\f2d1"; } - -.fa-window-restore::before { - content: "\f2d2"; } - -.fa-wine-bottle::before { - content: "\f72f"; } - -.fa-wine-glass::before { - content: "\f4e3"; } - -.fa-wine-glass-empty::before { - content: "\f5ce"; } - -.fa-wine-glass-alt::before { - content: "\f5ce"; } - -.fa-won-sign::before { - content: "\f159"; } - -.fa-krw::before { - content: "\f159"; } - -.fa-won::before { - content: "\f159"; } - -.fa-worm::before { - content: "\e599"; } - -.fa-wrench::before { - content: "\f0ad"; } - -.fa-x::before { - content: "\58"; } - -.fa-x-ray::before { - content: "\f497"; } - -.fa-xmark::before { - content: "\f00d"; } - -.fa-close::before { - content: "\f00d"; } - -.fa-multiply::before { - content: "\f00d"; } - -.fa-remove::before { - content: "\f00d"; } - -.fa-times::before { - content: "\f00d"; } - -.fa-xmarks-lines::before { - content: "\e59a"; } - -.fa-y::before { - content: "\59"; } - -.fa-yen-sign::before { - content: "\f157"; } - -.fa-cny::before { - content: "\f157"; } - -.fa-jpy::before { - content: "\f157"; } - -.fa-rmb::before { - content: "\f157"; } - -.fa-yen::before { - content: "\f157"; } - -.fa-yin-yang::before { - content: "\f6ad"; } - -.fa-z::before { - content: "\5a"; } - -.sr-only, -.fa-sr-only { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } - -.sr-only-focusable:not(:focus), -.fa-sr-only-focusable:not(:focus) { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } -:root, :host { - --fa-font-brands: normal 400 1em/1 "Font Awesome 6 Brands"; } - -@font-face { - font-family: 'Font Awesome 6 Brands'; - font-style: normal; - font-weight: 400; - font-display: block; - src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } - -.fab, -.fa-brands { - font-family: 'Font Awesome 6 Brands'; - font-weight: 400; } - -.fa-42-group:before { - content: "\e080"; } - -.fa-innosoft:before { - content: "\e080"; } - -.fa-500px:before { - content: "\f26e"; } - -.fa-accessible-icon:before { - content: "\f368"; } - -.fa-accusoft:before { - content: "\f369"; } - -.fa-adn:before { - content: "\f170"; } - -.fa-adversal:before { - content: "\f36a"; } - -.fa-affiliatetheme:before { - content: "\f36b"; } - -.fa-airbnb:before { - content: "\f834"; } - -.fa-algolia:before { - content: "\f36c"; } - -.fa-alipay:before { - content: "\f642"; } - -.fa-amazon:before { - content: "\f270"; } - -.fa-amazon-pay:before { - content: "\f42c"; } - -.fa-amilia:before { - content: "\f36d"; } - -.fa-android:before { - content: "\f17b"; } - -.fa-angellist:before { - content: "\f209"; } - -.fa-angrycreative:before { - content: "\f36e"; } - -.fa-angular:before { - content: "\f420"; } - -.fa-app-store:before { - content: "\f36f"; } - -.fa-app-store-ios:before { - content: "\f370"; } - -.fa-apper:before { - content: "\f371"; } - -.fa-apple:before { - content: "\f179"; } - -.fa-apple-pay:before { - content: "\f415"; } - -.fa-artstation:before { - content: "\f77a"; } - -.fa-asymmetrik:before { - content: "\f372"; } - -.fa-atlassian:before { - content: "\f77b"; } - -.fa-audible:before { - content: "\f373"; } - -.fa-autoprefixer:before { - content: "\f41c"; } - -.fa-avianex:before { - content: "\f374"; } - -.fa-aviato:before { - content: "\f421"; } - -.fa-aws:before { - content: "\f375"; } - -.fa-bandcamp:before { - content: "\f2d5"; } - -.fa-battle-net:before { - content: "\f835"; } - -.fa-behance:before { - content: "\f1b4"; } - -.fa-behance-square:before { - content: "\f1b5"; } - -.fa-bilibili:before { - content: "\e3d9"; } - -.fa-bimobject:before { - content: "\f378"; } - -.fa-bitbucket:before { - content: "\f171"; } - -.fa-bitcoin:before { - content: "\f379"; } - -.fa-bity:before { - content: "\f37a"; } - -.fa-black-tie:before { - content: "\f27e"; } - -.fa-blackberry:before { - content: "\f37b"; } - -.fa-blogger:before { - content: "\f37c"; } - -.fa-blogger-b:before { - content: "\f37d"; } - -.fa-bluetooth:before { - content: "\f293"; } - -.fa-bluetooth-b:before { - content: "\f294"; } - -.fa-bootstrap:before { - content: "\f836"; } - -.fa-bots:before { - content: "\e340"; } - -.fa-btc:before { - content: "\f15a"; } - -.fa-buffer:before { - content: "\f837"; } - -.fa-buromobelexperte:before { - content: "\f37f"; } - -.fa-buy-n-large:before { - content: "\f8a6"; } - -.fa-buysellads:before { - content: "\f20d"; } - -.fa-canadian-maple-leaf:before { - content: "\f785"; } - -.fa-cc-amazon-pay:before { - content: "\f42d"; } - -.fa-cc-amex:before { - content: "\f1f3"; } - -.fa-cc-apple-pay:before { - content: "\f416"; } - -.fa-cc-diners-club:before { - content: "\f24c"; } - -.fa-cc-discover:before { - content: "\f1f2"; } - -.fa-cc-jcb:before { - content: "\f24b"; } - -.fa-cc-mastercard:before { - content: "\f1f1"; } - -.fa-cc-paypal:before { - content: "\f1f4"; } - -.fa-cc-stripe:before { - content: "\f1f5"; } - -.fa-cc-visa:before { - content: "\f1f0"; } - -.fa-centercode:before { - content: "\f380"; } - -.fa-centos:before { - content: "\f789"; } - -.fa-chrome:before { - content: "\f268"; } - -.fa-chromecast:before { - content: "\f838"; } - -.fa-cloudflare:before { - content: "\e07d"; } - -.fa-cloudscale:before { - content: "\f383"; } - -.fa-cloudsmith:before { - content: "\f384"; } - -.fa-cloudversify:before { - content: "\f385"; } - -.fa-cmplid:before { - content: "\e360"; } - -.fa-codepen:before { - content: "\f1cb"; } - -.fa-codiepie:before { - content: "\f284"; } - -.fa-confluence:before { - content: "\f78d"; } - -.fa-connectdevelop:before { - content: "\f20e"; } - -.fa-contao:before { - content: "\f26d"; } - -.fa-cotton-bureau:before { - content: "\f89e"; } - -.fa-cpanel:before { - content: "\f388"; } - -.fa-creative-commons:before { - content: "\f25e"; } - -.fa-creative-commons-by:before { - content: "\f4e7"; } - -.fa-creative-commons-nc:before { - content: "\f4e8"; } - -.fa-creative-commons-nc-eu:before { - content: "\f4e9"; } - -.fa-creative-commons-nc-jp:before { - content: "\f4ea"; } - -.fa-creative-commons-nd:before { - content: "\f4eb"; } - -.fa-creative-commons-pd:before { - content: "\f4ec"; } - -.fa-creative-commons-pd-alt:before { - content: "\f4ed"; } - -.fa-creative-commons-remix:before { - content: "\f4ee"; } - -.fa-creative-commons-sa:before { - content: "\f4ef"; } - -.fa-creative-commons-sampling:before { - content: "\f4f0"; } - -.fa-creative-commons-sampling-plus:before { - content: "\f4f1"; } - -.fa-creative-commons-share:before { - content: "\f4f2"; } - -.fa-creative-commons-zero:before { - content: "\f4f3"; } - -.fa-critical-role:before { - content: "\f6c9"; } - -.fa-css3:before { - content: "\f13c"; } - -.fa-css3-alt:before { - content: "\f38b"; } - -.fa-cuttlefish:before { - content: "\f38c"; } - -.fa-d-and-d:before { - content: "\f38d"; } - -.fa-d-and-d-beyond:before { - content: "\f6ca"; } - -.fa-dailymotion:before { - content: "\e052"; } - -.fa-dashcube:before { - content: "\f210"; } - -.fa-deezer:before { - content: "\e077"; } - -.fa-delicious:before { - content: "\f1a5"; } - -.fa-deploydog:before { - content: "\f38e"; } - -.fa-deskpro:before { - content: "\f38f"; } - -.fa-dev:before { - content: "\f6cc"; } - -.fa-deviantart:before { - content: "\f1bd"; } - -.fa-dhl:before { - content: "\f790"; } - -.fa-diaspora:before { - content: "\f791"; } - -.fa-digg:before { - content: "\f1a6"; } - -.fa-digital-ocean:before { - content: "\f391"; } - -.fa-discord:before { - content: "\f392"; } - -.fa-discourse:before { - content: "\f393"; } - -.fa-dochub:before { - content: "\f394"; } - -.fa-docker:before { - content: "\f395"; } - -.fa-draft2digital:before { - content: "\f396"; } - -.fa-dribbble:before { - content: "\f17d"; } - -.fa-dribbble-square:before { - content: "\f397"; } - -.fa-dropbox:before { - content: "\f16b"; } - -.fa-drupal:before { - content: "\f1a9"; } - -.fa-dyalog:before { - content: "\f399"; } - -.fa-earlybirds:before { - content: "\f39a"; } - -.fa-ebay:before { - content: "\f4f4"; } - -.fa-edge:before { - content: "\f282"; } - -.fa-edge-legacy:before { - content: "\e078"; } - -.fa-elementor:before { - content: "\f430"; } - -.fa-ello:before { - content: "\f5f1"; } - -.fa-ember:before { - content: "\f423"; } - -.fa-empire:before { - content: "\f1d1"; } - -.fa-envira:before { - content: "\f299"; } - -.fa-erlang:before { - content: "\f39d"; } - -.fa-ethereum:before { - content: "\f42e"; } - -.fa-etsy:before { - content: "\f2d7"; } - -.fa-evernote:before { - content: "\f839"; } - -.fa-expeditedssl:before { - content: "\f23e"; } - -.fa-facebook:before { - content: "\f09a"; } - -.fa-facebook-f:before { - content: "\f39e"; } - -.fa-facebook-messenger:before { - content: "\f39f"; } - -.fa-facebook-square:before { - content: "\f082"; } - -.fa-fantasy-flight-games:before { - content: "\f6dc"; } - -.fa-fedex:before { - content: "\f797"; } - -.fa-fedora:before { - content: "\f798"; } - -.fa-figma:before { - content: "\f799"; } - -.fa-firefox:before { - content: "\f269"; } - -.fa-firefox-browser:before { - content: "\e007"; } - -.fa-first-order:before { - content: "\f2b0"; } - -.fa-first-order-alt:before { - content: "\f50a"; } - -.fa-firstdraft:before { - content: "\f3a1"; } - -.fa-flickr:before { - content: "\f16e"; } - -.fa-flipboard:before { - content: "\f44d"; } - -.fa-fly:before { - content: "\f417"; } - -.fa-font-awesome:before { - content: "\f2b4"; } - -.fa-font-awesome-flag:before { - content: "\f2b4"; } - -.fa-font-awesome-logo-full:before { - content: "\f2b4"; } - -.fa-fonticons:before { - content: "\f280"; } - -.fa-fonticons-fi:before { - content: "\f3a2"; } - -.fa-fort-awesome:before { - content: "\f286"; } - -.fa-fort-awesome-alt:before { - content: "\f3a3"; } - -.fa-forumbee:before { - content: "\f211"; } - -.fa-foursquare:before { - content: "\f180"; } - -.fa-free-code-camp:before { - content: "\f2c5"; } - -.fa-freebsd:before { - content: "\f3a4"; } - -.fa-fulcrum:before { - content: "\f50b"; } - -.fa-galactic-republic:before { - content: "\f50c"; } - -.fa-galactic-senate:before { - content: "\f50d"; } - -.fa-get-pocket:before { - content: "\f265"; } - -.fa-gg:before { - content: "\f260"; } - -.fa-gg-circle:before { - content: "\f261"; } - -.fa-git:before { - content: "\f1d3"; } - -.fa-git-alt:before { - content: "\f841"; } - -.fa-git-square:before { - content: "\f1d2"; } - -.fa-github:before { - content: "\f09b"; } - -.fa-github-alt:before { - content: "\f113"; } - -.fa-github-square:before { - content: "\f092"; } - -.fa-gitkraken:before { - content: "\f3a6"; } - -.fa-gitlab:before { - content: "\f296"; } - -.fa-gitter:before { - content: "\f426"; } - -.fa-glide:before { - content: "\f2a5"; } - -.fa-glide-g:before { - content: "\f2a6"; } - -.fa-gofore:before { - content: "\f3a7"; } - -.fa-golang:before { - content: "\e40f"; } - -.fa-goodreads:before { - content: "\f3a8"; } - -.fa-goodreads-g:before { - content: "\f3a9"; } - -.fa-google:before { - content: "\f1a0"; } - -.fa-google-drive:before { - content: "\f3aa"; } - -.fa-google-pay:before { - content: "\e079"; } - -.fa-google-play:before { - content: "\f3ab"; } - -.fa-google-plus:before { - content: "\f2b3"; } - -.fa-google-plus-g:before { - content: "\f0d5"; } - -.fa-google-plus-square:before { - content: "\f0d4"; } - -.fa-google-wallet:before { - content: "\f1ee"; } - -.fa-gratipay:before { - content: "\f184"; } - -.fa-grav:before { - content: "\f2d6"; } - -.fa-gripfire:before { - content: "\f3ac"; } - -.fa-grunt:before { - content: "\f3ad"; } - -.fa-guilded:before { - content: "\e07e"; } - -.fa-gulp:before { - content: "\f3ae"; } - -.fa-hacker-news:before { - content: "\f1d4"; } - -.fa-hacker-news-square:before { - content: "\f3af"; } - -.fa-hackerrank:before { - content: "\f5f7"; } - -.fa-hashnode:before { - content: "\e499"; } - -.fa-hips:before { - content: "\f452"; } - -.fa-hire-a-helper:before { - content: "\f3b0"; } - -.fa-hive:before { - content: "\e07f"; } - -.fa-hooli:before { - content: "\f427"; } - -.fa-hornbill:before { - content: "\f592"; } - -.fa-hotjar:before { - content: "\f3b1"; } - -.fa-houzz:before { - content: "\f27c"; } - -.fa-html5:before { - content: "\f13b"; } - -.fa-hubspot:before { - content: "\f3b2"; } - -.fa-ideal:before { - content: "\e013"; } - -.fa-imdb:before { - content: "\f2d8"; } - -.fa-instagram:before { - content: "\f16d"; } - -.fa-instagram-square:before { - content: "\e055"; } - -.fa-instalod:before { - content: "\e081"; } - -.fa-intercom:before { - content: "\f7af"; } - -.fa-internet-explorer:before { - content: "\f26b"; } - -.fa-invision:before { - content: "\f7b0"; } - -.fa-ioxhost:before { - content: "\f208"; } - -.fa-itch-io:before { - content: "\f83a"; } - -.fa-itunes:before { - content: "\f3b4"; } - -.fa-itunes-note:before { - content: "\f3b5"; } - -.fa-java:before { - content: "\f4e4"; } - -.fa-jedi-order:before { - content: "\f50e"; } - -.fa-jenkins:before { - content: "\f3b6"; } - -.fa-jira:before { - content: "\f7b1"; } - -.fa-joget:before { - content: "\f3b7"; } - -.fa-joomla:before { - content: "\f1aa"; } - -.fa-js:before { - content: "\f3b8"; } - -.fa-js-square:before { - content: "\f3b9"; } - -.fa-jsfiddle:before { - content: "\f1cc"; } - -.fa-kaggle:before { - content: "\f5fa"; } - -.fa-keybase:before { - content: "\f4f5"; } - -.fa-keycdn:before { - content: "\f3ba"; } - -.fa-kickstarter:before { - content: "\f3bb"; } - -.fa-kickstarter-k:before { - content: "\f3bc"; } - -.fa-korvue:before { - content: "\f42f"; } - -.fa-laravel:before { - content: "\f3bd"; } - -.fa-lastfm:before { - content: "\f202"; } - -.fa-lastfm-square:before { - content: "\f203"; } - -.fa-leanpub:before { - content: "\f212"; } - -.fa-less:before { - content: "\f41d"; } - -.fa-line:before { - content: "\f3c0"; } - -.fa-linkedin:before { - content: "\f08c"; } - -.fa-linkedin-in:before { - content: "\f0e1"; } - -.fa-linode:before { - content: "\f2b8"; } - -.fa-linux:before { - content: "\f17c"; } - -.fa-lyft:before { - content: "\f3c3"; } - -.fa-magento:before { - content: "\f3c4"; } - -.fa-mailchimp:before { - content: "\f59e"; } - -.fa-mandalorian:before { - content: "\f50f"; } - -.fa-markdown:before { - content: "\f60f"; } - -.fa-mastodon:before { - content: "\f4f6"; } - -.fa-maxcdn:before { - content: "\f136"; } - -.fa-mdb:before { - content: "\f8ca"; } - -.fa-medapps:before { - content: "\f3c6"; } - -.fa-medium:before { - content: "\f23a"; } - -.fa-medium-m:before { - content: "\f23a"; } - -.fa-medrt:before { - content: "\f3c8"; } - -.fa-meetup:before { - content: "\f2e0"; } - -.fa-megaport:before { - content: "\f5a3"; } - -.fa-mendeley:before { - content: "\f7b3"; } - -.fa-microblog:before { - content: "\e01a"; } - -.fa-microsoft:before { - content: "\f3ca"; } - -.fa-mix:before { - content: "\f3cb"; } - -.fa-mixcloud:before { - content: "\f289"; } - -.fa-mixer:before { - content: "\e056"; } - -.fa-mizuni:before { - content: "\f3cc"; } - -.fa-modx:before { - content: "\f285"; } - -.fa-monero:before { - content: "\f3d0"; } - -.fa-napster:before { - content: "\f3d2"; } - -.fa-neos:before { - content: "\f612"; } - -.fa-nfc-directional:before { - content: "\e530"; } - -.fa-nfc-symbol:before { - content: "\e531"; } - -.fa-nimblr:before { - content: "\f5a8"; } - -.fa-node:before { - content: "\f419"; } - -.fa-node-js:before { - content: "\f3d3"; } - -.fa-npm:before { - content: "\f3d4"; } - -.fa-ns8:before { - content: "\f3d5"; } - -.fa-nutritionix:before { - content: "\f3d6"; } - -.fa-octopus-deploy:before { - content: "\e082"; } - -.fa-odnoklassniki:before { - content: "\f263"; } - -.fa-odnoklassniki-square:before { - content: "\f264"; } - -.fa-old-republic:before { - content: "\f510"; } - -.fa-opencart:before { - content: "\f23d"; } - -.fa-openid:before { - content: "\f19b"; } - -.fa-opera:before { - content: "\f26a"; } - -.fa-optin-monster:before { - content: "\f23c"; } - -.fa-orcid:before { - content: "\f8d2"; } - -.fa-osi:before { - content: "\f41a"; } - -.fa-padlet:before { - content: "\e4a0"; } - -.fa-page4:before { - content: "\f3d7"; } - -.fa-pagelines:before { - content: "\f18c"; } - -.fa-palfed:before { - content: "\f3d8"; } - -.fa-patreon:before { - content: "\f3d9"; } - -.fa-paypal:before { - content: "\f1ed"; } - -.fa-perbyte:before { - content: "\e083"; } - -.fa-periscope:before { - content: "\f3da"; } - -.fa-phabricator:before { - content: "\f3db"; } - -.fa-phoenix-framework:before { - content: "\f3dc"; } - -.fa-phoenix-squadron:before { - content: "\f511"; } - -.fa-php:before { - content: "\f457"; } - -.fa-pied-piper:before { - content: "\f2ae"; } - -.fa-pied-piper-alt:before { - content: "\f1a8"; } - -.fa-pied-piper-hat:before { - content: "\f4e5"; } - -.fa-pied-piper-pp:before { - content: "\f1a7"; } - -.fa-pied-piper-square:before { - content: "\e01e"; } - -.fa-pinterest:before { - content: "\f0d2"; } - -.fa-pinterest-p:before { - content: "\f231"; } - -.fa-pinterest-square:before { - content: "\f0d3"; } - -.fa-pix:before { - content: "\e43a"; } - -.fa-playstation:before { - content: "\f3df"; } - -.fa-product-hunt:before { - content: "\f288"; } - -.fa-pushed:before { - content: "\f3e1"; } - -.fa-python:before { - content: "\f3e2"; } - -.fa-qq:before { - content: "\f1d6"; } - -.fa-quinscape:before { - content: "\f459"; } - -.fa-quora:before { - content: "\f2c4"; } - -.fa-r-project:before { - content: "\f4f7"; } - -.fa-raspberry-pi:before { - content: "\f7bb"; } - -.fa-ravelry:before { - content: "\f2d9"; } - -.fa-react:before { - content: "\f41b"; } - -.fa-reacteurope:before { - content: "\f75d"; } - -.fa-readme:before { - content: "\f4d5"; } - -.fa-rebel:before { - content: "\f1d0"; } - -.fa-red-river:before { - content: "\f3e3"; } - -.fa-reddit:before { - content: "\f1a1"; } - -.fa-reddit-alien:before { - content: "\f281"; } - -.fa-reddit-square:before { - content: "\f1a2"; } - -.fa-redhat:before { - content: "\f7bc"; } - -.fa-renren:before { - content: "\f18b"; } - -.fa-replyd:before { - content: "\f3e6"; } - -.fa-researchgate:before { - content: "\f4f8"; } - -.fa-resolving:before { - content: "\f3e7"; } - -.fa-rev:before { - content: "\f5b2"; } - -.fa-rocketchat:before { - content: "\f3e8"; } - -.fa-rockrms:before { - content: "\f3e9"; } - -.fa-rust:before { - content: "\e07a"; } - -.fa-safari:before { - content: "\f267"; } - -.fa-salesforce:before { - content: "\f83b"; } - -.fa-sass:before { - content: "\f41e"; } - -.fa-schlix:before { - content: "\f3ea"; } - -.fa-screenpal:before { - content: "\e570"; } - -.fa-scribd:before { - content: "\f28a"; } - -.fa-searchengin:before { - content: "\f3eb"; } - -.fa-sellcast:before { - content: "\f2da"; } - -.fa-sellsy:before { - content: "\f213"; } - -.fa-servicestack:before { - content: "\f3ec"; } - -.fa-shirtsinbulk:before { - content: "\f214"; } - -.fa-shopify:before { - content: "\e057"; } - -.fa-shopware:before { - content: "\f5b5"; } - -.fa-simplybuilt:before { - content: "\f215"; } - -.fa-sistrix:before { - content: "\f3ee"; } - -.fa-sith:before { - content: "\f512"; } - -.fa-sitrox:before { - content: "\e44a"; } - -.fa-sketch:before { - content: "\f7c6"; } - -.fa-skyatlas:before { - content: "\f216"; } - -.fa-skype:before { - content: "\f17e"; } - -.fa-slack:before { - content: "\f198"; } - -.fa-slack-hash:before { - content: "\f198"; } - -.fa-slideshare:before { - content: "\f1e7"; } - -.fa-snapchat:before { - content: "\f2ab"; } - -.fa-snapchat-ghost:before { - content: "\f2ab"; } - -.fa-snapchat-square:before { - content: "\f2ad"; } - -.fa-soundcloud:before { - content: "\f1be"; } - -.fa-sourcetree:before { - content: "\f7d3"; } - -.fa-speakap:before { - content: "\f3f3"; } - -.fa-speaker-deck:before { - content: "\f83c"; } - -.fa-spotify:before { - content: "\f1bc"; } - -.fa-square-font-awesome:before { - content: "\f425"; } - -.fa-square-font-awesome-stroke:before { - content: "\f35c"; } - -.fa-font-awesome-alt:before { - content: "\f35c"; } - -.fa-squarespace:before { - content: "\f5be"; } - -.fa-stack-exchange:before { - content: "\f18d"; } - -.fa-stack-overflow:before { - content: "\f16c"; } - -.fa-stackpath:before { - content: "\f842"; } - -.fa-staylinked:before { - content: "\f3f5"; } - 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content: "\f5c6"; } - -.fa-themeisle:before { - content: "\f2b2"; } - -.fa-think-peaks:before { - content: "\f731"; } - -.fa-tiktok:before { - content: "\e07b"; } - -.fa-trade-federation:before { - content: "\f513"; } - -.fa-trello:before { - content: "\f181"; } - -.fa-tumblr:before { - content: "\f173"; } - -.fa-tumblr-square:before { - content: "\f174"; } - -.fa-twitch:before { - content: "\f1e8"; } - -.fa-twitter:before { - content: "\f099"; } - -.fa-twitter-square:before { - content: "\f081"; } - -.fa-typo3:before { - content: "\f42b"; } - -.fa-uber:before { - content: "\f402"; } - -.fa-ubuntu:before { - content: "\f7df"; } - -.fa-uikit:before { - content: "\f403"; } - -.fa-umbraco:before { - content: "\f8e8"; } - -.fa-uncharted:before { - content: "\e084"; } - -.fa-uniregistry:before { - content: "\f404"; } - -.fa-unity:before { - content: "\e049"; } - -.fa-unsplash:before { - content: "\e07c"; } - -.fa-untappd:before { - content: "\f405"; } - -.fa-ups:before { - content: "\f7e0"; } - -.fa-usb:before { - content: "\f287"; } - -.fa-usps:before { - content: "\f7e1"; } - -.fa-ussunnah:before { - content: "\f407"; } - -.fa-vaadin:before { - content: "\f408"; } - -.fa-viacoin:before { - content: "\f237"; } - -.fa-viadeo:before { - content: "\f2a9"; } - -.fa-viadeo-square:before { - content: "\f2aa"; } - -.fa-viber:before { - content: "\f409"; } - -.fa-vimeo:before { - content: "\f40a"; } - -.fa-vimeo-square:before { - content: "\f194"; } - -.fa-vimeo-v:before { - content: "\f27d"; } - -.fa-vine:before { - content: "\f1ca"; } - -.fa-vk:before { - content: "\f189"; } - -.fa-vnv:before { - content: "\f40b"; } - -.fa-vuejs:before { - content: "\f41f"; } - -.fa-watchman-monitoring:before { - content: "\e087"; } - -.fa-waze:before { - content: "\f83f"; } - -.fa-weebly:before { - content: "\f5cc"; } - -.fa-weibo:before { - content: "\f18a"; } - -.fa-weixin:before { - content: "\f1d7"; } - -.fa-whatsapp:before { - content: "\f232"; } - -.fa-whatsapp-square:before { - content: "\f40c"; } - -.fa-whmcs:before { - content: "\f40d"; } - -.fa-wikipedia-w:before { - content: "\f266"; } - -.fa-windows:before { - content: "\f17a"; } - -.fa-wirsindhandwerk:before { - content: "\e2d0"; } - -.fa-wsh:before { - content: "\e2d0"; } - -.fa-wix:before { - content: "\f5cf"; } - -.fa-wizards-of-the-coast:before { - content: "\f730"; } - -.fa-wodu:before { - content: "\e088"; } - -.fa-wolf-pack-battalion:before { - content: "\f514"; } - -.fa-wordpress:before { - content: "\f19a"; } - -.fa-wordpress-simple:before { - content: "\f411"; } - -.fa-wpbeginner:before { - content: "\f297"; } - -.fa-wpexplorer:before { - content: "\f2de"; } - -.fa-wpforms:before { - content: "\f298"; } - -.fa-wpressr:before { - content: "\f3e4"; } - -.fa-xbox:before { - content: "\f412"; } - -.fa-xing:before { - content: "\f168"; } - -.fa-xing-square:before { - content: "\f169"; } - -.fa-y-combinator:before { - content: "\f23b"; } - -.fa-yahoo:before { - content: "\f19e"; } - 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animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin-reverse { - --fa-animation-direction: reverse; } - -.fa-pulse, -.fa-spin-pulse { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); - animation-timing-function: var(--fa-animation-timing, steps(8)); } - -@media (prefers-reduced-motion: reduce) { - .fa-beat, - .fa-bounce, - .fa-fade, - .fa-beat-fade, - .fa-flip, - .fa-pulse, - .fa-shake, - .fa-spin, - .fa-spin-pulse { - -webkit-animation-delay: -1ms; - animation-delay: -1ms; - -webkit-animation-duration: 1ms; - animation-duration: 1ms; - -webkit-animation-iteration-count: 1; - animation-iteration-count: 1; - transition-delay: 0s; - transition-duration: 0s; } } - -@-webkit-keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@-webkit-keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@-webkit-keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@-webkit-keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@-webkit-keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@-webkit-keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@-webkit-keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -@keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -.fa-rotate-90 { - -webkit-transform: rotate(90deg); - transform: rotate(90deg); } - -.fa-rotate-180 { - -webkit-transform: rotate(180deg); - transform: rotate(180deg); } - -.fa-rotate-270 { - -webkit-transform: rotate(270deg); - transform: rotate(270deg); } - -.fa-flip-horizontal { - -webkit-transform: scale(-1, 1); - transform: scale(-1, 1); } - -.fa-flip-vertical { - -webkit-transform: scale(1, -1); - transform: scale(1, -1); } - -.fa-flip-both, -.fa-flip-horizontal.fa-flip-vertical { - -webkit-transform: scale(-1, -1); - transform: scale(-1, -1); } - -.fa-rotate-by { - -webkit-transform: rotate(var(--fa-rotate-angle, none)); - transform: rotate(var(--fa-rotate-angle, none)); } - -.fa-stack { - display: inline-block; - height: 2em; - line-height: 2em; - position: relative; - vertical-align: middle; - width: 2.5em; } - -.fa-stack-1x, -.fa-stack-2x { - left: 0; - position: absolute; - text-align: center; - width: 100%; - z-index: var(--fa-stack-z-index, auto); } - -.fa-stack-1x { - line-height: inherit; } - -.fa-stack-2x { - font-size: 2em; } - -.fa-inverse { - color: var(--fa-inverse, #fff); } - -/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen -readers do not read off random characters that represent icons */ -.fa-0::before { - content: "\30"; } - -.fa-1::before { - content: "\31"; } - -.fa-2::before { - content: "\32"; } - -.fa-3::before { - content: "\33"; } - -.fa-4::before { - content: "\34"; } - -.fa-5::before { - content: "\35"; } - -.fa-6::before { - content: "\36"; } - -.fa-7::before { - content: "\37"; } - -.fa-8::before { - content: "\38"; } - -.fa-9::before { - content: "\39"; } - -.fa-a::before { - content: "\41"; } - -.fa-address-book::before { - content: "\f2b9"; } - -.fa-contact-book::before { - content: "\f2b9"; } - -.fa-address-card::before { - content: "\f2bb"; } - -.fa-contact-card::before { - content: "\f2bb"; } - -.fa-vcard::before { - content: "\f2bb"; } - -.fa-align-center::before { - content: "\f037"; } - -.fa-align-justify::before { - content: "\f039"; } - -.fa-align-left::before { - content: "\f036"; } - -.fa-align-right::before { - content: "\f038"; } - -.fa-anchor::before { - content: "\f13d"; } - -.fa-anchor-circle-check::before { - content: "\e4aa"; } - -.fa-anchor-circle-exclamation::before { - content: "\e4ab"; } - -.fa-anchor-circle-xmark::before { - content: "\e4ac"; } - -.fa-anchor-lock::before { - content: "\e4ad"; } - -.fa-angle-down::before { - content: "\f107"; } - -.fa-angle-left::before { - content: "\f104"; } - -.fa-angle-right::before { - content: "\f105"; } - -.fa-angle-up::before { - content: "\f106"; } - -.fa-angles-down::before { - content: "\f103"; } - -.fa-angle-double-down::before { - content: "\f103"; } - -.fa-angles-left::before { - content: "\f100"; } - -.fa-angle-double-left::before { - content: "\f100"; } - -.fa-angles-right::before { - content: "\f101"; } - -.fa-angle-double-right::before { - content: "\f101"; } - -.fa-angles-up::before { - content: "\f102"; } - -.fa-angle-double-up::before { - content: "\f102"; } - -.fa-ankh::before { - content: "\f644"; } - -.fa-apple-whole::before { - content: "\f5d1"; } - -.fa-apple-alt::before { - content: "\f5d1"; } - -.fa-archway::before { - content: "\f557"; } - -.fa-arrow-down::before { - content: "\f063"; } - -.fa-arrow-down-1-9::before { - content: "\f162"; } - -.fa-sort-numeric-asc::before { - content: "\f162"; } - -.fa-sort-numeric-down::before { - content: "\f162"; } - -.fa-arrow-down-9-1::before { - content: "\f886"; } - -.fa-sort-numeric-desc::before { - content: "\f886"; } - -.fa-sort-numeric-down-alt::before { - content: "\f886"; } - -.fa-arrow-down-a-z::before { - content: "\f15d"; } - -.fa-sort-alpha-asc::before { - content: "\f15d"; } - -.fa-sort-alpha-down::before { - content: "\f15d"; } - -.fa-arrow-down-long::before { - content: "\f175"; } - -.fa-long-arrow-down::before { - content: "\f175"; } - -.fa-arrow-down-short-wide::before { - content: "\f884"; } - -.fa-sort-amount-desc::before { - content: "\f884"; } - -.fa-sort-amount-down-alt::before { - content: "\f884"; } - -.fa-arrow-down-up-across-line::before { - content: "\e4af"; } - -.fa-arrow-down-up-lock::before { - content: "\e4b0"; } - -.fa-arrow-down-wide-short::before { - content: "\f160"; } - -.fa-sort-amount-asc::before { - content: "\f160"; } - -.fa-sort-amount-down::before { - content: "\f160"; } - -.fa-arrow-down-z-a::before { - content: "\f881"; } - -.fa-sort-alpha-desc::before { - content: "\f881"; } - -.fa-sort-alpha-down-alt::before { - content: "\f881"; } - -.fa-arrow-left::before { - content: "\f060"; } - -.fa-arrow-left-long::before { - content: "\f177"; } - -.fa-long-arrow-left::before { - content: "\f177"; } - -.fa-arrow-pointer::before { - content: "\f245"; } - -.fa-mouse-pointer::before { - content: "\f245"; } - -.fa-arrow-right::before { - content: "\f061"; } - -.fa-arrow-right-arrow-left::before { - content: "\f0ec"; } - -.fa-exchange::before { - content: "\f0ec"; } - -.fa-arrow-right-from-bracket::before { - content: "\f08b"; } - -.fa-sign-out::before { - content: "\f08b"; } - -.fa-arrow-right-long::before { - content: "\f178"; } - -.fa-long-arrow-right::before { - content: "\f178"; } - -.fa-arrow-right-to-bracket::before { - content: "\f090"; } - -.fa-sign-in::before { - content: "\f090"; } - -.fa-arrow-right-to-city::before { - content: "\e4b3"; } - -.fa-arrow-rotate-left::before { - content: "\f0e2"; } - -.fa-arrow-left-rotate::before { - content: "\f0e2"; } - -.fa-arrow-rotate-back::before { - content: "\f0e2"; } - -.fa-arrow-rotate-backward::before { - content: "\f0e2"; } - -.fa-undo::before { - content: "\f0e2"; } - -.fa-arrow-rotate-right::before { - content: "\f01e"; } - -.fa-arrow-right-rotate::before { - content: "\f01e"; } - -.fa-arrow-rotate-forward::before { - content: "\f01e"; } - -.fa-redo::before { - content: "\f01e"; } - -.fa-arrow-trend-down::before { - content: "\e097"; } - -.fa-arrow-trend-up::before { - content: "\e098"; } - -.fa-arrow-turn-down::before { - content: "\f149"; } - -.fa-level-down::before { - content: "\f149"; } - -.fa-arrow-turn-up::before { - content: "\f148"; } - -.fa-level-up::before { - content: "\f148"; } - -.fa-arrow-up::before { - content: "\f062"; } - -.fa-arrow-up-1-9::before { - content: "\f163"; } - -.fa-sort-numeric-up::before { - content: "\f163"; } - -.fa-arrow-up-9-1::before { - content: "\f887"; } - -.fa-sort-numeric-up-alt::before { - content: "\f887"; } - -.fa-arrow-up-a-z::before { - content: "\f15e"; } - -.fa-sort-alpha-up::before { - content: "\f15e"; } - -.fa-arrow-up-from-bracket::before { - content: "\e09a"; } - -.fa-arrow-up-from-ground-water::before { - content: "\e4b5"; } - -.fa-arrow-up-from-water-pump::before { - content: "\e4b6"; } - -.fa-arrow-up-long::before { - content: "\f176"; } - -.fa-long-arrow-up::before { - content: "\f176"; } - -.fa-arrow-up-right-dots::before { - content: "\e4b7"; } - -.fa-arrow-up-right-from-square::before { - content: "\f08e"; } - -.fa-external-link::before { - content: "\f08e"; } - -.fa-arrow-up-short-wide::before { - content: "\f885"; } - -.fa-sort-amount-up-alt::before { - content: "\f885"; } - -.fa-arrow-up-wide-short::before { - content: "\f161"; } - -.fa-sort-amount-up::before { - content: "\f161"; } - -.fa-arrow-up-z-a::before { - content: "\f882"; } - -.fa-sort-alpha-up-alt::before { - content: "\f882"; } - -.fa-arrows-down-to-line::before { - content: "\e4b8"; } - -.fa-arrows-down-to-people::before { - content: "\e4b9"; } - -.fa-arrows-left-right::before { - content: "\f07e"; } - -.fa-arrows-h::before { - content: "\f07e"; } - -.fa-arrows-left-right-to-line::before { - content: "\e4ba"; } - -.fa-arrows-rotate::before { - content: "\f021"; } - -.fa-refresh::before { - content: "\f021"; } - -.fa-sync::before { - content: "\f021"; } - -.fa-arrows-spin::before { - content: "\e4bb"; } - -.fa-arrows-split-up-and-left::before { - content: "\e4bc"; } - -.fa-arrows-to-circle::before { - content: "\e4bd"; } - -.fa-arrows-to-dot::before { - content: "\e4be"; } - -.fa-arrows-to-eye::before { - content: "\e4bf"; } - -.fa-arrows-turn-right::before { - content: "\e4c0"; } - -.fa-arrows-turn-to-dots::before { - content: "\e4c1"; } - -.fa-arrows-up-down::before { - content: "\f07d"; } - -.fa-arrows-v::before { - content: "\f07d"; } - -.fa-arrows-up-down-left-right::before { - content: "\f047"; } - -.fa-arrows::before { - content: "\f047"; } - -.fa-arrows-up-to-line::before { - content: "\e4c2"; } - -.fa-asterisk::before { - content: "\2a"; } - -.fa-at::before { - content: "\40"; } - -.fa-atom::before { - content: "\f5d2"; } - -.fa-audio-description::before { - content: "\f29e"; } - -.fa-austral-sign::before { - content: "\e0a9"; } - -.fa-award::before { - content: "\f559"; } - -.fa-b::before { - content: "\42"; } - -.fa-baby::before { - content: "\f77c"; } - -.fa-baby-carriage::before { - content: "\f77d"; } - -.fa-carriage-baby::before { - content: "\f77d"; } - -.fa-backward::before { - content: "\f04a"; } - -.fa-backward-fast::before { - content: "\f049"; } - -.fa-fast-backward::before { - content: "\f049"; } - -.fa-backward-step::before { - content: "\f048"; } - -.fa-step-backward::before { - content: "\f048"; } - -.fa-bacon::before { - content: "\f7e5"; } - -.fa-bacteria::before { - content: "\e059"; } - -.fa-bacterium::before { - content: "\e05a"; } - -.fa-bag-shopping::before { - content: "\f290"; } - -.fa-shopping-bag::before { - content: "\f290"; } - -.fa-bahai::before { - content: "\f666"; } - -.fa-baht-sign::before { - content: "\e0ac"; } - -.fa-ban::before { - content: "\f05e"; } - -.fa-cancel::before { - content: "\f05e"; } - -.fa-ban-smoking::before { - content: "\f54d"; } - -.fa-smoking-ban::before { - content: "\f54d"; } - -.fa-bandage::before { - content: "\f462"; } - -.fa-band-aid::before { - content: "\f462"; } - -.fa-barcode::before { - content: "\f02a"; } - -.fa-bars::before { - content: "\f0c9"; } - -.fa-navicon::before { - content: "\f0c9"; } - -.fa-bars-progress::before { - content: "\f828"; } - -.fa-tasks-alt::before { - content: "\f828"; } - -.fa-bars-staggered::before { - content: "\f550"; } - -.fa-reorder::before { - content: "\f550"; } - -.fa-stream::before { - content: "\f550"; } - -.fa-baseball::before { - content: "\f433"; } - -.fa-baseball-ball::before { - content: "\f433"; } - -.fa-baseball-bat-ball::before { - content: "\f432"; } - -.fa-basket-shopping::before { - content: "\f291"; } - -.fa-shopping-basket::before { - content: "\f291"; } - -.fa-basketball::before { - content: "\f434"; } - -.fa-basketball-ball::before { - content: "\f434"; } - -.fa-bath::before { - content: "\f2cd"; } - -.fa-bathtub::before { - content: "\f2cd"; } - -.fa-battery-empty::before { - content: "\f244"; } - -.fa-battery-0::before { - content: "\f244"; } - -.fa-battery-full::before { - content: "\f240"; } - -.fa-battery::before { - content: "\f240"; } - -.fa-battery-5::before { - content: "\f240"; } - -.fa-battery-half::before { - content: "\f242"; } - -.fa-battery-3::before { - content: "\f242"; } - -.fa-battery-quarter::before { - content: "\f243"; } - -.fa-battery-2::before { - content: "\f243"; } - -.fa-battery-three-quarters::before { - content: "\f241"; } - -.fa-battery-4::before { - content: "\f241"; } - -.fa-bed::before { - content: "\f236"; } - -.fa-bed-pulse::before { - content: "\f487"; } - -.fa-procedures::before { - content: "\f487"; } - -.fa-beer-mug-empty::before { - content: "\f0fc"; } - -.fa-beer::before { - content: "\f0fc"; } - -.fa-bell::before { - content: "\f0f3"; } - -.fa-bell-concierge::before { - content: "\f562"; } - -.fa-concierge-bell::before { - content: "\f562"; } - -.fa-bell-slash::before { - content: "\f1f6"; } - -.fa-bezier-curve::before { - content: "\f55b"; } - -.fa-bicycle::before { - content: "\f206"; } - -.fa-binoculars::before { - content: "\f1e5"; } - -.fa-biohazard::before { - content: "\f780"; } - -.fa-bitcoin-sign::before { - content: "\e0b4"; } - -.fa-blender::before { - content: "\f517"; } - -.fa-blender-phone::before { - content: "\f6b6"; } - -.fa-blog::before { - content: "\f781"; } - -.fa-bold::before { - content: "\f032"; } - -.fa-bolt::before { - content: "\f0e7"; } - -.fa-zap::before { - content: "\f0e7"; } - -.fa-bolt-lightning::before { - content: "\e0b7"; } - -.fa-bomb::before { - content: "\f1e2"; } - -.fa-bone::before { - content: "\f5d7"; } - -.fa-bong::before { - content: "\f55c"; } - -.fa-book::before { - content: "\f02d"; } - -.fa-book-atlas::before { - content: "\f558"; } - -.fa-atlas::before { - content: "\f558"; } - -.fa-book-bible::before { - content: "\f647"; } - -.fa-bible::before { - content: "\f647"; } - -.fa-book-bookmark::before { - content: "\e0bb"; } - -.fa-book-journal-whills::before { - content: "\f66a"; } - -.fa-journal-whills::before { - content: "\f66a"; } - -.fa-book-medical::before { - content: "\f7e6"; } - -.fa-book-open::before { - content: "\f518"; } - -.fa-book-open-reader::before { - content: "\f5da"; } - -.fa-book-reader::before { - content: "\f5da"; } - -.fa-book-quran::before { - content: "\f687"; } - -.fa-quran::before { - content: "\f687"; } - -.fa-book-skull::before { - content: "\f6b7"; } - -.fa-book-dead::before { - content: "\f6b7"; } - -.fa-bookmark::before { - content: "\f02e"; } - -.fa-border-all::before { - content: "\f84c"; } - -.fa-border-none::before { - content: "\f850"; } - -.fa-border-top-left::before { - content: "\f853"; } - -.fa-border-style::before { - content: "\f853"; } - -.fa-bore-hole::before { - content: "\e4c3"; } - -.fa-bottle-droplet::before { - content: "\e4c4"; } - -.fa-bottle-water::before { - content: "\e4c5"; } - -.fa-bowl-food::before { - content: "\e4c6"; } - -.fa-bowl-rice::before { - content: "\e2eb"; } - -.fa-bowling-ball::before { - content: "\f436"; } - -.fa-box::before { - content: "\f466"; } - -.fa-box-archive::before { - content: "\f187"; } - -.fa-archive::before { - content: "\f187"; } - -.fa-box-open::before { - content: "\f49e"; } - -.fa-box-tissue::before { - content: "\e05b"; } - -.fa-boxes-packing::before { - content: "\e4c7"; } - -.fa-boxes-stacked::before { - content: "\f468"; } - -.fa-boxes::before { - content: "\f468"; } - -.fa-boxes-alt::before { - content: "\f468"; } - -.fa-braille::before { - content: "\f2a1"; } - -.fa-brain::before { - content: "\f5dc"; } - -.fa-brazilian-real-sign::before { - content: "\e46c"; } - -.fa-bread-slice::before { - content: "\f7ec"; } - -.fa-bridge::before { - content: "\e4c8"; } - -.fa-bridge-circle-check::before { - content: "\e4c9"; } - -.fa-bridge-circle-exclamation::before { - content: "\e4ca"; } - -.fa-bridge-circle-xmark::before { - content: "\e4cb"; } - -.fa-bridge-lock::before { - content: "\e4cc"; } - -.fa-bridge-water::before { - content: "\e4ce"; } - -.fa-briefcase::before { - content: "\f0b1"; } - -.fa-briefcase-medical::before { - content: "\f469"; } - -.fa-broom::before { - content: "\f51a"; } - -.fa-broom-ball::before { - content: "\f458"; } - -.fa-quidditch::before { - content: "\f458"; } - -.fa-quidditch-broom-ball::before { - content: "\f458"; } - -.fa-brush::before { - content: "\f55d"; } - -.fa-bucket::before { - content: "\e4cf"; } - -.fa-bug::before { - content: "\f188"; } - -.fa-bug-slash::before { - content: "\e490"; } - -.fa-bugs::before { - content: "\e4d0"; } - -.fa-building::before { - content: "\f1ad"; } - -.fa-building-circle-arrow-right::before { - content: "\e4d1"; } - -.fa-building-circle-check::before { - content: "\e4d2"; } - -.fa-building-circle-exclamation::before { - content: "\e4d3"; } - -.fa-building-circle-xmark::before { - content: "\e4d4"; } - -.fa-building-columns::before { - content: "\f19c"; } - -.fa-bank::before { - content: "\f19c"; } - -.fa-institution::before { - content: "\f19c"; } - -.fa-museum::before { - content: "\f19c"; } - -.fa-university::before { - content: "\f19c"; } - -.fa-building-flag::before { - content: "\e4d5"; } - -.fa-building-lock::before { - content: "\e4d6"; } - -.fa-building-ngo::before { - content: "\e4d7"; } - -.fa-building-shield::before { - content: "\e4d8"; } - -.fa-building-un::before { - content: "\e4d9"; } - -.fa-building-user::before { - content: "\e4da"; } - -.fa-building-wheat::before { - content: "\e4db"; } - -.fa-bullhorn::before { - content: "\f0a1"; } - -.fa-bullseye::before { - content: "\f140"; } - -.fa-burger::before { - content: "\f805"; } - -.fa-hamburger::before { - content: "\f805"; } - -.fa-burst::before { - content: "\e4dc"; } - -.fa-bus::before { - content: "\f207"; } - -.fa-bus-simple::before { - content: "\f55e"; } - -.fa-bus-alt::before { - content: "\f55e"; } - -.fa-business-time::before { - content: "\f64a"; } - -.fa-briefcase-clock::before { - content: "\f64a"; } - -.fa-c::before { - content: "\43"; } - -.fa-cake-candles::before { - content: "\f1fd"; } - -.fa-birthday-cake::before { - content: "\f1fd"; } - -.fa-cake::before { - content: "\f1fd"; } - -.fa-calculator::before { - content: "\f1ec"; } - -.fa-calendar::before { - content: "\f133"; } - -.fa-calendar-check::before { - content: "\f274"; } - -.fa-calendar-day::before { - content: "\f783"; } - -.fa-calendar-days::before { - content: "\f073"; } - -.fa-calendar-alt::before { - content: "\f073"; } - -.fa-calendar-minus::before { - content: "\f272"; } - -.fa-calendar-plus::before { - content: "\f271"; } - -.fa-calendar-week::before { - content: "\f784"; } - -.fa-calendar-xmark::before { - content: "\f273"; } - -.fa-calendar-times::before { - content: "\f273"; } - -.fa-camera::before { - content: "\f030"; } - -.fa-camera-alt::before { - content: "\f030"; } - -.fa-camera-retro::before { - content: "\f083"; } - -.fa-camera-rotate::before { - content: "\e0d8"; } - -.fa-campground::before { - content: "\f6bb"; } - -.fa-candy-cane::before { - content: "\f786"; } - -.fa-cannabis::before { - content: "\f55f"; } - -.fa-capsules::before { - content: "\f46b"; } - -.fa-car::before { - content: "\f1b9"; } - -.fa-automobile::before { - content: "\f1b9"; } - -.fa-car-battery::before { - content: "\f5df"; } - -.fa-battery-car::before { - content: "\f5df"; } - -.fa-car-burst::before { - content: "\f5e1"; } - -.fa-car-crash::before { - content: "\f5e1"; } - -.fa-car-on::before { - content: "\e4dd"; } - -.fa-car-rear::before { - content: "\f5de"; } - -.fa-car-alt::before { - content: "\f5de"; } - -.fa-car-side::before { - content: "\f5e4"; } - -.fa-car-tunnel::before { - content: "\e4de"; } - -.fa-caravan::before { - content: "\f8ff"; } - -.fa-caret-down::before { - content: "\f0d7"; } - -.fa-caret-left::before { - content: "\f0d9"; } - -.fa-caret-right::before { - content: "\f0da"; } - -.fa-caret-up::before { - content: "\f0d8"; } - -.fa-carrot::before { - content: "\f787"; } - -.fa-cart-arrow-down::before { - content: "\f218"; } - -.fa-cart-flatbed::before { - content: "\f474"; } - -.fa-dolly-flatbed::before { - content: "\f474"; } - -.fa-cart-flatbed-suitcase::before { - content: "\f59d"; } - -.fa-luggage-cart::before { - content: "\f59d"; } - -.fa-cart-plus::before { - content: "\f217"; } - -.fa-cart-shopping::before { - content: "\f07a"; } - -.fa-shopping-cart::before { - content: "\f07a"; } - -.fa-cash-register::before { - content: "\f788"; } - -.fa-cat::before { - content: "\f6be"; } - -.fa-cedi-sign::before { - content: "\e0df"; } - -.fa-cent-sign::before { - content: "\e3f5"; } - -.fa-certificate::before { - content: "\f0a3"; } - -.fa-chair::before { - content: "\f6c0"; } - -.fa-chalkboard::before { - content: "\f51b"; } - -.fa-blackboard::before { - content: "\f51b"; } - -.fa-chalkboard-user::before { - content: "\f51c"; } - -.fa-chalkboard-teacher::before { - content: "\f51c"; } - -.fa-champagne-glasses::before { - content: "\f79f"; } - -.fa-glass-cheers::before { - content: "\f79f"; } - -.fa-charging-station::before { - content: "\f5e7"; } - -.fa-chart-area::before { - content: "\f1fe"; } - -.fa-area-chart::before { - content: "\f1fe"; } - -.fa-chart-bar::before { - content: "\f080"; } - -.fa-bar-chart::before { - content: "\f080"; } - -.fa-chart-column::before { - content: "\e0e3"; } - -.fa-chart-gantt::before { - content: "\e0e4"; } - -.fa-chart-line::before { - content: "\f201"; } - -.fa-line-chart::before { - content: "\f201"; } - -.fa-chart-pie::before { - content: "\f200"; } - -.fa-pie-chart::before { - content: "\f200"; } - -.fa-chart-simple::before { - content: "\e473"; } - -.fa-check::before { - content: "\f00c"; } - -.fa-check-double::before { - content: "\f560"; } - -.fa-check-to-slot::before { - content: "\f772"; } - -.fa-vote-yea::before { - content: "\f772"; } - -.fa-cheese::before { - content: "\f7ef"; } - -.fa-chess::before { - content: "\f439"; } - -.fa-chess-bishop::before { - content: "\f43a"; } - -.fa-chess-board::before { - content: "\f43c"; } - -.fa-chess-king::before { - content: "\f43f"; } - -.fa-chess-knight::before { - content: "\f441"; } - -.fa-chess-pawn::before { - content: "\f443"; } - -.fa-chess-queen::before { - content: "\f445"; } - -.fa-chess-rook::before { - content: "\f447"; } - -.fa-chevron-down::before { - content: "\f078"; } - -.fa-chevron-left::before { - content: "\f053"; } - -.fa-chevron-right::before { - content: "\f054"; } - -.fa-chevron-up::before { - content: "\f077"; } - -.fa-child::before { - content: "\f1ae"; } - -.fa-child-dress::before { - content: "\e59c"; } - -.fa-child-reaching::before { - content: "\e59d"; } - -.fa-child-rifle::before { - content: "\e4e0"; } - -.fa-children::before { - content: "\e4e1"; } - -.fa-church::before { - content: "\f51d"; } - -.fa-circle::before { - content: "\f111"; } - -.fa-circle-arrow-down::before { - content: "\f0ab"; } - -.fa-arrow-circle-down::before { - content: "\f0ab"; } - -.fa-circle-arrow-left::before { - content: "\f0a8"; } - -.fa-arrow-circle-left::before { - content: "\f0a8"; } - -.fa-circle-arrow-right::before { - content: "\f0a9"; } - -.fa-arrow-circle-right::before { - content: "\f0a9"; } - -.fa-circle-arrow-up::before { - content: "\f0aa"; } - -.fa-arrow-circle-up::before { - content: "\f0aa"; } - -.fa-circle-check::before { - content: "\f058"; } - -.fa-check-circle::before { - content: "\f058"; } - -.fa-circle-chevron-down::before { - content: "\f13a"; } - -.fa-chevron-circle-down::before { - content: "\f13a"; } - -.fa-circle-chevron-left::before { - content: "\f137"; } - -.fa-chevron-circle-left::before { - content: "\f137"; } - -.fa-circle-chevron-right::before { - content: "\f138"; } - -.fa-chevron-circle-right::before { - content: "\f138"; } - -.fa-circle-chevron-up::before { - content: "\f139"; } - -.fa-chevron-circle-up::before { - content: "\f139"; } - -.fa-circle-dollar-to-slot::before { - content: "\f4b9"; } - -.fa-donate::before { - content: "\f4b9"; } - -.fa-circle-dot::before { - content: "\f192"; } - -.fa-dot-circle::before { - content: "\f192"; } - -.fa-circle-down::before { - content: "\f358"; } - -.fa-arrow-alt-circle-down::before { - content: "\f358"; } - -.fa-circle-exclamation::before { - content: "\f06a"; } - -.fa-exclamation-circle::before { - content: "\f06a"; } - -.fa-circle-h::before { - content: "\f47e"; } - -.fa-hospital-symbol::before { - content: "\f47e"; } - -.fa-circle-half-stroke::before { - content: "\f042"; } - -.fa-adjust::before { - content: "\f042"; } - -.fa-circle-info::before { - content: "\f05a"; } - -.fa-info-circle::before { - content: "\f05a"; } - -.fa-circle-left::before { - content: "\f359"; } - -.fa-arrow-alt-circle-left::before { - content: "\f359"; } - -.fa-circle-minus::before { - content: "\f056"; } - -.fa-minus-circle::before { - content: "\f056"; } - -.fa-circle-nodes::before { - content: "\e4e2"; } - -.fa-circle-notch::before { - content: "\f1ce"; } - -.fa-circle-pause::before { - content: "\f28b"; } - -.fa-pause-circle::before { - content: "\f28b"; } - -.fa-circle-play::before { - content: "\f144"; } - -.fa-play-circle::before { - content: "\f144"; } - -.fa-circle-plus::before { - content: "\f055"; } - -.fa-plus-circle::before { - content: "\f055"; } - -.fa-circle-question::before { - content: "\f059"; } - -.fa-question-circle::before { - content: "\f059"; } - -.fa-circle-radiation::before { - content: "\f7ba"; } - -.fa-radiation-alt::before { - content: "\f7ba"; } - -.fa-circle-right::before { - content: "\f35a"; } - -.fa-arrow-alt-circle-right::before { - content: "\f35a"; } - -.fa-circle-stop::before { - content: "\f28d"; } - -.fa-stop-circle::before { - content: "\f28d"; } - -.fa-circle-up::before { - content: "\f35b"; } - -.fa-arrow-alt-circle-up::before { - content: "\f35b"; } - -.fa-circle-user::before { - content: "\f2bd"; } - -.fa-user-circle::before { - content: "\f2bd"; } - -.fa-circle-xmark::before { - content: "\f057"; } - -.fa-times-circle::before { - content: "\f057"; } - -.fa-xmark-circle::before { - content: "\f057"; } - -.fa-city::before { - content: "\f64f"; } - -.fa-clapperboard::before { - content: "\e131"; } - -.fa-clipboard::before { - content: "\f328"; } - -.fa-clipboard-check::before { - content: "\f46c"; } - -.fa-clipboard-list::before { - content: "\f46d"; } - -.fa-clipboard-question::before { - content: "\e4e3"; } - -.fa-clipboard-user::before { - content: "\f7f3"; } - -.fa-clock::before { - content: "\f017"; } - -.fa-clock-four::before { - content: "\f017"; } - -.fa-clock-rotate-left::before { - content: "\f1da"; } - -.fa-history::before { - content: "\f1da"; } - -.fa-clone::before { - content: "\f24d"; } - -.fa-closed-captioning::before { - content: "\f20a"; } - -.fa-cloud::before { - content: "\f0c2"; } - -.fa-cloud-arrow-down::before { - content: "\f0ed"; } - -.fa-cloud-download::before { - content: "\f0ed"; } - -.fa-cloud-download-alt::before { - content: "\f0ed"; } - -.fa-cloud-arrow-up::before { - content: "\f0ee"; } - -.fa-cloud-upload::before { - content: "\f0ee"; } - -.fa-cloud-upload-alt::before { - content: "\f0ee"; } - -.fa-cloud-bolt::before { - content: "\f76c"; } - -.fa-thunderstorm::before { - content: "\f76c"; } - -.fa-cloud-meatball::before { - content: "\f73b"; } - -.fa-cloud-moon::before { - content: "\f6c3"; } - -.fa-cloud-moon-rain::before { - content: "\f73c"; } - -.fa-cloud-rain::before { - content: "\f73d"; } - -.fa-cloud-showers-heavy::before { - content: "\f740"; } - -.fa-cloud-showers-water::before { - content: "\e4e4"; } - -.fa-cloud-sun::before { - content: "\f6c4"; } - -.fa-cloud-sun-rain::before { - content: "\f743"; } - -.fa-clover::before { - content: "\e139"; } - -.fa-code::before { - content: "\f121"; } - -.fa-code-branch::before { - content: "\f126"; } - -.fa-code-commit::before { - content: "\f386"; } - -.fa-code-compare::before { - content: "\e13a"; } - -.fa-code-fork::before { - content: "\e13b"; } - -.fa-code-merge::before { - content: "\f387"; } - -.fa-code-pull-request::before { - content: "\e13c"; } - -.fa-coins::before { - content: "\f51e"; } - -.fa-colon-sign::before { - content: "\e140"; } - -.fa-comment::before { - content: "\f075"; } - -.fa-comment-dollar::before { - content: "\f651"; } - -.fa-comment-dots::before { - content: "\f4ad"; } - -.fa-commenting::before { - content: "\f4ad"; } - -.fa-comment-medical::before { - content: "\f7f5"; } - -.fa-comment-slash::before { - content: "\f4b3"; } - -.fa-comment-sms::before { - content: "\f7cd"; } - -.fa-sms::before { - content: "\f7cd"; } - -.fa-comments::before { - content: "\f086"; } - -.fa-comments-dollar::before { - content: "\f653"; } - -.fa-compact-disc::before { - content: "\f51f"; } - -.fa-compass::before { - content: "\f14e"; } - -.fa-compass-drafting::before { - content: "\f568"; } - -.fa-drafting-compass::before { - content: "\f568"; } - -.fa-compress::before { - content: "\f066"; } - -.fa-computer::before { - content: "\e4e5"; } - -.fa-computer-mouse::before { - content: "\f8cc"; } - -.fa-mouse::before { - content: "\f8cc"; } - -.fa-cookie::before { - content: "\f563"; } - -.fa-cookie-bite::before { - content: "\f564"; } - -.fa-copy::before { - content: "\f0c5"; } - -.fa-copyright::before { - content: "\f1f9"; } - -.fa-couch::before { - content: "\f4b8"; } - -.fa-cow::before { - content: "\f6c8"; } - -.fa-credit-card::before { - content: "\f09d"; } - -.fa-credit-card-alt::before { - content: "\f09d"; } - -.fa-crop::before { - content: "\f125"; } - -.fa-crop-simple::before { - content: "\f565"; } - -.fa-crop-alt::before { - content: "\f565"; } - -.fa-cross::before { - content: "\f654"; } - -.fa-crosshairs::before { - content: "\f05b"; } - -.fa-crow::before { - content: "\f520"; } - -.fa-crown::before { - content: "\f521"; } - -.fa-crutch::before { - content: "\f7f7"; } - -.fa-cruzeiro-sign::before { - content: "\e152"; } - -.fa-cube::before { - content: "\f1b2"; } - -.fa-cubes::before { - content: "\f1b3"; } - -.fa-cubes-stacked::before { - content: "\e4e6"; } - -.fa-d::before { - content: "\44"; } - -.fa-database::before { - content: "\f1c0"; } - -.fa-delete-left::before { - content: "\f55a"; } - -.fa-backspace::before { - content: "\f55a"; } - -.fa-democrat::before { - content: "\f747"; } - -.fa-desktop::before { - content: "\f390"; } - -.fa-desktop-alt::before { - content: "\f390"; } - -.fa-dharmachakra::before { - content: "\f655"; } - -.fa-diagram-next::before { - content: "\e476"; } - -.fa-diagram-predecessor::before { - content: "\e477"; } - -.fa-diagram-project::before { - content: "\f542"; } - -.fa-project-diagram::before { - content: "\f542"; } - -.fa-diagram-successor::before { - content: "\e47a"; } - -.fa-diamond::before { - content: "\f219"; } - -.fa-diamond-turn-right::before { - content: "\f5eb"; } - -.fa-directions::before { - content: "\f5eb"; } - -.fa-dice::before { - content: "\f522"; } - -.fa-dice-d20::before { - content: "\f6cf"; } - -.fa-dice-d6::before { - content: "\f6d1"; } - -.fa-dice-five::before { - content: "\f523"; } - -.fa-dice-four::before { - content: "\f524"; } - -.fa-dice-one::before { - content: "\f525"; } - -.fa-dice-six::before { - content: "\f526"; } - -.fa-dice-three::before { - content: "\f527"; } - -.fa-dice-two::before { - content: "\f528"; } - -.fa-disease::before { - content: "\f7fa"; } - -.fa-display::before { - content: "\e163"; } - -.fa-divide::before { - content: "\f529"; } - -.fa-dna::before { - content: "\f471"; } - -.fa-dog::before { - content: "\f6d3"; } - -.fa-dollar-sign::before { - content: "\24"; } - -.fa-dollar::before { - content: "\24"; } - -.fa-usd::before { - content: "\24"; } - -.fa-dolly::before { - content: "\f472"; } - -.fa-dolly-box::before { - content: "\f472"; } - -.fa-dong-sign::before { - content: "\e169"; } - -.fa-door-closed::before { - content: "\f52a"; } - -.fa-door-open::before { - content: "\f52b"; } - -.fa-dove::before { - content: "\f4ba"; } - -.fa-down-left-and-up-right-to-center::before { - content: "\f422"; } - -.fa-compress-alt::before { - content: "\f422"; } - -.fa-down-long::before { - content: "\f309"; } - -.fa-long-arrow-alt-down::before { - content: "\f309"; } - -.fa-download::before { - content: "\f019"; } - -.fa-dragon::before { - content: "\f6d5"; } - -.fa-draw-polygon::before { - content: "\f5ee"; } - -.fa-droplet::before { - content: "\f043"; } - -.fa-tint::before { - content: "\f043"; } - -.fa-droplet-slash::before { - content: "\f5c7"; } - -.fa-tint-slash::before { - content: "\f5c7"; } - -.fa-drum::before { - content: "\f569"; } - -.fa-drum-steelpan::before { - content: "\f56a"; } - -.fa-drumstick-bite::before { - content: "\f6d7"; } - -.fa-dumbbell::before { - content: "\f44b"; } - -.fa-dumpster::before { - content: "\f793"; } - -.fa-dumpster-fire::before { - content: "\f794"; } - -.fa-dungeon::before { - content: "\f6d9"; } - -.fa-e::before { - content: "\45"; } - -.fa-ear-deaf::before { - content: "\f2a4"; } - -.fa-deaf::before { - content: "\f2a4"; } - -.fa-deafness::before { - content: "\f2a4"; } - -.fa-hard-of-hearing::before { - content: "\f2a4"; } - -.fa-ear-listen::before { - content: "\f2a2"; } - -.fa-assistive-listening-systems::before { - content: "\f2a2"; } - -.fa-earth-africa::before { - content: "\f57c"; } - -.fa-globe-africa::before { - content: "\f57c"; } - -.fa-earth-americas::before { - content: "\f57d"; } - -.fa-earth::before { - content: "\f57d"; } - -.fa-earth-america::before { - content: "\f57d"; } - -.fa-globe-americas::before { - content: "\f57d"; } - -.fa-earth-asia::before { - content: "\f57e"; } - -.fa-globe-asia::before { - content: "\f57e"; } - -.fa-earth-europe::before { - content: "\f7a2"; } - -.fa-globe-europe::before { - content: "\f7a2"; } - -.fa-earth-oceania::before { - content: "\e47b"; } - -.fa-globe-oceania::before { - content: "\e47b"; } - -.fa-egg::before { - content: "\f7fb"; } - -.fa-eject::before { - content: "\f052"; } - -.fa-elevator::before { - content: "\e16d"; } - -.fa-ellipsis::before { - content: "\f141"; } - -.fa-ellipsis-h::before { - content: "\f141"; } - -.fa-ellipsis-vertical::before { - content: "\f142"; } - -.fa-ellipsis-v::before { - content: "\f142"; } - -.fa-envelope::before { - content: "\f0e0"; } - -.fa-envelope-circle-check::before { - content: "\e4e8"; } - -.fa-envelope-open::before { - content: "\f2b6"; } - -.fa-envelope-open-text::before { - content: "\f658"; } - -.fa-envelopes-bulk::before { - content: "\f674"; } - -.fa-mail-bulk::before { - content: "\f674"; } - -.fa-equals::before { - content: "\3d"; } - -.fa-eraser::before { - content: "\f12d"; } - -.fa-ethernet::before { - content: "\f796"; } - -.fa-euro-sign::before { - content: "\f153"; } - -.fa-eur::before { - content: "\f153"; } - -.fa-euro::before { - content: "\f153"; } - -.fa-exclamation::before { - content: "\21"; } - -.fa-expand::before { - content: "\f065"; } - -.fa-explosion::before { - content: "\e4e9"; } - -.fa-eye::before { - content: "\f06e"; } - -.fa-eye-dropper::before { - content: "\f1fb"; } - -.fa-eye-dropper-empty::before { - content: "\f1fb"; } - -.fa-eyedropper::before { - content: "\f1fb"; } - -.fa-eye-low-vision::before { - content: "\f2a8"; } - -.fa-low-vision::before { - content: "\f2a8"; } - -.fa-eye-slash::before { - content: "\f070"; } - -.fa-f::before { - content: "\46"; } - -.fa-face-angry::before { - content: "\f556"; } - -.fa-angry::before { - content: "\f556"; } - -.fa-face-dizzy::before { - content: "\f567"; } - -.fa-dizzy::before { - content: "\f567"; } - -.fa-face-flushed::before { - content: "\f579"; } - -.fa-flushed::before { - content: "\f579"; } - -.fa-face-frown::before { - content: "\f119"; } - -.fa-frown::before { - content: "\f119"; } - -.fa-face-frown-open::before { - content: "\f57a"; } - -.fa-frown-open::before { - content: "\f57a"; } - -.fa-face-grimace::before { - content: "\f57f"; } - -.fa-grimace::before { - content: "\f57f"; } - -.fa-face-grin::before { - content: "\f580"; } - -.fa-grin::before { - content: "\f580"; } - -.fa-face-grin-beam::before { - content: "\f582"; } - -.fa-grin-beam::before { - content: "\f582"; } - -.fa-face-grin-beam-sweat::before { - content: "\f583"; } - -.fa-grin-beam-sweat::before { - content: "\f583"; } - -.fa-face-grin-hearts::before { - content: "\f584"; } - -.fa-grin-hearts::before { - content: "\f584"; } - -.fa-face-grin-squint::before { - content: "\f585"; } - -.fa-grin-squint::before { - content: "\f585"; } - -.fa-face-grin-squint-tears::before { - content: "\f586"; } - -.fa-grin-squint-tears::before { - content: "\f586"; } - -.fa-face-grin-stars::before { - content: "\f587"; } - -.fa-grin-stars::before { - content: "\f587"; } - -.fa-face-grin-tears::before { - content: "\f588"; } - -.fa-grin-tears::before { - content: "\f588"; } - -.fa-face-grin-tongue::before { - content: "\f589"; } - -.fa-grin-tongue::before { - content: "\f589"; } - -.fa-face-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-face-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-face-grin-wide::before { - content: "\f581"; } - -.fa-grin-alt::before { - content: "\f581"; } - -.fa-face-grin-wink::before { - content: "\f58c"; } - -.fa-grin-wink::before { - content: "\f58c"; } - -.fa-face-kiss::before { - content: "\f596"; } - -.fa-kiss::before { - content: "\f596"; } - -.fa-face-kiss-beam::before { - content: "\f597"; } - -.fa-kiss-beam::before { - content: "\f597"; } - -.fa-face-kiss-wink-heart::before { - content: "\f598"; } - -.fa-kiss-wink-heart::before { - content: "\f598"; } - -.fa-face-laugh::before { - content: "\f599"; } - -.fa-laugh::before { - content: "\f599"; } - -.fa-face-laugh-beam::before { - content: "\f59a"; } - -.fa-laugh-beam::before { - content: "\f59a"; } - -.fa-face-laugh-squint::before { - content: "\f59b"; } - -.fa-laugh-squint::before { - content: "\f59b"; } - -.fa-face-laugh-wink::before { - content: "\f59c"; } - -.fa-laugh-wink::before { - content: "\f59c"; } - -.fa-face-meh::before { - content: "\f11a"; } - -.fa-meh::before { - content: "\f11a"; } - -.fa-face-meh-blank::before { - content: "\f5a4"; } - -.fa-meh-blank::before { - content: "\f5a4"; } - -.fa-face-rolling-eyes::before { - content: "\f5a5"; } - -.fa-meh-rolling-eyes::before { - content: "\f5a5"; } - -.fa-face-sad-cry::before { - content: "\f5b3"; } - -.fa-sad-cry::before { - content: "\f5b3"; } - -.fa-face-sad-tear::before { - content: "\f5b4"; } - -.fa-sad-tear::before { - content: "\f5b4"; } - -.fa-face-smile::before { - content: "\f118"; } - -.fa-smile::before { - content: "\f118"; } - -.fa-face-smile-beam::before { - content: "\f5b8"; } - -.fa-smile-beam::before { - content: "\f5b8"; } - -.fa-face-smile-wink::before { - content: "\f4da"; } - -.fa-smile-wink::before { - content: "\f4da"; } - -.fa-face-surprise::before { - content: "\f5c2"; } - -.fa-surprise::before { - content: "\f5c2"; } - -.fa-face-tired::before { - content: "\f5c8"; } - -.fa-tired::before { - content: "\f5c8"; } - -.fa-fan::before { - content: "\f863"; } - -.fa-faucet::before { - content: "\e005"; } - -.fa-faucet-drip::before { - content: "\e006"; } - -.fa-fax::before { - content: "\f1ac"; } - -.fa-feather::before { - content: "\f52d"; } - -.fa-feather-pointed::before { - content: "\f56b"; } - -.fa-feather-alt::before { - content: "\f56b"; } - -.fa-ferry::before { - content: "\e4ea"; } - -.fa-file::before { - content: "\f15b"; } - -.fa-file-arrow-down::before { - content: "\f56d"; } - -.fa-file-download::before { - content: "\f56d"; } - -.fa-file-arrow-up::before { - content: "\f574"; } - -.fa-file-upload::before { - content: "\f574"; } - -.fa-file-audio::before { - content: "\f1c7"; } - -.fa-file-circle-check::before { - content: "\e493"; } - -.fa-file-circle-exclamation::before { - content: "\e4eb"; } - -.fa-file-circle-minus::before { - content: "\e4ed"; } - -.fa-file-circle-plus::before { - content: "\e4ee"; } - -.fa-file-circle-question::before { - content: "\e4ef"; } - -.fa-file-circle-xmark::before { - content: "\e494"; } - -.fa-file-code::before { - content: "\f1c9"; } - -.fa-file-contract::before { - content: "\f56c"; } - -.fa-file-csv::before { - content: "\f6dd"; } - -.fa-file-excel::before { - content: "\f1c3"; } - -.fa-file-export::before { - content: "\f56e"; } - -.fa-arrow-right-from-file::before { - content: "\f56e"; } - -.fa-file-image::before { - content: "\f1c5"; } - -.fa-file-import::before { - content: "\f56f"; } - -.fa-arrow-right-to-file::before { - content: "\f56f"; } - -.fa-file-invoice::before { - content: "\f570"; } - -.fa-file-invoice-dollar::before { - content: "\f571"; } - -.fa-file-lines::before { - content: "\f15c"; } - -.fa-file-alt::before { - content: "\f15c"; } - -.fa-file-text::before { - content: "\f15c"; } - -.fa-file-medical::before { - content: "\f477"; } - -.fa-file-pdf::before { - content: "\f1c1"; } - -.fa-file-pen::before { - content: "\f31c"; } - -.fa-file-edit::before { - content: "\f31c"; } - -.fa-file-powerpoint::before { - content: "\f1c4"; } - -.fa-file-prescription::before { - content: "\f572"; } - -.fa-file-shield::before { - content: "\e4f0"; } - -.fa-file-signature::before { - content: "\f573"; } - -.fa-file-video::before { - content: "\f1c8"; } - -.fa-file-waveform::before { - content: "\f478"; } - -.fa-file-medical-alt::before { - content: "\f478"; } - -.fa-file-word::before { - content: "\f1c2"; } - -.fa-file-zipper::before { - content: "\f1c6"; } - -.fa-file-archive::before { - content: "\f1c6"; } - -.fa-fill::before { - content: "\f575"; } - -.fa-fill-drip::before { - content: "\f576"; } - -.fa-film::before { - content: "\f008"; } - -.fa-filter::before { - content: "\f0b0"; } - -.fa-filter-circle-dollar::before { - content: "\f662"; } - -.fa-funnel-dollar::before { - content: "\f662"; } - -.fa-filter-circle-xmark::before { - content: "\e17b"; } - -.fa-fingerprint::before { - content: "\f577"; } - -.fa-fire::before { - content: "\f06d"; } - -.fa-fire-burner::before { - content: "\e4f1"; } - -.fa-fire-extinguisher::before { - content: "\f134"; } - -.fa-fire-flame-curved::before { - content: "\f7e4"; } - -.fa-fire-alt::before { - content: "\f7e4"; } - -.fa-fire-flame-simple::before { - content: "\f46a"; } - -.fa-burn::before { - content: "\f46a"; } - -.fa-fish::before { - content: "\f578"; } - -.fa-fish-fins::before { - content: "\e4f2"; } - -.fa-flag::before { - content: "\f024"; } - -.fa-flag-checkered::before { - content: "\f11e"; } - -.fa-flag-usa::before { - content: "\f74d"; } - -.fa-flask::before { - content: "\f0c3"; } - -.fa-flask-vial::before { - content: "\e4f3"; } - -.fa-floppy-disk::before { - content: "\f0c7"; } - -.fa-save::before { - content: "\f0c7"; } - -.fa-florin-sign::before { - content: "\e184"; } - -.fa-folder::before { - content: "\f07b"; } - -.fa-folder-blank::before { - content: "\f07b"; } - -.fa-folder-closed::before { - content: "\e185"; } - -.fa-folder-minus::before { - content: "\f65d"; } - -.fa-folder-open::before { - content: "\f07c"; } - -.fa-folder-plus::before { - content: "\f65e"; } - -.fa-folder-tree::before { - content: "\f802"; } - -.fa-font::before { - content: "\f031"; } - -.fa-football::before { - content: "\f44e"; } - -.fa-football-ball::before { - content: "\f44e"; } - -.fa-forward::before { - content: "\f04e"; } - -.fa-forward-fast::before { - content: "\f050"; } - -.fa-fast-forward::before { - content: "\f050"; } - -.fa-forward-step::before { - content: "\f051"; } - -.fa-step-forward::before { - content: "\f051"; } - -.fa-franc-sign::before { - content: "\e18f"; } - -.fa-frog::before { - content: "\f52e"; } - -.fa-futbol::before { - content: "\f1e3"; } - -.fa-futbol-ball::before { - content: "\f1e3"; } - -.fa-soccer-ball::before { - content: "\f1e3"; } - -.fa-g::before { - content: "\47"; } - -.fa-gamepad::before { - content: "\f11b"; } - -.fa-gas-pump::before { - content: "\f52f"; } - -.fa-gauge::before { - content: "\f624"; } - -.fa-dashboard::before { - content: "\f624"; } - -.fa-gauge-med::before { - content: "\f624"; } - -.fa-tachometer-alt-average::before { - content: "\f624"; } - -.fa-gauge-high::before { - content: "\f625"; } - -.fa-tachometer-alt::before { - content: "\f625"; } - -.fa-tachometer-alt-fast::before { - content: "\f625"; } - -.fa-gauge-simple::before { - content: "\f629"; } - -.fa-gauge-simple-med::before { - content: "\f629"; } - -.fa-tachometer-average::before { - content: "\f629"; } - -.fa-gauge-simple-high::before { - content: "\f62a"; } - -.fa-tachometer::before { - content: "\f62a"; } - -.fa-tachometer-fast::before { - content: "\f62a"; } - -.fa-gavel::before { - content: "\f0e3"; } - -.fa-legal::before { - content: "\f0e3"; } - -.fa-gear::before { - content: "\f013"; } - -.fa-cog::before { - content: "\f013"; } - -.fa-gears::before { - content: "\f085"; } - -.fa-cogs::before { - content: "\f085"; } - -.fa-gem::before { - content: "\f3a5"; } - -.fa-genderless::before { - content: "\f22d"; } - -.fa-ghost::before { - content: "\f6e2"; } - -.fa-gift::before { - content: "\f06b"; } - -.fa-gifts::before { - content: "\f79c"; } - -.fa-glass-water::before { - content: "\e4f4"; } - -.fa-glass-water-droplet::before { - content: "\e4f5"; } - -.fa-glasses::before { - content: "\f530"; } - -.fa-globe::before { - content: "\f0ac"; } - -.fa-golf-ball-tee::before { - content: "\f450"; } - -.fa-golf-ball::before { - content: "\f450"; } - -.fa-gopuram::before { - content: "\f664"; } - -.fa-graduation-cap::before { - content: "\f19d"; } - -.fa-mortar-board::before { - content: "\f19d"; } - -.fa-greater-than::before { - content: "\3e"; } - -.fa-greater-than-equal::before { - content: "\f532"; } - -.fa-grip::before { - content: "\f58d"; } - -.fa-grip-horizontal::before { - content: "\f58d"; } - -.fa-grip-lines::before { - content: "\f7a4"; } - -.fa-grip-lines-vertical::before { - content: "\f7a5"; } - -.fa-grip-vertical::before { - content: "\f58e"; } - -.fa-group-arrows-rotate::before { - content: "\e4f6"; } - -.fa-guarani-sign::before { - content: "\e19a"; } - -.fa-guitar::before { - content: "\f7a6"; } - -.fa-gun::before { - content: "\e19b"; } - -.fa-h::before { - content: "\48"; } - -.fa-hammer::before { - content: "\f6e3"; } - -.fa-hamsa::before { - content: "\f665"; } - -.fa-hand::before { - content: "\f256"; } - -.fa-hand-paper::before { - content: "\f256"; } - -.fa-hand-back-fist::before { - content: "\f255"; } - -.fa-hand-rock::before { - content: "\f255"; } - -.fa-hand-dots::before { - content: "\f461"; } - -.fa-allergies::before { - content: "\f461"; } - -.fa-hand-fist::before { - content: "\f6de"; } - -.fa-fist-raised::before { - content: "\f6de"; } - -.fa-hand-holding::before { - content: "\f4bd"; } - -.fa-hand-holding-dollar::before { - content: "\f4c0"; } - -.fa-hand-holding-usd::before { - content: "\f4c0"; } - -.fa-hand-holding-droplet::before { - content: "\f4c1"; } - -.fa-hand-holding-water::before { - content: "\f4c1"; } - -.fa-hand-holding-hand::before { - content: "\e4f7"; } - -.fa-hand-holding-heart::before { - content: "\f4be"; } - -.fa-hand-holding-medical::before { - content: "\e05c"; } - -.fa-hand-lizard::before { - content: "\f258"; } - -.fa-hand-middle-finger::before { - content: "\f806"; } - -.fa-hand-peace::before { - content: "\f25b"; } - -.fa-hand-point-down::before { - content: "\f0a7"; } - -.fa-hand-point-left::before { - content: "\f0a5"; } - -.fa-hand-point-right::before { - content: "\f0a4"; } - -.fa-hand-point-up::before { - content: "\f0a6"; } - -.fa-hand-pointer::before { - content: "\f25a"; } - -.fa-hand-scissors::before { - content: "\f257"; } - -.fa-hand-sparkles::before { - content: "\e05d"; } - -.fa-hand-spock::before { - content: "\f259"; } - -.fa-handcuffs::before { - content: "\e4f8"; } - -.fa-hands::before { - content: "\f2a7"; } - -.fa-sign-language::before { - content: "\f2a7"; } - -.fa-signing::before { - content: "\f2a7"; } - -.fa-hands-asl-interpreting::before { - content: "\f2a3"; } - -.fa-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-asl-interpreting::before { - content: "\f2a3"; } - -.fa-hands-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-hands-bound::before { - content: "\e4f9"; } - -.fa-hands-bubbles::before { - content: "\e05e"; } - -.fa-hands-wash::before { - content: "\e05e"; } - -.fa-hands-clapping::before { - content: "\e1a8"; } - -.fa-hands-holding::before { - content: "\f4c2"; } - -.fa-hands-holding-child::before { - content: "\e4fa"; } - -.fa-hands-holding-circle::before { - content: "\e4fb"; } - -.fa-hands-praying::before { - content: "\f684"; } - -.fa-praying-hands::before { - content: "\f684"; } - -.fa-handshake::before { - content: "\f2b5"; } - -.fa-handshake-angle::before { - content: "\f4c4"; } - -.fa-hands-helping::before { - content: "\f4c4"; } - -.fa-handshake-simple::before { - content: "\f4c6"; } - -.fa-handshake-alt::before { - content: "\f4c6"; } - -.fa-handshake-simple-slash::before { - content: "\e05f"; } - -.fa-handshake-alt-slash::before { - content: "\e05f"; } - -.fa-handshake-slash::before { - content: "\e060"; } - -.fa-hanukiah::before { - content: "\f6e6"; } - -.fa-hard-drive::before { - content: "\f0a0"; } - -.fa-hdd::before { - content: "\f0a0"; } - -.fa-hashtag::before { - content: "\23"; } - -.fa-hat-cowboy::before { - content: "\f8c0"; } - -.fa-hat-cowboy-side::before { - content: "\f8c1"; } - -.fa-hat-wizard::before { - content: "\f6e8"; } - -.fa-head-side-cough::before { - content: "\e061"; } - -.fa-head-side-cough-slash::before { - content: "\e062"; } - -.fa-head-side-mask::before { - content: "\e063"; } - -.fa-head-side-virus::before { - content: "\e064"; } - -.fa-heading::before { - content: "\f1dc"; } - -.fa-header::before { - content: "\f1dc"; } - -.fa-headphones::before { - content: "\f025"; } - -.fa-headphones-simple::before { - content: "\f58f"; } - -.fa-headphones-alt::before { - content: "\f58f"; } - -.fa-headset::before { - content: "\f590"; } - -.fa-heart::before { - content: "\f004"; } - -.fa-heart-circle-bolt::before { - content: "\e4fc"; } - -.fa-heart-circle-check::before { - content: "\e4fd"; } - -.fa-heart-circle-exclamation::before { - content: "\e4fe"; } - -.fa-heart-circle-minus::before { - content: "\e4ff"; } - -.fa-heart-circle-plus::before { - content: "\e500"; } - -.fa-heart-circle-xmark::before { - content: "\e501"; } - -.fa-heart-crack::before { - content: "\f7a9"; } - -.fa-heart-broken::before { - content: "\f7a9"; } - -.fa-heart-pulse::before { - content: "\f21e"; } - -.fa-heartbeat::before { - content: "\f21e"; } - -.fa-helicopter::before { - content: "\f533"; } - -.fa-helicopter-symbol::before { - content: "\e502"; } - -.fa-helmet-safety::before { - content: "\f807"; } - -.fa-hard-hat::before { - content: "\f807"; } - -.fa-hat-hard::before { - content: "\f807"; } - -.fa-helmet-un::before { - content: "\e503"; } - -.fa-highlighter::before { - content: "\f591"; } - -.fa-hill-avalanche::before { - content: "\e507"; } - -.fa-hill-rockslide::before { - content: "\e508"; } - -.fa-hippo::before { - content: "\f6ed"; } - -.fa-hockey-puck::before { - content: "\f453"; } - -.fa-holly-berry::before { - content: "\f7aa"; } - -.fa-horse::before { - content: "\f6f0"; } - -.fa-horse-head::before { - content: "\f7ab"; } - -.fa-hospital::before { - content: "\f0f8"; } - -.fa-hospital-alt::before { - content: "\f0f8"; } - -.fa-hospital-wide::before { - content: "\f0f8"; } - -.fa-hospital-user::before { - content: "\f80d"; } - -.fa-hot-tub-person::before { - content: "\f593"; } - -.fa-hot-tub::before { - content: "\f593"; } - -.fa-hotdog::before { - content: "\f80f"; } - -.fa-hotel::before { - content: "\f594"; } - -.fa-hourglass::before { - content: "\f254"; } - -.fa-hourglass-2::before { - content: "\f254"; } - -.fa-hourglass-half::before { - content: "\f254"; } - -.fa-hourglass-empty::before { - content: "\f252"; } - -.fa-hourglass-end::before { - content: "\f253"; } - -.fa-hourglass-3::before { - content: "\f253"; } - -.fa-hourglass-start::before { - content: "\f251"; } - -.fa-hourglass-1::before { - content: "\f251"; } - -.fa-house::before { - content: "\f015"; } - -.fa-home::before { - content: "\f015"; } - -.fa-home-alt::before { - content: "\f015"; } - -.fa-home-lg-alt::before { - content: "\f015"; } - -.fa-house-chimney::before { - content: "\e3af"; } - -.fa-home-lg::before { - content: "\e3af"; } - -.fa-house-chimney-crack::before { - content: "\f6f1"; } - -.fa-house-damage::before { - content: "\f6f1"; } - -.fa-house-chimney-medical::before { - content: "\f7f2"; } - -.fa-clinic-medical::before { - content: "\f7f2"; } - -.fa-house-chimney-user::before { - content: "\e065"; } - -.fa-house-chimney-window::before { - content: "\e00d"; } - -.fa-house-circle-check::before { - content: "\e509"; } - -.fa-house-circle-exclamation::before { - content: "\e50a"; } - -.fa-house-circle-xmark::before { - content: "\e50b"; } - -.fa-house-crack::before { - content: "\e3b1"; } - -.fa-house-fire::before { - content: "\e50c"; } - -.fa-house-flag::before { - content: "\e50d"; } - -.fa-house-flood-water::before { - content: "\e50e"; } - -.fa-house-flood-water-circle-arrow-right::before { - content: "\e50f"; } - -.fa-house-laptop::before { - content: "\e066"; } - -.fa-laptop-house::before { - content: "\e066"; } - -.fa-house-lock::before { - content: "\e510"; } - -.fa-house-medical::before { - content: "\e3b2"; } - -.fa-house-medical-circle-check::before { - content: "\e511"; } - -.fa-house-medical-circle-exclamation::before { - content: "\e512"; } - -.fa-house-medical-circle-xmark::before { - content: "\e513"; } - -.fa-house-medical-flag::before { - content: "\e514"; } - -.fa-house-signal::before { - content: "\e012"; } - -.fa-house-tsunami::before { - content: "\e515"; } - -.fa-house-user::before { - content: "\e1b0"; } - -.fa-home-user::before { - content: "\e1b0"; } - -.fa-hryvnia-sign::before { - content: "\f6f2"; } - -.fa-hryvnia::before { - content: "\f6f2"; } - -.fa-hurricane::before { - content: "\f751"; } - -.fa-i::before { - content: "\49"; } - -.fa-i-cursor::before { - content: "\f246"; } - -.fa-ice-cream::before { - content: "\f810"; } - -.fa-icicles::before { - content: "\f7ad"; } - -.fa-icons::before { - content: "\f86d"; } - -.fa-heart-music-camera-bolt::before { - content: "\f86d"; } - -.fa-id-badge::before { - content: "\f2c1"; } - -.fa-id-card::before { - content: "\f2c2"; } - -.fa-drivers-license::before { - content: "\f2c2"; } - -.fa-id-card-clip::before { - content: "\f47f"; } - -.fa-id-card-alt::before { - content: "\f47f"; } - -.fa-igloo::before { - content: "\f7ae"; } - -.fa-image::before { - content: "\f03e"; } - -.fa-image-portrait::before { - content: "\f3e0"; } - -.fa-portrait::before { - content: "\f3e0"; } - -.fa-images::before { - content: "\f302"; } - -.fa-inbox::before { - content: "\f01c"; } - -.fa-indent::before { - content: "\f03c"; } - -.fa-indian-rupee-sign::before { - content: "\e1bc"; } - -.fa-indian-rupee::before { - content: "\e1bc"; } - -.fa-inr::before { - content: "\e1bc"; } - -.fa-industry::before { - content: "\f275"; } - -.fa-infinity::before { - content: "\f534"; } - -.fa-info::before { - content: "\f129"; } - -.fa-italic::before { - content: "\f033"; } - -.fa-j::before { - content: "\4a"; } - -.fa-jar::before { - content: "\e516"; } - -.fa-jar-wheat::before { - content: "\e517"; } - -.fa-jedi::before { - content: "\f669"; } - -.fa-jet-fighter::before { - content: "\f0fb"; } - -.fa-fighter-jet::before { - content: "\f0fb"; } - -.fa-jet-fighter-up::before { - content: "\e518"; } - -.fa-joint::before { - content: "\f595"; } - -.fa-jug-detergent::before { - content: "\e519"; } - -.fa-k::before { - content: "\4b"; } - -.fa-kaaba::before { - content: "\f66b"; } - -.fa-key::before { - content: "\f084"; } - -.fa-keyboard::before { - content: "\f11c"; } - -.fa-khanda::before { - content: "\f66d"; } - -.fa-kip-sign::before { - content: "\e1c4"; } - -.fa-kit-medical::before { - content: "\f479"; } - -.fa-first-aid::before { - content: "\f479"; } - -.fa-kitchen-set::before { - content: "\e51a"; } - -.fa-kiwi-bird::before { - content: "\f535"; } - -.fa-l::before { - content: "\4c"; } - -.fa-land-mine-on::before { - content: "\e51b"; } - -.fa-landmark::before { - content: "\f66f"; } - -.fa-landmark-dome::before { - content: "\f752"; } - -.fa-landmark-alt::before { - content: "\f752"; } - -.fa-landmark-flag::before { - content: "\e51c"; } - -.fa-language::before { - content: "\f1ab"; } - -.fa-laptop::before { - content: "\f109"; } - -.fa-laptop-code::before { - content: "\f5fc"; } - -.fa-laptop-file::before { - content: "\e51d"; } - -.fa-laptop-medical::before { - content: "\f812"; } - -.fa-lari-sign::before { - content: "\e1c8"; } - -.fa-layer-group::before { - content: "\f5fd"; } - -.fa-leaf::before { - content: "\f06c"; } - -.fa-left-long::before { - content: "\f30a"; } - -.fa-long-arrow-alt-left::before { - content: "\f30a"; } - -.fa-left-right::before { - content: "\f337"; } - -.fa-arrows-alt-h::before { - content: "\f337"; } - -.fa-lemon::before { - content: "\f094"; } - -.fa-less-than::before { - content: "\3c"; } - -.fa-less-than-equal::before { - content: "\f537"; } - -.fa-life-ring::before { - content: "\f1cd"; } - -.fa-lightbulb::before { - content: "\f0eb"; } - -.fa-lines-leaning::before { - content: "\e51e"; } - -.fa-link::before { - content: "\f0c1"; } - -.fa-chain::before { - content: "\f0c1"; } - -.fa-link-slash::before { - content: "\f127"; } - -.fa-chain-broken::before { - content: "\f127"; } - -.fa-chain-slash::before { - content: "\f127"; } - -.fa-unlink::before { - content: "\f127"; } - -.fa-lira-sign::before { - content: "\f195"; } - -.fa-list::before { - content: "\f03a"; } - -.fa-list-squares::before { - content: "\f03a"; } - -.fa-list-check::before { - content: "\f0ae"; } - -.fa-tasks::before { - content: "\f0ae"; } - -.fa-list-ol::before { - content: "\f0cb"; } - -.fa-list-1-2::before { - content: "\f0cb"; } - -.fa-list-numeric::before { - content: "\f0cb"; } - -.fa-list-ul::before { - content: "\f0ca"; } - -.fa-list-dots::before { - content: "\f0ca"; } - -.fa-litecoin-sign::before { - content: "\e1d3"; } - -.fa-location-arrow::before { - content: "\f124"; } - -.fa-location-crosshairs::before { - content: "\f601"; } - -.fa-location::before { - content: "\f601"; } - -.fa-location-dot::before { - content: "\f3c5"; } - -.fa-map-marker-alt::before { - content: "\f3c5"; } - -.fa-location-pin::before { - content: "\f041"; } - -.fa-map-marker::before { - content: "\f041"; } - -.fa-location-pin-lock::before { - content: "\e51f"; } - -.fa-lock::before { - content: "\f023"; } - -.fa-lock-open::before { - content: "\f3c1"; } - -.fa-locust::before { - content: "\e520"; } - -.fa-lungs::before { - content: "\f604"; } - -.fa-lungs-virus::before { - content: "\e067"; } - -.fa-m::before { - content: "\4d"; } - -.fa-magnet::before { - content: "\f076"; } - -.fa-magnifying-glass::before { - content: "\f002"; } - -.fa-search::before { - content: "\f002"; } - -.fa-magnifying-glass-arrow-right::before { - content: "\e521"; } - -.fa-magnifying-glass-chart::before { - content: "\e522"; } - -.fa-magnifying-glass-dollar::before { - content: "\f688"; } - -.fa-search-dollar::before { - content: "\f688"; } - -.fa-magnifying-glass-location::before { - content: "\f689"; } - -.fa-search-location::before { - content: "\f689"; } - -.fa-magnifying-glass-minus::before { - content: "\f010"; } - -.fa-search-minus::before { - content: "\f010"; } - -.fa-magnifying-glass-plus::before { - content: "\f00e"; } - -.fa-search-plus::before { - content: "\f00e"; } - -.fa-manat-sign::before { - content: "\e1d5"; } - -.fa-map::before { - content: "\f279"; } - -.fa-map-location::before { - content: "\f59f"; } - -.fa-map-marked::before { - content: "\f59f"; } - -.fa-map-location-dot::before { - content: "\f5a0"; } - -.fa-map-marked-alt::before { - content: "\f5a0"; } - -.fa-map-pin::before { - content: "\f276"; } - -.fa-marker::before { - content: "\f5a1"; } - -.fa-mars::before { - content: "\f222"; } - -.fa-mars-and-venus::before { - content: "\f224"; } - -.fa-mars-and-venus-burst::before { - content: "\e523"; } - -.fa-mars-double::before { - content: "\f227"; } - -.fa-mars-stroke::before { - content: "\f229"; } - -.fa-mars-stroke-right::before { - content: "\f22b"; } - -.fa-mars-stroke-h::before { - content: "\f22b"; } - -.fa-mars-stroke-up::before { - content: "\f22a"; } - -.fa-mars-stroke-v::before { - content: "\f22a"; } - -.fa-martini-glass::before { - content: "\f57b"; } - -.fa-glass-martini-alt::before { - content: "\f57b"; } - -.fa-martini-glass-citrus::before { - content: "\f561"; } - -.fa-cocktail::before { - content: "\f561"; } - -.fa-martini-glass-empty::before { - content: "\f000"; } - -.fa-glass-martini::before { - content: "\f000"; } - -.fa-mask::before { - content: "\f6fa"; } - -.fa-mask-face::before { - content: "\e1d7"; } - -.fa-mask-ventilator::before { - content: "\e524"; } - -.fa-masks-theater::before { - content: "\f630"; } - -.fa-theater-masks::before { - content: "\f630"; } - -.fa-mattress-pillow::before { - content: "\e525"; } - -.fa-maximize::before { - content: "\f31e"; } - -.fa-expand-arrows-alt::before { - content: "\f31e"; } - -.fa-medal::before { - content: "\f5a2"; } - -.fa-memory::before { - content: "\f538"; } - -.fa-menorah::before { - content: "\f676"; } - -.fa-mercury::before { - content: "\f223"; } - -.fa-message::before { - content: "\f27a"; } - -.fa-comment-alt::before { - content: "\f27a"; } - -.fa-meteor::before { - content: "\f753"; } - -.fa-microchip::before { - content: "\f2db"; } - -.fa-microphone::before { - content: "\f130"; } - -.fa-microphone-lines::before { - content: "\f3c9"; } - -.fa-microphone-alt::before { - content: "\f3c9"; } - -.fa-microphone-lines-slash::before { - content: "\f539"; } - -.fa-microphone-alt-slash::before { - content: "\f539"; } - -.fa-microphone-slash::before { - content: "\f131"; } - -.fa-microscope::before { - content: "\f610"; } - -.fa-mill-sign::before { - content: "\e1ed"; } - -.fa-minimize::before { - content: "\f78c"; } - -.fa-compress-arrows-alt::before { - content: "\f78c"; } - -.fa-minus::before { - content: "\f068"; } - -.fa-subtract::before { - content: "\f068"; } - -.fa-mitten::before { - content: "\f7b5"; } - -.fa-mobile::before { - content: "\f3ce"; } - -.fa-mobile-android::before { - content: "\f3ce"; } - -.fa-mobile-phone::before { - content: "\f3ce"; } - -.fa-mobile-button::before { - content: "\f10b"; } - -.fa-mobile-retro::before { - content: "\e527"; } - -.fa-mobile-screen::before { - content: "\f3cf"; } - -.fa-mobile-android-alt::before { - content: "\f3cf"; } - -.fa-mobile-screen-button::before { - content: "\f3cd"; } - -.fa-mobile-alt::before { - content: "\f3cd"; } - -.fa-money-bill::before { - content: "\f0d6"; } - -.fa-money-bill-1::before { - content: "\f3d1"; } - -.fa-money-bill-alt::before { - content: "\f3d1"; } - -.fa-money-bill-1-wave::before { - content: "\f53b"; } - -.fa-money-bill-wave-alt::before { - content: "\f53b"; } - -.fa-money-bill-transfer::before { - content: "\e528"; } - -.fa-money-bill-trend-up::before { - content: "\e529"; } - -.fa-money-bill-wave::before { - content: "\f53a"; } - -.fa-money-bill-wheat::before { - content: "\e52a"; } - -.fa-money-bills::before { - content: "\e1f3"; } - -.fa-money-check::before { - content: "\f53c"; } - -.fa-money-check-dollar::before { - content: "\f53d"; } - -.fa-money-check-alt::before { - content: "\f53d"; } - -.fa-monument::before { - content: "\f5a6"; } - -.fa-moon::before { - content: "\f186"; } - -.fa-mortar-pestle::before { - content: "\f5a7"; } - -.fa-mosque::before { - content: "\f678"; } - -.fa-mosquito::before { - content: "\e52b"; } - -.fa-mosquito-net::before { - content: "\e52c"; } - -.fa-motorcycle::before { - content: "\f21c"; } - -.fa-mound::before { - content: "\e52d"; } - -.fa-mountain::before { - content: "\f6fc"; } - -.fa-mountain-city::before { - content: "\e52e"; } - -.fa-mountain-sun::before { - content: "\e52f"; } - -.fa-mug-hot::before { - content: "\f7b6"; } - -.fa-mug-saucer::before { - content: "\f0f4"; } - -.fa-coffee::before { - content: "\f0f4"; } - -.fa-music::before { - content: "\f001"; } - -.fa-n::before { - content: "\4e"; } - -.fa-naira-sign::before { - content: "\e1f6"; } - -.fa-network-wired::before { - content: "\f6ff"; } - -.fa-neuter::before { - content: "\f22c"; } - -.fa-newspaper::before { - content: "\f1ea"; } - -.fa-not-equal::before { - content: "\f53e"; } - -.fa-note-sticky::before { - content: "\f249"; } - -.fa-sticky-note::before { - content: "\f249"; } - -.fa-notes-medical::before { - content: "\f481"; } - -.fa-o::before { - content: "\4f"; } - -.fa-object-group::before { - content: "\f247"; } - -.fa-object-ungroup::before { - content: "\f248"; } - -.fa-oil-can::before { - content: "\f613"; } - -.fa-oil-well::before { - content: "\e532"; } - -.fa-om::before { - content: "\f679"; } - -.fa-otter::before { - content: "\f700"; } - -.fa-outdent::before { - content: "\f03b"; } - -.fa-dedent::before { - content: "\f03b"; } - -.fa-p::before { - content: "\50"; } - -.fa-pager::before { - content: "\f815"; } - -.fa-paint-roller::before { - content: "\f5aa"; } - -.fa-paintbrush::before { - content: "\f1fc"; } - -.fa-paint-brush::before { - content: "\f1fc"; } - -.fa-palette::before { - content: "\f53f"; } - -.fa-pallet::before { - content: "\f482"; } - -.fa-panorama::before { - content: "\e209"; } - -.fa-paper-plane::before { - content: "\f1d8"; } - -.fa-paperclip::before { - content: "\f0c6"; } - -.fa-parachute-box::before { - content: "\f4cd"; } - -.fa-paragraph::before { - content: "\f1dd"; } - -.fa-passport::before { - content: "\f5ab"; } - -.fa-paste::before { - content: "\f0ea"; } - -.fa-file-clipboard::before { - content: "\f0ea"; } - -.fa-pause::before { - content: "\f04c"; } - -.fa-paw::before { - content: "\f1b0"; } - -.fa-peace::before { - content: "\f67c"; } - -.fa-pen::before { - content: "\f304"; } - -.fa-pen-clip::before { - content: "\f305"; } - -.fa-pen-alt::before { - content: "\f305"; } - -.fa-pen-fancy::before { - content: "\f5ac"; } - -.fa-pen-nib::before { - content: "\f5ad"; } - -.fa-pen-ruler::before { - content: "\f5ae"; } - -.fa-pencil-ruler::before { - content: "\f5ae"; } - -.fa-pen-to-square::before { - content: "\f044"; } - -.fa-edit::before { - content: "\f044"; } - -.fa-pencil::before { - content: "\f303"; } - -.fa-pencil-alt::before { - content: "\f303"; } - -.fa-people-arrows-left-right::before { - content: "\e068"; } - -.fa-people-arrows::before { - content: "\e068"; } - -.fa-people-carry-box::before { - content: "\f4ce"; } - -.fa-people-carry::before { - content: "\f4ce"; } - -.fa-people-group::before { - content: "\e533"; } - -.fa-people-line::before { - content: "\e534"; } - -.fa-people-pulling::before { - content: "\e535"; } - -.fa-people-robbery::before { - content: "\e536"; } - -.fa-people-roof::before { - content: "\e537"; } - -.fa-pepper-hot::before { - content: "\f816"; } - -.fa-percent::before { - content: "\25"; } - -.fa-percentage::before { - content: "\25"; } - -.fa-person::before { - content: "\f183"; } - -.fa-male::before { - content: "\f183"; } - -.fa-person-arrow-down-to-line::before { - content: "\e538"; } - -.fa-person-arrow-up-from-line::before { - content: "\e539"; } - -.fa-person-biking::before { - content: "\f84a"; } - -.fa-biking::before { - content: "\f84a"; } - -.fa-person-booth::before { - content: "\f756"; } - -.fa-person-breastfeeding::before { - content: "\e53a"; } - -.fa-person-burst::before { - content: "\e53b"; } - -.fa-person-cane::before { - content: "\e53c"; } - -.fa-person-chalkboard::before { - content: "\e53d"; } - -.fa-person-circle-check::before { - content: "\e53e"; } - -.fa-person-circle-exclamation::before { - content: "\e53f"; } - -.fa-person-circle-minus::before { - content: "\e540"; } - -.fa-person-circle-plus::before { - content: "\e541"; } - -.fa-person-circle-question::before { - content: "\e542"; } - -.fa-person-circle-xmark::before { - content: "\e543"; } - -.fa-person-digging::before { - content: "\f85e"; } - -.fa-digging::before { - content: "\f85e"; } - -.fa-person-dots-from-line::before { - content: "\f470"; } - -.fa-diagnoses::before { - content: "\f470"; } - -.fa-person-dress::before { - content: "\f182"; } - -.fa-female::before { - content: "\f182"; } - -.fa-person-dress-burst::before { - content: "\e544"; } - -.fa-person-drowning::before { - content: "\e545"; } - -.fa-person-falling::before { - content: "\e546"; } - -.fa-person-falling-burst::before { - content: "\e547"; } - -.fa-person-half-dress::before { - content: "\e548"; } - -.fa-person-harassing::before { - content: "\e549"; } - -.fa-person-hiking::before { - content: "\f6ec"; } - -.fa-hiking::before { - content: "\f6ec"; } - -.fa-person-military-pointing::before { - content: "\e54a"; } - -.fa-person-military-rifle::before { - content: "\e54b"; } - -.fa-person-military-to-person::before { - content: "\e54c"; } - -.fa-person-praying::before { - content: "\f683"; } - -.fa-pray::before { - content: "\f683"; } - -.fa-person-pregnant::before { - content: "\e31e"; } - -.fa-person-rays::before { - content: "\e54d"; } - -.fa-person-rifle::before { - content: "\e54e"; } - -.fa-person-running::before { - content: "\f70c"; } - -.fa-running::before { - content: "\f70c"; } - -.fa-person-shelter::before { - content: "\e54f"; } - -.fa-person-skating::before { - content: "\f7c5"; } - -.fa-skating::before { - content: "\f7c5"; } - -.fa-person-skiing::before { - content: "\f7c9"; } - -.fa-skiing::before { - content: "\f7c9"; } - -.fa-person-skiing-nordic::before { - content: "\f7ca"; } - -.fa-skiing-nordic::before { - content: "\f7ca"; } - -.fa-person-snowboarding::before { - content: "\f7ce"; } - -.fa-snowboarding::before { - content: "\f7ce"; } - -.fa-person-swimming::before { - content: "\f5c4"; } - -.fa-swimmer::before { - content: "\f5c4"; } - -.fa-person-through-window::before { - content: "\e433"; } - -.fa-person-walking::before { - content: "\f554"; } - -.fa-walking::before { - content: "\f554"; } - -.fa-person-walking-arrow-loop-left::before { - content: "\e551"; } - -.fa-person-walking-arrow-right::before { - content: "\e552"; } - -.fa-person-walking-dashed-line-arrow-right::before { - content: "\e553"; } - -.fa-person-walking-luggage::before { - content: "\e554"; } - -.fa-person-walking-with-cane::before { - content: "\f29d"; } - -.fa-blind::before { - content: "\f29d"; } - -.fa-peseta-sign::before { - content: "\e221"; } - -.fa-peso-sign::before { - content: "\e222"; } - -.fa-phone::before { - content: "\f095"; } - -.fa-phone-flip::before { - content: "\f879"; } - -.fa-phone-alt::before { - content: "\f879"; } - -.fa-phone-slash::before { - content: "\f3dd"; } - -.fa-phone-volume::before { - content: "\f2a0"; } - -.fa-volume-control-phone::before { - content: "\f2a0"; } - -.fa-photo-film::before { - content: "\f87c"; } - -.fa-photo-video::before { - content: "\f87c"; } - -.fa-piggy-bank::before { - content: "\f4d3"; } - -.fa-pills::before { - content: "\f484"; } - -.fa-pizza-slice::before { - content: "\f818"; } - -.fa-place-of-worship::before { - content: "\f67f"; } - -.fa-plane::before { - content: "\f072"; } - -.fa-plane-arrival::before { - content: "\f5af"; } - -.fa-plane-circle-check::before { - content: "\e555"; } - -.fa-plane-circle-exclamation::before { - content: "\e556"; } - -.fa-plane-circle-xmark::before { - content: "\e557"; } - -.fa-plane-departure::before { - content: "\f5b0"; } - -.fa-plane-lock::before { - content: "\e558"; } - -.fa-plane-slash::before { - content: "\e069"; } - -.fa-plane-up::before { - content: "\e22d"; } - -.fa-plant-wilt::before { - content: "\e43b"; } - -.fa-plate-wheat::before { - content: "\e55a"; } - -.fa-play::before { - content: "\f04b"; } - -.fa-plug::before { - content: "\f1e6"; } - -.fa-plug-circle-bolt::before { - content: "\e55b"; } - -.fa-plug-circle-check::before { - content: "\e55c"; } - -.fa-plug-circle-exclamation::before { - content: "\e55d"; } - -.fa-plug-circle-minus::before { - content: "\e55e"; } - -.fa-plug-circle-plus::before { - content: "\e55f"; } - -.fa-plug-circle-xmark::before { - content: "\e560"; } - -.fa-plus::before { - content: "\2b"; } - -.fa-add::before { - content: "\2b"; } - -.fa-plus-minus::before { - content: "\e43c"; } - -.fa-podcast::before { - content: "\f2ce"; } - -.fa-poo::before { - content: "\f2fe"; } - -.fa-poo-storm::before { - content: "\f75a"; } - -.fa-poo-bolt::before { - content: "\f75a"; } - -.fa-poop::before { - content: "\f619"; } - -.fa-power-off::before { - content: "\f011"; } - -.fa-prescription::before { - content: "\f5b1"; } - -.fa-prescription-bottle::before { - content: "\f485"; } - -.fa-prescription-bottle-medical::before { - content: "\f486"; } - -.fa-prescription-bottle-alt::before { - content: "\f486"; } - -.fa-print::before { - content: "\f02f"; } - -.fa-pump-medical::before { - content: "\e06a"; } - -.fa-pump-soap::before { - content: "\e06b"; } - -.fa-puzzle-piece::before { - content: "\f12e"; } - -.fa-q::before { - content: "\51"; } - -.fa-qrcode::before { - content: "\f029"; } - -.fa-question::before { - content: "\3f"; } - -.fa-quote-left::before { - content: "\f10d"; } - -.fa-quote-left-alt::before { - content: "\f10d"; } - -.fa-quote-right::before { - content: "\f10e"; } - -.fa-quote-right-alt::before { - content: "\f10e"; } - -.fa-r::before { - content: "\52"; } - -.fa-radiation::before { - content: "\f7b9"; } - -.fa-radio::before { - content: "\f8d7"; } - -.fa-rainbow::before { - content: "\f75b"; } - -.fa-ranking-star::before { - content: "\e561"; } - -.fa-receipt::before { - content: "\f543"; } - -.fa-record-vinyl::before { - content: "\f8d9"; } - -.fa-rectangle-ad::before { - content: "\f641"; } - -.fa-ad::before { - content: "\f641"; } - -.fa-rectangle-list::before { - content: "\f022"; } - -.fa-list-alt::before { - content: "\f022"; } - -.fa-rectangle-xmark::before { - content: "\f410"; } - -.fa-rectangle-times::before { - content: "\f410"; } - -.fa-times-rectangle::before { - content: "\f410"; } - -.fa-window-close::before { - content: "\f410"; } - -.fa-recycle::before { - content: "\f1b8"; } - -.fa-registered::before { - content: "\f25d"; } - -.fa-repeat::before { - content: "\f363"; } - -.fa-reply::before { - content: "\f3e5"; } - -.fa-mail-reply::before { - content: "\f3e5"; } - -.fa-reply-all::before { - content: "\f122"; } - -.fa-mail-reply-all::before { - content: "\f122"; } - -.fa-republican::before { - content: "\f75e"; } - -.fa-restroom::before { - content: "\f7bd"; } - -.fa-retweet::before { - content: "\f079"; } - -.fa-ribbon::before { - content: "\f4d6"; } - -.fa-right-from-bracket::before { - content: "\f2f5"; } - -.fa-sign-out-alt::before { - content: "\f2f5"; } - -.fa-right-left::before { - content: "\f362"; } - -.fa-exchange-alt::before { - content: "\f362"; } - -.fa-right-long::before { - content: "\f30b"; } - -.fa-long-arrow-alt-right::before { - content: "\f30b"; } - -.fa-right-to-bracket::before { - content: "\f2f6"; } - -.fa-sign-in-alt::before { - content: "\f2f6"; } - -.fa-ring::before { - content: "\f70b"; } - -.fa-road::before { - content: "\f018"; } - -.fa-road-barrier::before { - content: "\e562"; } - -.fa-road-bridge::before { - content: "\e563"; } - -.fa-road-circle-check::before { - content: "\e564"; } - -.fa-road-circle-exclamation::before { - content: "\e565"; } - -.fa-road-circle-xmark::before { - content: "\e566"; } - -.fa-road-lock::before { - content: "\e567"; } - -.fa-road-spikes::before { - content: "\e568"; } - -.fa-robot::before { - content: "\f544"; } - -.fa-rocket::before { - content: "\f135"; } - -.fa-rotate::before { - content: "\f2f1"; } - -.fa-sync-alt::before { - content: "\f2f1"; } - -.fa-rotate-left::before { - content: "\f2ea"; } - -.fa-rotate-back::before { - content: "\f2ea"; } - -.fa-rotate-backward::before { - content: "\f2ea"; } - -.fa-undo-alt::before { - content: "\f2ea"; } - -.fa-rotate-right::before { - content: "\f2f9"; } - -.fa-redo-alt::before { - content: "\f2f9"; } - -.fa-rotate-forward::before { - content: "\f2f9"; } - -.fa-route::before { - content: "\f4d7"; } - -.fa-rss::before { - content: "\f09e"; } - -.fa-feed::before { - content: "\f09e"; } - -.fa-ruble-sign::before { - content: "\f158"; } - -.fa-rouble::before { - content: "\f158"; } - -.fa-rub::before { - content: "\f158"; } - -.fa-ruble::before { - content: "\f158"; } - -.fa-rug::before { - content: "\e569"; } - -.fa-ruler::before { - content: "\f545"; } - -.fa-ruler-combined::before { - content: "\f546"; } - -.fa-ruler-horizontal::before { - content: "\f547"; } - -.fa-ruler-vertical::before { - content: "\f548"; } - -.fa-rupee-sign::before { - content: "\f156"; } - -.fa-rupee::before { - content: "\f156"; } - -.fa-rupiah-sign::before { - content: "\e23d"; } - -.fa-s::before { - content: "\53"; } - -.fa-sack-dollar::before { - content: "\f81d"; } - -.fa-sack-xmark::before { - content: "\e56a"; } - -.fa-sailboat::before { - content: "\e445"; } - -.fa-satellite::before { - content: "\f7bf"; } - -.fa-satellite-dish::before { - content: "\f7c0"; } - -.fa-scale-balanced::before { - content: "\f24e"; } - -.fa-balance-scale::before { - content: "\f24e"; } - -.fa-scale-unbalanced::before { - content: "\f515"; } - -.fa-balance-scale-left::before { - content: "\f515"; } - -.fa-scale-unbalanced-flip::before { - content: "\f516"; } - -.fa-balance-scale-right::before { - content: "\f516"; } - -.fa-school::before { - content: "\f549"; } - -.fa-school-circle-check::before { - content: "\e56b"; } - -.fa-school-circle-exclamation::before { - content: "\e56c"; } - -.fa-school-circle-xmark::before { - content: "\e56d"; } - -.fa-school-flag::before { - content: "\e56e"; } - -.fa-school-lock::before { - content: "\e56f"; } - -.fa-scissors::before { - content: "\f0c4"; } - -.fa-cut::before { - content: "\f0c4"; } - -.fa-screwdriver::before { - content: "\f54a"; } - -.fa-screwdriver-wrench::before { - content: "\f7d9"; } - -.fa-tools::before { - content: "\f7d9"; } - -.fa-scroll::before { - content: "\f70e"; } - -.fa-scroll-torah::before { - content: "\f6a0"; } - -.fa-torah::before { - content: "\f6a0"; } - -.fa-sd-card::before { - content: "\f7c2"; } - -.fa-section::before { - content: "\e447"; } - -.fa-seedling::before { - content: "\f4d8"; } - -.fa-sprout::before { - content: "\f4d8"; } - -.fa-server::before { - content: "\f233"; } - -.fa-shapes::before { - content: "\f61f"; } - -.fa-triangle-circle-square::before { - content: "\f61f"; } - -.fa-share::before { - content: "\f064"; } - -.fa-arrow-turn-right::before { - content: "\f064"; } - -.fa-mail-forward::before { - content: "\f064"; } - -.fa-share-from-square::before { - content: "\f14d"; } - -.fa-share-square::before { - content: "\f14d"; } - -.fa-share-nodes::before { - content: "\f1e0"; } - -.fa-share-alt::before { - content: "\f1e0"; } - -.fa-sheet-plastic::before { - content: "\e571"; } - -.fa-shekel-sign::before { - content: "\f20b"; } - -.fa-ils::before { - content: "\f20b"; } - -.fa-shekel::before { - content: "\f20b"; } - -.fa-sheqel::before { - content: "\f20b"; } - -.fa-sheqel-sign::before { - content: "\f20b"; } - -.fa-shield::before { - content: "\f132"; } - -.fa-shield-blank::before { - content: "\f132"; } - -.fa-shield-cat::before { - content: "\e572"; } - -.fa-shield-dog::before { - content: "\e573"; } - -.fa-shield-halved::before { - content: "\f3ed"; } - -.fa-shield-alt::before { - content: "\f3ed"; } - -.fa-shield-heart::before { - content: "\e574"; } - -.fa-shield-virus::before { - content: "\e06c"; } - -.fa-ship::before { - content: "\f21a"; } - -.fa-shirt::before { - content: "\f553"; } - -.fa-t-shirt::before { - content: "\f553"; } - -.fa-tshirt::before { - content: "\f553"; } - -.fa-shoe-prints::before { - content: "\f54b"; } - -.fa-shop::before { - content: "\f54f"; } - -.fa-store-alt::before { - content: "\f54f"; } - -.fa-shop-lock::before { - content: "\e4a5"; } - -.fa-shop-slash::before { - content: "\e070"; } - -.fa-store-alt-slash::before { - content: "\e070"; } - -.fa-shower::before { - content: "\f2cc"; } - -.fa-shrimp::before { - content: "\e448"; } - -.fa-shuffle::before { - content: "\f074"; } - -.fa-random::before { - content: "\f074"; } - -.fa-shuttle-space::before { - content: "\f197"; } - -.fa-space-shuttle::before { - content: "\f197"; } - -.fa-sign-hanging::before { - content: "\f4d9"; } - -.fa-sign::before { - content: "\f4d9"; } - -.fa-signal::before { - content: "\f012"; } - -.fa-signal-5::before { - content: "\f012"; } - -.fa-signal-perfect::before { - content: "\f012"; } - -.fa-signature::before { - content: "\f5b7"; } - -.fa-signs-post::before { - content: "\f277"; } - -.fa-map-signs::before { - content: "\f277"; } - -.fa-sim-card::before { - content: "\f7c4"; } - -.fa-sink::before { - content: "\e06d"; } - -.fa-sitemap::before { - content: "\f0e8"; } - -.fa-skull::before { - content: "\f54c"; } - -.fa-skull-crossbones::before { - content: "\f714"; } - -.fa-slash::before { - content: "\f715"; } - -.fa-sleigh::before { - content: "\f7cc"; } - -.fa-sliders::before { - content: "\f1de"; } - -.fa-sliders-h::before { - content: "\f1de"; } - -.fa-smog::before { - content: "\f75f"; } - -.fa-smoking::before { - content: "\f48d"; } - -.fa-snowflake::before { - content: "\f2dc"; } - -.fa-snowman::before { - content: "\f7d0"; } - -.fa-snowplow::before { - content: "\f7d2"; } - -.fa-soap::before { - content: "\e06e"; } - -.fa-socks::before { - content: "\f696"; } - -.fa-solar-panel::before { - content: "\f5ba"; } - -.fa-sort::before { - content: "\f0dc"; } - -.fa-unsorted::before { - content: "\f0dc"; } - -.fa-sort-down::before { - content: "\f0dd"; } - -.fa-sort-desc::before { - content: "\f0dd"; } - -.fa-sort-up::before { - content: "\f0de"; } - -.fa-sort-asc::before { - content: "\f0de"; } - -.fa-spa::before { - content: "\f5bb"; } - -.fa-spaghetti-monster-flying::before { - content: "\f67b"; } - -.fa-pastafarianism::before { - content: "\f67b"; } - -.fa-spell-check::before { - content: "\f891"; } - -.fa-spider::before { - content: "\f717"; } - -.fa-spinner::before { - content: "\f110"; } - -.fa-splotch::before { - content: "\f5bc"; } - -.fa-spoon::before { - content: "\f2e5"; } - -.fa-utensil-spoon::before { - content: "\f2e5"; } - -.fa-spray-can::before { - content: "\f5bd"; } - -.fa-spray-can-sparkles::before { - content: "\f5d0"; } - -.fa-air-freshener::before { - content: "\f5d0"; } - -.fa-square::before { - content: "\f0c8"; } - -.fa-square-arrow-up-right::before { - content: "\f14c"; } - -.fa-external-link-square::before { - content: "\f14c"; } - -.fa-square-caret-down::before { - content: "\f150"; } - -.fa-caret-square-down::before { - content: "\f150"; } - -.fa-square-caret-left::before { - content: "\f191"; } - -.fa-caret-square-left::before { - content: "\f191"; } - -.fa-square-caret-right::before { - content: "\f152"; } - -.fa-caret-square-right::before { - content: "\f152"; } - -.fa-square-caret-up::before { - content: "\f151"; } - -.fa-caret-square-up::before { - content: "\f151"; } - -.fa-square-check::before { - content: "\f14a"; } - -.fa-check-square::before { - content: "\f14a"; } - -.fa-square-envelope::before { - content: "\f199"; } - -.fa-envelope-square::before { - content: "\f199"; } - -.fa-square-full::before { - content: "\f45c"; } - -.fa-square-h::before { - content: "\f0fd"; } - -.fa-h-square::before { - content: "\f0fd"; } - -.fa-square-minus::before { - content: "\f146"; } - -.fa-minus-square::before { - content: "\f146"; } - -.fa-square-nfi::before { - content: "\e576"; } - -.fa-square-parking::before { - content: "\f540"; } - -.fa-parking::before { - content: "\f540"; } - -.fa-square-pen::before { - content: "\f14b"; } - -.fa-pen-square::before { - content: "\f14b"; } - -.fa-pencil-square::before { - content: "\f14b"; } - -.fa-square-person-confined::before { - content: "\e577"; } - -.fa-square-phone::before { - content: "\f098"; } - -.fa-phone-square::before { - content: "\f098"; } - -.fa-square-phone-flip::before { - content: "\f87b"; } - -.fa-phone-square-alt::before { - content: "\f87b"; } - -.fa-square-plus::before { - content: "\f0fe"; } - -.fa-plus-square::before { - content: "\f0fe"; } - -.fa-square-poll-horizontal::before { - content: "\f682"; } - -.fa-poll-h::before { - content: "\f682"; } - -.fa-square-poll-vertical::before { - content: "\f681"; } - -.fa-poll::before { - content: "\f681"; } - -.fa-square-root-variable::before { - content: "\f698"; } - -.fa-square-root-alt::before { - content: "\f698"; } - -.fa-square-rss::before { - content: "\f143"; } - -.fa-rss-square::before { - content: "\f143"; } - -.fa-square-share-nodes::before { - content: "\f1e1"; } - -.fa-share-alt-square::before { - content: "\f1e1"; } - -.fa-square-up-right::before { - content: "\f360"; } - -.fa-external-link-square-alt::before { - content: "\f360"; } - -.fa-square-virus::before { - content: "\e578"; } - -.fa-square-xmark::before { - content: "\f2d3"; } - -.fa-times-square::before { - content: "\f2d3"; } - -.fa-xmark-square::before { - content: "\f2d3"; } - -.fa-staff-aesculapius::before { - content: "\e579"; } - -.fa-rod-asclepius::before { - content: "\e579"; } - -.fa-rod-snake::before { - content: "\e579"; } - -.fa-staff-snake::before { - content: "\e579"; } - -.fa-stairs::before { - content: "\e289"; } - -.fa-stamp::before { - content: "\f5bf"; } - -.fa-star::before { - content: "\f005"; } - -.fa-star-and-crescent::before { - content: "\f699"; } - -.fa-star-half::before { - content: "\f089"; } - -.fa-star-half-stroke::before { - content: "\f5c0"; } - -.fa-star-half-alt::before { - content: "\f5c0"; } - -.fa-star-of-david::before { - content: "\f69a"; } - -.fa-star-of-life::before { - content: "\f621"; } - -.fa-sterling-sign::before { - content: "\f154"; } - -.fa-gbp::before { - content: "\f154"; } - -.fa-pound-sign::before { - content: "\f154"; } - -.fa-stethoscope::before { - content: "\f0f1"; } - -.fa-stop::before { - content: "\f04d"; } - -.fa-stopwatch::before { - content: "\f2f2"; } - -.fa-stopwatch-20::before { - content: "\e06f"; } - -.fa-store::before { - content: "\f54e"; } - -.fa-store-slash::before { - content: "\e071"; } - -.fa-street-view::before { - content: "\f21d"; } - -.fa-strikethrough::before { - content: "\f0cc"; } - -.fa-stroopwafel::before { - content: "\f551"; } - -.fa-subscript::before { - content: "\f12c"; } - -.fa-suitcase::before { - content: "\f0f2"; } - -.fa-suitcase-medical::before { - content: "\f0fa"; } - -.fa-medkit::before { - content: "\f0fa"; } - -.fa-suitcase-rolling::before { - content: "\f5c1"; } - -.fa-sun::before { - content: "\f185"; } - -.fa-sun-plant-wilt::before { - content: "\e57a"; } - -.fa-superscript::before { - content: "\f12b"; } - -.fa-swatchbook::before { - content: "\f5c3"; } - -.fa-synagogue::before { - content: "\f69b"; } - -.fa-syringe::before { - content: "\f48e"; } - -.fa-t::before { - content: "\54"; } - -.fa-table::before { - content: "\f0ce"; } - -.fa-table-cells::before { - content: "\f00a"; } - -.fa-th::before { - content: "\f00a"; } - -.fa-table-cells-large::before { - content: "\f009"; } - -.fa-th-large::before { - content: "\f009"; } - -.fa-table-columns::before { - content: "\f0db"; } - -.fa-columns::before { - content: "\f0db"; } - -.fa-table-list::before { - content: "\f00b"; } - -.fa-th-list::before { - content: "\f00b"; } - -.fa-table-tennis-paddle-ball::before { - content: "\f45d"; } - -.fa-ping-pong-paddle-ball::before { - content: "\f45d"; } - -.fa-table-tennis::before { - content: "\f45d"; } - -.fa-tablet::before { - content: "\f3fb"; } - -.fa-tablet-android::before { - content: "\f3fb"; } - -.fa-tablet-button::before { - content: "\f10a"; } - -.fa-tablet-screen-button::before { - content: "\f3fa"; } - -.fa-tablet-alt::before { - content: "\f3fa"; } - -.fa-tablets::before { - content: "\f490"; } - -.fa-tachograph-digital::before { - content: "\f566"; } - -.fa-digital-tachograph::before { - content: "\f566"; } - -.fa-tag::before { - content: "\f02b"; } - -.fa-tags::before { - content: "\f02c"; } - -.fa-tape::before { - content: "\f4db"; } - -.fa-tarp::before { - content: "\e57b"; } - -.fa-tarp-droplet::before { - content: "\e57c"; } - -.fa-taxi::before { - content: "\f1ba"; } - -.fa-cab::before { - content: "\f1ba"; } - -.fa-teeth::before { - content: "\f62e"; } - -.fa-teeth-open::before { - content: "\f62f"; } - -.fa-temperature-arrow-down::before { - content: "\e03f"; } - -.fa-temperature-down::before { - content: "\e03f"; } - -.fa-temperature-arrow-up::before { - content: "\e040"; } - -.fa-temperature-up::before { - content: "\e040"; } - -.fa-temperature-empty::before { - content: "\f2cb"; } - -.fa-temperature-0::before { - content: "\f2cb"; } - -.fa-thermometer-0::before { - content: "\f2cb"; } - -.fa-thermometer-empty::before { - content: "\f2cb"; } - -.fa-temperature-full::before { - content: "\f2c7"; } - -.fa-temperature-4::before { - content: "\f2c7"; } - -.fa-thermometer-4::before { - content: "\f2c7"; } - -.fa-thermometer-full::before { - content: "\f2c7"; } - -.fa-temperature-half::before { - content: "\f2c9"; } - -.fa-temperature-2::before { - content: "\f2c9"; } - -.fa-thermometer-2::before { - content: "\f2c9"; } - -.fa-thermometer-half::before { - content: "\f2c9"; } - -.fa-temperature-high::before { - content: "\f769"; } - -.fa-temperature-low::before { - content: "\f76b"; } - -.fa-temperature-quarter::before { - content: "\f2ca"; } - -.fa-temperature-1::before { - content: "\f2ca"; } - -.fa-thermometer-1::before { - content: "\f2ca"; } - -.fa-thermometer-quarter::before { - content: "\f2ca"; } - -.fa-temperature-three-quarters::before { - content: "\f2c8"; } - -.fa-temperature-3::before { - content: "\f2c8"; } - -.fa-thermometer-3::before { - content: "\f2c8"; } - -.fa-thermometer-three-quarters::before { - content: "\f2c8"; } - -.fa-tenge-sign::before { - content: "\f7d7"; } - -.fa-tenge::before { - content: "\f7d7"; } - -.fa-tent::before { - content: "\e57d"; } - -.fa-tent-arrow-down-to-line::before { - content: "\e57e"; } - -.fa-tent-arrow-left-right::before { - content: "\e57f"; } - -.fa-tent-arrow-turn-left::before { - content: "\e580"; } - -.fa-tent-arrows-down::before { - content: "\e581"; } - -.fa-tents::before { - content: "\e582"; } - -.fa-terminal::before { - content: "\f120"; } - -.fa-text-height::before { - content: "\f034"; } - -.fa-text-slash::before { - content: "\f87d"; } - -.fa-remove-format::before { - content: "\f87d"; } - -.fa-text-width::before { - content: "\f035"; } - -.fa-thermometer::before { - content: "\f491"; } - -.fa-thumbs-down::before { - content: "\f165"; } - -.fa-thumbs-up::before { - content: "\f164"; } - -.fa-thumbtack::before { - content: "\f08d"; } - -.fa-thumb-tack::before { - content: "\f08d"; } - -.fa-ticket::before { - content: "\f145"; } - -.fa-ticket-simple::before { - content: "\f3ff"; } - -.fa-ticket-alt::before { - content: "\f3ff"; } - -.fa-timeline::before { - content: "\e29c"; } - -.fa-toggle-off::before { - content: "\f204"; } - -.fa-toggle-on::before { - content: "\f205"; } - -.fa-toilet::before { - content: "\f7d8"; } - -.fa-toilet-paper::before { - content: "\f71e"; } - -.fa-toilet-paper-slash::before { - content: "\e072"; } - -.fa-toilet-portable::before { - content: "\e583"; } - -.fa-toilets-portable::before { - content: "\e584"; } - -.fa-toolbox::before { - content: "\f552"; } - -.fa-tooth::before { - content: "\f5c9"; } - -.fa-torii-gate::before { - content: "\f6a1"; } - -.fa-tornado::before { - content: "\f76f"; } - -.fa-tower-broadcast::before { - content: "\f519"; } - -.fa-broadcast-tower::before { - content: "\f519"; } - -.fa-tower-cell::before { - content: "\e585"; } - -.fa-tower-observation::before { - content: "\e586"; } - -.fa-tractor::before { - content: "\f722"; } - -.fa-trademark::before { - content: "\f25c"; } - -.fa-traffic-light::before { - content: "\f637"; } - -.fa-trailer::before { - content: "\e041"; } - -.fa-train::before { - content: "\f238"; } - -.fa-train-subway::before { - content: "\f239"; } - -.fa-subway::before { - content: "\f239"; } - -.fa-train-tram::before { - content: "\f7da"; } - -.fa-tram::before { - content: "\f7da"; } - -.fa-transgender::before { - content: "\f225"; } - -.fa-transgender-alt::before { - content: "\f225"; } - -.fa-trash::before { - content: "\f1f8"; } - -.fa-trash-arrow-up::before { - content: "\f829"; } - -.fa-trash-restore::before { - content: "\f829"; } - -.fa-trash-can::before { - content: "\f2ed"; } - -.fa-trash-alt::before { - content: "\f2ed"; } - -.fa-trash-can-arrow-up::before { - content: "\f82a"; } - -.fa-trash-restore-alt::before { - content: "\f82a"; } - -.fa-tree::before { - content: "\f1bb"; } - -.fa-tree-city::before { - content: "\e587"; } - -.fa-triangle-exclamation::before { - content: "\f071"; } - -.fa-exclamation-triangle::before { - content: "\f071"; } - -.fa-warning::before { - content: "\f071"; } - -.fa-trophy::before { - content: "\f091"; } - -.fa-trowel::before { - content: "\e589"; } - -.fa-trowel-bricks::before { - content: "\e58a"; } - -.fa-truck::before { - content: "\f0d1"; } - -.fa-truck-arrow-right::before { - content: "\e58b"; } - -.fa-truck-droplet::before { - content: "\e58c"; } - -.fa-truck-fast::before { - content: "\f48b"; } - -.fa-shipping-fast::before { - content: "\f48b"; } - -.fa-truck-field::before { - content: "\e58d"; } - -.fa-truck-field-un::before { - content: "\e58e"; } - -.fa-truck-front::before { - content: "\e2b7"; } - -.fa-truck-medical::before { - content: "\f0f9"; } - -.fa-ambulance::before { - content: "\f0f9"; } - -.fa-truck-monster::before { - content: "\f63b"; } - -.fa-truck-moving::before { - content: "\f4df"; } - -.fa-truck-pickup::before { - content: "\f63c"; } - -.fa-truck-plane::before { - content: "\e58f"; } - -.fa-truck-ramp-box::before { - content: "\f4de"; } - -.fa-truck-loading::before { - content: "\f4de"; } - -.fa-tty::before { - content: "\f1e4"; } - -.fa-teletype::before { - content: "\f1e4"; } - -.fa-turkish-lira-sign::before { - content: "\e2bb"; } - -.fa-try::before { - content: "\e2bb"; } - -.fa-turkish-lira::before { - content: "\e2bb"; } - -.fa-turn-down::before { - content: "\f3be"; } - -.fa-level-down-alt::before { - content: "\f3be"; } - -.fa-turn-up::before { - content: "\f3bf"; } - -.fa-level-up-alt::before { - content: "\f3bf"; } - -.fa-tv::before { - content: "\f26c"; } - -.fa-television::before { - content: "\f26c"; } - -.fa-tv-alt::before { - content: "\f26c"; } - -.fa-u::before { - content: "\55"; } - -.fa-umbrella::before { - content: "\f0e9"; } - -.fa-umbrella-beach::before { - content: "\f5ca"; } - -.fa-underline::before { - content: "\f0cd"; } - -.fa-universal-access::before { - content: "\f29a"; } - -.fa-unlock::before { - content: "\f09c"; } - -.fa-unlock-keyhole::before { - content: "\f13e"; } - -.fa-unlock-alt::before { - content: "\f13e"; } - -.fa-up-down::before { - content: "\f338"; } - -.fa-arrows-alt-v::before { - content: "\f338"; } - -.fa-up-down-left-right::before { - content: "\f0b2"; } - -.fa-arrows-alt::before { - content: "\f0b2"; } - -.fa-up-long::before { - content: "\f30c"; } - -.fa-long-arrow-alt-up::before { - content: "\f30c"; } - -.fa-up-right-and-down-left-from-center::before { - content: "\f424"; } - -.fa-expand-alt::before { - content: "\f424"; } - -.fa-up-right-from-square::before { - content: "\f35d"; } - -.fa-external-link-alt::before { - content: "\f35d"; } - -.fa-upload::before { - content: "\f093"; } - -.fa-user::before { - content: "\f007"; } - -.fa-user-astronaut::before { - content: "\f4fb"; } - -.fa-user-check::before { - content: "\f4fc"; } - -.fa-user-clock::before { - content: "\f4fd"; } - -.fa-user-doctor::before { - content: "\f0f0"; } - -.fa-user-md::before { - content: "\f0f0"; } - -.fa-user-gear::before { - content: "\f4fe"; } - -.fa-user-cog::before { - content: "\f4fe"; } - -.fa-user-graduate::before { - content: "\f501"; } - -.fa-user-group::before { - content: "\f500"; } - -.fa-user-friends::before { - content: "\f500"; } - -.fa-user-injured::before { - content: "\f728"; } - -.fa-user-large::before { - content: "\f406"; } - -.fa-user-alt::before { - content: "\f406"; } - -.fa-user-large-slash::before { - content: "\f4fa"; } - -.fa-user-alt-slash::before { - content: "\f4fa"; } - -.fa-user-lock::before { - content: "\f502"; } - -.fa-user-minus::before { - content: "\f503"; } - -.fa-user-ninja::before { - content: "\f504"; } - -.fa-user-nurse::before { - content: "\f82f"; } - -.fa-user-pen::before { - content: "\f4ff"; } - -.fa-user-edit::before { - content: "\f4ff"; } - -.fa-user-plus::before { - content: "\f234"; } - -.fa-user-secret::before { - content: "\f21b"; } - -.fa-user-shield::before { - content: "\f505"; } - -.fa-user-slash::before { - content: "\f506"; } - -.fa-user-tag::before { - content: "\f507"; } - -.fa-user-tie::before { - content: "\f508"; } - -.fa-user-xmark::before { - content: "\f235"; } - -.fa-user-times::before { - content: "\f235"; } - -.fa-users::before { - content: "\f0c0"; } - -.fa-users-between-lines::before { - content: "\e591"; } - -.fa-users-gear::before { - content: "\f509"; } - -.fa-users-cog::before { - content: "\f509"; } - -.fa-users-line::before { - content: "\e592"; } - -.fa-users-rays::before { - content: "\e593"; } - -.fa-users-rectangle::before { - content: "\e594"; } - -.fa-users-slash::before { - content: "\e073"; } - -.fa-users-viewfinder::before { - content: "\e595"; } - -.fa-utensils::before { - content: "\f2e7"; } - -.fa-cutlery::before { - content: "\f2e7"; } - -.fa-v::before { - content: "\56"; } - -.fa-van-shuttle::before { - content: "\f5b6"; } - -.fa-shuttle-van::before { - content: "\f5b6"; } - -.fa-vault::before { - content: "\e2c5"; } - -.fa-vector-square::before { - content: "\f5cb"; } - -.fa-venus::before { - content: "\f221"; } - -.fa-venus-double::before { - content: "\f226"; } - -.fa-venus-mars::before { - content: "\f228"; } - -.fa-vest::before { - content: "\e085"; } - -.fa-vest-patches::before { - content: "\e086"; } - -.fa-vial::before { - content: "\f492"; } - -.fa-vial-circle-check::before { - content: "\e596"; } - -.fa-vial-virus::before { - content: "\e597"; } - -.fa-vials::before { - content: "\f493"; } - -.fa-video::before { - content: "\f03d"; } - -.fa-video-camera::before { - content: "\f03d"; } - -.fa-video-slash::before { - content: "\f4e2"; } - -.fa-vihara::before { - content: "\f6a7"; } - -.fa-virus::before { - content: "\e074"; } - -.fa-virus-covid::before { - content: "\e4a8"; } - -.fa-virus-covid-slash::before { - content: "\e4a9"; } - -.fa-virus-slash::before { - content: "\e075"; } - -.fa-viruses::before { - content: "\e076"; } - -.fa-voicemail::before { - content: "\f897"; } - -.fa-volcano::before { - content: "\f770"; } - -.fa-volleyball::before { - content: "\f45f"; } - -.fa-volleyball-ball::before { - content: "\f45f"; } - -.fa-volume-high::before { - content: "\f028"; } - -.fa-volume-up::before { - content: "\f028"; } - -.fa-volume-low::before { - content: "\f027"; } - -.fa-volume-down::before { - content: "\f027"; } - -.fa-volume-off::before { - content: "\f026"; } - -.fa-volume-xmark::before { - content: "\f6a9"; } - -.fa-volume-mute::before { - content: "\f6a9"; } - -.fa-volume-times::before { - content: "\f6a9"; } - -.fa-vr-cardboard::before { - content: "\f729"; } - -.fa-w::before { - content: "\57"; } - -.fa-walkie-talkie::before { - content: "\f8ef"; } - -.fa-wallet::before { - content: "\f555"; } - -.fa-wand-magic::before { - content: "\f0d0"; } - -.fa-magic::before { - content: "\f0d0"; } - -.fa-wand-magic-sparkles::before { - content: "\e2ca"; } - -.fa-magic-wand-sparkles::before { - content: "\e2ca"; } - -.fa-wand-sparkles::before { - content: "\f72b"; } - -.fa-warehouse::before { - content: "\f494"; } - -.fa-water::before { - content: "\f773"; } - -.fa-water-ladder::before { - content: "\f5c5"; } - -.fa-ladder-water::before { - content: "\f5c5"; } - -.fa-swimming-pool::before { - content: "\f5c5"; } - -.fa-wave-square::before { - content: "\f83e"; } - -.fa-weight-hanging::before { - content: "\f5cd"; } - -.fa-weight-scale::before { - content: "\f496"; } - -.fa-weight::before { - content: "\f496"; } - -.fa-wheat-awn::before { - content: "\e2cd"; } - -.fa-wheat-alt::before { - content: "\e2cd"; } - -.fa-wheat-awn-circle-exclamation::before { - content: "\e598"; } - -.fa-wheelchair::before { - content: "\f193"; } - -.fa-wheelchair-move::before { - content: "\e2ce"; } - -.fa-wheelchair-alt::before { - content: "\e2ce"; } - -.fa-whiskey-glass::before { - content: "\f7a0"; } - -.fa-glass-whiskey::before { - content: "\f7a0"; } - -.fa-wifi::before { - content: "\f1eb"; } - -.fa-wifi-3::before { - content: "\f1eb"; } - -.fa-wifi-strong::before { - content: "\f1eb"; } - -.fa-wind::before { - content: "\f72e"; } - -.fa-window-maximize::before { - content: "\f2d0"; } - -.fa-window-minimize::before { - content: "\f2d1"; } - -.fa-window-restore::before { - content: "\f2d2"; } - -.fa-wine-bottle::before { - content: "\f72f"; } - -.fa-wine-glass::before { - content: "\f4e3"; } - -.fa-wine-glass-empty::before { - content: "\f5ce"; } - -.fa-wine-glass-alt::before { - content: "\f5ce"; } - -.fa-won-sign::before { - content: "\f159"; } - -.fa-krw::before { - content: "\f159"; } - -.fa-won::before { - content: "\f159"; } - -.fa-worm::before { - content: "\e599"; } - -.fa-wrench::before { - content: "\f0ad"; } - -.fa-x::before { - content: "\58"; } - -.fa-x-ray::before { - content: "\f497"; } - -.fa-xmark::before { - content: "\f00d"; } - -.fa-close::before { - content: "\f00d"; } - -.fa-multiply::before { - content: "\f00d"; } - -.fa-remove::before { - content: "\f00d"; } - -.fa-times::before { - content: "\f00d"; } - -.fa-xmarks-lines::before { - content: "\e59a"; } - -.fa-y::before { - content: "\59"; } - -.fa-yen-sign::before { - content: "\f157"; } - -.fa-cny::before { - content: "\f157"; } - -.fa-jpy::before { - content: "\f157"; } - -.fa-rmb::before { - content: "\f157"; } - -.fa-yen::before { - content: "\f157"; } - -.fa-yin-yang::before { - content: "\f6ad"; } - -.fa-z::before { - content: "\5a"; } - -.sr-only, -.fa-sr-only { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } - -.sr-only-focusable:not(:focus), -.fa-sr-only-focusable:not(:focus) { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } -:root, :host { - --fa-font-brands: normal 400 1em/1 "Font Awesome 6 Brands"; } - -@font-face { - font-family: 'Font Awesome 6 Brands'; - font-style: normal; - font-weight: 400; - font-display: block; - src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } - -.fab, -.fa-brands { - font-family: 'Font Awesome 6 Brands'; - font-weight: 400; } - -.fa-42-group:before { - content: "\e080"; } - -.fa-innosoft:before { - content: "\e080"; } - -.fa-500px:before { - content: "\f26e"; } - -.fa-accessible-icon:before { - content: "\f368"; } - -.fa-accusoft:before { - content: "\f369"; } - -.fa-adn:before { - content: "\f170"; } - -.fa-adversal:before { - content: "\f36a"; } - -.fa-affiliatetheme:before { - content: "\f36b"; } - -.fa-airbnb:before { - content: "\f834"; } - -.fa-algolia:before { - content: "\f36c"; } - -.fa-alipay:before { - content: "\f642"; } - -.fa-amazon:before { - content: "\f270"; } - -.fa-amazon-pay:before { - content: "\f42c"; } - -.fa-amilia:before { - content: "\f36d"; } - -.fa-android:before { - content: "\f17b"; } - -.fa-angellist:before { - content: "\f209"; } - -.fa-angrycreative:before { - content: "\f36e"; } - -.fa-angular:before { - content: "\f420"; } - -.fa-app-store:before { - content: "\f36f"; } - -.fa-app-store-ios:before { - content: "\f370"; } - -.fa-apper:before { - content: "\f371"; } - -.fa-apple:before { - content: "\f179"; } - -.fa-apple-pay:before { - content: "\f415"; } - -.fa-artstation:before { - content: "\f77a"; } - -.fa-asymmetrik:before { - content: "\f372"; } - -.fa-atlassian:before { - content: "\f77b"; } - -.fa-audible:before { - content: "\f373"; } - -.fa-autoprefixer:before { - content: "\f41c"; } - -.fa-avianex:before { - content: "\f374"; } - -.fa-aviato:before { - content: "\f421"; } - -.fa-aws:before { - content: "\f375"; } - -.fa-bandcamp:before { - content: "\f2d5"; } - -.fa-battle-net:before { - content: "\f835"; } - -.fa-behance:before { - content: "\f1b4"; } - -.fa-behance-square:before { - content: "\f1b5"; } - -.fa-bilibili:before { - content: "\e3d9"; } - -.fa-bimobject:before { - content: "\f378"; } - -.fa-bitbucket:before { - content: "\f171"; } - -.fa-bitcoin:before { - content: "\f379"; } - -.fa-bity:before { - content: "\f37a"; } - -.fa-black-tie:before { - content: "\f27e"; } - -.fa-blackberry:before { - content: "\f37b"; } - -.fa-blogger:before { - content: "\f37c"; } - -.fa-blogger-b:before { - content: "\f37d"; } - -.fa-bluetooth:before { - content: "\f293"; } - -.fa-bluetooth-b:before { - content: "\f294"; } - -.fa-bootstrap:before { - content: "\f836"; } - -.fa-bots:before { - content: "\e340"; } - -.fa-btc:before { - content: "\f15a"; } - -.fa-buffer:before { - content: "\f837"; } - -.fa-buromobelexperte:before { - content: "\f37f"; } - -.fa-buy-n-large:before { - content: "\f8a6"; } - -.fa-buysellads:before { - content: "\f20d"; } - -.fa-canadian-maple-leaf:before { - content: "\f785"; } - -.fa-cc-amazon-pay:before { - content: "\f42d"; } - -.fa-cc-amex:before { - content: "\f1f3"; } - -.fa-cc-apple-pay:before { - content: "\f416"; } - -.fa-cc-diners-club:before { - content: "\f24c"; } - -.fa-cc-discover:before { - content: "\f1f2"; } - -.fa-cc-jcb:before { - content: "\f24b"; } - -.fa-cc-mastercard:before { - content: "\f1f1"; } - -.fa-cc-paypal:before { - content: "\f1f4"; } - -.fa-cc-stripe:before { - content: "\f1f5"; } - -.fa-cc-visa:before { - content: "\f1f0"; } - -.fa-centercode:before { - content: "\f380"; } - -.fa-centos:before { - content: "\f789"; } - -.fa-chrome:before { - content: "\f268"; } - -.fa-chromecast:before { - content: "\f838"; } - -.fa-cloudflare:before { - content: "\e07d"; } - -.fa-cloudscale:before { - content: "\f383"; } - -.fa-cloudsmith:before { - content: "\f384"; } - -.fa-cloudversify:before { - content: "\f385"; } - -.fa-cmplid:before { - content: "\e360"; } - -.fa-codepen:before { - content: "\f1cb"; } - -.fa-codiepie:before { - content: "\f284"; } - -.fa-confluence:before { - content: "\f78d"; } - -.fa-connectdevelop:before { - content: "\f20e"; } - -.fa-contao:before { - content: "\f26d"; } - -.fa-cotton-bureau:before { - content: "\f89e"; } - -.fa-cpanel:before { - content: "\f388"; } - -.fa-creative-commons:before { - content: "\f25e"; } - -.fa-creative-commons-by:before { - content: "\f4e7"; } - -.fa-creative-commons-nc:before { - content: "\f4e8"; } - -.fa-creative-commons-nc-eu:before { - content: "\f4e9"; } - -.fa-creative-commons-nc-jp:before { - content: "\f4ea"; } - -.fa-creative-commons-nd:before { - content: "\f4eb"; } - -.fa-creative-commons-pd:before { - content: "\f4ec"; } - -.fa-creative-commons-pd-alt:before { - content: "\f4ed"; } - -.fa-creative-commons-remix:before { - content: "\f4ee"; } - -.fa-creative-commons-sa:before { - content: "\f4ef"; } - -.fa-creative-commons-sampling:before { - content: "\f4f0"; } - -.fa-creative-commons-sampling-plus:before { - content: "\f4f1"; } - -.fa-creative-commons-share:before { - content: "\f4f2"; } - -.fa-creative-commons-zero:before { - content: "\f4f3"; } - -.fa-critical-role:before { - content: "\f6c9"; } - -.fa-css3:before { - content: "\f13c"; } - -.fa-css3-alt:before { - content: "\f38b"; } - -.fa-cuttlefish:before { - content: "\f38c"; } - -.fa-d-and-d:before { - content: "\f38d"; } - -.fa-d-and-d-beyond:before { - content: "\f6ca"; } - -.fa-dailymotion:before { - content: "\e052"; } - -.fa-dashcube:before { - content: "\f210"; } - -.fa-deezer:before { - content: "\e077"; } - -.fa-delicious:before { - content: "\f1a5"; } - -.fa-deploydog:before { - content: "\f38e"; } - -.fa-deskpro:before { - content: "\f38f"; } - -.fa-dev:before { - content: "\f6cc"; } - -.fa-deviantart:before { - content: "\f1bd"; } - -.fa-dhl:before { - content: "\f790"; } - -.fa-diaspora:before { - content: "\f791"; } - -.fa-digg:before { - content: "\f1a6"; } - -.fa-digital-ocean:before { - content: "\f391"; } - -.fa-discord:before { - content: "\f392"; } - -.fa-discourse:before { - content: "\f393"; } - -.fa-dochub:before { - content: "\f394"; } - -.fa-docker:before { - content: "\f395"; } - -.fa-draft2digital:before { - content: "\f396"; } - -.fa-dribbble:before { - content: "\f17d"; } - -.fa-dribbble-square:before { - content: "\f397"; } - -.fa-dropbox:before { - content: "\f16b"; } - -.fa-drupal:before { - content: "\f1a9"; } - -.fa-dyalog:before { - content: "\f399"; } - -.fa-earlybirds:before { - content: "\f39a"; } - -.fa-ebay:before { - content: "\f4f4"; } - -.fa-edge:before { - content: "\f282"; } - -.fa-edge-legacy:before { - content: "\e078"; } - -.fa-elementor:before { - content: "\f430"; } - -.fa-ello:before { - content: "\f5f1"; } - -.fa-ember:before { - content: "\f423"; } - -.fa-empire:before { - content: "\f1d1"; } - -.fa-envira:before { - content: "\f299"; } - 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content: "\f417"; } - -.fa-font-awesome:before { - content: "\f2b4"; } - -.fa-font-awesome-flag:before { - content: "\f2b4"; } - -.fa-font-awesome-logo-full:before { - content: "\f2b4"; } - -.fa-fonticons:before { - content: "\f280"; } - -.fa-fonticons-fi:before { - content: "\f3a2"; } - -.fa-fort-awesome:before { - content: "\f286"; } - -.fa-fort-awesome-alt:before { - content: "\f3a3"; } - -.fa-forumbee:before { - content: "\f211"; } - -.fa-foursquare:before { - content: "\f180"; } - -.fa-free-code-camp:before { - content: "\f2c5"; } - -.fa-freebsd:before { - content: "\f3a4"; } - -.fa-fulcrum:before { - content: "\f50b"; } - -.fa-galactic-republic:before { - content: "\f50c"; } - -.fa-galactic-senate:before { - content: "\f50d"; } - -.fa-get-pocket:before { - content: "\f265"; } - -.fa-gg:before { - content: "\f260"; } - -.fa-gg-circle:before { - content: "\f261"; } - -.fa-git:before { - content: "\f1d3"; } - -.fa-git-alt:before { - content: "\f841"; } - -.fa-git-square:before { - content: "\f1d2"; } - -.fa-github:before { - content: "\f09b"; } - -.fa-github-alt:before { - content: "\f113"; } - -.fa-github-square:before { - content: "\f092"; } - -.fa-gitkraken:before { - content: "\f3a6"; } - -.fa-gitlab:before { - content: "\f296"; } - -.fa-gitter:before { - content: "\f426"; } - -.fa-glide:before { - content: "\f2a5"; } - -.fa-glide-g:before { - content: "\f2a6"; } - -.fa-gofore:before { - content: "\f3a7"; } - -.fa-golang:before { - content: "\e40f"; } - -.fa-goodreads:before { - content: "\f3a8"; } - -.fa-goodreads-g:before { - content: "\f3a9"; } - -.fa-google:before { - content: "\f1a0"; } - -.fa-google-drive:before { - content: "\f3aa"; } - -.fa-google-pay:before { - content: "\e079"; } - -.fa-google-play:before { - content: "\f3ab"; } - -.fa-google-plus:before { - content: "\f2b3"; } - -.fa-google-plus-g:before { - content: "\f0d5"; } - -.fa-google-plus-square:before { - content: "\f0d4"; } - -.fa-google-wallet:before { - content: "\f1ee"; } - 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content: "\f16d"; } - -.fa-instagram-square:before { - content: "\e055"; } - -.fa-instalod:before { - content: "\e081"; } - -.fa-intercom:before { - content: "\f7af"; } - -.fa-internet-explorer:before { - content: "\f26b"; } - -.fa-invision:before { - content: "\f7b0"; } - -.fa-ioxhost:before { - content: "\f208"; } - -.fa-itch-io:before { - content: "\f83a"; } - -.fa-itunes:before { - content: "\f3b4"; } - -.fa-itunes-note:before { - content: "\f3b5"; } - -.fa-java:before { - content: "\f4e4"; } - -.fa-jedi-order:before { - content: "\f50e"; } - -.fa-jenkins:before { - content: "\f3b6"; } - -.fa-jira:before { - content: "\f7b1"; } - -.fa-joget:before { - content: "\f3b7"; } - -.fa-joomla:before { - content: "\f1aa"; } - -.fa-js:before { - content: "\f3b8"; } - -.fa-js-square:before { - content: "\f3b9"; } - -.fa-jsfiddle:before { - content: "\f1cc"; } - -.fa-kaggle:before { - content: "\f5fa"; } - -.fa-keybase:before { - content: "\f4f5"; } - -.fa-keycdn:before { - content: "\f3ba"; } 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- content: "\f3c6"; } - -.fa-medium:before { - content: "\f23a"; } - -.fa-medium-m:before { - content: "\f23a"; } - -.fa-medrt:before { - content: "\f3c8"; } - -.fa-meetup:before { - content: "\f2e0"; } - -.fa-megaport:before { - content: "\f5a3"; } - -.fa-mendeley:before { - content: "\f7b3"; } - -.fa-microblog:before { - content: "\e01a"; } - -.fa-microsoft:before { - content: "\f3ca"; } - -.fa-mix:before { - content: "\f3cb"; } - -.fa-mixcloud:before { - content: "\f289"; } - -.fa-mixer:before { - content: "\e056"; } - -.fa-mizuni:before { - content: "\f3cc"; } - -.fa-modx:before { - content: "\f285"; } - -.fa-monero:before { - content: "\f3d0"; } - -.fa-napster:before { - content: "\f3d2"; } - -.fa-neos:before { - content: "\f612"; } - -.fa-nfc-directional:before { - content: "\e530"; } - -.fa-nfc-symbol:before { - content: "\e531"; } - -.fa-nimblr:before { - content: "\f5a8"; } - -.fa-node:before { - content: "\f419"; } - -.fa-node-js:before { - content: "\f3d3"; } - -.fa-npm:before { - content: "\f3d4"; } - -.fa-ns8:before { - content: "\f3d5"; } - -.fa-nutritionix:before { - content: "\f3d6"; } - -.fa-octopus-deploy:before { - content: "\e082"; } - -.fa-odnoklassniki:before { - content: "\f263"; } - -.fa-odnoklassniki-square:before { - content: "\f264"; } - -.fa-old-republic:before { - content: "\f510"; } - -.fa-opencart:before { - content: "\f23d"; } - -.fa-openid:before { - content: "\f19b"; } - -.fa-opera:before { - content: "\f26a"; } - -.fa-optin-monster:before { - content: "\f23c"; } - -.fa-orcid:before { - content: "\f8d2"; } - -.fa-osi:before { - content: "\f41a"; } - -.fa-padlet:before { - content: "\e4a0"; } - -.fa-page4:before { - content: "\f3d7"; } - -.fa-pagelines:before { - content: "\f18c"; } - -.fa-palfed:before { - content: "\f3d8"; } - -.fa-patreon:before { - content: "\f3d9"; } - -.fa-paypal:before { - content: "\f1ed"; } - -.fa-perbyte:before { - content: "\e083"; } - -.fa-periscope:before { - content: "\f3da"; } - 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-.fa-r-project:before { - content: "\f4f7"; } - -.fa-raspberry-pi:before { - content: "\f7bb"; } - -.fa-ravelry:before { - content: "\f2d9"; } - -.fa-react:before { - content: "\f41b"; } - -.fa-reacteurope:before { - content: "\f75d"; } - -.fa-readme:before { - content: "\f4d5"; } - -.fa-rebel:before { - content: "\f1d0"; } - -.fa-red-river:before { - content: "\f3e3"; } - -.fa-reddit:before { - content: "\f1a1"; } - -.fa-reddit-alien:before { - content: "\f281"; } - -.fa-reddit-square:before { - content: "\f1a2"; } - -.fa-redhat:before { - content: "\f7bc"; } - -.fa-renren:before { - content: "\f18b"; } - -.fa-replyd:before { - content: "\f3e6"; } - -.fa-researchgate:before { - content: "\f4f8"; } - -.fa-resolving:before { - content: "\f3e7"; } - -.fa-rev:before { - content: "\f5b2"; } - -.fa-rocketchat:before { - content: "\f3e8"; } - -.fa-rockrms:before { - content: "\f3e9"; } - -.fa-rust:before { - content: "\e07a"; } - -.fa-safari:before { - content: "\f267"; } - -.fa-salesforce:before { - content: "\f83b"; } - -.fa-sass:before { - content: "\f41e"; } - -.fa-schlix:before { - content: "\f3ea"; } - -.fa-screenpal:before { - content: "\e570"; } - -.fa-scribd:before { - content: "\f28a"; } - -.fa-searchengin:before { - content: "\f3eb"; } - -.fa-sellcast:before { - content: "\f2da"; } - -.fa-sellsy:before { - content: "\f213"; } - -.fa-servicestack:before { - content: "\f3ec"; } - -.fa-shirtsinbulk:before { - content: "\f214"; } - -.fa-shopify:before { - content: "\e057"; } - -.fa-shopware:before { - content: "\f5b5"; } - -.fa-simplybuilt:before { - content: "\f215"; } - -.fa-sistrix:before { - content: "\f3ee"; } - -.fa-sith:before { - content: "\f512"; } - -.fa-sitrox:before { - content: "\e44a"; } - -.fa-sketch:before { - content: "\f7c6"; } - -.fa-skyatlas:before { - content: "\f216"; } - -.fa-skype:before { - content: "\f17e"; } - -.fa-slack:before { - content: "\f198"; } - -.fa-slack-hash:before { - content: "\f198"; } - 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content: "\f3f6"; } - -.fa-sticker-mule:before { - content: "\f3f7"; } - -.fa-strava:before { - content: "\f428"; } - -.fa-stripe:before { - content: "\f429"; } - -.fa-stripe-s:before { - content: "\f42a"; } - -.fa-studiovinari:before { - content: "\f3f8"; } - -.fa-stumbleupon:before { - content: "\f1a4"; } - -.fa-stumbleupon-circle:before { - content: "\f1a3"; } - -.fa-superpowers:before { - content: "\f2dd"; } - -.fa-supple:before { - content: "\f3f9"; } - -.fa-suse:before { - content: "\f7d6"; } - -.fa-swift:before { - content: "\f8e1"; } - -.fa-symfony:before { - content: "\f83d"; } - -.fa-teamspeak:before { - content: "\f4f9"; } - -.fa-telegram:before { - content: "\f2c6"; } - -.fa-telegram-plane:before { - content: "\f2c6"; } - -.fa-tencent-weibo:before { - content: "\f1d5"; } - -.fa-the-red-yeti:before { - content: "\f69d"; } - -.fa-themeco:before { - content: "\f5c6"; } - -.fa-themeisle:before { - content: "\f2b2"; } - -.fa-think-peaks:before { - content: "\f731"; } - -.fa-tiktok:before { - content: "\e07b"; } - -.fa-trade-federation:before { - content: "\f513"; } - -.fa-trello:before { - content: "\f181"; } - -.fa-tumblr:before { - content: "\f173"; } - -.fa-tumblr-square:before { - content: "\f174"; } - -.fa-twitch:before { - content: "\f1e8"; } - -.fa-twitter:before { - content: "\f099"; } - -.fa-twitter-square:before { - content: "\f081"; } - -.fa-typo3:before { - content: "\f42b"; } - -.fa-uber:before { - content: "\f402"; } - -.fa-ubuntu:before { - content: "\f7df"; } - -.fa-uikit:before { - content: "\f403"; } - -.fa-umbraco:before { - content: "\f8e8"; } - -.fa-uncharted:before { - content: "\e084"; } - -.fa-uniregistry:before { - content: "\f404"; } - -.fa-unity:before { - content: "\e049"; } - -.fa-unsplash:before { - content: "\e07c"; } - -.fa-untappd:before { - content: "\f405"; } - -.fa-ups:before { - content: "\f7e0"; } - -.fa-usb:before { - content: "\f287"; } - -.fa-usps:before { - content: "\f7e1"; } - -.fa-ussunnah:before { - content: "\f407"; } - -.fa-vaadin:before { - content: "\f408"; } - -.fa-viacoin:before { - content: "\f237"; } - -.fa-viadeo:before { - content: "\f2a9"; } - -.fa-viadeo-square:before { - content: "\f2aa"; } - -.fa-viber:before { - content: "\f409"; } - -.fa-vimeo:before { - content: "\f40a"; } - -.fa-vimeo-square:before { - content: "\f194"; } - -.fa-vimeo-v:before { - content: "\f27d"; } - -.fa-vine:before { - content: "\f1ca"; } - -.fa-vk:before { - content: "\f189"; } - -.fa-vnv:before { - content: "\f40b"; } - -.fa-vuejs:before { - content: "\f41f"; } - -.fa-watchman-monitoring:before { - content: "\e087"; } - -.fa-waze:before { - content: "\f83f"; } - -.fa-weebly:before { - content: "\f5cc"; } - -.fa-weibo:before { - content: "\f18a"; } - -.fa-weixin:before { - content: "\f1d7"; } - -.fa-whatsapp:before { - content: "\f232"; } - -.fa-whatsapp-square:before { - content: "\f40c"; } - -.fa-whmcs:before { - content: "\f40d"; } - -.fa-wikipedia-w:before { - content: "\f266"; } - -.fa-windows:before { - content: "\f17a"; } - -.fa-wirsindhandwerk:before { - content: "\e2d0"; } - -.fa-wsh:before { - content: "\e2d0"; } - -.fa-wix:before { - content: "\f5cf"; } - -.fa-wizards-of-the-coast:before { - content: "\f730"; } - -.fa-wodu:before { - content: "\e088"; } - -.fa-wolf-pack-battalion:before { - content: "\f514"; } - -.fa-wordpress:before { - content: "\f19a"; } - -.fa-wordpress-simple:before { - content: "\f411"; } - -.fa-wpbeginner:before { - content: "\f297"; } - -.fa-wpexplorer:before { - content: "\f2de"; } - -.fa-wpforms:before { - content: "\f298"; } - -.fa-wpressr:before { - content: "\f3e4"; } - -.fa-xbox:before { - content: "\f412"; } - -.fa-xing:before { - content: "\f168"; } - -.fa-xing-square:before { - content: "\f169"; } - -.fa-y-combinator:before { - content: "\f23b"; } - -.fa-yahoo:before { - content: "\f19e"; } - -.fa-yammer:before { - content: "\f840"; } - -.fa-yandex:before { - content: "\f413"; } - -.fa-yandex-international:before { - content: "\f414"; } - -.fa-yarn:before { - content: "\f7e3"; } - -.fa-yelp:before { - content: "\f1e9"; } - -.fa-yoast:before { - content: "\f2b1"; } - -.fa-youtube:before { - content: "\f167"; } - -.fa-youtube-square:before { - content: "\f431"; } - -.fa-zhihu:before { - content: "\f63f"; } -:root, :host { - --fa-font-regular: normal 400 1em/1 "Font Awesome 6 Free"; } - -@font-face { - font-family: 'Font Awesome 6 Free'; - font-style: normal; - font-weight: 400; - font-display: block; - src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } - -.far, -.fa-regular { - font-family: 'Font Awesome 6 Free'; - font-weight: 400; } -:root, :host { - --fa-font-solid: normal 900 1em/1 "Font Awesome 6 Free"; } - -@font-face { - font-family: 'Font Awesome 6 Free'; - font-style: normal; - font-weight: 900; - font-display: block; - src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } - -.fas, -.fa-solid { - font-family: 'Font Awesome 6 Free'; - font-weight: 900; } -@font-face { - font-family: "Font Awesome 5 Brands"; - font-display: block; - font-weight: 400; - src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } - -@font-face { - font-family: "Font Awesome 5 Free"; - font-display: block; - font-weight: 900; - src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } - -@font-face { - font-family: "Font Awesome 5 Free"; - font-display: block; - font-weight: 400; - src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } -@font-face { - font-family: "FontAwesome"; - font-display: block; - src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } - -@font-face { - font-family: "FontAwesome"; - font-display: block; - src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } - -@font-face { - font-family: "FontAwesome"; - font-display: block; - src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); - unicode-range: U+F003,U+F006,U+F014,U+F016-F017,U+F01A-F01B,U+F01D,U+F022,U+F03E,U+F044,U+F046,U+F05C-F05D,U+F06E,U+F070,U+F087-F088,U+F08A,U+F094,U+F096-F097,U+F09D,U+F0A0,U+F0A2,U+F0A4-F0A7,U+F0C5,U+F0C7,U+F0E5-F0E6,U+F0EB,U+F0F6-F0F8,U+F10C,U+F114-F115,U+F118-F11A,U+F11C-F11D,U+F133,U+F147,U+F14E,U+F150-F152,U+F185-F186,U+F18E,U+F190-F192,U+F196,U+F1C1-F1C9,U+F1D9,U+F1DB,U+F1E3,U+F1EA,U+F1F7,U+F1F9,U+F20A,U+F247-F248,U+F24A,U+F24D,U+F255-F25B,U+F25D,U+F271-F274,U+F278,U+F27B,U+F28C,U+F28E,U+F29C,U+F2B5,U+F2B7,U+F2BA,U+F2BC,U+F2BE,U+F2C0-F2C1,U+F2C3,U+F2D0,U+F2D2,U+F2D4,U+F2DC; } - -@font-face { - font-family: "FontAwesome"; - font-display: block; - src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); - unicode-range: U+F041,U+F047,U+F065-F066,U+F07D-F07E,U+F080,U+F08B,U+F08E,U+F090,U+F09A,U+F0AC,U+F0AE,U+F0B2,U+F0D0,U+F0D6,U+F0E4,U+F0EC,U+F10A-F10B,U+F123,U+F13E,U+F148-F149,U+F14C,U+F156,U+F15E,U+F160-F161,U+F163,U+F175-F178,U+F195,U+F1F8,U+F219,U+F250,U+F252,U+F27A; } diff --git a/_extensions/cambiotraining/fontawesome/assets/css/all 4.css b/_extensions/cambiotraining/fontawesome/assets/css/all 4.css deleted file mode 100644 index 9c2adee..0000000 --- a/_extensions/cambiotraining/fontawesome/assets/css/all 4.css +++ /dev/null @@ -1,7831 +0,0 @@ -/*! - * Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com - * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) - * Copyright 2022 Fonticons, Inc. - */ -.fa { - font-family: var(--fa-style-family, "Font Awesome 6 Free"); - font-weight: var(--fa-style, 900); } - -.fa, -.fas, -.fa-solid, -.far, -.fa-regular, -.fal, -.fa-light, -.fat, -.fa-thin, -.fad, -.fa-duotone, -.fab, -.fa-brands { - -moz-osx-font-smoothing: grayscale; - -webkit-font-smoothing: antialiased; - display: var(--fa-display, inline-block); - font-style: normal; - font-variant: normal; - line-height: 1; - text-rendering: auto; } - -.fa-1x { - font-size: 1em; } - -.fa-2x { - font-size: 2em; } - -.fa-3x { - font-size: 3em; } - -.fa-4x { - font-size: 4em; } - -.fa-5x { - font-size: 5em; } - -.fa-6x { - font-size: 6em; } - -.fa-7x { - font-size: 7em; } - -.fa-8x { - font-size: 8em; } - -.fa-9x { - font-size: 9em; } - -.fa-10x { - font-size: 10em; } - -.fa-2xs { - font-size: 0.625em; - line-height: 0.1em; - vertical-align: 0.225em; } - -.fa-xs { - font-size: 0.75em; - line-height: 0.08333em; - vertical-align: 0.125em; } - -.fa-sm { - font-size: 0.875em; - line-height: 0.07143em; - vertical-align: 0.05357em; } - -.fa-lg { - font-size: 1.25em; - line-height: 0.05em; - vertical-align: -0.075em; } - -.fa-xl { - font-size: 1.5em; - line-height: 0.04167em; - vertical-align: -0.125em; } - -.fa-2xl { - font-size: 2em; - line-height: 0.03125em; - vertical-align: -0.1875em; } - -.fa-fw { - text-align: center; - width: 1.25em; } - -.fa-ul { - list-style-type: none; - margin-left: var(--fa-li-margin, 2.5em); - padding-left: 0; } - .fa-ul > li { - position: relative; } - -.fa-li { - left: calc(var(--fa-li-width, 2em) * -1); - position: absolute; - text-align: center; - width: var(--fa-li-width, 2em); - line-height: inherit; } - -.fa-border { - border-color: var(--fa-border-color, #eee); - border-radius: var(--fa-border-radius, 0.1em); - border-style: var(--fa-border-style, solid); - border-width: var(--fa-border-width, 0.08em); - padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } - -.fa-pull-left { - float: left; - margin-right: var(--fa-pull-margin, 0.3em); } - -.fa-pull-right { - float: right; - margin-left: var(--fa-pull-margin, 0.3em); } - -.fa-beat { - -webkit-animation-name: fa-beat; - animation-name: fa-beat; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); - animation-timing-function: var(--fa-animation-timing, ease-in-out); } - -.fa-bounce { - -webkit-animation-name: fa-bounce; - animation-name: fa-bounce; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } - -.fa-fade { - -webkit-animation-name: fa-fade; - animation-name: fa-fade; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } - -.fa-beat-fade { - -webkit-animation-name: fa-beat-fade; - animation-name: fa-beat-fade; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } - -.fa-flip { - -webkit-animation-name: fa-flip; - animation-name: fa-flip; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); - animation-timing-function: var(--fa-animation-timing, ease-in-out); } - -.fa-shake { - -webkit-animation-name: fa-shake; - animation-name: fa-shake; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 2s); - animation-duration: var(--fa-animation-duration, 2s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin-reverse { - --fa-animation-direction: reverse; } - -.fa-pulse, -.fa-spin-pulse { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); - animation-timing-function: var(--fa-animation-timing, steps(8)); } - -@media (prefers-reduced-motion: reduce) { - .fa-beat, - .fa-bounce, - .fa-fade, - .fa-beat-fade, - .fa-flip, - .fa-pulse, - .fa-shake, - .fa-spin, - .fa-spin-pulse { - -webkit-animation-delay: -1ms; - animation-delay: -1ms; - -webkit-animation-duration: 1ms; - animation-duration: 1ms; - -webkit-animation-iteration-count: 1; - animation-iteration-count: 1; - transition-delay: 0s; - transition-duration: 0s; } } - -@-webkit-keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@-webkit-keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@-webkit-keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@-webkit-keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@-webkit-keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@-webkit-keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@-webkit-keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -@keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -.fa-rotate-90 { - -webkit-transform: rotate(90deg); - transform: rotate(90deg); } - -.fa-rotate-180 { - -webkit-transform: rotate(180deg); - transform: rotate(180deg); } - -.fa-rotate-270 { - -webkit-transform: rotate(270deg); - transform: rotate(270deg); } - -.fa-flip-horizontal { - -webkit-transform: scale(-1, 1); - transform: scale(-1, 1); } - -.fa-flip-vertical { - -webkit-transform: scale(1, -1); - transform: scale(1, -1); } - -.fa-flip-both, -.fa-flip-horizontal.fa-flip-vertical { - -webkit-transform: scale(-1, -1); - transform: scale(-1, -1); } - -.fa-rotate-by { - -webkit-transform: rotate(var(--fa-rotate-angle, none)); - transform: rotate(var(--fa-rotate-angle, none)); } - -.fa-stack { - display: inline-block; - height: 2em; - line-height: 2em; - position: relative; - vertical-align: middle; - width: 2.5em; } - -.fa-stack-1x, -.fa-stack-2x { - left: 0; - position: absolute; - text-align: center; - width: 100%; - z-index: var(--fa-stack-z-index, auto); } - -.fa-stack-1x { - line-height: inherit; } - -.fa-stack-2x { - font-size: 2em; } - -.fa-inverse { - color: var(--fa-inverse, #fff); } - -/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen -readers do not read off random characters that represent icons */ -.fa-0::before { - content: "\30"; } - -.fa-1::before { - content: "\31"; } - -.fa-2::before { - content: "\32"; } - -.fa-3::before { - content: "\33"; } - -.fa-4::before { - content: "\34"; } - -.fa-5::before { - content: "\35"; } - -.fa-6::before { - content: "\36"; } - -.fa-7::before { - content: "\37"; } - -.fa-8::before { - content: "\38"; } - -.fa-9::before { - content: "\39"; } - -.fa-a::before { - content: "\41"; } - -.fa-address-book::before { - content: "\f2b9"; } - -.fa-contact-book::before { - content: "\f2b9"; } - -.fa-address-card::before { - content: "\f2bb"; } - -.fa-contact-card::before { - content: "\f2bb"; } - -.fa-vcard::before { - content: "\f2bb"; } - -.fa-align-center::before { - content: "\f037"; } - -.fa-align-justify::before { - content: "\f039"; } - -.fa-align-left::before { - content: "\f036"; } - -.fa-align-right::before { - content: "\f038"; } - -.fa-anchor::before { - content: "\f13d"; } - -.fa-anchor-circle-check::before { - content: "\e4aa"; } - -.fa-anchor-circle-exclamation::before { - content: "\e4ab"; } - -.fa-anchor-circle-xmark::before { - content: "\e4ac"; } - -.fa-anchor-lock::before { - content: "\e4ad"; } - -.fa-angle-down::before { - content: "\f107"; } - -.fa-angle-left::before { - content: "\f104"; } - -.fa-angle-right::before { - content: "\f105"; } - -.fa-angle-up::before { - content: "\f106"; } - -.fa-angles-down::before { - content: "\f103"; } - -.fa-angle-double-down::before { - content: "\f103"; } - -.fa-angles-left::before { - content: "\f100"; } - -.fa-angle-double-left::before { - content: "\f100"; } - -.fa-angles-right::before { - content: "\f101"; } - -.fa-angle-double-right::before { - content: "\f101"; } - -.fa-angles-up::before { - content: "\f102"; } - -.fa-angle-double-up::before { - content: "\f102"; } - -.fa-ankh::before { - content: "\f644"; } - -.fa-apple-whole::before { - content: "\f5d1"; } - -.fa-apple-alt::before { - content: "\f5d1"; } - -.fa-archway::before { - content: "\f557"; } - -.fa-arrow-down::before { - content: "\f063"; } - -.fa-arrow-down-1-9::before { - content: "\f162"; } - -.fa-sort-numeric-asc::before { - content: "\f162"; } - -.fa-sort-numeric-down::before { - content: "\f162"; } - -.fa-arrow-down-9-1::before { - content: "\f886"; } - -.fa-sort-numeric-desc::before { - content: "\f886"; } - -.fa-sort-numeric-down-alt::before { - content: "\f886"; } - -.fa-arrow-down-a-z::before { - content: "\f15d"; } - -.fa-sort-alpha-asc::before { - content: "\f15d"; } - -.fa-sort-alpha-down::before { - content: "\f15d"; } - -.fa-arrow-down-long::before { - content: "\f175"; } - -.fa-long-arrow-down::before { - content: "\f175"; } - -.fa-arrow-down-short-wide::before { - content: "\f884"; } - -.fa-sort-amount-desc::before { - content: "\f884"; } - -.fa-sort-amount-down-alt::before { - content: "\f884"; } - -.fa-arrow-down-up-across-line::before { - content: "\e4af"; } - -.fa-arrow-down-up-lock::before { - content: "\e4b0"; } - -.fa-arrow-down-wide-short::before { - content: "\f160"; } - -.fa-sort-amount-asc::before { - content: "\f160"; } - -.fa-sort-amount-down::before { - content: "\f160"; } - -.fa-arrow-down-z-a::before { - content: "\f881"; } - -.fa-sort-alpha-desc::before { - content: "\f881"; } - -.fa-sort-alpha-down-alt::before { - content: "\f881"; } - -.fa-arrow-left::before { - content: "\f060"; } - -.fa-arrow-left-long::before { - content: "\f177"; } - -.fa-long-arrow-left::before { - content: "\f177"; } - -.fa-arrow-pointer::before { - content: "\f245"; } - -.fa-mouse-pointer::before { - content: "\f245"; } - -.fa-arrow-right::before { - content: "\f061"; } - -.fa-arrow-right-arrow-left::before { - content: "\f0ec"; } - -.fa-exchange::before { - content: "\f0ec"; } - -.fa-arrow-right-from-bracket::before { - content: "\f08b"; } - -.fa-sign-out::before { - content: "\f08b"; } - -.fa-arrow-right-long::before { - content: "\f178"; } - -.fa-long-arrow-right::before { - content: "\f178"; } - -.fa-arrow-right-to-bracket::before { - content: "\f090"; } - -.fa-sign-in::before { - content: "\f090"; } - -.fa-arrow-right-to-city::before { - content: "\e4b3"; } - -.fa-arrow-rotate-left::before { - content: "\f0e2"; } - -.fa-arrow-left-rotate::before { - content: "\f0e2"; } - -.fa-arrow-rotate-back::before { - content: "\f0e2"; } - -.fa-arrow-rotate-backward::before { - content: "\f0e2"; } - -.fa-undo::before { - content: "\f0e2"; } - -.fa-arrow-rotate-right::before { - content: "\f01e"; } - -.fa-arrow-right-rotate::before { - content: "\f01e"; } - -.fa-arrow-rotate-forward::before { - content: "\f01e"; } - -.fa-redo::before { - content: "\f01e"; } - -.fa-arrow-trend-down::before { - content: "\e097"; } - -.fa-arrow-trend-up::before { - content: "\e098"; } - -.fa-arrow-turn-down::before { - content: "\f149"; } - -.fa-level-down::before { - content: "\f149"; } - -.fa-arrow-turn-up::before { - content: "\f148"; } - -.fa-level-up::before { - content: "\f148"; } - -.fa-arrow-up::before { - content: "\f062"; } - -.fa-arrow-up-1-9::before { - content: "\f163"; } - -.fa-sort-numeric-up::before { - content: "\f163"; } - -.fa-arrow-up-9-1::before { - content: "\f887"; } - -.fa-sort-numeric-up-alt::before { - content: "\f887"; } - -.fa-arrow-up-a-z::before { - content: "\f15e"; } - -.fa-sort-alpha-up::before { - content: "\f15e"; } - -.fa-arrow-up-from-bracket::before { - content: "\e09a"; } - -.fa-arrow-up-from-ground-water::before { - content: "\e4b5"; } - -.fa-arrow-up-from-water-pump::before { - content: "\e4b6"; } - -.fa-arrow-up-long::before { - content: "\f176"; } - -.fa-long-arrow-up::before { - content: "\f176"; } - -.fa-arrow-up-right-dots::before { - content: "\e4b7"; } - -.fa-arrow-up-right-from-square::before { - content: "\f08e"; } - -.fa-external-link::before { - content: "\f08e"; } - -.fa-arrow-up-short-wide::before { - content: "\f885"; } - -.fa-sort-amount-up-alt::before { - content: "\f885"; } - -.fa-arrow-up-wide-short::before { - content: "\f161"; } - -.fa-sort-amount-up::before { - content: "\f161"; } - -.fa-arrow-up-z-a::before { - content: "\f882"; } - -.fa-sort-alpha-up-alt::before { - content: "\f882"; } - -.fa-arrows-down-to-line::before { - content: "\e4b8"; } - -.fa-arrows-down-to-people::before { - content: "\e4b9"; } - -.fa-arrows-left-right::before { - content: "\f07e"; } - -.fa-arrows-h::before { - content: "\f07e"; } - -.fa-arrows-left-right-to-line::before { - content: "\e4ba"; } - -.fa-arrows-rotate::before { - content: "\f021"; } - -.fa-refresh::before { - content: "\f021"; } - -.fa-sync::before { - content: "\f021"; } - -.fa-arrows-spin::before { - content: "\e4bb"; } - -.fa-arrows-split-up-and-left::before { - content: "\e4bc"; } - -.fa-arrows-to-circle::before { - content: "\e4bd"; } - -.fa-arrows-to-dot::before { - content: "\e4be"; } - -.fa-arrows-to-eye::before { - content: "\e4bf"; } - -.fa-arrows-turn-right::before { - content: "\e4c0"; } - -.fa-arrows-turn-to-dots::before { - content: "\e4c1"; } - -.fa-arrows-up-down::before { - content: "\f07d"; } - -.fa-arrows-v::before { - content: "\f07d"; } - -.fa-arrows-up-down-left-right::before { - content: "\f047"; } - -.fa-arrows::before { - content: "\f047"; } - -.fa-arrows-up-to-line::before { - content: "\e4c2"; } - -.fa-asterisk::before { - content: "\2a"; } - -.fa-at::before { - content: "\40"; } - -.fa-atom::before { - content: "\f5d2"; } - -.fa-audio-description::before { - content: "\f29e"; } - -.fa-austral-sign::before { - content: "\e0a9"; } - -.fa-award::before { - content: "\f559"; } - -.fa-b::before { - content: "\42"; } - -.fa-baby::before { - content: "\f77c"; } - -.fa-baby-carriage::before { - content: "\f77d"; } - -.fa-carriage-baby::before { - content: "\f77d"; } - -.fa-backward::before { - content: "\f04a"; } - -.fa-backward-fast::before { - content: "\f049"; } - -.fa-fast-backward::before { - content: "\f049"; } - -.fa-backward-step::before { - content: "\f048"; } - -.fa-step-backward::before { - content: "\f048"; } - -.fa-bacon::before { - content: "\f7e5"; } - -.fa-bacteria::before { - content: "\e059"; } - -.fa-bacterium::before { - content: "\e05a"; } - -.fa-bag-shopping::before { - content: "\f290"; } - -.fa-shopping-bag::before { - content: "\f290"; } - -.fa-bahai::before { - content: "\f666"; } - -.fa-baht-sign::before { - content: "\e0ac"; } - -.fa-ban::before { - content: "\f05e"; } - -.fa-cancel::before { - content: "\f05e"; } - -.fa-ban-smoking::before { - content: "\f54d"; } - -.fa-smoking-ban::before { - content: "\f54d"; } - -.fa-bandage::before { - content: "\f462"; } - -.fa-band-aid::before { - content: "\f462"; } - -.fa-barcode::before { - content: "\f02a"; } - -.fa-bars::before { - content: "\f0c9"; } - -.fa-navicon::before { - content: "\f0c9"; } - -.fa-bars-progress::before { - content: "\f828"; } - -.fa-tasks-alt::before { - content: "\f828"; } - -.fa-bars-staggered::before { - content: "\f550"; } - -.fa-reorder::before { - content: "\f550"; } - -.fa-stream::before { - content: "\f550"; } - -.fa-baseball::before { - content: "\f433"; } - -.fa-baseball-ball::before { - content: "\f433"; } - -.fa-baseball-bat-ball::before { - content: "\f432"; } - -.fa-basket-shopping::before { - content: "\f291"; } - -.fa-shopping-basket::before { - content: "\f291"; } - -.fa-basketball::before { - content: "\f434"; } - -.fa-basketball-ball::before { - content: "\f434"; } - -.fa-bath::before { - content: "\f2cd"; } - -.fa-bathtub::before { - content: "\f2cd"; } - -.fa-battery-empty::before { - content: "\f244"; } - -.fa-battery-0::before { - content: "\f244"; } - -.fa-battery-full::before { - content: "\f240"; } - -.fa-battery::before { - content: "\f240"; } - -.fa-battery-5::before { - content: "\f240"; } - -.fa-battery-half::before { - content: "\f242"; } - -.fa-battery-3::before { - content: "\f242"; } - -.fa-battery-quarter::before { - content: "\f243"; } - -.fa-battery-2::before { - content: "\f243"; } - -.fa-battery-three-quarters::before { - content: "\f241"; } - -.fa-battery-4::before { - content: "\f241"; } - -.fa-bed::before { - content: "\f236"; } - -.fa-bed-pulse::before { - content: "\f487"; } - -.fa-procedures::before { - content: "\f487"; } - -.fa-beer-mug-empty::before { - content: "\f0fc"; } - -.fa-beer::before { - content: "\f0fc"; } - -.fa-bell::before { - content: "\f0f3"; } - -.fa-bell-concierge::before { - content: "\f562"; } - -.fa-concierge-bell::before { - content: "\f562"; } - -.fa-bell-slash::before { - content: "\f1f6"; } - -.fa-bezier-curve::before { - content: "\f55b"; } - -.fa-bicycle::before { - content: "\f206"; } - -.fa-binoculars::before { - content: "\f1e5"; } - -.fa-biohazard::before { - content: "\f780"; } - -.fa-bitcoin-sign::before { - content: "\e0b4"; } - -.fa-blender::before { - content: "\f517"; } - -.fa-blender-phone::before { - content: "\f6b6"; } - -.fa-blog::before { - content: "\f781"; } - -.fa-bold::before { - content: "\f032"; } - -.fa-bolt::before { - content: "\f0e7"; } - -.fa-zap::before { - content: "\f0e7"; } - -.fa-bolt-lightning::before { - content: "\e0b7"; } - -.fa-bomb::before { - content: "\f1e2"; } - -.fa-bone::before { - content: "\f5d7"; } - -.fa-bong::before { - content: "\f55c"; } - -.fa-book::before { - content: "\f02d"; } - -.fa-book-atlas::before { - content: "\f558"; } - -.fa-atlas::before { - content: "\f558"; } - -.fa-book-bible::before { - content: "\f647"; } - -.fa-bible::before { - content: "\f647"; } - -.fa-book-bookmark::before { - content: "\e0bb"; } - -.fa-book-journal-whills::before { - content: "\f66a"; } - -.fa-journal-whills::before { - content: "\f66a"; } - -.fa-book-medical::before { - content: "\f7e6"; } - -.fa-book-open::before { - content: "\f518"; } - -.fa-book-open-reader::before { - content: "\f5da"; } - -.fa-book-reader::before { - content: "\f5da"; } - -.fa-book-quran::before { - content: "\f687"; } - -.fa-quran::before { - content: "\f687"; } - -.fa-book-skull::before { - content: "\f6b7"; } - -.fa-book-dead::before { - content: "\f6b7"; } - -.fa-bookmark::before { - content: "\f02e"; } - -.fa-border-all::before { - content: "\f84c"; } - -.fa-border-none::before { - content: "\f850"; } - -.fa-border-top-left::before { - content: "\f853"; } - -.fa-border-style::before { - content: "\f853"; } - -.fa-bore-hole::before { - content: "\e4c3"; } - -.fa-bottle-droplet::before { - content: "\e4c4"; } - -.fa-bottle-water::before { - content: "\e4c5"; } - -.fa-bowl-food::before { - content: "\e4c6"; } - -.fa-bowl-rice::before { - content: "\e2eb"; } - -.fa-bowling-ball::before { - content: "\f436"; } - -.fa-box::before { - content: "\f466"; } - -.fa-box-archive::before { - content: "\f187"; } - -.fa-archive::before { - content: "\f187"; } - -.fa-box-open::before { - content: "\f49e"; } - -.fa-box-tissue::before { - content: "\e05b"; } - -.fa-boxes-packing::before { - content: "\e4c7"; } - -.fa-boxes-stacked::before { - content: "\f468"; } - -.fa-boxes::before { - content: "\f468"; } - -.fa-boxes-alt::before { - content: "\f468"; } - -.fa-braille::before { - content: "\f2a1"; } - -.fa-brain::before { - content: "\f5dc"; } - -.fa-brazilian-real-sign::before { - content: "\e46c"; } - -.fa-bread-slice::before { - content: "\f7ec"; } - -.fa-bridge::before { - content: "\e4c8"; } - -.fa-bridge-circle-check::before { - content: "\e4c9"; } - -.fa-bridge-circle-exclamation::before { - content: "\e4ca"; } - -.fa-bridge-circle-xmark::before { - content: "\e4cb"; } - -.fa-bridge-lock::before { - content: "\e4cc"; } - -.fa-bridge-water::before { - content: "\e4ce"; } - -.fa-briefcase::before { - content: "\f0b1"; } - -.fa-briefcase-medical::before { - content: "\f469"; } - -.fa-broom::before { - content: "\f51a"; } - -.fa-broom-ball::before { - content: "\f458"; } - -.fa-quidditch::before { - content: "\f458"; } - -.fa-quidditch-broom-ball::before { - content: "\f458"; } - -.fa-brush::before { - content: "\f55d"; } - -.fa-bucket::before { - content: "\e4cf"; } - -.fa-bug::before { - content: "\f188"; } - -.fa-bug-slash::before { - content: "\e490"; } - -.fa-bugs::before { - content: "\e4d0"; } - -.fa-building::before { - content: "\f1ad"; } - -.fa-building-circle-arrow-right::before { - content: "\e4d1"; } - -.fa-building-circle-check::before { - content: "\e4d2"; } - -.fa-building-circle-exclamation::before { - content: "\e4d3"; } - -.fa-building-circle-xmark::before { - content: "\e4d4"; } - -.fa-building-columns::before { - content: "\f19c"; } - -.fa-bank::before { - content: "\f19c"; } - -.fa-institution::before { - content: "\f19c"; } - -.fa-museum::before { - content: "\f19c"; } - -.fa-university::before { - content: "\f19c"; } - -.fa-building-flag::before { - content: "\e4d5"; } - -.fa-building-lock::before { - content: "\e4d6"; } - -.fa-building-ngo::before { - content: "\e4d7"; } - -.fa-building-shield::before { - content: "\e4d8"; } - -.fa-building-un::before { - content: "\e4d9"; } - -.fa-building-user::before { - content: "\e4da"; } - -.fa-building-wheat::before { - content: "\e4db"; } - -.fa-bullhorn::before { - content: "\f0a1"; } - -.fa-bullseye::before { - content: "\f140"; } - -.fa-burger::before { - content: "\f805"; } - -.fa-hamburger::before { - content: "\f805"; } - -.fa-burst::before { - content: "\e4dc"; } - -.fa-bus::before { - content: "\f207"; } - -.fa-bus-simple::before { - content: "\f55e"; } - -.fa-bus-alt::before { - content: "\f55e"; } - -.fa-business-time::before { - content: "\f64a"; } - -.fa-briefcase-clock::before { - content: "\f64a"; } - -.fa-c::before { - content: "\43"; } - -.fa-cake-candles::before { - content: "\f1fd"; } - -.fa-birthday-cake::before { - content: "\f1fd"; } - -.fa-cake::before { - content: "\f1fd"; } - -.fa-calculator::before { - content: "\f1ec"; } - -.fa-calendar::before { - content: "\f133"; } - -.fa-calendar-check::before { - content: "\f274"; } - -.fa-calendar-day::before { - content: "\f783"; } - -.fa-calendar-days::before { - content: "\f073"; } - -.fa-calendar-alt::before { - content: "\f073"; } - -.fa-calendar-minus::before { - content: "\f272"; } - -.fa-calendar-plus::before { - content: "\f271"; } - -.fa-calendar-week::before { - content: "\f784"; } - -.fa-calendar-xmark::before { - content: "\f273"; } - -.fa-calendar-times::before { - content: "\f273"; } - -.fa-camera::before { - content: "\f030"; } - -.fa-camera-alt::before { - content: "\f030"; } - -.fa-camera-retro::before { - content: "\f083"; } - -.fa-camera-rotate::before { - content: "\e0d8"; } - -.fa-campground::before { - content: "\f6bb"; } - -.fa-candy-cane::before { - content: "\f786"; } - -.fa-cannabis::before { - content: "\f55f"; } - -.fa-capsules::before { - content: "\f46b"; } - -.fa-car::before { - content: "\f1b9"; } - -.fa-automobile::before { - content: "\f1b9"; } - -.fa-car-battery::before { - content: "\f5df"; } - -.fa-battery-car::before { - content: "\f5df"; } - -.fa-car-burst::before { - content: "\f5e1"; } - -.fa-car-crash::before { - content: "\f5e1"; } - -.fa-car-on::before { - content: "\e4dd"; } - -.fa-car-rear::before { - content: "\f5de"; } - -.fa-car-alt::before { - content: "\f5de"; } - -.fa-car-side::before { - content: "\f5e4"; } - -.fa-car-tunnel::before { - content: "\e4de"; } - -.fa-caravan::before { - content: "\f8ff"; } - -.fa-caret-down::before { - content: "\f0d7"; } - -.fa-caret-left::before { - content: "\f0d9"; } - -.fa-caret-right::before { - content: "\f0da"; } - -.fa-caret-up::before { - content: "\f0d8"; } - -.fa-carrot::before { - content: "\f787"; } - -.fa-cart-arrow-down::before { - content: "\f218"; } - -.fa-cart-flatbed::before { - content: "\f474"; } - -.fa-dolly-flatbed::before { - content: "\f474"; } - -.fa-cart-flatbed-suitcase::before { - content: "\f59d"; } - -.fa-luggage-cart::before { - content: "\f59d"; } - -.fa-cart-plus::before { - content: "\f217"; } - -.fa-cart-shopping::before { - content: "\f07a"; } - -.fa-shopping-cart::before { - content: "\f07a"; } - -.fa-cash-register::before { - content: "\f788"; } - -.fa-cat::before { - content: "\f6be"; } - -.fa-cedi-sign::before { - content: "\e0df"; } - -.fa-cent-sign::before { - content: "\e3f5"; } - -.fa-certificate::before { - content: "\f0a3"; } - -.fa-chair::before { - content: "\f6c0"; } - -.fa-chalkboard::before { - content: "\f51b"; } - -.fa-blackboard::before { - content: "\f51b"; } - -.fa-chalkboard-user::before { - content: "\f51c"; } - -.fa-chalkboard-teacher::before { - content: "\f51c"; } - -.fa-champagne-glasses::before { - content: "\f79f"; } - -.fa-glass-cheers::before { - content: "\f79f"; } - -.fa-charging-station::before { - content: "\f5e7"; } - -.fa-chart-area::before { - content: "\f1fe"; } - -.fa-area-chart::before { - content: "\f1fe"; } - -.fa-chart-bar::before { - content: "\f080"; } - -.fa-bar-chart::before { - content: "\f080"; } - -.fa-chart-column::before { - content: "\e0e3"; } - -.fa-chart-gantt::before { - content: "\e0e4"; } - -.fa-chart-line::before { - content: "\f201"; } - -.fa-line-chart::before { - content: "\f201"; } - -.fa-chart-pie::before { - content: "\f200"; } - -.fa-pie-chart::before { - content: "\f200"; } - -.fa-chart-simple::before { - content: "\e473"; } - -.fa-check::before { - content: "\f00c"; } - -.fa-check-double::before { - content: "\f560"; } - -.fa-check-to-slot::before { - content: "\f772"; } - -.fa-vote-yea::before { - content: "\f772"; } - -.fa-cheese::before { - content: "\f7ef"; } - -.fa-chess::before { - content: "\f439"; } - -.fa-chess-bishop::before { - content: "\f43a"; } - -.fa-chess-board::before { - content: "\f43c"; } - -.fa-chess-king::before { - content: "\f43f"; } - -.fa-chess-knight::before { - content: "\f441"; } - -.fa-chess-pawn::before { - content: "\f443"; } - -.fa-chess-queen::before { - content: "\f445"; } - -.fa-chess-rook::before { - content: "\f447"; } - -.fa-chevron-down::before { - content: "\f078"; } - -.fa-chevron-left::before { - content: "\f053"; } - -.fa-chevron-right::before { - content: "\f054"; } - -.fa-chevron-up::before { - content: "\f077"; } - -.fa-child::before { - content: "\f1ae"; } - -.fa-child-dress::before { - content: "\e59c"; } - -.fa-child-reaching::before { - content: "\e59d"; } - -.fa-child-rifle::before { - content: "\e4e0"; } - -.fa-children::before { - content: "\e4e1"; } - -.fa-church::before { - content: "\f51d"; } - -.fa-circle::before { - content: "\f111"; } - -.fa-circle-arrow-down::before { - content: "\f0ab"; } - -.fa-arrow-circle-down::before { - content: "\f0ab"; } - -.fa-circle-arrow-left::before { - content: "\f0a8"; } - -.fa-arrow-circle-left::before { - content: "\f0a8"; } - -.fa-circle-arrow-right::before { - content: "\f0a9"; } - -.fa-arrow-circle-right::before { - content: "\f0a9"; } - -.fa-circle-arrow-up::before { - content: "\f0aa"; } - -.fa-arrow-circle-up::before { - content: "\f0aa"; } - -.fa-circle-check::before { - content: "\f058"; } - -.fa-check-circle::before { - content: "\f058"; } - -.fa-circle-chevron-down::before { - content: "\f13a"; } - -.fa-chevron-circle-down::before { - content: "\f13a"; } - -.fa-circle-chevron-left::before { - content: "\f137"; } - -.fa-chevron-circle-left::before { - content: "\f137"; } - -.fa-circle-chevron-right::before { - content: "\f138"; } - -.fa-chevron-circle-right::before { - content: "\f138"; } - -.fa-circle-chevron-up::before { - content: "\f139"; } - -.fa-chevron-circle-up::before { - content: "\f139"; } - -.fa-circle-dollar-to-slot::before { - content: "\f4b9"; } - -.fa-donate::before { - content: "\f4b9"; } - -.fa-circle-dot::before { - content: "\f192"; } - -.fa-dot-circle::before { - content: "\f192"; } - -.fa-circle-down::before { - content: "\f358"; } - -.fa-arrow-alt-circle-down::before { - content: "\f358"; } - -.fa-circle-exclamation::before { - content: "\f06a"; } - -.fa-exclamation-circle::before { - content: "\f06a"; } - -.fa-circle-h::before { - content: "\f47e"; } - -.fa-hospital-symbol::before { - content: "\f47e"; } - -.fa-circle-half-stroke::before { - content: "\f042"; } - -.fa-adjust::before { - content: "\f042"; } - -.fa-circle-info::before { - content: "\f05a"; } - -.fa-info-circle::before { - content: "\f05a"; } - -.fa-circle-left::before { - content: "\f359"; } - -.fa-arrow-alt-circle-left::before { - content: "\f359"; } - -.fa-circle-minus::before { - content: "\f056"; } - -.fa-minus-circle::before { - content: "\f056"; } - -.fa-circle-nodes::before { - content: "\e4e2"; } - -.fa-circle-notch::before { - content: "\f1ce"; } - -.fa-circle-pause::before { - content: "\f28b"; } - -.fa-pause-circle::before { - content: "\f28b"; } - -.fa-circle-play::before { - content: "\f144"; } - -.fa-play-circle::before { - content: "\f144"; } - -.fa-circle-plus::before { - content: "\f055"; } - -.fa-plus-circle::before { - content: "\f055"; } - -.fa-circle-question::before { - content: "\f059"; } - -.fa-question-circle::before { - content: "\f059"; } - -.fa-circle-radiation::before { - content: "\f7ba"; } - -.fa-radiation-alt::before { - content: "\f7ba"; } - -.fa-circle-right::before { - content: "\f35a"; } - -.fa-arrow-alt-circle-right::before { - content: "\f35a"; } - -.fa-circle-stop::before { - content: "\f28d"; } - -.fa-stop-circle::before { - content: "\f28d"; } - -.fa-circle-up::before { - content: "\f35b"; } - -.fa-arrow-alt-circle-up::before { - content: "\f35b"; } - -.fa-circle-user::before { - content: "\f2bd"; } - -.fa-user-circle::before { - content: "\f2bd"; } - -.fa-circle-xmark::before { - content: "\f057"; } - -.fa-times-circle::before { - content: "\f057"; } - -.fa-xmark-circle::before { - content: "\f057"; } - -.fa-city::before { - content: "\f64f"; } - -.fa-clapperboard::before { - content: "\e131"; } - -.fa-clipboard::before { - content: "\f328"; } - -.fa-clipboard-check::before { - content: "\f46c"; } - -.fa-clipboard-list::before { - content: "\f46d"; } - -.fa-clipboard-question::before { - content: "\e4e3"; } - -.fa-clipboard-user::before { - content: "\f7f3"; } - -.fa-clock::before { - content: "\f017"; } - -.fa-clock-four::before { - content: "\f017"; } - -.fa-clock-rotate-left::before { - content: "\f1da"; } - -.fa-history::before { - content: "\f1da"; } - -.fa-clone::before { - content: "\f24d"; } - -.fa-closed-captioning::before { - content: "\f20a"; } - -.fa-cloud::before { - content: "\f0c2"; } - -.fa-cloud-arrow-down::before { - content: "\f0ed"; } - -.fa-cloud-download::before { - content: "\f0ed"; } - -.fa-cloud-download-alt::before { - content: "\f0ed"; } - -.fa-cloud-arrow-up::before { - content: "\f0ee"; } - -.fa-cloud-upload::before { - content: "\f0ee"; } - -.fa-cloud-upload-alt::before { - content: "\f0ee"; } - -.fa-cloud-bolt::before { - content: "\f76c"; } - -.fa-thunderstorm::before { - content: "\f76c"; } - -.fa-cloud-meatball::before { - content: "\f73b"; } - -.fa-cloud-moon::before { - content: "\f6c3"; } - -.fa-cloud-moon-rain::before { - content: "\f73c"; } - -.fa-cloud-rain::before { - content: "\f73d"; } - -.fa-cloud-showers-heavy::before { - content: "\f740"; } - -.fa-cloud-showers-water::before { - content: "\e4e4"; } - -.fa-cloud-sun::before { - content: "\f6c4"; } - -.fa-cloud-sun-rain::before { - content: "\f743"; } - -.fa-clover::before { - content: "\e139"; } - -.fa-code::before { - content: "\f121"; } - -.fa-code-branch::before { - content: "\f126"; } - -.fa-code-commit::before { - content: "\f386"; } - -.fa-code-compare::before { - content: "\e13a"; } - -.fa-code-fork::before { - content: "\e13b"; } - -.fa-code-merge::before { - content: "\f387"; } - -.fa-code-pull-request::before { - content: "\e13c"; } - -.fa-coins::before { - content: "\f51e"; } - -.fa-colon-sign::before { - content: "\e140"; } - -.fa-comment::before { - content: "\f075"; } - -.fa-comment-dollar::before { - content: "\f651"; } - -.fa-comment-dots::before { - content: "\f4ad"; } - -.fa-commenting::before { - content: "\f4ad"; } - -.fa-comment-medical::before { - content: "\f7f5"; } - -.fa-comment-slash::before { - content: "\f4b3"; } - -.fa-comment-sms::before { - content: "\f7cd"; } - -.fa-sms::before { - content: "\f7cd"; } - -.fa-comments::before { - content: "\f086"; } - -.fa-comments-dollar::before { - content: "\f653"; } - -.fa-compact-disc::before { - content: "\f51f"; } - -.fa-compass::before { - content: "\f14e"; } - -.fa-compass-drafting::before { - content: "\f568"; } - -.fa-drafting-compass::before { - content: "\f568"; } - -.fa-compress::before { - content: "\f066"; } - -.fa-computer::before { - content: "\e4e5"; } - -.fa-computer-mouse::before { - content: "\f8cc"; } - -.fa-mouse::before { - content: "\f8cc"; } - -.fa-cookie::before { - content: "\f563"; } - -.fa-cookie-bite::before { - content: "\f564"; } - -.fa-copy::before { - content: "\f0c5"; } - -.fa-copyright::before { - content: "\f1f9"; } - -.fa-couch::before { - content: "\f4b8"; } - -.fa-cow::before { - content: "\f6c8"; } - -.fa-credit-card::before { - content: "\f09d"; } - -.fa-credit-card-alt::before { - content: "\f09d"; } - -.fa-crop::before { - content: "\f125"; } - -.fa-crop-simple::before { - content: "\f565"; } - -.fa-crop-alt::before { - content: "\f565"; } - -.fa-cross::before { - content: "\f654"; } - -.fa-crosshairs::before { - content: "\f05b"; } - -.fa-crow::before { - content: "\f520"; } - -.fa-crown::before { - content: "\f521"; } - -.fa-crutch::before { - content: "\f7f7"; } - -.fa-cruzeiro-sign::before { - content: "\e152"; } - -.fa-cube::before { - content: "\f1b2"; } - -.fa-cubes::before { - content: "\f1b3"; } - -.fa-cubes-stacked::before { - content: "\e4e6"; } - -.fa-d::before { - content: "\44"; } - -.fa-database::before { - content: "\f1c0"; } - -.fa-delete-left::before { - content: "\f55a"; } - -.fa-backspace::before { - content: "\f55a"; } - -.fa-democrat::before { - content: "\f747"; } - -.fa-desktop::before { - content: "\f390"; } - -.fa-desktop-alt::before { - content: "\f390"; } - -.fa-dharmachakra::before { - content: "\f655"; } - -.fa-diagram-next::before { - content: "\e476"; } - -.fa-diagram-predecessor::before { - content: "\e477"; } - -.fa-diagram-project::before { - content: "\f542"; } - -.fa-project-diagram::before { - content: "\f542"; } - -.fa-diagram-successor::before { - content: "\e47a"; } - -.fa-diamond::before { - content: "\f219"; } - -.fa-diamond-turn-right::before { - content: "\f5eb"; } - -.fa-directions::before { - content: "\f5eb"; } - -.fa-dice::before { - content: "\f522"; } - -.fa-dice-d20::before { - content: "\f6cf"; } - -.fa-dice-d6::before { - content: "\f6d1"; } - -.fa-dice-five::before { - content: "\f523"; } - -.fa-dice-four::before { - content: "\f524"; } - -.fa-dice-one::before { - content: "\f525"; } - -.fa-dice-six::before { - content: "\f526"; } - -.fa-dice-three::before { - content: "\f527"; } - -.fa-dice-two::before { - content: "\f528"; } - -.fa-disease::before { - content: "\f7fa"; } - -.fa-display::before { - content: "\e163"; } - -.fa-divide::before { - content: "\f529"; } - -.fa-dna::before { - content: "\f471"; } - -.fa-dog::before { - content: "\f6d3"; } - -.fa-dollar-sign::before { - content: "\24"; } - -.fa-dollar::before { - content: "\24"; } - -.fa-usd::before { - content: "\24"; } - -.fa-dolly::before { - content: "\f472"; } - -.fa-dolly-box::before { - content: "\f472"; } - -.fa-dong-sign::before { - content: "\e169"; } - -.fa-door-closed::before { - content: "\f52a"; } - -.fa-door-open::before { - content: "\f52b"; } - -.fa-dove::before { - content: "\f4ba"; } - -.fa-down-left-and-up-right-to-center::before { - content: "\f422"; } - -.fa-compress-alt::before { - content: "\f422"; } - -.fa-down-long::before { - content: "\f309"; } - -.fa-long-arrow-alt-down::before { - content: "\f309"; } - -.fa-download::before { - content: "\f019"; } - -.fa-dragon::before { - content: "\f6d5"; } - -.fa-draw-polygon::before { - content: "\f5ee"; } - -.fa-droplet::before { - content: "\f043"; } - -.fa-tint::before { - content: "\f043"; } - -.fa-droplet-slash::before { - content: "\f5c7"; } - -.fa-tint-slash::before { - content: "\f5c7"; } - -.fa-drum::before { - content: "\f569"; } - -.fa-drum-steelpan::before { - content: "\f56a"; } - -.fa-drumstick-bite::before { - content: "\f6d7"; } - -.fa-dumbbell::before { - content: "\f44b"; } - -.fa-dumpster::before { - content: "\f793"; } - -.fa-dumpster-fire::before { - content: "\f794"; } - -.fa-dungeon::before { - content: "\f6d9"; } - -.fa-e::before { - content: "\45"; } - -.fa-ear-deaf::before { - content: "\f2a4"; } - -.fa-deaf::before { - content: "\f2a4"; } - -.fa-deafness::before { - content: "\f2a4"; } - -.fa-hard-of-hearing::before { - content: "\f2a4"; } - -.fa-ear-listen::before { - content: "\f2a2"; } - -.fa-assistive-listening-systems::before { - content: "\f2a2"; } - -.fa-earth-africa::before { - content: "\f57c"; } - -.fa-globe-africa::before { - content: "\f57c"; } - -.fa-earth-americas::before { - content: "\f57d"; } - -.fa-earth::before { - content: "\f57d"; } - -.fa-earth-america::before { - content: "\f57d"; } - -.fa-globe-americas::before { - content: "\f57d"; } - -.fa-earth-asia::before { - content: "\f57e"; } - -.fa-globe-asia::before { - content: "\f57e"; } - -.fa-earth-europe::before { - content: "\f7a2"; } - -.fa-globe-europe::before { - content: "\f7a2"; } - -.fa-earth-oceania::before { - content: "\e47b"; } - -.fa-globe-oceania::before { - content: "\e47b"; } - -.fa-egg::before { - content: "\f7fb"; } - -.fa-eject::before { - content: "\f052"; } - -.fa-elevator::before { - content: "\e16d"; } - -.fa-ellipsis::before { - content: "\f141"; } - -.fa-ellipsis-h::before { - content: "\f141"; } - -.fa-ellipsis-vertical::before { - content: "\f142"; } - -.fa-ellipsis-v::before { - content: "\f142"; } - -.fa-envelope::before { - content: "\f0e0"; } - -.fa-envelope-circle-check::before { - content: "\e4e8"; } - -.fa-envelope-open::before { - content: "\f2b6"; } - -.fa-envelope-open-text::before { - content: "\f658"; } - -.fa-envelopes-bulk::before { - content: "\f674"; } - -.fa-mail-bulk::before { - content: "\f674"; } - -.fa-equals::before { - content: "\3d"; } - -.fa-eraser::before { - content: "\f12d"; } - -.fa-ethernet::before { - content: "\f796"; } - -.fa-euro-sign::before { - content: "\f153"; } - -.fa-eur::before { - content: "\f153"; } - -.fa-euro::before { - content: "\f153"; } - -.fa-exclamation::before { - content: "\21"; } - -.fa-expand::before { - content: "\f065"; } - -.fa-explosion::before { - content: "\e4e9"; } - -.fa-eye::before { - content: "\f06e"; } - -.fa-eye-dropper::before { - content: "\f1fb"; } - -.fa-eye-dropper-empty::before { - content: "\f1fb"; } - -.fa-eyedropper::before { - content: "\f1fb"; } - -.fa-eye-low-vision::before { - content: "\f2a8"; } - -.fa-low-vision::before { - content: "\f2a8"; } - -.fa-eye-slash::before { - content: "\f070"; } - -.fa-f::before { - content: "\46"; } - -.fa-face-angry::before { - content: "\f556"; } - -.fa-angry::before { - content: "\f556"; } - -.fa-face-dizzy::before { - content: "\f567"; } - -.fa-dizzy::before { - content: "\f567"; } - -.fa-face-flushed::before { - content: "\f579"; } - -.fa-flushed::before { - content: "\f579"; } - -.fa-face-frown::before { - content: "\f119"; } - -.fa-frown::before { - content: "\f119"; } - -.fa-face-frown-open::before { - content: "\f57a"; } - -.fa-frown-open::before { - content: "\f57a"; } - -.fa-face-grimace::before { - content: "\f57f"; } - -.fa-grimace::before { - content: "\f57f"; } - -.fa-face-grin::before { - content: "\f580"; } - -.fa-grin::before { - content: "\f580"; } - -.fa-face-grin-beam::before { - content: "\f582"; } - -.fa-grin-beam::before { - content: "\f582"; } - -.fa-face-grin-beam-sweat::before { - content: "\f583"; } - -.fa-grin-beam-sweat::before { - content: "\f583"; } - -.fa-face-grin-hearts::before { - content: "\f584"; } - -.fa-grin-hearts::before { - content: "\f584"; } - -.fa-face-grin-squint::before { - content: "\f585"; } - -.fa-grin-squint::before { - content: "\f585"; } - -.fa-face-grin-squint-tears::before { - content: "\f586"; } - -.fa-grin-squint-tears::before { - content: "\f586"; } - -.fa-face-grin-stars::before { - content: "\f587"; } - -.fa-grin-stars::before { - content: "\f587"; } - -.fa-face-grin-tears::before { - content: "\f588"; } - -.fa-grin-tears::before { - content: "\f588"; } - -.fa-face-grin-tongue::before { - content: "\f589"; } - -.fa-grin-tongue::before { - content: "\f589"; } - -.fa-face-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-face-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-face-grin-wide::before { - content: "\f581"; } - -.fa-grin-alt::before { - content: "\f581"; } - -.fa-face-grin-wink::before { - content: "\f58c"; } - -.fa-grin-wink::before { - content: "\f58c"; } - -.fa-face-kiss::before { - content: "\f596"; } - -.fa-kiss::before { - content: "\f596"; } - -.fa-face-kiss-beam::before { - content: "\f597"; } - -.fa-kiss-beam::before { - content: "\f597"; } - -.fa-face-kiss-wink-heart::before { - content: "\f598"; } - -.fa-kiss-wink-heart::before { - content: "\f598"; } - -.fa-face-laugh::before { - content: "\f599"; } - -.fa-laugh::before { - content: "\f599"; } - -.fa-face-laugh-beam::before { - content: "\f59a"; } - -.fa-laugh-beam::before { - content: "\f59a"; } - -.fa-face-laugh-squint::before { - content: "\f59b"; } - -.fa-laugh-squint::before { - content: "\f59b"; } - -.fa-face-laugh-wink::before { - content: "\f59c"; } - -.fa-laugh-wink::before { - content: "\f59c"; } - -.fa-face-meh::before { - content: "\f11a"; } - -.fa-meh::before { - content: "\f11a"; } - -.fa-face-meh-blank::before { - content: "\f5a4"; } - -.fa-meh-blank::before { - content: "\f5a4"; } - -.fa-face-rolling-eyes::before { - content: "\f5a5"; } - -.fa-meh-rolling-eyes::before { - content: "\f5a5"; } - -.fa-face-sad-cry::before { - content: "\f5b3"; } - -.fa-sad-cry::before { - content: "\f5b3"; } - -.fa-face-sad-tear::before { - content: "\f5b4"; } - -.fa-sad-tear::before { - content: "\f5b4"; } - -.fa-face-smile::before { - content: "\f118"; } - -.fa-smile::before { - content: "\f118"; } - -.fa-face-smile-beam::before { - content: "\f5b8"; } - -.fa-smile-beam::before { - content: "\f5b8"; } - -.fa-face-smile-wink::before { - content: "\f4da"; } - -.fa-smile-wink::before { - content: "\f4da"; } - -.fa-face-surprise::before { - content: "\f5c2"; } - -.fa-surprise::before { - content: "\f5c2"; } - -.fa-face-tired::before { - content: "\f5c8"; } - -.fa-tired::before { - content: "\f5c8"; } - -.fa-fan::before { - content: "\f863"; } - -.fa-faucet::before { - content: "\e005"; } - -.fa-faucet-drip::before { - content: "\e006"; } - -.fa-fax::before { - content: "\f1ac"; } - -.fa-feather::before { - content: "\f52d"; } - -.fa-feather-pointed::before { - content: "\f56b"; } - -.fa-feather-alt::before { - content: "\f56b"; } - -.fa-ferry::before { - content: "\e4ea"; } - -.fa-file::before { - content: "\f15b"; } - -.fa-file-arrow-down::before { - content: "\f56d"; } - -.fa-file-download::before { - content: "\f56d"; } - -.fa-file-arrow-up::before { - content: "\f574"; } - -.fa-file-upload::before { - content: "\f574"; } - -.fa-file-audio::before { - content: "\f1c7"; } - -.fa-file-circle-check::before { - content: "\e493"; } - -.fa-file-circle-exclamation::before { - content: "\e4eb"; } - -.fa-file-circle-minus::before { - content: "\e4ed"; } - -.fa-file-circle-plus::before { - content: "\e4ee"; } - -.fa-file-circle-question::before { - content: "\e4ef"; } - -.fa-file-circle-xmark::before { - content: "\e494"; } - -.fa-file-code::before { - content: "\f1c9"; } - -.fa-file-contract::before { - content: "\f56c"; } - -.fa-file-csv::before { - content: "\f6dd"; } - -.fa-file-excel::before { - content: "\f1c3"; } - -.fa-file-export::before { - content: "\f56e"; } - -.fa-arrow-right-from-file::before { - content: "\f56e"; } - -.fa-file-image::before { - content: "\f1c5"; } - -.fa-file-import::before { - content: "\f56f"; } - -.fa-arrow-right-to-file::before { - content: "\f56f"; } - -.fa-file-invoice::before { - content: "\f570"; } - -.fa-file-invoice-dollar::before { - content: "\f571"; } - -.fa-file-lines::before { - content: "\f15c"; } - -.fa-file-alt::before { - content: "\f15c"; } - -.fa-file-text::before { - content: "\f15c"; } - -.fa-file-medical::before { - content: "\f477"; } - -.fa-file-pdf::before { - content: "\f1c1"; } - -.fa-file-pen::before { - content: "\f31c"; } - -.fa-file-edit::before { - content: "\f31c"; } - -.fa-file-powerpoint::before { - content: "\f1c4"; } - -.fa-file-prescription::before { - content: "\f572"; } - -.fa-file-shield::before { - content: "\e4f0"; } - -.fa-file-signature::before { - content: "\f573"; } - -.fa-file-video::before { - content: "\f1c8"; } - -.fa-file-waveform::before { - content: "\f478"; } - -.fa-file-medical-alt::before { - content: "\f478"; } - -.fa-file-word::before { - content: "\f1c2"; } - -.fa-file-zipper::before { - content: "\f1c6"; } - -.fa-file-archive::before { - content: "\f1c6"; } - -.fa-fill::before { - content: "\f575"; } - -.fa-fill-drip::before { - content: "\f576"; } - -.fa-film::before { - content: "\f008"; } - -.fa-filter::before { - content: "\f0b0"; } - -.fa-filter-circle-dollar::before { - content: "\f662"; } - -.fa-funnel-dollar::before { - content: "\f662"; } - -.fa-filter-circle-xmark::before { - content: "\e17b"; } - -.fa-fingerprint::before { - content: "\f577"; } - -.fa-fire::before { - content: "\f06d"; } - -.fa-fire-burner::before { - content: "\e4f1"; } - -.fa-fire-extinguisher::before { - content: "\f134"; } - -.fa-fire-flame-curved::before { - content: "\f7e4"; } - -.fa-fire-alt::before { - content: "\f7e4"; } - -.fa-fire-flame-simple::before { - content: "\f46a"; } - -.fa-burn::before { - content: "\f46a"; } - -.fa-fish::before { - content: "\f578"; } - -.fa-fish-fins::before { - content: "\e4f2"; } - -.fa-flag::before { - content: "\f024"; } - -.fa-flag-checkered::before { - content: "\f11e"; } - -.fa-flag-usa::before { - content: "\f74d"; } - -.fa-flask::before { - content: "\f0c3"; } - -.fa-flask-vial::before { - content: "\e4f3"; } - -.fa-floppy-disk::before { - content: "\f0c7"; } - -.fa-save::before { - content: "\f0c7"; } - -.fa-florin-sign::before { - content: "\e184"; } - -.fa-folder::before { - content: "\f07b"; } - -.fa-folder-blank::before { - content: "\f07b"; } - -.fa-folder-closed::before { - content: "\e185"; } - -.fa-folder-minus::before { - content: "\f65d"; } - -.fa-folder-open::before { - content: "\f07c"; } - -.fa-folder-plus::before { - content: "\f65e"; } - -.fa-folder-tree::before { - content: "\f802"; } - -.fa-font::before { - content: "\f031"; } - -.fa-football::before { - content: "\f44e"; } - -.fa-football-ball::before { - content: "\f44e"; } - -.fa-forward::before { - content: "\f04e"; } - -.fa-forward-fast::before { - content: "\f050"; } - -.fa-fast-forward::before { - content: "\f050"; } - -.fa-forward-step::before { - content: "\f051"; } - -.fa-step-forward::before { - content: "\f051"; } - -.fa-franc-sign::before { - content: "\e18f"; } - -.fa-frog::before { - content: "\f52e"; } - -.fa-futbol::before { - content: "\f1e3"; } - -.fa-futbol-ball::before { - content: "\f1e3"; } - -.fa-soccer-ball::before { - content: "\f1e3"; } - -.fa-g::before { - content: "\47"; } - -.fa-gamepad::before { - content: "\f11b"; } - -.fa-gas-pump::before { - content: "\f52f"; } - -.fa-gauge::before { - content: "\f624"; } - -.fa-dashboard::before { - content: "\f624"; } - -.fa-gauge-med::before { - content: "\f624"; } - -.fa-tachometer-alt-average::before { - content: "\f624"; } - -.fa-gauge-high::before { - content: "\f625"; } - -.fa-tachometer-alt::before { - content: "\f625"; } - -.fa-tachometer-alt-fast::before { - content: "\f625"; } - -.fa-gauge-simple::before { - content: "\f629"; } - -.fa-gauge-simple-med::before { - content: "\f629"; } - -.fa-tachometer-average::before { - content: "\f629"; } - -.fa-gauge-simple-high::before { - content: "\f62a"; } - -.fa-tachometer::before { - content: "\f62a"; } - -.fa-tachometer-fast::before { - content: "\f62a"; } - -.fa-gavel::before { - content: "\f0e3"; } - -.fa-legal::before { - content: "\f0e3"; } - -.fa-gear::before { - content: "\f013"; } - -.fa-cog::before { - content: "\f013"; } - -.fa-gears::before { - content: "\f085"; } - -.fa-cogs::before { - content: "\f085"; } - -.fa-gem::before { - content: "\f3a5"; } - -.fa-genderless::before { - content: "\f22d"; } - -.fa-ghost::before { - content: "\f6e2"; } - -.fa-gift::before { - content: "\f06b"; } - -.fa-gifts::before { - content: "\f79c"; } - -.fa-glass-water::before { - content: "\e4f4"; } - -.fa-glass-water-droplet::before { - content: "\e4f5"; } - -.fa-glasses::before { - content: "\f530"; } - -.fa-globe::before { - content: "\f0ac"; } - -.fa-golf-ball-tee::before { - content: "\f450"; } - -.fa-golf-ball::before { - content: "\f450"; } - -.fa-gopuram::before { - content: "\f664"; } - -.fa-graduation-cap::before { - content: "\f19d"; } - -.fa-mortar-board::before { - content: "\f19d"; } - -.fa-greater-than::before { - content: "\3e"; } - -.fa-greater-than-equal::before { - content: "\f532"; } - -.fa-grip::before { - content: "\f58d"; } - -.fa-grip-horizontal::before { - content: "\f58d"; } - -.fa-grip-lines::before { - content: "\f7a4"; } - -.fa-grip-lines-vertical::before { - content: "\f7a5"; } - -.fa-grip-vertical::before { - content: "\f58e"; } - -.fa-group-arrows-rotate::before { - content: "\e4f6"; } - -.fa-guarani-sign::before { - content: "\e19a"; } - -.fa-guitar::before { - content: "\f7a6"; } - -.fa-gun::before { - content: "\e19b"; } - -.fa-h::before { - content: "\48"; } - -.fa-hammer::before { - content: "\f6e3"; } - -.fa-hamsa::before { - content: "\f665"; } - -.fa-hand::before { - content: "\f256"; } - -.fa-hand-paper::before { - content: "\f256"; } - -.fa-hand-back-fist::before { - content: "\f255"; } - -.fa-hand-rock::before { - content: "\f255"; } - -.fa-hand-dots::before { - content: "\f461"; } - -.fa-allergies::before { - content: "\f461"; } - -.fa-hand-fist::before { - content: "\f6de"; } - -.fa-fist-raised::before { - content: "\f6de"; } - -.fa-hand-holding::before { - content: "\f4bd"; } - -.fa-hand-holding-dollar::before { - content: "\f4c0"; } - -.fa-hand-holding-usd::before { - content: "\f4c0"; } - -.fa-hand-holding-droplet::before { - content: "\f4c1"; } - -.fa-hand-holding-water::before { - content: "\f4c1"; } - -.fa-hand-holding-hand::before { - content: "\e4f7"; } - -.fa-hand-holding-heart::before { - content: "\f4be"; } - -.fa-hand-holding-medical::before { - content: "\e05c"; } - -.fa-hand-lizard::before { - content: "\f258"; } - -.fa-hand-middle-finger::before { - content: "\f806"; } - -.fa-hand-peace::before { - content: "\f25b"; } - -.fa-hand-point-down::before { - content: "\f0a7"; } - -.fa-hand-point-left::before { - content: "\f0a5"; } - -.fa-hand-point-right::before { - content: "\f0a4"; } - -.fa-hand-point-up::before { - content: "\f0a6"; } - -.fa-hand-pointer::before { - content: "\f25a"; } - -.fa-hand-scissors::before { - content: "\f257"; } - -.fa-hand-sparkles::before { - content: "\e05d"; } - -.fa-hand-spock::before { - content: "\f259"; } - -.fa-handcuffs::before { - content: "\e4f8"; } - -.fa-hands::before { - content: "\f2a7"; } - -.fa-sign-language::before { - content: "\f2a7"; } - -.fa-signing::before { - content: "\f2a7"; } - -.fa-hands-asl-interpreting::before { - content: "\f2a3"; } - -.fa-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-asl-interpreting::before { - content: "\f2a3"; } - -.fa-hands-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-hands-bound::before { - content: "\e4f9"; } - -.fa-hands-bubbles::before { - content: "\e05e"; } - -.fa-hands-wash::before { - content: "\e05e"; } - -.fa-hands-clapping::before { - content: "\e1a8"; } - -.fa-hands-holding::before { - content: "\f4c2"; } - -.fa-hands-holding-child::before { - content: "\e4fa"; } - -.fa-hands-holding-circle::before { - content: "\e4fb"; } - -.fa-hands-praying::before { - content: "\f684"; } - -.fa-praying-hands::before { - content: "\f684"; } - -.fa-handshake::before { - content: "\f2b5"; } - -.fa-handshake-angle::before { - content: "\f4c4"; } - -.fa-hands-helping::before { - content: "\f4c4"; } - -.fa-handshake-simple::before { - content: "\f4c6"; } - -.fa-handshake-alt::before { - content: "\f4c6"; } - -.fa-handshake-simple-slash::before { - content: "\e05f"; } - -.fa-handshake-alt-slash::before { - content: "\e05f"; } - -.fa-handshake-slash::before { - content: "\e060"; } - -.fa-hanukiah::before { - content: "\f6e6"; } - -.fa-hard-drive::before { - content: "\f0a0"; } - -.fa-hdd::before { - content: "\f0a0"; } - -.fa-hashtag::before { - content: "\23"; } - -.fa-hat-cowboy::before { - content: "\f8c0"; } - -.fa-hat-cowboy-side::before { - content: "\f8c1"; } - -.fa-hat-wizard::before { - content: "\f6e8"; } - -.fa-head-side-cough::before { - content: "\e061"; } - -.fa-head-side-cough-slash::before { - content: "\e062"; } - -.fa-head-side-mask::before { - content: "\e063"; } - -.fa-head-side-virus::before { - content: "\e064"; } - -.fa-heading::before { - content: "\f1dc"; } - -.fa-header::before { - content: "\f1dc"; } - -.fa-headphones::before { - content: "\f025"; } - -.fa-headphones-simple::before { - content: "\f58f"; } - -.fa-headphones-alt::before { - content: "\f58f"; } - -.fa-headset::before { - content: "\f590"; } - -.fa-heart::before { - content: "\f004"; } - -.fa-heart-circle-bolt::before { - content: "\e4fc"; } - -.fa-heart-circle-check::before { - content: "\e4fd"; } - -.fa-heart-circle-exclamation::before { - content: "\e4fe"; } - -.fa-heart-circle-minus::before { - content: "\e4ff"; } - -.fa-heart-circle-plus::before { - content: "\e500"; } - -.fa-heart-circle-xmark::before { - content: "\e501"; } - -.fa-heart-crack::before { - content: "\f7a9"; } - -.fa-heart-broken::before { - content: "\f7a9"; } - -.fa-heart-pulse::before { - content: "\f21e"; } - -.fa-heartbeat::before { - content: "\f21e"; } - -.fa-helicopter::before { - content: "\f533"; } - -.fa-helicopter-symbol::before { - content: "\e502"; } - -.fa-helmet-safety::before { - content: "\f807"; } - -.fa-hard-hat::before { - content: "\f807"; } - -.fa-hat-hard::before { - content: "\f807"; } - -.fa-helmet-un::before { - content: "\e503"; } - -.fa-highlighter::before { - content: "\f591"; } - -.fa-hill-avalanche::before { - content: "\e507"; } - -.fa-hill-rockslide::before { - content: "\e508"; } - -.fa-hippo::before { - content: "\f6ed"; } - -.fa-hockey-puck::before { - content: "\f453"; } - -.fa-holly-berry::before { - content: "\f7aa"; } - -.fa-horse::before { - content: "\f6f0"; } - -.fa-horse-head::before { - content: "\f7ab"; } - -.fa-hospital::before { - content: "\f0f8"; } - -.fa-hospital-alt::before { - content: "\f0f8"; } - -.fa-hospital-wide::before { - content: "\f0f8"; } - -.fa-hospital-user::before { - content: "\f80d"; } - -.fa-hot-tub-person::before { - content: "\f593"; } - -.fa-hot-tub::before { - content: "\f593"; } - -.fa-hotdog::before { - content: "\f80f"; } - -.fa-hotel::before { - content: "\f594"; } - -.fa-hourglass::before { - content: "\f254"; } - -.fa-hourglass-2::before { - content: "\f254"; } - -.fa-hourglass-half::before { - content: "\f254"; } - -.fa-hourglass-empty::before { - content: "\f252"; } - -.fa-hourglass-end::before { - content: "\f253"; } - -.fa-hourglass-3::before { - content: "\f253"; } - -.fa-hourglass-start::before { - content: "\f251"; } - -.fa-hourglass-1::before { - content: "\f251"; } - -.fa-house::before { - content: "\f015"; } - -.fa-home::before { - content: "\f015"; } - -.fa-home-alt::before { - content: "\f015"; } - -.fa-home-lg-alt::before { - content: "\f015"; } - -.fa-house-chimney::before { - content: "\e3af"; } - -.fa-home-lg::before { - content: "\e3af"; } - -.fa-house-chimney-crack::before { - content: "\f6f1"; } - -.fa-house-damage::before { - content: "\f6f1"; } - -.fa-house-chimney-medical::before { - content: "\f7f2"; } - -.fa-clinic-medical::before { - content: "\f7f2"; } - -.fa-house-chimney-user::before { - content: "\e065"; } - -.fa-house-chimney-window::before { - content: "\e00d"; } - -.fa-house-circle-check::before { - content: "\e509"; } - -.fa-house-circle-exclamation::before { - content: "\e50a"; } - -.fa-house-circle-xmark::before { - content: "\e50b"; } - -.fa-house-crack::before { - content: "\e3b1"; } - -.fa-house-fire::before { - content: "\e50c"; } - -.fa-house-flag::before { - content: "\e50d"; } - -.fa-house-flood-water::before { - content: "\e50e"; } - -.fa-house-flood-water-circle-arrow-right::before { - content: "\e50f"; } - -.fa-house-laptop::before { - content: "\e066"; } - -.fa-laptop-house::before { - content: "\e066"; } - -.fa-house-lock::before { - content: "\e510"; } - -.fa-house-medical::before { - content: "\e3b2"; } - -.fa-house-medical-circle-check::before { - content: "\e511"; } - -.fa-house-medical-circle-exclamation::before { - content: "\e512"; } - -.fa-house-medical-circle-xmark::before { - content: "\e513"; } - -.fa-house-medical-flag::before { - content: "\e514"; } - -.fa-house-signal::before { - content: "\e012"; } - -.fa-house-tsunami::before { - content: "\e515"; } - -.fa-house-user::before { - content: "\e1b0"; } - -.fa-home-user::before { - content: "\e1b0"; } - -.fa-hryvnia-sign::before { - content: "\f6f2"; } - -.fa-hryvnia::before { - content: "\f6f2"; } - -.fa-hurricane::before { - content: "\f751"; } - -.fa-i::before { - content: "\49"; } - -.fa-i-cursor::before { - content: "\f246"; } - -.fa-ice-cream::before { - content: "\f810"; } - -.fa-icicles::before { - content: "\f7ad"; } - -.fa-icons::before { - content: "\f86d"; } - -.fa-heart-music-camera-bolt::before { - content: "\f86d"; } - -.fa-id-badge::before { - content: "\f2c1"; } - -.fa-id-card::before { - content: "\f2c2"; } - -.fa-drivers-license::before { - content: "\f2c2"; } - -.fa-id-card-clip::before { - content: "\f47f"; } - -.fa-id-card-alt::before { - content: "\f47f"; } - -.fa-igloo::before { - content: "\f7ae"; } - -.fa-image::before { - content: "\f03e"; } - -.fa-image-portrait::before { - content: "\f3e0"; } - -.fa-portrait::before { - content: "\f3e0"; } - -.fa-images::before { - content: "\f302"; } - -.fa-inbox::before { - content: "\f01c"; } - -.fa-indent::before { - content: "\f03c"; } - -.fa-indian-rupee-sign::before { - content: "\e1bc"; } - -.fa-indian-rupee::before { - content: "\e1bc"; } - -.fa-inr::before { - content: "\e1bc"; } - -.fa-industry::before { - content: "\f275"; } - -.fa-infinity::before { - content: "\f534"; } - -.fa-info::before { - content: "\f129"; } - -.fa-italic::before { - content: "\f033"; } - -.fa-j::before { - content: "\4a"; } - -.fa-jar::before { - content: "\e516"; } - -.fa-jar-wheat::before { - content: "\e517"; } - -.fa-jedi::before { - content: "\f669"; } - -.fa-jet-fighter::before { - content: "\f0fb"; } - -.fa-fighter-jet::before { - content: "\f0fb"; } - -.fa-jet-fighter-up::before { - content: "\e518"; } - -.fa-joint::before { - content: "\f595"; } - -.fa-jug-detergent::before { - content: "\e519"; } - -.fa-k::before { - content: "\4b"; } - -.fa-kaaba::before { - content: "\f66b"; } - -.fa-key::before { - content: "\f084"; } - -.fa-keyboard::before { - content: "\f11c"; } - -.fa-khanda::before { - content: "\f66d"; } - -.fa-kip-sign::before { - content: "\e1c4"; } - -.fa-kit-medical::before { - content: "\f479"; } - -.fa-first-aid::before { - content: "\f479"; } - -.fa-kitchen-set::before { - content: "\e51a"; } - -.fa-kiwi-bird::before { - content: "\f535"; } - -.fa-l::before { - content: "\4c"; } - -.fa-land-mine-on::before { - content: "\e51b"; } - -.fa-landmark::before { - content: "\f66f"; } - -.fa-landmark-dome::before { - content: "\f752"; } - -.fa-landmark-alt::before { - content: "\f752"; } - -.fa-landmark-flag::before { - content: "\e51c"; } - -.fa-language::before { - content: "\f1ab"; } - -.fa-laptop::before { - content: "\f109"; } - -.fa-laptop-code::before { - content: "\f5fc"; } - -.fa-laptop-file::before { - content: "\e51d"; } - -.fa-laptop-medical::before { - content: "\f812"; } - -.fa-lari-sign::before { - content: "\e1c8"; } - -.fa-layer-group::before { - content: "\f5fd"; } - -.fa-leaf::before { - content: "\f06c"; } - -.fa-left-long::before { - content: "\f30a"; } - -.fa-long-arrow-alt-left::before { - content: "\f30a"; } - -.fa-left-right::before { - content: "\f337"; } - -.fa-arrows-alt-h::before { - content: "\f337"; } - -.fa-lemon::before { - content: "\f094"; } - -.fa-less-than::before { - content: "\3c"; } - -.fa-less-than-equal::before { - content: "\f537"; } - -.fa-life-ring::before { - content: "\f1cd"; } - -.fa-lightbulb::before { - content: "\f0eb"; } - -.fa-lines-leaning::before { - content: "\e51e"; } - -.fa-link::before { - content: "\f0c1"; } - -.fa-chain::before { - content: "\f0c1"; } - -.fa-link-slash::before { - content: "\f127"; } - -.fa-chain-broken::before { - content: "\f127"; } - -.fa-chain-slash::before { - content: "\f127"; } - -.fa-unlink::before { - content: "\f127"; } - -.fa-lira-sign::before { - content: "\f195"; } - -.fa-list::before { - content: "\f03a"; } - -.fa-list-squares::before { - content: "\f03a"; } - -.fa-list-check::before { - content: "\f0ae"; } - -.fa-tasks::before { - content: "\f0ae"; } - -.fa-list-ol::before { - content: "\f0cb"; } - -.fa-list-1-2::before { - content: "\f0cb"; } - -.fa-list-numeric::before { - content: "\f0cb"; } - -.fa-list-ul::before { - content: "\f0ca"; } - -.fa-list-dots::before { - content: "\f0ca"; } - -.fa-litecoin-sign::before { - content: "\e1d3"; } - -.fa-location-arrow::before { - content: "\f124"; } - -.fa-location-crosshairs::before { - content: "\f601"; } - -.fa-location::before { - content: "\f601"; } - -.fa-location-dot::before { - content: "\f3c5"; } - -.fa-map-marker-alt::before { - content: "\f3c5"; } - -.fa-location-pin::before { - content: "\f041"; } - -.fa-map-marker::before { - content: "\f041"; } - -.fa-location-pin-lock::before { - content: "\e51f"; } - -.fa-lock::before { - content: "\f023"; } - -.fa-lock-open::before { - content: "\f3c1"; } - -.fa-locust::before { - content: "\e520"; } - -.fa-lungs::before { - content: "\f604"; } - -.fa-lungs-virus::before { - content: "\e067"; } - -.fa-m::before { - content: "\4d"; } - -.fa-magnet::before { - content: "\f076"; } - -.fa-magnifying-glass::before { - content: "\f002"; } - -.fa-search::before { - content: "\f002"; } - -.fa-magnifying-glass-arrow-right::before { - content: "\e521"; } - -.fa-magnifying-glass-chart::before { - content: "\e522"; } - -.fa-magnifying-glass-dollar::before { - content: "\f688"; } - -.fa-search-dollar::before { - content: "\f688"; } - -.fa-magnifying-glass-location::before { - content: "\f689"; } - -.fa-search-location::before { - content: "\f689"; } - -.fa-magnifying-glass-minus::before { - content: "\f010"; } - -.fa-search-minus::before { - content: "\f010"; } - -.fa-magnifying-glass-plus::before { - content: "\f00e"; } - -.fa-search-plus::before { - content: "\f00e"; } - -.fa-manat-sign::before { - content: "\e1d5"; } - -.fa-map::before { - content: "\f279"; } - -.fa-map-location::before { - content: "\f59f"; } - -.fa-map-marked::before { - content: "\f59f"; } - -.fa-map-location-dot::before { - content: "\f5a0"; } - -.fa-map-marked-alt::before { - content: "\f5a0"; } - -.fa-map-pin::before { - content: "\f276"; } - -.fa-marker::before { - content: "\f5a1"; } - -.fa-mars::before { - content: "\f222"; } - -.fa-mars-and-venus::before { - content: "\f224"; } - -.fa-mars-and-venus-burst::before { - content: "\e523"; } - -.fa-mars-double::before { - content: "\f227"; } - -.fa-mars-stroke::before { - content: "\f229"; } - -.fa-mars-stroke-right::before { - content: "\f22b"; } - -.fa-mars-stroke-h::before { - content: "\f22b"; } - -.fa-mars-stroke-up::before { - content: "\f22a"; } - -.fa-mars-stroke-v::before { - content: "\f22a"; } - -.fa-martini-glass::before { - content: "\f57b"; } - -.fa-glass-martini-alt::before { - content: "\f57b"; } - -.fa-martini-glass-citrus::before { - content: "\f561"; } - -.fa-cocktail::before { - content: "\f561"; } - -.fa-martini-glass-empty::before { - content: "\f000"; } - -.fa-glass-martini::before { - content: "\f000"; } - -.fa-mask::before { - content: "\f6fa"; } - -.fa-mask-face::before { - content: "\e1d7"; } - -.fa-mask-ventilator::before { - content: "\e524"; } - -.fa-masks-theater::before { - content: "\f630"; } - -.fa-theater-masks::before { - content: "\f630"; } - -.fa-mattress-pillow::before { - content: "\e525"; } - -.fa-maximize::before { - content: "\f31e"; } - -.fa-expand-arrows-alt::before { - content: "\f31e"; } - -.fa-medal::before { - content: "\f5a2"; } - -.fa-memory::before { - content: "\f538"; } - -.fa-menorah::before { - content: "\f676"; } - -.fa-mercury::before { - content: "\f223"; } - -.fa-message::before { - content: "\f27a"; } - -.fa-comment-alt::before { - content: "\f27a"; } - -.fa-meteor::before { - content: "\f753"; } - -.fa-microchip::before { - content: "\f2db"; } - -.fa-microphone::before { - content: "\f130"; } - -.fa-microphone-lines::before { - content: "\f3c9"; } - -.fa-microphone-alt::before { - content: "\f3c9"; } - -.fa-microphone-lines-slash::before { - content: "\f539"; } - -.fa-microphone-alt-slash::before { - content: "\f539"; } - -.fa-microphone-slash::before { - content: "\f131"; } - -.fa-microscope::before { - content: "\f610"; } - -.fa-mill-sign::before { - content: "\e1ed"; } - -.fa-minimize::before { - content: "\f78c"; } - -.fa-compress-arrows-alt::before { - content: "\f78c"; } - -.fa-minus::before { - content: "\f068"; } - -.fa-subtract::before { - content: "\f068"; } - -.fa-mitten::before { - content: "\f7b5"; } - -.fa-mobile::before { - content: "\f3ce"; } - -.fa-mobile-android::before { - content: "\f3ce"; } - -.fa-mobile-phone::before { - content: "\f3ce"; } - -.fa-mobile-button::before { - content: "\f10b"; } - -.fa-mobile-retro::before { - content: "\e527"; } - -.fa-mobile-screen::before { - content: "\f3cf"; } - -.fa-mobile-android-alt::before { - content: "\f3cf"; } - -.fa-mobile-screen-button::before { - content: "\f3cd"; } - -.fa-mobile-alt::before { - content: "\f3cd"; } - -.fa-money-bill::before { - content: "\f0d6"; } - -.fa-money-bill-1::before { - content: "\f3d1"; } - -.fa-money-bill-alt::before { - content: "\f3d1"; } - -.fa-money-bill-1-wave::before { - content: "\f53b"; } - -.fa-money-bill-wave-alt::before { - content: "\f53b"; } - -.fa-money-bill-transfer::before { - content: "\e528"; } - -.fa-money-bill-trend-up::before { - content: "\e529"; } - -.fa-money-bill-wave::before { - content: "\f53a"; } - -.fa-money-bill-wheat::before { - content: "\e52a"; } - -.fa-money-bills::before { - content: "\e1f3"; } - -.fa-money-check::before { - content: "\f53c"; } - -.fa-money-check-dollar::before { - content: "\f53d"; } - -.fa-money-check-alt::before { - content: "\f53d"; } - -.fa-monument::before { - content: "\f5a6"; } - -.fa-moon::before { - content: "\f186"; } - -.fa-mortar-pestle::before { - content: "\f5a7"; } - -.fa-mosque::before { - content: "\f678"; } - -.fa-mosquito::before { - content: "\e52b"; } - -.fa-mosquito-net::before { - content: "\e52c"; } - -.fa-motorcycle::before { - content: "\f21c"; } - -.fa-mound::before { - content: "\e52d"; } - -.fa-mountain::before { - content: "\f6fc"; } - -.fa-mountain-city::before { - content: "\e52e"; } - -.fa-mountain-sun::before { - content: "\e52f"; } - -.fa-mug-hot::before { - content: "\f7b6"; } - -.fa-mug-saucer::before { - content: "\f0f4"; } - -.fa-coffee::before { - content: "\f0f4"; } - -.fa-music::before { - content: "\f001"; } - -.fa-n::before { - content: "\4e"; } - -.fa-naira-sign::before { - content: "\e1f6"; } - -.fa-network-wired::before { - content: "\f6ff"; } - -.fa-neuter::before { - content: "\f22c"; } - -.fa-newspaper::before { - content: "\f1ea"; } - -.fa-not-equal::before { - content: "\f53e"; } - -.fa-note-sticky::before { - content: "\f249"; } - -.fa-sticky-note::before { - content: "\f249"; } - -.fa-notes-medical::before { - content: "\f481"; } - -.fa-o::before { - content: "\4f"; } - -.fa-object-group::before { - content: "\f247"; } - -.fa-object-ungroup::before { - content: "\f248"; } - -.fa-oil-can::before { - content: "\f613"; } - -.fa-oil-well::before { - content: "\e532"; } - -.fa-om::before { - content: "\f679"; } - -.fa-otter::before { - content: "\f700"; } - -.fa-outdent::before { - content: "\f03b"; } - -.fa-dedent::before { - content: "\f03b"; } - -.fa-p::before { - content: "\50"; } - -.fa-pager::before { - content: "\f815"; } - -.fa-paint-roller::before { - content: "\f5aa"; } - -.fa-paintbrush::before { - content: "\f1fc"; } - -.fa-paint-brush::before { - content: "\f1fc"; } - -.fa-palette::before { - content: "\f53f"; } - -.fa-pallet::before { - content: "\f482"; } - -.fa-panorama::before { - content: "\e209"; } - -.fa-paper-plane::before { - content: "\f1d8"; } - -.fa-paperclip::before { - content: "\f0c6"; } - -.fa-parachute-box::before { - content: "\f4cd"; } - -.fa-paragraph::before { - content: "\f1dd"; } - -.fa-passport::before { - content: "\f5ab"; } - -.fa-paste::before { - content: "\f0ea"; } - -.fa-file-clipboard::before { - content: "\f0ea"; } - -.fa-pause::before { - content: "\f04c"; } - -.fa-paw::before { - content: "\f1b0"; } - -.fa-peace::before { - content: "\f67c"; } - -.fa-pen::before { - content: "\f304"; } - -.fa-pen-clip::before { - content: "\f305"; } - -.fa-pen-alt::before { - content: "\f305"; } - -.fa-pen-fancy::before { - content: "\f5ac"; } - -.fa-pen-nib::before { - content: "\f5ad"; } - -.fa-pen-ruler::before { - content: "\f5ae"; } - -.fa-pencil-ruler::before { - content: "\f5ae"; } - -.fa-pen-to-square::before { - content: "\f044"; } - -.fa-edit::before { - content: "\f044"; } - -.fa-pencil::before { - content: "\f303"; } - -.fa-pencil-alt::before { - content: "\f303"; } - -.fa-people-arrows-left-right::before { - content: "\e068"; } - -.fa-people-arrows::before { - content: "\e068"; } - -.fa-people-carry-box::before { - content: "\f4ce"; } - -.fa-people-carry::before { - content: "\f4ce"; } - -.fa-people-group::before { - content: "\e533"; } - -.fa-people-line::before { - content: "\e534"; } - -.fa-people-pulling::before { - content: "\e535"; } - -.fa-people-robbery::before { - content: "\e536"; } - -.fa-people-roof::before { - content: "\e537"; } - -.fa-pepper-hot::before { - content: "\f816"; } - -.fa-percent::before { - content: "\25"; } - -.fa-percentage::before { - content: "\25"; } - -.fa-person::before { - content: "\f183"; } - -.fa-male::before { - content: "\f183"; } - -.fa-person-arrow-down-to-line::before { - content: "\e538"; } - -.fa-person-arrow-up-from-line::before { - content: "\e539"; } - -.fa-person-biking::before { - content: "\f84a"; } - -.fa-biking::before { - content: "\f84a"; } - -.fa-person-booth::before { - content: "\f756"; } - -.fa-person-breastfeeding::before { - content: "\e53a"; } - -.fa-person-burst::before { - content: "\e53b"; } - -.fa-person-cane::before { - content: "\e53c"; } - -.fa-person-chalkboard::before { - content: "\e53d"; } - -.fa-person-circle-check::before { - content: "\e53e"; } - -.fa-person-circle-exclamation::before { - content: "\e53f"; } - -.fa-person-circle-minus::before { - content: "\e540"; } - -.fa-person-circle-plus::before { - content: "\e541"; } - -.fa-person-circle-question::before { - content: "\e542"; } - -.fa-person-circle-xmark::before { - content: "\e543"; } - -.fa-person-digging::before { - content: "\f85e"; } - -.fa-digging::before { - content: "\f85e"; } - -.fa-person-dots-from-line::before { - content: "\f470"; } - -.fa-diagnoses::before { - content: "\f470"; } - -.fa-person-dress::before { - content: "\f182"; } - -.fa-female::before { - content: "\f182"; } - -.fa-person-dress-burst::before { - content: "\e544"; } - -.fa-person-drowning::before { - content: "\e545"; } - -.fa-person-falling::before { - content: "\e546"; } - -.fa-person-falling-burst::before { - content: "\e547"; } - -.fa-person-half-dress::before { - content: "\e548"; } - -.fa-person-harassing::before { - content: "\e549"; } - -.fa-person-hiking::before { - content: "\f6ec"; } - -.fa-hiking::before { - content: "\f6ec"; } - -.fa-person-military-pointing::before { - content: "\e54a"; } - -.fa-person-military-rifle::before { - content: "\e54b"; } - -.fa-person-military-to-person::before { - content: "\e54c"; } - -.fa-person-praying::before { - content: "\f683"; } - -.fa-pray::before { - content: "\f683"; } - -.fa-person-pregnant::before { - content: "\e31e"; } - -.fa-person-rays::before { - content: "\e54d"; } - -.fa-person-rifle::before { - content: "\e54e"; } - -.fa-person-running::before { - content: "\f70c"; } - -.fa-running::before { - content: "\f70c"; } - -.fa-person-shelter::before { - content: "\e54f"; } - -.fa-person-skating::before { - content: "\f7c5"; } - -.fa-skating::before { - content: "\f7c5"; } - -.fa-person-skiing::before { - content: "\f7c9"; } - -.fa-skiing::before { - content: "\f7c9"; } - -.fa-person-skiing-nordic::before { - content: "\f7ca"; } - -.fa-skiing-nordic::before { - content: "\f7ca"; } - -.fa-person-snowboarding::before { - content: "\f7ce"; } - -.fa-snowboarding::before { - content: "\f7ce"; } - -.fa-person-swimming::before { - content: "\f5c4"; } - -.fa-swimmer::before { - content: "\f5c4"; } - -.fa-person-through-window::before { - content: "\e433"; } - -.fa-person-walking::before { - content: "\f554"; } - -.fa-walking::before { - content: "\f554"; } - -.fa-person-walking-arrow-loop-left::before { - content: "\e551"; } - -.fa-person-walking-arrow-right::before { - content: "\e552"; } - -.fa-person-walking-dashed-line-arrow-right::before { - content: "\e553"; } - -.fa-person-walking-luggage::before { - content: "\e554"; } - -.fa-person-walking-with-cane::before { - content: "\f29d"; } - -.fa-blind::before { - content: "\f29d"; } - -.fa-peseta-sign::before { - content: "\e221"; } - -.fa-peso-sign::before { - content: "\e222"; } - -.fa-phone::before { - content: "\f095"; } - -.fa-phone-flip::before { - content: "\f879"; } - -.fa-phone-alt::before { - content: "\f879"; } - -.fa-phone-slash::before { - content: "\f3dd"; } - -.fa-phone-volume::before { - content: "\f2a0"; } - -.fa-volume-control-phone::before { - content: "\f2a0"; } - -.fa-photo-film::before { - content: "\f87c"; } - -.fa-photo-video::before { - content: "\f87c"; } - -.fa-piggy-bank::before { - content: "\f4d3"; } - -.fa-pills::before { - content: "\f484"; } - -.fa-pizza-slice::before { - content: "\f818"; } - -.fa-place-of-worship::before { - content: "\f67f"; } - -.fa-plane::before { - content: "\f072"; } - -.fa-plane-arrival::before { - content: "\f5af"; } - -.fa-plane-circle-check::before { - content: "\e555"; } - -.fa-plane-circle-exclamation::before { - content: "\e556"; } - -.fa-plane-circle-xmark::before { - content: "\e557"; } - -.fa-plane-departure::before { - content: "\f5b0"; } - -.fa-plane-lock::before { - content: "\e558"; } - -.fa-plane-slash::before { - content: "\e069"; } - -.fa-plane-up::before { - content: "\e22d"; } - -.fa-plant-wilt::before { - content: "\e43b"; } - -.fa-plate-wheat::before { - content: "\e55a"; } - -.fa-play::before { - content: "\f04b"; } - -.fa-plug::before { - content: "\f1e6"; } - -.fa-plug-circle-bolt::before { - content: "\e55b"; } - -.fa-plug-circle-check::before { - content: "\e55c"; } - -.fa-plug-circle-exclamation::before { - content: "\e55d"; } - -.fa-plug-circle-minus::before { - content: "\e55e"; } - -.fa-plug-circle-plus::before { - content: "\e55f"; } - -.fa-plug-circle-xmark::before { - content: "\e560"; } - -.fa-plus::before { - content: "\2b"; } - -.fa-add::before { - content: "\2b"; } - -.fa-plus-minus::before { - content: "\e43c"; } - -.fa-podcast::before { - content: "\f2ce"; } - -.fa-poo::before { - content: "\f2fe"; } - -.fa-poo-storm::before { - content: "\f75a"; } - -.fa-poo-bolt::before { - content: "\f75a"; } - -.fa-poop::before { - content: "\f619"; } - -.fa-power-off::before { - content: "\f011"; } - -.fa-prescription::before { - content: "\f5b1"; } - -.fa-prescription-bottle::before { - content: "\f485"; } - -.fa-prescription-bottle-medical::before { - content: "\f486"; } - -.fa-prescription-bottle-alt::before { - content: "\f486"; } - -.fa-print::before { - content: "\f02f"; } - -.fa-pump-medical::before { - content: "\e06a"; } - -.fa-pump-soap::before { - content: "\e06b"; } - -.fa-puzzle-piece::before { - content: "\f12e"; } - -.fa-q::before { - content: "\51"; } - -.fa-qrcode::before { - content: "\f029"; } - -.fa-question::before { - content: "\3f"; } - -.fa-quote-left::before { - content: "\f10d"; } - -.fa-quote-left-alt::before { - content: "\f10d"; } - -.fa-quote-right::before { - content: "\f10e"; } - -.fa-quote-right-alt::before { - content: "\f10e"; } - -.fa-r::before { - content: "\52"; } - -.fa-radiation::before { - content: "\f7b9"; } - -.fa-radio::before { - content: "\f8d7"; } - -.fa-rainbow::before { - content: "\f75b"; } - -.fa-ranking-star::before { - content: "\e561"; } - -.fa-receipt::before { - content: "\f543"; } - -.fa-record-vinyl::before { - content: "\f8d9"; } - -.fa-rectangle-ad::before { - content: "\f641"; } - -.fa-ad::before { - content: "\f641"; } - -.fa-rectangle-list::before { - content: "\f022"; } - -.fa-list-alt::before { - content: "\f022"; } - -.fa-rectangle-xmark::before { - content: "\f410"; } - -.fa-rectangle-times::before { - content: "\f410"; } - -.fa-times-rectangle::before { - content: "\f410"; } - -.fa-window-close::before { - content: "\f410"; } - -.fa-recycle::before { - content: "\f1b8"; } - -.fa-registered::before { - content: "\f25d"; } - -.fa-repeat::before { - content: "\f363"; } - -.fa-reply::before { - content: "\f3e5"; } - -.fa-mail-reply::before { - content: "\f3e5"; } - -.fa-reply-all::before { - content: "\f122"; } - -.fa-mail-reply-all::before { - content: "\f122"; } - -.fa-republican::before { - content: "\f75e"; } - -.fa-restroom::before { - content: "\f7bd"; } - -.fa-retweet::before { - content: "\f079"; } - -.fa-ribbon::before { - content: "\f4d6"; } - -.fa-right-from-bracket::before { - content: "\f2f5"; } - -.fa-sign-out-alt::before { - content: "\f2f5"; } - -.fa-right-left::before { - content: "\f362"; } - -.fa-exchange-alt::before { - content: "\f362"; } - -.fa-right-long::before { - content: "\f30b"; } - -.fa-long-arrow-alt-right::before { - content: "\f30b"; } - -.fa-right-to-bracket::before { - content: "\f2f6"; } - -.fa-sign-in-alt::before { - content: "\f2f6"; } - -.fa-ring::before { - content: "\f70b"; } - -.fa-road::before { - content: "\f018"; } - -.fa-road-barrier::before { - content: "\e562"; } - -.fa-road-bridge::before { - content: "\e563"; } - -.fa-road-circle-check::before { - content: "\e564"; } - -.fa-road-circle-exclamation::before { - content: "\e565"; } - -.fa-road-circle-xmark::before { - content: "\e566"; } - -.fa-road-lock::before { - content: "\e567"; } - -.fa-road-spikes::before { - content: "\e568"; } - -.fa-robot::before { - content: "\f544"; } - -.fa-rocket::before { - content: "\f135"; } - -.fa-rotate::before { - content: "\f2f1"; } - -.fa-sync-alt::before { - content: "\f2f1"; } - -.fa-rotate-left::before { - content: "\f2ea"; } - -.fa-rotate-back::before { - content: "\f2ea"; } - -.fa-rotate-backward::before { - content: "\f2ea"; } - -.fa-undo-alt::before { - content: "\f2ea"; } - -.fa-rotate-right::before { - content: "\f2f9"; } - -.fa-redo-alt::before { - content: "\f2f9"; } - -.fa-rotate-forward::before { - content: "\f2f9"; } - -.fa-route::before { - content: "\f4d7"; } - -.fa-rss::before { - content: "\f09e"; } - -.fa-feed::before { - content: "\f09e"; } - -.fa-ruble-sign::before { - content: "\f158"; } - -.fa-rouble::before { - content: "\f158"; } - -.fa-rub::before { - content: "\f158"; } - -.fa-ruble::before { - content: "\f158"; } - -.fa-rug::before { - content: "\e569"; } - -.fa-ruler::before { - content: "\f545"; } - -.fa-ruler-combined::before { - content: "\f546"; } - -.fa-ruler-horizontal::before { - content: "\f547"; } - -.fa-ruler-vertical::before { - content: "\f548"; } - -.fa-rupee-sign::before { - content: "\f156"; } - -.fa-rupee::before { - content: "\f156"; } - -.fa-rupiah-sign::before { - content: "\e23d"; } - -.fa-s::before { - content: "\53"; } - -.fa-sack-dollar::before { - content: "\f81d"; } - -.fa-sack-xmark::before { - content: "\e56a"; } - -.fa-sailboat::before { - content: "\e445"; } - -.fa-satellite::before { - content: "\f7bf"; } - -.fa-satellite-dish::before { - content: "\f7c0"; } - -.fa-scale-balanced::before { - content: "\f24e"; } - -.fa-balance-scale::before { - content: "\f24e"; } - -.fa-scale-unbalanced::before { - content: "\f515"; } - -.fa-balance-scale-left::before { - content: "\f515"; } - -.fa-scale-unbalanced-flip::before { - content: "\f516"; } - -.fa-balance-scale-right::before { - content: "\f516"; } - -.fa-school::before { - content: "\f549"; } - -.fa-school-circle-check::before { - content: "\e56b"; } - -.fa-school-circle-exclamation::before { - content: "\e56c"; } - -.fa-school-circle-xmark::before { - content: "\e56d"; } - -.fa-school-flag::before { - content: "\e56e"; } - -.fa-school-lock::before { - content: "\e56f"; } - -.fa-scissors::before { - content: "\f0c4"; } - -.fa-cut::before { - content: "\f0c4"; } - -.fa-screwdriver::before { - content: "\f54a"; } - -.fa-screwdriver-wrench::before { - content: "\f7d9"; } - -.fa-tools::before { - content: "\f7d9"; } - -.fa-scroll::before { - content: "\f70e"; } - -.fa-scroll-torah::before { - content: "\f6a0"; } - -.fa-torah::before { - content: "\f6a0"; } - -.fa-sd-card::before { - content: "\f7c2"; } - -.fa-section::before { - content: "\e447"; } - -.fa-seedling::before { - content: "\f4d8"; } - -.fa-sprout::before { - content: "\f4d8"; } - -.fa-server::before { - content: "\f233"; } - -.fa-shapes::before { - content: "\f61f"; } - -.fa-triangle-circle-square::before { - content: "\f61f"; } - -.fa-share::before { - content: "\f064"; } - -.fa-arrow-turn-right::before { - content: "\f064"; } - -.fa-mail-forward::before { - content: "\f064"; } - -.fa-share-from-square::before { - content: "\f14d"; } - -.fa-share-square::before { - content: "\f14d"; } - -.fa-share-nodes::before { - content: "\f1e0"; } - -.fa-share-alt::before { - content: "\f1e0"; } - -.fa-sheet-plastic::before { - content: "\e571"; } - -.fa-shekel-sign::before { - content: "\f20b"; } - -.fa-ils::before { - content: "\f20b"; } - -.fa-shekel::before { - content: "\f20b"; } - -.fa-sheqel::before { - content: "\f20b"; } - -.fa-sheqel-sign::before { - content: "\f20b"; } - -.fa-shield::before { - content: "\f132"; } - -.fa-shield-blank::before { - content: "\f132"; } - -.fa-shield-cat::before { - content: "\e572"; } - -.fa-shield-dog::before { - content: "\e573"; } - -.fa-shield-halved::before { - content: "\f3ed"; } - -.fa-shield-alt::before { - content: "\f3ed"; } - -.fa-shield-heart::before { - content: "\e574"; } - -.fa-shield-virus::before { - content: "\e06c"; } - -.fa-ship::before { - content: "\f21a"; } - -.fa-shirt::before { - content: "\f553"; } - -.fa-t-shirt::before { - content: "\f553"; } - -.fa-tshirt::before { - content: "\f553"; } - -.fa-shoe-prints::before { - content: "\f54b"; } - -.fa-shop::before { - content: "\f54f"; } - -.fa-store-alt::before { - content: "\f54f"; } - -.fa-shop-lock::before { - content: "\e4a5"; } - -.fa-shop-slash::before { - content: "\e070"; } - -.fa-store-alt-slash::before { - content: "\e070"; } - -.fa-shower::before { - content: "\f2cc"; } - -.fa-shrimp::before { - content: "\e448"; } - -.fa-shuffle::before { - content: "\f074"; } - -.fa-random::before { - content: "\f074"; } - -.fa-shuttle-space::before { - content: "\f197"; } - -.fa-space-shuttle::before { - content: "\f197"; } - -.fa-sign-hanging::before { - content: "\f4d9"; } - -.fa-sign::before { - content: "\f4d9"; } - -.fa-signal::before { - content: "\f012"; } - -.fa-signal-5::before { - content: "\f012"; } - -.fa-signal-perfect::before { - content: "\f012"; } - -.fa-signature::before { - content: "\f5b7"; } - -.fa-signs-post::before { - content: "\f277"; } - -.fa-map-signs::before { - content: "\f277"; } - -.fa-sim-card::before { - content: "\f7c4"; } - -.fa-sink::before { - content: "\e06d"; } - -.fa-sitemap::before { - content: "\f0e8"; } - -.fa-skull::before { - content: "\f54c"; } - -.fa-skull-crossbones::before { - content: "\f714"; } - -.fa-slash::before { - content: "\f715"; } - -.fa-sleigh::before { - content: "\f7cc"; } - -.fa-sliders::before { - content: "\f1de"; } - -.fa-sliders-h::before { - content: "\f1de"; } - -.fa-smog::before { - content: "\f75f"; } - -.fa-smoking::before { - content: "\f48d"; } - -.fa-snowflake::before { - content: "\f2dc"; } - -.fa-snowman::before { - content: "\f7d0"; } - -.fa-snowplow::before { - content: "\f7d2"; } - -.fa-soap::before { - content: "\e06e"; } - -.fa-socks::before { - content: "\f696"; } - -.fa-solar-panel::before { - content: "\f5ba"; } - -.fa-sort::before { - content: "\f0dc"; } - -.fa-unsorted::before { - content: "\f0dc"; } - -.fa-sort-down::before { - content: "\f0dd"; } - -.fa-sort-desc::before { - content: "\f0dd"; } - -.fa-sort-up::before { - content: "\f0de"; } - -.fa-sort-asc::before { - content: "\f0de"; } - -.fa-spa::before { - content: "\f5bb"; } - -.fa-spaghetti-monster-flying::before { - content: "\f67b"; } - -.fa-pastafarianism::before { - content: "\f67b"; } - -.fa-spell-check::before { - content: "\f891"; } - -.fa-spider::before { - content: "\f717"; } - -.fa-spinner::before { - content: "\f110"; } - -.fa-splotch::before { - content: "\f5bc"; } - -.fa-spoon::before { - content: "\f2e5"; } - -.fa-utensil-spoon::before { - content: "\f2e5"; } - -.fa-spray-can::before { - content: "\f5bd"; } - -.fa-spray-can-sparkles::before { - content: "\f5d0"; } - -.fa-air-freshener::before { - content: "\f5d0"; } - -.fa-square::before { - content: "\f0c8"; } - -.fa-square-arrow-up-right::before { - content: "\f14c"; } - -.fa-external-link-square::before { - content: "\f14c"; } - -.fa-square-caret-down::before { - content: "\f150"; } - -.fa-caret-square-down::before { - content: "\f150"; } - -.fa-square-caret-left::before { - content: "\f191"; } - -.fa-caret-square-left::before { - content: "\f191"; } - -.fa-square-caret-right::before { - content: "\f152"; } - -.fa-caret-square-right::before { - content: "\f152"; } - -.fa-square-caret-up::before { - content: "\f151"; } - -.fa-caret-square-up::before { - content: "\f151"; } - -.fa-square-check::before { - content: "\f14a"; } - -.fa-check-square::before { - content: "\f14a"; } - -.fa-square-envelope::before { - content: "\f199"; } - -.fa-envelope-square::before { - content: "\f199"; } - -.fa-square-full::before { - content: "\f45c"; } - -.fa-square-h::before { - content: "\f0fd"; } - -.fa-h-square::before { - content: "\f0fd"; } - -.fa-square-minus::before { - content: "\f146"; } - -.fa-minus-square::before { - content: "\f146"; } - -.fa-square-nfi::before { - content: "\e576"; } - -.fa-square-parking::before { - content: "\f540"; } - -.fa-parking::before { - content: "\f540"; } - -.fa-square-pen::before { - content: "\f14b"; } - -.fa-pen-square::before { - content: "\f14b"; } - -.fa-pencil-square::before { - content: "\f14b"; } - -.fa-square-person-confined::before { - content: "\e577"; } - -.fa-square-phone::before { - content: "\f098"; } - -.fa-phone-square::before { - content: "\f098"; } - -.fa-square-phone-flip::before { - content: "\f87b"; } - -.fa-phone-square-alt::before { - content: "\f87b"; } - -.fa-square-plus::before { - content: "\f0fe"; } - -.fa-plus-square::before { - content: "\f0fe"; } - -.fa-square-poll-horizontal::before { - content: "\f682"; } - -.fa-poll-h::before { - content: "\f682"; } - -.fa-square-poll-vertical::before { - content: "\f681"; } - -.fa-poll::before { - content: "\f681"; } - -.fa-square-root-variable::before { - content: "\f698"; } - -.fa-square-root-alt::before { - content: "\f698"; } - -.fa-square-rss::before { - content: "\f143"; } - -.fa-rss-square::before { - content: "\f143"; } - -.fa-square-share-nodes::before { - content: "\f1e1"; } - -.fa-share-alt-square::before { - content: "\f1e1"; } - -.fa-square-up-right::before { - content: "\f360"; } - -.fa-external-link-square-alt::before { - content: "\f360"; } - -.fa-square-virus::before { - content: "\e578"; } - -.fa-square-xmark::before { - content: "\f2d3"; } - -.fa-times-square::before { - content: "\f2d3"; } - -.fa-xmark-square::before { - content: "\f2d3"; } - -.fa-staff-aesculapius::before { - content: "\e579"; } - -.fa-rod-asclepius::before { - content: "\e579"; } - -.fa-rod-snake::before { - content: "\e579"; } - -.fa-staff-snake::before { - content: "\e579"; } - -.fa-stairs::before { - content: "\e289"; } - -.fa-stamp::before { - content: "\f5bf"; } - -.fa-star::before { - content: "\f005"; } - -.fa-star-and-crescent::before { - content: "\f699"; } - -.fa-star-half::before { - content: "\f089"; } - -.fa-star-half-stroke::before { - content: "\f5c0"; } - -.fa-star-half-alt::before { - content: "\f5c0"; } - -.fa-star-of-david::before { - content: "\f69a"; } - -.fa-star-of-life::before { - content: "\f621"; } - -.fa-sterling-sign::before { - content: "\f154"; } - -.fa-gbp::before { - content: "\f154"; } - -.fa-pound-sign::before { - content: "\f154"; } - -.fa-stethoscope::before { - content: "\f0f1"; } - -.fa-stop::before { - content: "\f04d"; } - -.fa-stopwatch::before { - content: "\f2f2"; } - -.fa-stopwatch-20::before { - content: "\e06f"; } - -.fa-store::before { - content: "\f54e"; } - -.fa-store-slash::before { - content: "\e071"; } - -.fa-street-view::before { - content: "\f21d"; } - -.fa-strikethrough::before { - content: "\f0cc"; } - -.fa-stroopwafel::before { - content: "\f551"; } - -.fa-subscript::before { - content: "\f12c"; } - -.fa-suitcase::before { - content: "\f0f2"; } - -.fa-suitcase-medical::before { - content: "\f0fa"; } - -.fa-medkit::before { - content: "\f0fa"; } - -.fa-suitcase-rolling::before { - content: "\f5c1"; } - -.fa-sun::before { - content: "\f185"; } - -.fa-sun-plant-wilt::before { - content: "\e57a"; } - -.fa-superscript::before { - content: "\f12b"; } - -.fa-swatchbook::before { - content: "\f5c3"; } - -.fa-synagogue::before { - content: "\f69b"; } - -.fa-syringe::before { - content: "\f48e"; } - -.fa-t::before { - content: "\54"; } - -.fa-table::before { - content: "\f0ce"; } - -.fa-table-cells::before { - content: "\f00a"; } - -.fa-th::before { - content: "\f00a"; } - -.fa-table-cells-large::before { - content: "\f009"; } - -.fa-th-large::before { - content: "\f009"; } - -.fa-table-columns::before { - content: "\f0db"; } - -.fa-columns::before { - content: "\f0db"; } - -.fa-table-list::before { - content: "\f00b"; } - -.fa-th-list::before { - content: "\f00b"; } - -.fa-table-tennis-paddle-ball::before { - content: "\f45d"; } - -.fa-ping-pong-paddle-ball::before { - content: "\f45d"; } - -.fa-table-tennis::before { - content: "\f45d"; } - -.fa-tablet::before { - content: "\f3fb"; } - -.fa-tablet-android::before { - content: "\f3fb"; } - -.fa-tablet-button::before { - content: "\f10a"; } - -.fa-tablet-screen-button::before { - content: "\f3fa"; } - -.fa-tablet-alt::before { - content: "\f3fa"; } - -.fa-tablets::before { - content: "\f490"; } - -.fa-tachograph-digital::before { - content: "\f566"; } - -.fa-digital-tachograph::before { - content: "\f566"; } - -.fa-tag::before { - content: "\f02b"; } - -.fa-tags::before { - content: "\f02c"; } - -.fa-tape::before { - content: "\f4db"; } - -.fa-tarp::before { - content: "\e57b"; } - -.fa-tarp-droplet::before { - content: "\e57c"; } - -.fa-taxi::before { - content: "\f1ba"; } - -.fa-cab::before { - content: "\f1ba"; } - -.fa-teeth::before { - content: "\f62e"; } - -.fa-teeth-open::before { - content: "\f62f"; } - -.fa-temperature-arrow-down::before { - content: "\e03f"; } - -.fa-temperature-down::before { - content: "\e03f"; } - -.fa-temperature-arrow-up::before { - content: "\e040"; } - -.fa-temperature-up::before { - content: "\e040"; } - -.fa-temperature-empty::before { - content: "\f2cb"; } - -.fa-temperature-0::before { - content: "\f2cb"; } - -.fa-thermometer-0::before { - content: "\f2cb"; } - -.fa-thermometer-empty::before { - content: "\f2cb"; } - -.fa-temperature-full::before { - content: "\f2c7"; } - -.fa-temperature-4::before { - content: "\f2c7"; } - -.fa-thermometer-4::before { - content: "\f2c7"; } - -.fa-thermometer-full::before { - content: "\f2c7"; } - -.fa-temperature-half::before { - content: "\f2c9"; } - -.fa-temperature-2::before { - content: "\f2c9"; } - -.fa-thermometer-2::before { - content: "\f2c9"; } - -.fa-thermometer-half::before { - content: "\f2c9"; } - -.fa-temperature-high::before { - content: "\f769"; } - -.fa-temperature-low::before { - content: "\f76b"; } - -.fa-temperature-quarter::before { - content: "\f2ca"; } - -.fa-temperature-1::before { - content: "\f2ca"; } - -.fa-thermometer-1::before { - content: "\f2ca"; } - -.fa-thermometer-quarter::before { - content: "\f2ca"; } - -.fa-temperature-three-quarters::before { - content: "\f2c8"; } - -.fa-temperature-3::before { - content: "\f2c8"; } - -.fa-thermometer-3::before { - content: "\f2c8"; } - -.fa-thermometer-three-quarters::before { - content: "\f2c8"; } - -.fa-tenge-sign::before { - content: "\f7d7"; } - -.fa-tenge::before { - content: "\f7d7"; } - -.fa-tent::before { - content: "\e57d"; } - -.fa-tent-arrow-down-to-line::before { - content: "\e57e"; } - -.fa-tent-arrow-left-right::before { - content: "\e57f"; } - -.fa-tent-arrow-turn-left::before { - content: "\e580"; } - -.fa-tent-arrows-down::before { - content: "\e581"; } - -.fa-tents::before { - content: "\e582"; } - -.fa-terminal::before { - content: "\f120"; } - -.fa-text-height::before { - content: "\f034"; } - -.fa-text-slash::before { - content: "\f87d"; } - -.fa-remove-format::before { - content: "\f87d"; } - -.fa-text-width::before { - content: "\f035"; } - -.fa-thermometer::before { - content: "\f491"; } - -.fa-thumbs-down::before { - content: "\f165"; } - -.fa-thumbs-up::before { - content: "\f164"; } - -.fa-thumbtack::before { - content: "\f08d"; } - -.fa-thumb-tack::before { - content: "\f08d"; } - -.fa-ticket::before { - content: "\f145"; } - -.fa-ticket-simple::before { - content: "\f3ff"; } - -.fa-ticket-alt::before { - content: "\f3ff"; } - -.fa-timeline::before { - content: "\e29c"; } - -.fa-toggle-off::before { - content: "\f204"; } - -.fa-toggle-on::before { - content: "\f205"; } - -.fa-toilet::before { - content: "\f7d8"; } - -.fa-toilet-paper::before { - content: "\f71e"; } - -.fa-toilet-paper-slash::before { - content: "\e072"; } - -.fa-toilet-portable::before { - content: "\e583"; } - -.fa-toilets-portable::before { - content: "\e584"; } - -.fa-toolbox::before { - content: "\f552"; } - -.fa-tooth::before { - content: "\f5c9"; } - -.fa-torii-gate::before { - content: "\f6a1"; } - -.fa-tornado::before { - content: "\f76f"; } - -.fa-tower-broadcast::before { - content: "\f519"; } - -.fa-broadcast-tower::before { - content: "\f519"; } - -.fa-tower-cell::before { - content: "\e585"; } - -.fa-tower-observation::before { - content: "\e586"; } - -.fa-tractor::before { - content: "\f722"; } - -.fa-trademark::before { - content: "\f25c"; } - -.fa-traffic-light::before { - content: "\f637"; } - -.fa-trailer::before { - content: "\e041"; } - -.fa-train::before { - content: "\f238"; } - -.fa-train-subway::before { - content: "\f239"; } - -.fa-subway::before { - content: "\f239"; } - -.fa-train-tram::before { - content: "\f7da"; } - -.fa-tram::before { - content: "\f7da"; } - -.fa-transgender::before { - content: "\f225"; } - -.fa-transgender-alt::before { - content: "\f225"; } - -.fa-trash::before { - content: "\f1f8"; } - -.fa-trash-arrow-up::before { - content: "\f829"; } - -.fa-trash-restore::before { - content: "\f829"; } - -.fa-trash-can::before { - content: "\f2ed"; } - -.fa-trash-alt::before { - content: "\f2ed"; } - -.fa-trash-can-arrow-up::before { - content: "\f82a"; } - -.fa-trash-restore-alt::before { - content: "\f82a"; } - -.fa-tree::before { - content: "\f1bb"; } - -.fa-tree-city::before { - content: "\e587"; } - -.fa-triangle-exclamation::before { - content: "\f071"; } - -.fa-exclamation-triangle::before { - content: "\f071"; } - -.fa-warning::before { - content: "\f071"; } - -.fa-trophy::before { - content: "\f091"; } - -.fa-trowel::before { - content: "\e589"; } - -.fa-trowel-bricks::before { - content: "\e58a"; } - -.fa-truck::before { - content: "\f0d1"; } - -.fa-truck-arrow-right::before { - content: "\e58b"; } - -.fa-truck-droplet::before { - content: "\e58c"; } - -.fa-truck-fast::before { - content: "\f48b"; } - -.fa-shipping-fast::before { - content: "\f48b"; } - -.fa-truck-field::before { - content: "\e58d"; } - -.fa-truck-field-un::before { - content: "\e58e"; } - -.fa-truck-front::before { - content: "\e2b7"; } - -.fa-truck-medical::before { - content: "\f0f9"; } - -.fa-ambulance::before { - content: "\f0f9"; } - -.fa-truck-monster::before { - content: "\f63b"; } - -.fa-truck-moving::before { - content: "\f4df"; } - -.fa-truck-pickup::before { - content: "\f63c"; } - -.fa-truck-plane::before { - content: "\e58f"; } - -.fa-truck-ramp-box::before { - content: "\f4de"; } - -.fa-truck-loading::before { - content: "\f4de"; } - -.fa-tty::before { - content: "\f1e4"; } - -.fa-teletype::before { - content: "\f1e4"; } - -.fa-turkish-lira-sign::before { - content: "\e2bb"; } - -.fa-try::before { - content: "\e2bb"; } - -.fa-turkish-lira::before { - content: "\e2bb"; } - -.fa-turn-down::before { - content: "\f3be"; } - -.fa-level-down-alt::before { - content: "\f3be"; } - -.fa-turn-up::before { - content: "\f3bf"; } - -.fa-level-up-alt::before { - content: "\f3bf"; } - -.fa-tv::before { - content: "\f26c"; } - -.fa-television::before { - content: "\f26c"; } - -.fa-tv-alt::before { - content: "\f26c"; } - -.fa-u::before { - content: "\55"; } - -.fa-umbrella::before { - content: "\f0e9"; } - -.fa-umbrella-beach::before { - content: "\f5ca"; } - -.fa-underline::before { - content: "\f0cd"; } - -.fa-universal-access::before { - content: "\f29a"; } - -.fa-unlock::before { - content: "\f09c"; } - -.fa-unlock-keyhole::before { - content: "\f13e"; } - -.fa-unlock-alt::before { - content: "\f13e"; } - -.fa-up-down::before { - content: "\f338"; } - -.fa-arrows-alt-v::before { - content: "\f338"; } - -.fa-up-down-left-right::before { - content: "\f0b2"; } - -.fa-arrows-alt::before { - content: "\f0b2"; } - -.fa-up-long::before { - content: "\f30c"; } - -.fa-long-arrow-alt-up::before { - content: "\f30c"; } - -.fa-up-right-and-down-left-from-center::before { - content: "\f424"; } - -.fa-expand-alt::before { - content: "\f424"; } - -.fa-up-right-from-square::before { - content: "\f35d"; } - -.fa-external-link-alt::before { - content: "\f35d"; } - -.fa-upload::before { - content: "\f093"; } - -.fa-user::before { - content: "\f007"; } - -.fa-user-astronaut::before { - content: "\f4fb"; } - -.fa-user-check::before { - content: "\f4fc"; } - -.fa-user-clock::before { - content: "\f4fd"; } - -.fa-user-doctor::before { - content: "\f0f0"; } - -.fa-user-md::before { - content: "\f0f0"; } - -.fa-user-gear::before { - content: "\f4fe"; } - -.fa-user-cog::before { - content: "\f4fe"; } - -.fa-user-graduate::before { - content: "\f501"; } - -.fa-user-group::before { - content: "\f500"; } - -.fa-user-friends::before { - content: "\f500"; } - -.fa-user-injured::before { - content: "\f728"; } - -.fa-user-large::before { - content: "\f406"; } - -.fa-user-alt::before { - content: "\f406"; } - -.fa-user-large-slash::before { - content: "\f4fa"; } - -.fa-user-alt-slash::before { - content: "\f4fa"; } - -.fa-user-lock::before { - content: "\f502"; } - -.fa-user-minus::before { - content: "\f503"; } - -.fa-user-ninja::before { - content: "\f504"; } - -.fa-user-nurse::before { - content: "\f82f"; } - -.fa-user-pen::before { - content: "\f4ff"; } - -.fa-user-edit::before { - content: "\f4ff"; } - -.fa-user-plus::before { - content: "\f234"; } - -.fa-user-secret::before { - content: "\f21b"; } - -.fa-user-shield::before { - content: "\f505"; } - -.fa-user-slash::before { - content: "\f506"; } - -.fa-user-tag::before { - content: "\f507"; } - -.fa-user-tie::before { - content: "\f508"; } - -.fa-user-xmark::before { - content: "\f235"; } - -.fa-user-times::before { - content: "\f235"; } - -.fa-users::before { - content: "\f0c0"; } - -.fa-users-between-lines::before { - content: "\e591"; } - -.fa-users-gear::before { - content: "\f509"; } - -.fa-users-cog::before { - content: "\f509"; } - -.fa-users-line::before { - content: "\e592"; } - -.fa-users-rays::before { - content: "\e593"; } - -.fa-users-rectangle::before { - content: "\e594"; } - -.fa-users-slash::before { - content: "\e073"; } - -.fa-users-viewfinder::before { - content: "\e595"; } - -.fa-utensils::before { - content: "\f2e7"; } - -.fa-cutlery::before { - content: "\f2e7"; } - -.fa-v::before { - content: "\56"; } - -.fa-van-shuttle::before { - content: "\f5b6"; } - -.fa-shuttle-van::before { - content: "\f5b6"; } - -.fa-vault::before { - content: "\e2c5"; } - -.fa-vector-square::before { - content: "\f5cb"; } - -.fa-venus::before { - content: "\f221"; } - -.fa-venus-double::before { - content: "\f226"; } - -.fa-venus-mars::before { - content: "\f228"; } - -.fa-vest::before { - content: "\e085"; } - -.fa-vest-patches::before { - content: "\e086"; } - -.fa-vial::before { - content: "\f492"; } - -.fa-vial-circle-check::before { - content: "\e596"; } - -.fa-vial-virus::before { - content: "\e597"; } - -.fa-vials::before { - content: "\f493"; } - -.fa-video::before { - content: "\f03d"; } - -.fa-video-camera::before { - content: "\f03d"; } - -.fa-video-slash::before { - content: "\f4e2"; } - -.fa-vihara::before { - content: "\f6a7"; } - -.fa-virus::before { - content: "\e074"; } - -.fa-virus-covid::before { - content: "\e4a8"; } - -.fa-virus-covid-slash::before { - content: "\e4a9"; } - -.fa-virus-slash::before { - content: "\e075"; } - -.fa-viruses::before { - content: "\e076"; } - -.fa-voicemail::before { - content: "\f897"; } - -.fa-volcano::before { - content: "\f770"; } - -.fa-volleyball::before { - content: "\f45f"; } - -.fa-volleyball-ball::before { - content: "\f45f"; } - -.fa-volume-high::before { - content: "\f028"; } - -.fa-volume-up::before { - content: "\f028"; } - -.fa-volume-low::before { - content: "\f027"; } - -.fa-volume-down::before { - content: "\f027"; } - -.fa-volume-off::before { - content: "\f026"; } - -.fa-volume-xmark::before { - content: "\f6a9"; } - -.fa-volume-mute::before { - content: "\f6a9"; } - -.fa-volume-times::before { - content: "\f6a9"; } - -.fa-vr-cardboard::before { - content: "\f729"; } - -.fa-w::before { - content: "\57"; } - -.fa-walkie-talkie::before { - content: "\f8ef"; } - -.fa-wallet::before { - content: "\f555"; } - -.fa-wand-magic::before { - content: "\f0d0"; } - -.fa-magic::before { - content: "\f0d0"; } - -.fa-wand-magic-sparkles::before { - content: "\e2ca"; } - -.fa-magic-wand-sparkles::before { - content: "\e2ca"; } - -.fa-wand-sparkles::before { - content: "\f72b"; } - -.fa-warehouse::before { - content: "\f494"; } - -.fa-water::before { - content: "\f773"; } - -.fa-water-ladder::before { - content: "\f5c5"; } - -.fa-ladder-water::before { - content: "\f5c5"; } - -.fa-swimming-pool::before { - content: "\f5c5"; } - -.fa-wave-square::before { - content: "\f83e"; } - -.fa-weight-hanging::before { - content: "\f5cd"; } - -.fa-weight-scale::before { - content: "\f496"; } - -.fa-weight::before { - content: "\f496"; } - -.fa-wheat-awn::before { - content: "\e2cd"; } - -.fa-wheat-alt::before { - content: "\e2cd"; } - -.fa-wheat-awn-circle-exclamation::before { - content: "\e598"; } - -.fa-wheelchair::before { - content: "\f193"; } - -.fa-wheelchair-move::before { - content: "\e2ce"; } - -.fa-wheelchair-alt::before { - content: "\e2ce"; } - -.fa-whiskey-glass::before { - content: "\f7a0"; } - -.fa-glass-whiskey::before { - content: "\f7a0"; } - -.fa-wifi::before { - content: "\f1eb"; } - -.fa-wifi-3::before { - content: "\f1eb"; } - -.fa-wifi-strong::before { - content: "\f1eb"; } - -.fa-wind::before { - content: "\f72e"; } - -.fa-window-maximize::before { - content: "\f2d0"; } - -.fa-window-minimize::before { - content: "\f2d1"; } - -.fa-window-restore::before { - content: "\f2d2"; } - -.fa-wine-bottle::before { - content: "\f72f"; } - -.fa-wine-glass::before { - content: "\f4e3"; } - -.fa-wine-glass-empty::before { - content: "\f5ce"; } - -.fa-wine-glass-alt::before { - content: "\f5ce"; } - -.fa-won-sign::before { - content: "\f159"; } - -.fa-krw::before { - content: "\f159"; } - -.fa-won::before { - content: "\f159"; } - -.fa-worm::before { - content: "\e599"; } - -.fa-wrench::before { - content: "\f0ad"; } - -.fa-x::before { - content: "\58"; } - -.fa-x-ray::before { - content: "\f497"; } - -.fa-xmark::before { - content: "\f00d"; } - -.fa-close::before { - content: "\f00d"; } - -.fa-multiply::before { - content: "\f00d"; } - -.fa-remove::before { - content: "\f00d"; } - -.fa-times::before { - content: "\f00d"; } - -.fa-xmarks-lines::before { - content: "\e59a"; } - -.fa-y::before { - content: "\59"; } - -.fa-yen-sign::before { - content: "\f157"; } - -.fa-cny::before { - content: "\f157"; } - -.fa-jpy::before { - content: "\f157"; } - -.fa-rmb::before { - content: "\f157"; } - -.fa-yen::before { - content: "\f157"; } - -.fa-yin-yang::before { - content: "\f6ad"; } - -.fa-z::before { - content: "\5a"; } - -.sr-only, -.fa-sr-only { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } - -.sr-only-focusable:not(:focus), -.fa-sr-only-focusable:not(:focus) { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } -:root, :host { - --fa-font-brands: normal 400 1em/1 "Font Awesome 6 Brands"; } - -@font-face { - font-family: 'Font Awesome 6 Brands'; - font-style: normal; - font-weight: 400; - font-display: block; - src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } - -.fab, -.fa-brands { - font-family: 'Font Awesome 6 Brands'; - font-weight: 400; } - -.fa-42-group:before { - content: "\e080"; } - -.fa-innosoft:before { - content: "\e080"; } - -.fa-500px:before { - content: "\f26e"; } - -.fa-accessible-icon:before { - content: "\f368"; } - -.fa-accusoft:before { - content: "\f369"; } - -.fa-adn:before { - content: "\f170"; } - -.fa-adversal:before { - content: "\f36a"; } - -.fa-affiliatetheme:before { - content: "\f36b"; } - -.fa-airbnb:before { - content: "\f834"; } - -.fa-algolia:before { - content: "\f36c"; } - -.fa-alipay:before { - content: "\f642"; } - -.fa-amazon:before { - content: "\f270"; } - -.fa-amazon-pay:before { - content: "\f42c"; } - -.fa-amilia:before { - content: "\f36d"; } - -.fa-android:before { - content: "\f17b"; } - -.fa-angellist:before { - content: "\f209"; } - -.fa-angrycreative:before { - content: "\f36e"; } - -.fa-angular:before { - content: "\f420"; } - -.fa-app-store:before { - content: "\f36f"; } - -.fa-app-store-ios:before { - content: "\f370"; } - -.fa-apper:before { - content: "\f371"; } - -.fa-apple:before { - content: "\f179"; } - -.fa-apple-pay:before { - content: "\f415"; } - -.fa-artstation:before { - content: "\f77a"; } - -.fa-asymmetrik:before { - content: "\f372"; } - -.fa-atlassian:before { - content: "\f77b"; } - -.fa-audible:before { - content: "\f373"; } - -.fa-autoprefixer:before { - content: "\f41c"; } - -.fa-avianex:before { - content: "\f374"; } - -.fa-aviato:before { - content: "\f421"; } - -.fa-aws:before { - content: "\f375"; } - -.fa-bandcamp:before { - content: "\f2d5"; } - -.fa-battle-net:before { - content: "\f835"; } - -.fa-behance:before { - content: "\f1b4"; } - -.fa-behance-square:before { - content: "\f1b5"; } - -.fa-bilibili:before { - content: "\e3d9"; } - -.fa-bimobject:before { - content: "\f378"; } - -.fa-bitbucket:before { - content: "\f171"; } - -.fa-bitcoin:before { - content: "\f379"; } - -.fa-bity:before { - content: "\f37a"; } - -.fa-black-tie:before { - content: "\f27e"; } - -.fa-blackberry:before { - content: "\f37b"; } - -.fa-blogger:before { - content: "\f37c"; } - -.fa-blogger-b:before { - content: "\f37d"; } - -.fa-bluetooth:before { - content: "\f293"; } - -.fa-bluetooth-b:before { - content: "\f294"; } - -.fa-bootstrap:before { - content: "\f836"; } - -.fa-bots:before { - content: "\e340"; } - -.fa-btc:before { - content: "\f15a"; } - -.fa-buffer:before { - content: "\f837"; } - -.fa-buromobelexperte:before { - content: "\f37f"; } - -.fa-buy-n-large:before { - content: "\f8a6"; } - -.fa-buysellads:before { - content: "\f20d"; } - -.fa-canadian-maple-leaf:before { - content: "\f785"; } - -.fa-cc-amazon-pay:before { - content: "\f42d"; } - -.fa-cc-amex:before { - content: "\f1f3"; } - -.fa-cc-apple-pay:before { - content: "\f416"; } - -.fa-cc-diners-club:before { - content: "\f24c"; } - -.fa-cc-discover:before { - content: "\f1f2"; } - -.fa-cc-jcb:before { - content: "\f24b"; } - -.fa-cc-mastercard:before { - content: "\f1f1"; } - -.fa-cc-paypal:before { - content: "\f1f4"; } - -.fa-cc-stripe:before { - content: "\f1f5"; } - -.fa-cc-visa:before { - content: "\f1f0"; } - -.fa-centercode:before { - content: "\f380"; } - -.fa-centos:before { - content: "\f789"; } - -.fa-chrome:before { - content: "\f268"; } - -.fa-chromecast:before { - content: "\f838"; } - -.fa-cloudflare:before { - content: "\e07d"; } - -.fa-cloudscale:before { - content: "\f383"; } - -.fa-cloudsmith:before { - content: "\f384"; } - -.fa-cloudversify:before { - content: "\f385"; } - -.fa-cmplid:before { - content: "\e360"; } - -.fa-codepen:before { - content: "\f1cb"; } - -.fa-codiepie:before { - content: "\f284"; } - -.fa-confluence:before { - content: "\f78d"; } - -.fa-connectdevelop:before { - content: "\f20e"; } - -.fa-contao:before { - content: "\f26d"; } - -.fa-cotton-bureau:before { - content: "\f89e"; } - -.fa-cpanel:before { - content: "\f388"; } - -.fa-creative-commons:before { - content: "\f25e"; } - -.fa-creative-commons-by:before { - content: "\f4e7"; } - -.fa-creative-commons-nc:before { - content: "\f4e8"; } - -.fa-creative-commons-nc-eu:before { - content: "\f4e9"; } - -.fa-creative-commons-nc-jp:before { - content: "\f4ea"; } - -.fa-creative-commons-nd:before { - content: "\f4eb"; } - -.fa-creative-commons-pd:before { - content: "\f4ec"; } - -.fa-creative-commons-pd-alt:before { - content: "\f4ed"; } - -.fa-creative-commons-remix:before { - content: "\f4ee"; } - -.fa-creative-commons-sa:before { - content: "\f4ef"; } - -.fa-creative-commons-sampling:before { - content: "\f4f0"; } - -.fa-creative-commons-sampling-plus:before { - content: "\f4f1"; } - -.fa-creative-commons-share:before { - content: "\f4f2"; } - -.fa-creative-commons-zero:before { - content: "\f4f3"; } - -.fa-critical-role:before { - content: "\f6c9"; } - -.fa-css3:before { - content: "\f13c"; } - -.fa-css3-alt:before { - content: "\f38b"; } - -.fa-cuttlefish:before { - content: "\f38c"; } - -.fa-d-and-d:before { - content: "\f38d"; } - -.fa-d-and-d-beyond:before { - content: "\f6ca"; } - -.fa-dailymotion:before { - content: "\e052"; } - -.fa-dashcube:before { - content: "\f210"; } - -.fa-deezer:before { - content: "\e077"; } - -.fa-delicious:before { - content: "\f1a5"; } - -.fa-deploydog:before { - content: "\f38e"; } - -.fa-deskpro:before { - content: "\f38f"; } - -.fa-dev:before { - content: "\f6cc"; } - -.fa-deviantart:before { - content: "\f1bd"; } - -.fa-dhl:before { - content: "\f790"; } - -.fa-diaspora:before { - content: "\f791"; } - -.fa-digg:before { - content: "\f1a6"; } - -.fa-digital-ocean:before { - content: "\f391"; } - -.fa-discord:before { - content: "\f392"; } - -.fa-discourse:before { - content: "\f393"; } - -.fa-dochub:before { - content: "\f394"; } - -.fa-docker:before { - content: "\f395"; } - -.fa-draft2digital:before { - content: "\f396"; } - -.fa-dribbble:before { - content: "\f17d"; } - -.fa-dribbble-square:before { - content: "\f397"; } - -.fa-dropbox:before { - content: "\f16b"; } - -.fa-drupal:before { - content: "\f1a9"; } - -.fa-dyalog:before { - content: "\f399"; } - -.fa-earlybirds:before { - content: "\f39a"; } - -.fa-ebay:before { - content: "\f4f4"; } - -.fa-edge:before { - content: "\f282"; } - -.fa-edge-legacy:before { - content: "\e078"; } - -.fa-elementor:before { - content: "\f430"; } - -.fa-ello:before { - content: "\f5f1"; } - -.fa-ember:before { - content: "\f423"; } - -.fa-empire:before { - content: "\f1d1"; } - -.fa-envira:before { - content: "\f299"; } - -.fa-erlang:before { - content: "\f39d"; } - -.fa-ethereum:before { - content: "\f42e"; } - -.fa-etsy:before { - content: "\f2d7"; } - -.fa-evernote:before { - content: "\f839"; } - -.fa-expeditedssl:before { - content: "\f23e"; } - -.fa-facebook:before { - content: "\f09a"; } - -.fa-facebook-f:before { - content: "\f39e"; } - -.fa-facebook-messenger:before { - content: "\f39f"; } - -.fa-facebook-square:before { - content: "\f082"; } - -.fa-fantasy-flight-games:before { - content: "\f6dc"; } - -.fa-fedex:before { - content: "\f797"; } - -.fa-fedora:before { - content: "\f798"; } - -.fa-figma:before { - content: "\f799"; } - -.fa-firefox:before { - content: "\f269"; } - -.fa-firefox-browser:before { - content: "\e007"; } - -.fa-first-order:before { - content: "\f2b0"; } - -.fa-first-order-alt:before { - content: "\f50a"; } - -.fa-firstdraft:before { - content: "\f3a1"; } - -.fa-flickr:before { - content: "\f16e"; } - -.fa-flipboard:before { - content: "\f44d"; } - -.fa-fly:before { - content: "\f417"; } - -.fa-font-awesome:before { - content: "\f2b4"; } - -.fa-font-awesome-flag:before { - content: "\f2b4"; } - -.fa-font-awesome-logo-full:before { - content: "\f2b4"; } - -.fa-fonticons:before { - content: "\f280"; } - -.fa-fonticons-fi:before { - content: "\f3a2"; } - -.fa-fort-awesome:before { - content: "\f286"; } - -.fa-fort-awesome-alt:before { - content: "\f3a3"; } - -.fa-forumbee:before { - content: "\f211"; } - -.fa-foursquare:before { - content: "\f180"; } - -.fa-free-code-camp:before { - content: "\f2c5"; } - -.fa-freebsd:before { - content: "\f3a4"; } - -.fa-fulcrum:before { - content: "\f50b"; } - -.fa-galactic-republic:before { - content: "\f50c"; } - -.fa-galactic-senate:before { - content: "\f50d"; } - -.fa-get-pocket:before { - content: "\f265"; } - -.fa-gg:before { - content: "\f260"; } - -.fa-gg-circle:before { - content: "\f261"; } - -.fa-git:before { - content: "\f1d3"; } - -.fa-git-alt:before { - content: "\f841"; } - -.fa-git-square:before { - content: "\f1d2"; } - -.fa-github:before { - content: "\f09b"; } - -.fa-github-alt:before { - content: "\f113"; } - -.fa-github-square:before { - content: "\f092"; } - -.fa-gitkraken:before { - content: "\f3a6"; } - -.fa-gitlab:before { - content: "\f296"; } - -.fa-gitter:before { - content: "\f426"; } - -.fa-glide:before { - content: "\f2a5"; } - -.fa-glide-g:before { - content: "\f2a6"; } - -.fa-gofore:before { - content: "\f3a7"; } - -.fa-golang:before { - content: "\e40f"; } - -.fa-goodreads:before { - content: "\f3a8"; } - -.fa-goodreads-g:before { - content: "\f3a9"; } - -.fa-google:before { - content: "\f1a0"; } - -.fa-google-drive:before { - content: "\f3aa"; } - -.fa-google-pay:before { - content: "\e079"; } - -.fa-google-play:before { - content: "\f3ab"; } - -.fa-google-plus:before { - content: "\f2b3"; } - -.fa-google-plus-g:before { - content: "\f0d5"; } - -.fa-google-plus-square:before { - content: "\f0d4"; } - -.fa-google-wallet:before { - content: "\f1ee"; } - -.fa-gratipay:before { - content: "\f184"; } - -.fa-grav:before { - content: "\f2d6"; } - -.fa-gripfire:before { - content: "\f3ac"; } - -.fa-grunt:before { - content: "\f3ad"; } - -.fa-guilded:before { - content: "\e07e"; } - -.fa-gulp:before { - content: "\f3ae"; } - -.fa-hacker-news:before { - content: "\f1d4"; } - -.fa-hacker-news-square:before { - content: "\f3af"; } - -.fa-hackerrank:before { - content: "\f5f7"; } - -.fa-hashnode:before { - content: "\e499"; } - -.fa-hips:before { - content: "\f452"; } - -.fa-hire-a-helper:before { - content: "\f3b0"; } - -.fa-hive:before { - content: "\e07f"; } - -.fa-hooli:before { - content: "\f427"; } - -.fa-hornbill:before { - content: "\f592"; } - -.fa-hotjar:before { - content: "\f3b1"; } - -.fa-houzz:before { - content: "\f27c"; } - -.fa-html5:before { - content: "\f13b"; } - -.fa-hubspot:before { - content: "\f3b2"; } - -.fa-ideal:before { - content: "\e013"; } - -.fa-imdb:before { - content: "\f2d8"; } - -.fa-instagram:before { - content: "\f16d"; } - -.fa-instagram-square:before { - content: "\e055"; } - -.fa-instalod:before { - content: "\e081"; } - -.fa-intercom:before { - content: "\f7af"; } - -.fa-internet-explorer:before { - content: "\f26b"; } - -.fa-invision:before { - content: "\f7b0"; } - -.fa-ioxhost:before { - content: "\f208"; } - -.fa-itch-io:before { - content: "\f83a"; } - -.fa-itunes:before { - content: "\f3b4"; } - -.fa-itunes-note:before { - content: "\f3b5"; } - -.fa-java:before { - content: "\f4e4"; } - -.fa-jedi-order:before { - content: "\f50e"; } - -.fa-jenkins:before { - content: "\f3b6"; } - -.fa-jira:before { - content: "\f7b1"; } - -.fa-joget:before { - content: "\f3b7"; } - -.fa-joomla:before { - content: "\f1aa"; } - -.fa-js:before { - content: "\f3b8"; } - -.fa-js-square:before { - content: "\f3b9"; } - -.fa-jsfiddle:before { - content: "\f1cc"; } - -.fa-kaggle:before { - content: "\f5fa"; } - -.fa-keybase:before { - content: "\f4f5"; } - -.fa-keycdn:before { - content: "\f3ba"; } - -.fa-kickstarter:before { - content: "\f3bb"; } - -.fa-kickstarter-k:before { - content: "\f3bc"; } - -.fa-korvue:before { - content: "\f42f"; } - -.fa-laravel:before { - content: "\f3bd"; } - -.fa-lastfm:before { - content: "\f202"; } - -.fa-lastfm-square:before { - content: "\f203"; } - -.fa-leanpub:before { - content: "\f212"; } - -.fa-less:before { - content: "\f41d"; } - -.fa-line:before { - content: "\f3c0"; } - -.fa-linkedin:before { - content: "\f08c"; } - -.fa-linkedin-in:before { - content: "\f0e1"; } - -.fa-linode:before { - content: "\f2b8"; } - -.fa-linux:before { - content: "\f17c"; } - -.fa-lyft:before { - content: "\f3c3"; } - -.fa-magento:before { - content: "\f3c4"; } - -.fa-mailchimp:before { - content: "\f59e"; } - -.fa-mandalorian:before { - content: "\f50f"; } - -.fa-markdown:before { - content: "\f60f"; } - -.fa-mastodon:before { - content: "\f4f6"; } - -.fa-maxcdn:before { - content: "\f136"; } - -.fa-mdb:before { - content: "\f8ca"; } - -.fa-medapps:before { - content: "\f3c6"; } - -.fa-medium:before { - content: "\f23a"; } - -.fa-medium-m:before { - content: "\f23a"; } - -.fa-medrt:before { - content: "\f3c8"; } - -.fa-meetup:before { - content: "\f2e0"; } - -.fa-megaport:before { - content: "\f5a3"; } - -.fa-mendeley:before { - content: "\f7b3"; } - -.fa-microblog:before { - content: "\e01a"; } - -.fa-microsoft:before { - content: "\f3ca"; } - -.fa-mix:before { - content: "\f3cb"; } - -.fa-mixcloud:before { - content: "\f289"; } - -.fa-mixer:before { - content: "\e056"; } - -.fa-mizuni:before { - content: "\f3cc"; } - -.fa-modx:before { - content: "\f285"; } - -.fa-monero:before { - content: "\f3d0"; } - -.fa-napster:before { - content: "\f3d2"; } - -.fa-neos:before { - content: "\f612"; } - -.fa-nfc-directional:before { - content: "\e530"; } - -.fa-nfc-symbol:before { - content: 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deleted file mode 100644 index 3939135..0000000 --- a/_extensions/cambiotraining/fontawesome/fontawesome 2.lua +++ /dev/null @@ -1,77 +0,0 @@ -local function ensureLatexDeps() - quarto.doc.useLatexPackage("fontawesome5") -end - -local function ensureHtmlDeps() - quarto.doc.addHtmlDependency({ - name = 'fontawesome6', - version = '0.1.0', - stylesheets = {'assets/css/all.css', 'assets/css/latex-fontsize.css'} - }) -end - -local function isEmpty(s) - return s == nil or s == '' -end - -local function isValidSize(size) - local validSizes = { - "tiny", - "scriptsize", - "footnotesize", - "small", - "normalsize", - "large", - "Large", - "LARGE", - "huge", - "Huge" - } - for _, v in ipairs(validSizes) do - if v == size then - return size - end - end - return "" -end - -return { - ["fa"] = function(args, kwargs) - - local group = "solid" - local icon = pandoc.utils.stringify(args[1]) - if #args > 1 then - group = icon - icon = pandoc.utils.stringify(args[2]) - end - - local title = pandoc.utils.stringify(kwargs["title"]) - if not isEmpty(title) then - title = " title=\"" .. title .. "\"" - end - - local size = pandoc.utils.stringify(kwargs["size"]) - - -- detect html (excluding epub which won't handle fa) - if quarto.doc.isFormat("html:js") then - ensureHtmlDeps() - if not isEmpty(size) then - size = " fa-" .. size - end - return pandoc.RawInline( - 'html', - "" - ) - -- detect pdf / beamer / latex / etc - elseif quarto.doc.isFormat("pdf") then - ensureLatexDeps() - if isEmpty(isValidSize(size)) then - return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") - else - return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") - end - else - return pandoc.Null() - end - end -} diff --git a/_extensions/cambiotraining/fontawesome/fontawesome 3.lua b/_extensions/cambiotraining/fontawesome/fontawesome 3.lua deleted file mode 100644 index 3939135..0000000 --- a/_extensions/cambiotraining/fontawesome/fontawesome 3.lua +++ /dev/null @@ -1,77 +0,0 @@ -local function ensureLatexDeps() - quarto.doc.useLatexPackage("fontawesome5") -end - -local function ensureHtmlDeps() - quarto.doc.addHtmlDependency({ - name = 'fontawesome6', - version = '0.1.0', - stylesheets = {'assets/css/all.css', 'assets/css/latex-fontsize.css'} - }) -end - -local function isEmpty(s) - return s == nil or s == '' -end - -local function isValidSize(size) - local validSizes = { - "tiny", - "scriptsize", - "footnotesize", - "small", - "normalsize", - "large", - "Large", - "LARGE", - "huge", - "Huge" - } - for _, v in ipairs(validSizes) do - if v == size then - return size - end - end - return "" -end - -return { - ["fa"] = function(args, kwargs) - - local group = "solid" - local icon = pandoc.utils.stringify(args[1]) - if #args > 1 then - group = icon - icon = pandoc.utils.stringify(args[2]) - end - - local title = pandoc.utils.stringify(kwargs["title"]) - if not isEmpty(title) then - title = " title=\"" .. title .. "\"" - end - - local size = pandoc.utils.stringify(kwargs["size"]) - - -- detect html (excluding epub which won't handle fa) - if quarto.doc.isFormat("html:js") then - ensureHtmlDeps() - if not isEmpty(size) then - size = " fa-" .. size - end - return pandoc.RawInline( - 'html', - "" - ) - -- detect pdf / beamer / latex / etc - elseif quarto.doc.isFormat("pdf") then - ensureLatexDeps() - if isEmpty(isValidSize(size)) then - return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") - else - return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") - end - else - return pandoc.Null() - end - end -} diff --git a/_extensions/cambiotraining/fontawesome/fontawesome 4.lua b/_extensions/cambiotraining/fontawesome/fontawesome 4.lua deleted file mode 100644 index 3939135..0000000 --- a/_extensions/cambiotraining/fontawesome/fontawesome 4.lua +++ /dev/null @@ -1,77 +0,0 @@ -local function ensureLatexDeps() - quarto.doc.useLatexPackage("fontawesome5") -end - -local function ensureHtmlDeps() - quarto.doc.addHtmlDependency({ - name = 'fontawesome6', - version = '0.1.0', - stylesheets = {'assets/css/all.css', 'assets/css/latex-fontsize.css'} - }) -end - -local function isEmpty(s) - return s == nil or s == '' -end - -local function isValidSize(size) - local validSizes = { - "tiny", - "scriptsize", - "footnotesize", - "small", - "normalsize", - "large", - "Large", - "LARGE", - "huge", - "Huge" - } - for _, v in ipairs(validSizes) do - if v == size then - return size - end - end - return "" -end - -return { - ["fa"] = function(args, kwargs) - - local group = "solid" - local icon = pandoc.utils.stringify(args[1]) - if #args > 1 then - group = icon - icon = pandoc.utils.stringify(args[2]) - end - - local title = pandoc.utils.stringify(kwargs["title"]) - if not isEmpty(title) then - title = " title=\"" .. title .. "\"" - end - - local size = pandoc.utils.stringify(kwargs["size"]) - - -- detect html (excluding epub which won't handle fa) - if quarto.doc.isFormat("html:js") then - ensureHtmlDeps() - if not isEmpty(size) then - size = " fa-" .. size - end - return pandoc.RawInline( - 'html', - "" - ) - -- detect pdf / beamer / latex / etc - elseif quarto.doc.isFormat("pdf") then - ensureLatexDeps() - if isEmpty(isValidSize(size)) then - return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") - else - return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") - end - else - return pandoc.Null() - end - end -} diff --git a/_freeze/materials/cs1_practical_one-sample/execute-results/html.json b/_freeze/materials/cs1_practical_one-sample/execute-results/html.json index 85b79b7..02fdd0e 100644 --- a/_freeze/materials/cs1_practical_one-sample/execute-results/html.json +++ b/_freeze/materials/cs1_practical_one-sample/execute-results/html.json @@ -1,7 +1,8 @@ { - "hash": "f921d4dff19d85d89f0a1ded375e3bfd", + "hash": "030792c909148f943f1aa823668f2351", "result": { - "markdown": "---\ntitle: \"One-sample data\"\n---\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n::: {.callout-tip}\n#### Learning outcomes\n\n**Questions**\n\n- When do I perform a one-sample test?\n- What are the assumptions?\n- How do I interpret and present the results of the test?\n- How do I deal with non-normal data?\n\n**Objectives**\n\n- Set out your hypothesis for single sample continuous data\n- Be able to summarise and visualise the data\n- Understand and assess the underlying assumptions of the test\n- Perform a one-sample t-test\n- Be able to interpret and report the results\n- Be able to do these steps on non-normal data\n:::\n\n## Libraries and functions\n\n::: {.callout-note collapse=\"true\"}\n## Click to expand\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n### Libraries\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# A collection of R packages designed for data science\nlibrary(tidyverse)\n```\n:::\n\n\n### Functions\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Performs a one-sample t-test, Student's t-test and Welch's t-test in later sections\nstats::t.test()\n\n# Performs a Shapiro-Wilk test for normality\nstats::shapiro.test()\n\n# Performs one and two sample Wilcoxon tests\nstats::wilcox.test()\n\n# Plots a Q-Q plot for comparison with a normal distribution\nggplot2::stat_qq()\n\n# Adds a comparison line to the Q-Q plot\nggplot2::stat_qq_line()\n```\n:::\n\n\n## Python\n\n| Libraries | Description |\n|:-------------------|:---------------------------------------------------|\n| `plotnine` | The Python equivalent of `ggplot2`. |\n| `pandas` | A Python data analysis and manipulation tool. |\n| `pingouin` | A Python module developed to have simple yet exhaustive stats functions. |\n\n| Functions | Description |\n|:--------------------------|:--------------------------------------------|\n| [`pingouin.normality()`](https://pingouin-stats.org/generated/pingouin.normality.html) | Performs the Shapiro-Wilk test for normality. |\n| [`pingouin.ttest()`](https://pingouin-stats.org/generated/pingouin.ttest.html#pingouin.ttest) | Performs a t-test |\n| [`pingouin.wilcoxon()`](https://pingouin-stats.org/generated/pingouin.wilcoxon.html#pingouin.wilcoxon) | Wilcoxon signed rank test. |\n| `plotnine.stats.stat_qq()` | Plots a Q-Q plot for comparison with a normal distribution. |\n| `plotnine.stats.stat_qq_line()` | Adds a comparison line to the Q-Q plot. |\n:::\n:::\n\n## Purpose and aim\n\nOne sample tests are used when we have a single sample of continuous data. It is used to find out if the sample came from a parent distribution with a given mean (or median). This essentially boils down to finding out if the sample mean (or median) is \"close enough\" to our hypothesised parent population mean (or median). So, in the figure below, we could use these tests to see what the probability is that the sample of ten points comes from the distribution plotted above it i.e. a population with a mean of 20 mm.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n## Choosing a test\n\nThere are two tests that we are going to look at in this situation; the one-sample t-test, and the one-sample Wilcoxon signed rank-sum test. Both tests work on the sort of data that we're considering here, but they both have different assumptions.\n\nIf your data is normally distributed, then a one-sample t-test is appropriate. If your data aren't normally distributed, but their distribution is symmetric, and the sample size is small then a one-sample Wilcoxon signed rank-sum test is more appropriate.\n\nFor each statistical test we consider there will be five tasks. These will come back again and again, so pay extra close attention.\n\n::: callout-important\n1. Setting out of the hypothesis\n2. Summarise and visualisation of the data\n3. Assessment of assumptions\n4. Implementation of the statistical test\n5. Interpreting the output and presentation of results\n:::\n\nWe won't always carry these out in exactly the same order, but we will always consider each of the five tasks for every test.\n\n## Data and hypotheses\n\nFor example, suppose we measure the body lengths of male guppies (in mm) collected from the Guanapo River in Trinidad. We want to test whether the data support the hypothesis that the mean body is actually 20 mm. We form the following null and alternative hypotheses:\n\n- $H_0$: The mean body length is equal to 20mm ($\\mu =$ 20).\n- $H_1$: The mean body length is not equal to 20mm ($\\mu \\neq$ 20).\n\nWe will use a one-sample, two-tailed t-test to see if we should reject the null hypothesis or not.\n\n- We use a **one-sample** test because we only have one sample.\n- We use a **two-tailed** t-test because we want to know if our data suggest that the true (population) mean is different from 20 mm in either direction rather than just to see if it is greater than or less than 20 mm (in which case we would use a one-tailed test).\n- We're using a **t-test** because we don't know any better yet and because I'm telling you to. We'll look at what the precise assumptions/requirements need to be in a moment.\n\nMake sure you have downloaded the data (see: [Data section](../setup.qmd)) and placed it within your working directory.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nFirst we load the relevant libraries:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load tidyverse\nlibrary(tidyverse)\n```\n:::\n\n\nWe then read in the data and create a table containing the data.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# import the data\nfishlengthDF <- read_csv(\"data/CS1-onesample.csv\")\n\nfishlengthDF\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 29 × 3\n id river length\n \n 1 1 Guanapo 19.1\n 2 2 Guanapo 23.3\n 3 3 Guanapo 18.2\n 4 4 Guanapo 16.4\n 5 5 Guanapo 19.7\n 6 6 Guanapo 16.6\n 7 7 Guanapo 17.5\n 8 8 Guanapo 19.9\n 9 9 Guanapo 19.1\n10 10 Guanapo 18.8\n# ℹ 19 more rows\n```\n:::\n:::\n\n\nThe first line reads the data into R and creates an object called a **tibble**, which is a type of **data frame**. This data frame contains 3 columns: a unique `id`, `river` encoding the river and `length` with the measured guppy length.\n\n## Python\n\nWe then read the data in:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\nfishlength_py = pd.read_csv('data/CS1-onesample.csv')\n\n# inspect the data\nfishlength_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id river length\n0 1 Guanapo 19.1\n1 2 Guanapo 23.3\n2 3 Guanapo 18.2\n3 4 Guanapo 16.4\n4 5 Guanapo 19.7\n```\n:::\n:::\n\n:::\n\n## Summarise and visualise\n\nSummarise the data and visualise it:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(fishlengthDF)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id river length \n Min. : 1 Length:29 Min. :11.2 \n 1st Qu.: 8 Class :character 1st Qu.:17.5 \n Median :15 Mode :character Median :18.8 \n Mean :15 Mean :18.3 \n 3rd Qu.:22 3rd Qu.:19.7 \n Max. :29 Max. :23.3 \n```\n:::\n:::\n\n\nFrom the `summary()` output we can see that the mean and median of the `length` variable are quite close together. The `id` column also has minimum, maximum, mean etc. values - these are not useful! The numbers in the `id` column have no numerical value, but are just to ensure each observation can be traced back, if needed.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(fishlengthDF,\n aes(x = river,\n y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n## Python\n\nFirst we have a look at a numerical summary of the data:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nfishlength_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id length\ncount 29.000000 29.000000\nmean 15.000000 18.296552\nstd 8.514693 2.584636\nmin 1.000000 11.200000\n25% 8.000000 17.500000\n50% 15.000000 18.800000\n75% 22.000000 19.700000\nmax 29.000000 23.300000\n```\n:::\n:::\n\n\nFrom the `describe()` output we can see that the mean and median of the `length` variable are quite close together. The `id` column also has minimum, maximum, mean etc. values - these are not useful! The numbers in the `id` column have no numerical value, but are just to ensure each observation can be traced back, if needed.\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(fishlength_py,\n aes(x = 'river',\n y = 'length')) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-12-1.png){width=614}\n:::\n:::\n\n:::\n\nThe data do not appear to contain any obvious errors, and whilst both the mean and median are less than 20 (18.3 and 18.8 respectively) it is not absolutely certain that the sample mean is sufficiently different from this value to be \"statistically significant\", although we may anticipate such a result.\n\n## Assumptions\n\nWhen it comes to one-sample tests, we have two options:\n\na. t-test\nb. Wilcoxon signed-rank test\n\nFor us to use a t-test for this analysis (and for the results to be valid) we have to make two assumptions:\n\n1. The parent distribution from which the sample is taken is normally distributed (and as such the sample data are normally distributed themselves).\n\n::: callout-note\nIt is worth noting though that the t-test is actually pretty robust in situations where the sample data are not normal. For sufficiently large sample sizes (your guess is as good as mine, but conventionally this means about 30 data points), you can use a t-test without worrying about whether the underlying population is normally distributed or not.\n:::\n\n2. Each data point in the sample is independent of the others. This is in general not something that can be tested for and instead has to be considered from the sampling procedure. For example, taking repeated measurements from the same individual would generate data that are not independent.\n\nThe second point we know nothing about and so we ignore it here (this is an issue that needs to be considered from the experimental design), whereas the first assumption can be checked. There are three ways of checking for normality:\n\nIn increasing order of rigour, we have\n\n1. Histogram\n2. Quantile-quantile plot\n3. Shapiro-Wilk test\n\n### Histogram of the data\n\nPlot a histogram of the data, which gives:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(fishlengthDF,\n aes(x = length)) +\n geom_histogram(bins = 15)\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-13-3.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(fishlength_py,\n aes(x = \"length\")) +\n geom_histogram(bins = 15))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-14-1.png){width=614}\n:::\n:::\n\n:::\n\nThe distribution appears to be uni-modal and symmetric, and so it isn't obviously non-normal. However, there are a lot of distributions that have these simple properties but which aren't normal, so this isn't exactly rigorous. Thankfully there are other, more rigorous tests.\n\nNB. By even looking at this distribution to assess the assumption of normality we are already going far beyond what anyone else ever does. Nevertheless, we will continue.\n\n### Q-Q plot of the data\n\nQ-Q plot is the short for quantile-quantile plot. This **diagnostic** plot (as it is sometimes called) is a way of comparing two distributions. How Q-Q plots work won't be explained here but will be addressed in the next session.\n\nConstruct a Q-Q Plot of the quantiles of the data against the quantiles of a normal distribution:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(fishlengthDF,\n aes(sample = length)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-15-3.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(fishlength_py,\n aes(sample = \"length\")) +\n stat_qq() +\n stat_qq_line(colour = \"blue\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-16-1.png){width=614}\n:::\n:::\n\n:::\n\nWhat is important to know is that if the data were normally distributed then all of the points should lie on (or close to) the diagonal line in this graph.\n\nIn this case, the points lie quite close to the line for the most part but the sample quantiles (points) from either end of the sample distribution are either smaller (below the line on the left) or larger (above the line on the right) than expected if they were supposed to be normally distributed. This suggests that the sample distribution is a bit more spread out than would be expected if it came from a normal distribution.\n\nIt is important to recognise that there isn't a simple unambiguous answer when interpreting these types of graph, in terms of whether the assumption of normality has been well met or not and instead it often boils down to a matter of experience.\n\nIt is a very rare situation indeed where the assumptions necessary for a test will be met unequivocally and a certain degree of personal interpretation is always needed. Here you have to ask yourself whether the data are normal \"enough\" for you to be confident in the validity of the test.\n\nBelow are four examples of QQ plots for different types of distributions:\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-18-1.png){width=672}\n:::\n:::\n\n\nThese two graphs relate to 200 data points that have been drawn from a normal distribution. Even here you can see that the points do not all lie perfectly on the diagonal line in the QQ plot, and a certain amount of deviation at the top and bottom of the graph can happen just by chance (if I were to draw a different set of point then the graph would look slightly different).\n\n------------------------------------------------------------------------\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-19-1.png){width=672}\n:::\n:::\n\n\nThese two graphs relate to 200 data points that have been drawn from a uniform distribution. Uniform distributions are more condensed than normal distributions, and this is reflected in the QQ plot having a very pronounced S-shaped pattern to it (this is colloquially known as **snaking**).\n\n------------------------------------------------------------------------\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n\nThese two graphs relate to 200 data points that have been drawn from a t distribution. t distributions are more spread out than normal distributions, and this is reflected in the QQ plot again having a very pronounced S-shaped pattern to it, but this time the snaking is a reflection of that observed for the uniform distribution.\n\n------------------------------------------------------------------------\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-21-1.png){width=672}\n:::\n:::\n\n\nThese two graphs relate to 200 data points that have been drawn from an exponential distribution. Exponential distributions are not symmetric and are very skewed compared with normal distributions. The significant right-skew in this distribution is reflected in the QQ plot again having points that curve away above the diagonal line at both ends (a left-skew would have the points being below the line at both ends).\n\nIn all four cases it is worth noting that the deviations are only at the ends of the plot.\n\n### Shapiro-Wilk test\n\nThis is one of a number of formal statistical test that assess whether a given sample of numbers come from a normal distribution. It calculates the probability of getting the sample data if the underlying distribution is in fact normal. It is very easy to carry out in R.\n\nPerform a Shapiro-Wilk test on the data:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nThe `shapiro.test()` function needs a numerical vector as input. We get this by extracting the `length` column.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nshapiro.test(fishlengthDF$length)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: fishlengthDF$length\nW = 0.94938, p-value = 0.1764\n```\n:::\n:::\n\n\n- The 1st line gives the name of the test and the `data:` tells you which data are used.\n- The 3rd line contains the two key outputs from the test:\n- The calculated W-statistic is 0.9494 (we don't need to know this)\n- The p-value is 0.1764\n\n## Python\n\nWe take the `length` values from the `fishlength_py` data frame and pass that to the `normality()` function in `pingouin`:\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.normality(fishlength_py.length)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval normal\nlength 0.949384 0.176418 True\n```\n:::\n:::\n\n\n- the `W` column gives us the W-statistic\n- the `pval` column gives us the p-value\n- the `normal` column gives us the outcome of the test in `True/False`\n:::\n\nAs the p-value is bigger than 0.05 (say) then we can say that there is insufficient evidence to reject the null hypothesis that the sample came from a normal distribution.\n\nIt is important to recognise that the Shapiro-Wilk test is not without limitations. It is rather sensitive to the sample size being considered. In general, for small sample sizes, the test is very relaxed about normality (and nearly all data sets are considered normal), whereas for large sample sizes the test can be overly strict, and it can fail to recognise data sets that are very nearly normal indeed.\n\n### Assumptions overview\n\n::: callout-important\nIn terms of assessing the assumptions of a test it is always worth considering several methods, both graphical and analytic, and not just relying on a single method.\n:::\n\nIn the `fishlength` example, the graphical Q-Q plot analysis was not especially conclusive as there was some suggestion of snaking in the plots, but the Shapiro-Wilk test gave a non-significant p-value (0.1764). Putting these two together, along with the original histogram and the recognition that there were only 30 data points in the data set I personally would be happy that the assumptions of the t-test were met well enough to trust the result of the t-test, but you may not be...\n\nIn which case we would consider an alternative test that has less stringent assumptions (but is less powerful): the [one-sample Wilcoxon signed-rank test](#cs1-onesample-wilcoxon-signed-rank).\n\n## Implement and interpret the test\n\nPerform a one-sample, two-tailed t-test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nt.test(fishlengthDF$length,\n mu = 20, alternative = \"two.sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tOne Sample t-test\n\ndata: fishlengthDF$length\nt = -3.5492, df = 28, p-value = 0.001387\nalternative hypothesis: true mean is not equal to 20\n95 percent confidence interval:\n 17.31341 19.27969\nsample estimates:\nmean of x \n 18.29655 \n```\n:::\n:::\n\n\n- The first argument must be a numerical vector of data values. In our case it's the `length` values.\n- The second argument must be a number and is the mean to be tested under the null hypothesis.\n- The third argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`. We have no prior assumptions on whether the alternative fish length would be greater or less than 20, so we choose `two.sided`.\n\nIn the output:\n\n- The 1st line gives the name of the test and the 2nd line reminds you what the dataset was called\n- The 3rd line contains the three key outputs from the test:\n - The calculated t-value is -3.5492 (we'll need this for reporting)\n - There are 28 degrees of freedom (again we'll need this for reporting)\n - The p-value is 0.001387.\n- The 4th line simply states the alternative hypothesis\n- The 5th and 6th lines give the 95th confidence interval (we don't need to know this)\n- The 7th, 8th and 9th lines give the sample mean again (18.29655).\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(x = fishlength_py.length,\n y = 20,\n alternative = \"two-sided\").round(3)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T dof alternative p-val CI95% cohen-d BF10 power\nT-test -3.549 28 two-sided 0.001 [17.31, 19.28] 0.659 25.071 0.929\n```\n:::\n:::\n\n\n- the `x` argument must be a numerical series of data values\n- the `y` argument must be a number and is the mean to be tested under the null hypothesis\n- the `alternative` argument defines the alternative hypothesis (we have no expectation that the fish length is smaller or larger than 20, if the null hypothesis does not hold up)\n- with `.round(3)` we're rounding the outcome to 3 digits\n\nIn the output:\n\nWe're not focussing on *all* of the output just yet, but\n\n- `T` gives us the value of the t-statistic\n- `dof` gives us the degrees of freedom (we'll need this for reporting)\n- `pval` gives us the p-value\n:::\n\nThe p-value is what we're mostly interested in. It gives the probability of us getting a sample such as ours if the null hypothesis were actually true.\n\n::: callout-important\n## p-value interpretation\n\n- a high p-value means that there is a high probability of observing a sample such as ours and the null hypothesis is probably true whereas\n- a low p-value means that there is a low probability of observing a sample such as ours and the null hypothesis is probably not true.\n\nIt is important to realise that the p-value is just an indication and there is no absolute certainty here in this interpretation.\n\nPeople, however like more definite answers and so we pick an artificial probability threshold (called a significance level) in order to be able to say something more decisive. The standard significance level is 0.05 and since our p-value is smaller than this we choose to say that \"it is very unlikely that we would have this particular sample if the null hypothesis were true\".\n:::\n\nIn this case the p-value is much smaller than 0.05, so we reject our null hypothesis and state that:\n\n> A one-sample t-test indicated that the mean body length of male guppies ($\\bar{x}$ = 18.29mm) differs significantly from 20 mm (p = 0.0014).\n\nThe above sentence is an adequate concluding statement for this test and is what we would write in any paper or report. Note that we have included (in brackets) information on the actual mean value of our group($\\bar{x}$ = 18.29mm) and the p-value (p = 0.0014). In some journals you are only required to report whether the p-value is less than the critical value (e.g. p \\< 0.05) but I would always recommend reporting the actual p-value obtained.\n\n::: callout-important\nAdditional information, such as the test statistic and degrees of freedom, are sometimes also reported. This is more of a legacy from the time where people did the calculations by hand and used tables. I personally find it much more useful to report as above and supply the data and analysis, so other people can see what I've done and why!\n:::\n\n## Dealing with non-normal data\n\nYour data might not always be normally distributed. That's not a huge issue and there are statistical tests that can deal with this. For a one-sample data set there is the Wilcoxon signed rank test. This test, in contrast to the one-sample t-test does not assume that the parent distribution is normally distributed. We do still need the parent distribution (and consequently the sample) to be the same shape and scale.\n\nThe Wilcoxon signed rank test checks if the rank-transformed values are symmetric around the median. As such, using this test we look to see if the *median* of the parent distributions differs significantly from a given hypothesised value (in contrast with the t-test that looks at the *mean*).\n\n### Data and hypotheses\n\nAgain, we use the `fishlength` data set. The one-sample Wilcoxon signed rank test allows to see if the *median* body length is different from a specified value. Here we want to test whether the data support the hypothesis that the median body is actually 20 mm. The following null and alternative hypotheses are very similar to those used for the one sample t-test:\n\n- $H_0$: The median body length is equal to 20 mm ($\\mu =$ 20).\n- $H_1$: The median body length is not equal to 20 mm ($\\mu \\neq$ 20).\n\nWe will use a one-sample, two-tailed Wilcoxon signed rank test to see if we should reject the null hypothesis or not.\n\n### Summarise and visualise\n\nWe did this before in the previous section, nothing really should have changed between now and then (if it has then you're not off to a good start on this practical!)\n\n### Assumptions\n\nIn order to use a one-sample Wilcoxon signed rank test for this analysis (and for the results to be strictly valid) we have to make two assumptions:\n\n1. The data are distributed symmetrically around the median\n2. Each data point in the sample is independent of the others. This is the same as for the t-test and is a common feature of nearly all statistical tests. Lack of independence in your data is really tough to deal with (if not impossible) and a large part of proper experimental design is ensuring this.\n\nWhilst there are formal statistical tests for symmetry we will opt for a simple visual inspection using both a box plot and a histogram.\n\nPlot a histogram and a box plot of the data:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nLet's first determine the median, so we can use that to compare our data to. We'll also store the value in an object called `median_fishlength`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# determine the median\nmedian_fishlength <- median(fishlengthDF$length)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a histogram\nfishlengthDF %>% \n ggplot(aes(x = length)) +\n geom_histogram(bins = 10) +\n geom_vline(xintercept = median_fishlength,\n colour = \"red\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-27-1.png){width=672}\n:::\n\n```{.r .cell-code}\n# create box plot\nfishlengthDF %>% \n ggplot(aes(y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-27-2.png){width=672}\n:::\n:::\n\n\n## Python\n\nLet's first determine the median, so we can use that to compare our data to.\n\n\n::: {.cell}\n\n```{.python .cell-code}\nmedian_fishlength = fishlength_py.length.median()\n```\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# create a histogram\n(ggplot(fishlength_py,\n aes(x = \"length\")) +\n geom_histogram(bins = 10) +\n geom_vline(xintercept = median_fishlength,\n colour = \"red\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-29-1.png){width=614}\n:::\n\n```{.python .cell-code}\n# create box plot\n(ggplot(fishlength_py,\n aes(x = 1,\n y = \"length\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-29-2.png){width=614}\n:::\n:::\n\n:::\n\nHere we can see that whilst the distribution isn't perfectly symmetric, neither is it heavily skewed to the left or right and we can make the call that the distribution is *symmetric enough* for us to be happy with the results of the test.\n\n### Implement and interpret the test\n\nPerform a one-sample, two-tailed Wilcoxon signed rank test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox.test(fishlengthDF$length,\n mu = 20, alternative = \"two.sided\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning in wilcox.test.default(fishlengthDF$length, mu = 20, alternative =\n\"two.sided\"): cannot compute exact p-value with ties\n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tWilcoxon signed rank test with continuity correction\n\ndata: fishlengthDF$length\nV = 67.5, p-value = 0.001222\nalternative hypothesis: true location is not equal to 20\n```\n:::\n:::\n\n\nThe syntax is identical to the one-sample t-test we carried out earlier.\n\n- The first argument must be a numerical vector of data values.\n\n- The second argument must be a number and is the median to be tested under the null hypothesis.\n\n- The third argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less.`\n\n- The first line gives a warning (not an error) message regarding the implementation of this test. This can be safely ignored in this case as the p-value is so small, but essentially, it's letting you know that some of the data values are identical to each other. This is not supposed to happen as we should be dealing with continuous data for this test, but in practice it's not something that we need to worry ourselves with.\n\n- It then gives you the name of the test and `data:` reminds you what the data set was called (here it's the numerical `length` values coming from the pipe)\n\n- Next are the two key outputs from the test:\n\n - The calculated V statistic is 67.5\n - The p-value is 0.001222.\n\n- The last line simply states the alternative hypothesis\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.wilcoxon(fishlength_py.length - 20,\n alternative = \"two-sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W-val alternative p-val RBC CLES\nWilcoxon 67.5 two-sided 0.000669 -0.689655 NaN\n```\n:::\n:::\n\n\nThe syntax is similar to what we did earlier:\n\n- The 1st argument we give to the `wilcoxon()` function is an array of the differences between our data points and the median to be tested under the null hypothesis, i.e. our data points (`fishlength_py.length`) minus the test median (20, in this case).\n- The 2nd argument gives us the type of alternative hypothesis and must be one of \"two-sided\", \"larger\", or \"smaller\".\n:::\n\nAgain, the p-value is what we're most interested in. It gives the probability of us getting a sample such as ours if the null hypothesis were actually true. So, in this case since our p-value is less than 0.05 we can reject our null hypothesis and state that:\n\n> A one-sample Wilcoxon signed rank test indicated that the median body length of male guppies ($\\tilde{x}$ = 18.8 mm) differs significantly from 20 mm (p = 0.0012).\n\nThe above sentence is an adequate concluding statement for this test and is what we would write in any paper or report. Note that we have included (in brackets) information on the median value of the group ($\\tilde{x}$ = 18.8 mm) and the p-value (p = 0.0012). Keep in mind that, when publishing, you'd also submit your data and scripts, so people can follow your analysis.\n\n## Exercises\n\n### Gastric juices {#sec-exr_gastric}\n\n:::{.callout-exercise}\n\n{{< level 2 >}}\n\n\n\nThe following data are the dissolving times (in seconds) of a drug in agitated gastric juice:\n\n`42.7, 43.4, 44.6, 45.1, 45.6, 45.9, 46.8, 47.6`\n\nThese data are stored in `data/CS1-gastric_juices.csv`.\n\nDo these results provide any evidence to suggest that dissolving time for this drug is different from 45 seconds?\n\n1. Here the data are already formatted for your convenience into a [tidy](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) format\n2. Load the data from `data/CS1-gastric_juices.csv`\n3. Write down the null and alternative hypotheses.\n4. Summarise and visualise the data and perform an appropriate one-sample t-test.\n - What can you say about the dissolving time? (what sentence would you use to report this)\n5. Check the assumptions for the test.\n - Was the test valid?\n\n::: {.callout-answer collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : mean $=$ 45s\n\n$H_1$ : mean $\\neq$ 45s\n\n#### Data, summarise & visualise\n\nWe read in the data from `CS1-gastric_juices.csv`. It contains two columns, an `id` column and a `dissolving_time` column with the measured values.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\ndissolving <- read_csv(\"data/CS1-gastric_juices.csv\")\n\n# have a look at the data\ndissolving\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 8 × 2\n id dissolving_time\n \n1 1 42.7\n2 2 43.4\n3 3 44.6\n4 4 45.1\n5 5 45.6\n6 6 45.9\n7 7 46.8\n8 8 47.6\n```\n:::\n\n```{.r .cell-code}\n# summarise the data\nsummary(dissolving)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id dissolving_time\n Min. :1.00 Min. :42.70 \n 1st Qu.:2.75 1st Qu.:44.30 \n Median :4.50 Median :45.35 \n Mean :4.50 Mean :45.21 \n 3rd Qu.:6.25 3rd Qu.:46.12 \n Max. :8.00 Max. :47.60 \n```\n:::\n:::\n\n\nWe can look at the histogram and box plot of the data:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a histogram\nggplot(dissolving,\n aes(x = dissolving_time)) +\n geom_histogram(bins = 4)\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-33-1.png){width=672}\n:::\n\n```{.r .cell-code}\n# create a boxplot\nggplot(dissolving,\n aes(y = dissolving_time)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-33-2.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\ndissolving_py = pd.read_csv(\"data/CS1-gastric_juices.csv\")\n\n# have a look at the data\ndissolving_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id dissolving_time\n0 1 42.7\n1 2 43.4\n2 3 44.6\n3 4 45.1\n4 5 45.6\n```\n:::\n\n```{.python .cell-code}\n# summarise the data\ndissolving_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id dissolving_time\ncount 8.00000 8.000000\nmean 4.50000 45.212500\nstd 2.44949 1.640068\nmin 1.00000 42.700000\n25% 2.75000 44.300000\n50% 4.50000 45.350000\n75% 6.25000 46.125000\nmax 8.00000 47.600000\n```\n:::\n:::\n\n\nWe can look at the histogram and box plot of the data:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create a histogram\n(ggplot(dissolving_py,\n aes(x = \"dissolving_time\")) +\n geom_histogram(bins = 4))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-35-1.png){width=614}\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# create a box plot\n(ggplot(dissolving_py,\n aes(x = 1,\n y = \"dissolving_time\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-36-3.png){width=614}\n:::\n:::\n\n\nPython (or `plotnine` in particular) gets a bit cranky if you try to create a `geom_boxplot` but do not define the `x` aesthetic. Hence us putting it as `1`. The value has no numerical meaning, however.\n:::\n\nThere are only 8 data points, so the histogram is rather uninformative. Thankfully the box plot is a bit more useful here. We can see:\n\n1. There don't appear to be any major errors in data entry and there aren't any huge outliers\n2. The median value in the box-plot (the thick black line) is pretty close to 45 and so I wouldn't be surprised if the mean of the data isn't significantly different from 45. We can confirm that by looking at the mean and median values that we calculated using the summary command from earlier.\n3. The data appear to be symmetric, and so whilst we can't tell if they're normal they're a least not massively skewed.\n\n#### Assumptions\n\nNormality:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test\nshapiro.test(dissolving$dissolving_time)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: dissolving$dissolving_time\nW = 0.98023, p-value = 0.9641\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a Q-Q plot\nggplot(dissolving,\n aes(sample = dissolving_time)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-38-1.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Perform Shapiro-Wilk test to check normality\npg.normality(dissolving_py.dissolving_time)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval normal\ndissolving_time 0.980234 0.964054 True\n```\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# Create a Q-Q plot\n(ggplot(dissolving_py,\n aes(sample = \"dissolving_time\")) +\n stat_qq() +\n stat_qq_line(colour = \"blue\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-40-1.png){width=614}\n:::\n:::\n\n:::\n\n- The Shapiro test has a p-value of 0.964 which (given that it is bigger than 0.05) suggests that the data are normal enough.\n- The Q-Q plot isn't perfect, with some deviation of the points away from the line but since the points aren't accelerating away from the line and, since we only have 8 points, we can claim, with some slight reservations, that the assumption of normality appears to be adequately well met.\n\nOverall, we are somewhat confident that the assumption of normality is well-enough met for the t-test to be an appropriate method for analysing the data. Note the ridiculous number of caveats here and the slightly political/slippery language I'm using. This is intentional and reflects the ambiguous nature of assumption checking. This is an important approach to doing statistics that you need to embrace.\n\nIn reality, if I found myself in this situation I would also try doing a non-parametric test on the data (Wilcoxon signed-rank test) and see whether I get the same conclusion about whether the median dissolving time differs from 45s. Technically, you don't know about the Wilcoxon test yet as you haven't done that section of the materials. Anyway, if I get the same conclusion then my confidence in the result of the test goes up considerably; it doesn't matter how well an assumption has been met, I get the same result. If on the other hand I get a completely different conclusion from carrying out the non-parametric test then all bets are off; I now have very little confidence in my test result as I don't know which one to believe (in the case that the assumptions of the test are a bit unclear). In this example a Wilcoxon test also gives us a non-significant result and so all is good.\n\n#### Implement test\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform one-sample t-test\nt.test(dissolving$dissolving_time,\n mu = 45, alternative = \"two.sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tOne Sample t-test\n\ndata: dissolving$dissolving_time\nt = 0.36647, df = 7, p-value = 0.7248\nalternative hypothesis: true mean is not equal to 45\n95 percent confidence interval:\n 43.84137 46.58363\nsample estimates:\nmean of x \n 45.2125 \n```\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(x = dissolving_py.dissolving_time,\n y = 45,\n alternative = \"two-sided\").round(3)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T dof alternative p-val CI95% cohen-d BF10 power\nT-test 0.366 7 two-sided 0.725 [43.84, 46.58] 0.13 0.356 0.062\n```\n:::\n:::\n\n:::\n\n> A one-sample t-test indicated that the mean dissolving time of the drug is not significantly different from 45s ($\\bar{x}$ = 45.2, p = 0.725).\n\nAnd that, is that.\n:::\n:::\n\n### Gastric juices (revisited) {#sec-exr_gastricrevisit}\n\n:::{.callout-exercise}\n\n\n{{< level 2 >}}\n\n\n\nWhat if we were unsure if we could assume normality here? In that case we'd have to perform a Wilcoxon signed rank test.\n\n1. Analyse the drug data set from before using a one-sample Wilcoxon signed rank test\n2. Discuss with a (virtual) neighbour which of the two tests you feel is best suited to the data.\n3. Does it matter in this case?\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : median $=$ 45s\n\n$H_1$ : median $\\neq$ 45s\n\n#### Assumptions\n\nFrom the box plot from the previous exercise we already know that the data are symmetric enough for the test to be valid.\n\n#### Wilcoxon signed rank test\n\nCalculating the median and performing the test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmedian(dissolving$dissolving_time)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n[1] 45.35\n```\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox.test(dissolving$dissolving_time,\n mu = 45, alternative = \"two.sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tWilcoxon signed rank exact test\n\ndata: dissolving$dissolving_time\nV = 22, p-value = 0.6406\nalternative hypothesis: true location is not equal to 45\n```\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndissolving_py.dissolving_time.median()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n45.35\n```\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\npg.wilcoxon(dissolving_py.dissolving_time - 45,\n alternative = \"two-sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W-val alternative p-val RBC CLES\nWilcoxon 14.0 two-sided 0.640625 0.222222 NaN\n```\n:::\n:::\n\n:::\n\n> A one-sample Wilcoxon-signed rank test indicated that the median dissolving time of the drug is not significantly different from 45 s ($\\tilde{x}$ = 45.35 , p = 0.64)\n\n#### Discussion\n\nIn terms of choosing between the two test we can see that both meet their respective assumptions and so both tests are valid. In this case both tests also agree in terms of their conclusions i.e. that the average dissolving time (either mean or median) doesn't differ significantly from the proposed value of 45 s.\n\n- So one answer would be that it doesn't matter which test you use.\n- Another answer would be that you should pick the test that measures the quantity you're interested in *i.e.* if you care about medians then use the Wilcoxon test, whereas if you care about means then use the t-test.\n- A final answer would be that, since both test are valid we would prefer to use the test with greater **power**. t-tests always have more power than Wilcoxon tests (as long as they're valid) and so we could report that one. (We'll talk about this in the last session but power is effectively the capacity of a test to detect a significant difference - so more power is better).\n:::\n:::\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- One-sample tests are used when you have a single sample of continuous data\n- The t-test assumes that the data are normally distributed and independent of each other\n- A good way of assessing the assumption of normality is by checking the data against a Q-Q plot\n- The Wilcoxon signed rank test is used when you have a single sample of continuous data, which is not normally distributed\n:::\n", + "engine": "knitr", + "markdown": "---\ntitle: \"One-sample data\"\n---\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n\n\n::: {.callout-tip}\n#### Learning outcomes\n\n**Questions**\n\n- When do I perform a one-sample test?\n- What are the assumptions?\n- How do I interpret and present the results of the test?\n- How do I deal with non-normal data?\n\n**Objectives**\n\n- Set out your hypothesis for single sample continuous data\n- Be able to summarise and visualise the data\n- Understand and assess the underlying assumptions of the test\n- Perform a one-sample t-test\n- Be able to interpret and report the results\n- Be able to do these steps on non-normal data\n:::\n\n## Libraries and functions\n\n::: {.callout-note collapse=\"true\"}\n## Click to expand\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n### Libraries\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# A collection of R packages designed for data science\nlibrary(tidyverse)\n\n# Converts stats functions to a tidyverse-friendly format\nlibrary(rstatix)\n```\n:::\n\n\n\n\n### Functions\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Performs a one-sample t-test, Student's t-test and Welch's t-test in later sections\nrstatix::t_test()\n\n# Performs a Shapiro-Wilk test for normality\nstats::shapiro.test()\n\n# Performs one and two sample Wilcoxon tests\nrstatix::wilcox_test()\n\n# Plots a Q-Q plot for comparison with a normal distribution\nggplot2::stat_qq()\n\n# Adds a comparison line to the Q-Q plot\nggplot2::stat_qq_line()\n```\n:::\n\n\n\n\n## Python\n\n| Libraries | Description |\n|:-------------------|:---------------------------------------------------|\n| `plotnine` | The Python equivalent of `ggplot2`. |\n| `pandas` | A Python data analysis and manipulation tool. |\n| `pingouin` | A Python module developed to have simple yet exhaustive stats functions. |\n\n| Functions | Description |\n|:--------------------------|:--------------------------------------------|\n| [`pingouin.normality()`](https://pingouin-stats.org/generated/pingouin.normality.html) | Performs the Shapiro-Wilk test for normality. |\n| [`pingouin.ttest()`](https://pingouin-stats.org/generated/pingouin.ttest.html#pingouin.ttest) | Performs a t-test |\n| [`pingouin.wilcoxon()`](https://pingouin-stats.org/generated/pingouin.wilcoxon.html#pingouin.wilcoxon) | Wilcoxon signed rank test. |\n| `plotnine.stats.stat_qq()` | Plots a Q-Q plot for comparison with a normal distribution. |\n| `plotnine.stats.stat_qq_line()` | Adds a comparison line to the Q-Q plot. |\n:::\n:::\n\n## Purpose and aim\n\nOne sample tests are used when we have a single sample of continuous data. It is used to find out if the sample came from a parent distribution with a given mean (or median). This essentially boils down to finding out if the sample mean (or median) is \"close enough\" to our hypothesised parent population mean (or median). So, in the figure below, we could use these tests to see what the probability is that the sample of ten points comes from the distribution plotted above it i.e. a population with a mean of 20 mm.\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n\n\n## Choosing a test\n\nThere are two tests that we are going to look at in this situation; the one-sample t-test, and the one-sample Wilcoxon signed rank-sum test. Both tests work on the sort of data that we're considering here, but they both have different assumptions.\n\nIf your data is normally distributed, then a one-sample t-test is appropriate. If your data aren't normally distributed, but their distribution is symmetric, and the sample size is small then a one-sample Wilcoxon signed rank-sum test is more appropriate.\n\nFor each statistical test we consider there will be five tasks. These will come back again and again, so pay extra close attention.\n\n::: callout-important\n1. Setting out of the hypothesis\n2. Summarise and visualisation of the data\n3. Assessment of assumptions\n4. Implementation of the statistical test\n5. Interpreting the output and presentation of results\n:::\n\nWe won't always carry these out in exactly the same order, but we will always consider each of the five tasks for every test.\n\n## Data and hypotheses\n\nFor example, suppose we measure the body lengths of male guppies (in mm) collected from the Guanapo River in Trinidad. We want to test whether the data support the hypothesis that the mean body is actually 20 mm. We form the following null and alternative hypotheses:\n\n- $H_0$: The mean body length is equal to 20mm ($\\mu =$ 20).\n- $H_1$: The mean body length is not equal to 20mm ($\\mu \\neq$ 20).\n\nWe will use a one-sample, two-tailed t-test to see if we should reject the null hypothesis or not.\n\n- We use a **one-sample** test because we only have one sample.\n- We use a **two-tailed** t-test because we want to know if our data suggest that the true (population) mean is different from 20 mm in either direction rather than just to see if it is greater than or less than 20 mm (in which case we would use a one-tailed test).\n- We're using a **t-test** because we don't know any better yet and because I'm telling you to. We'll look at what the precise assumptions/requirements need to be in a moment.\n\nMake sure you have downloaded the data (see: [Data section](../setup.qmd)) and placed it within your working directory.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nFirst we load the relevant libraries:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load tidyverse\nlibrary(tidyverse)\n```\n:::\n\n\n\n\nWe then read in the data and create a table containing the data.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# import the data\nfishlengthDF <- read_csv(\"data/CS1-onesample.csv\")\n\nfishlengthDF\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 29 × 3\n id river length\n \n 1 1 Guanapo 19.1\n 2 2 Guanapo 23.3\n 3 3 Guanapo 18.2\n 4 4 Guanapo 16.4\n 5 5 Guanapo 19.7\n 6 6 Guanapo 16.6\n 7 7 Guanapo 17.5\n 8 8 Guanapo 19.9\n 9 9 Guanapo 19.1\n10 10 Guanapo 18.8\n# ℹ 19 more rows\n```\n\n\n:::\n:::\n\n\n\n\nThe first line reads the data into R and creates an object called a **tibble**, which is a type of **data frame**. This data frame contains 3 columns: a unique `id`, `river` encoding the river and `length` with the measured guppy length.\n\n## Python\n\nWe then read the data in:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\nfishlength_py = pd.read_csv('data/CS1-onesample.csv')\n\n# inspect the data\nfishlength_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id river length\n0 1 Guanapo 19.1\n1 2 Guanapo 23.3\n2 3 Guanapo 18.2\n3 4 Guanapo 16.4\n4 5 Guanapo 19.7\n```\n\n\n:::\n:::\n\n\n\n:::\n\n## Summarise and visualise\n\nSummarise the data and visualise it:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(fishlengthDF)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id river length \n Min. : 1 Length:29 Min. :11.2 \n 1st Qu.: 8 Class :character 1st Qu.:17.5 \n Median :15 Mode :character Median :18.8 \n Mean :15 Mean :18.3 \n 3rd Qu.:22 3rd Qu.:19.7 \n Max. :29 Max. :23.3 \n```\n\n\n:::\n:::\n\n\n\n\nFrom the `summary()` output we can see that the mean and median of the `length` variable are quite close together. The `id` column also has minimum, maximum, mean etc. values - these are not useful! The numbers in the `id` column have no numerical value, but are just to ensure each observation can be traced back, if needed.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(fishlengthDF,\n aes(x = river,\n y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\nFirst we have a look at a numerical summary of the data:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nfishlength_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id length\ncount 29.000000 29.000000\nmean 15.000000 18.296552\nstd 8.514693 2.584636\nmin 1.000000 11.200000\n25% 8.000000 17.500000\n50% 15.000000 18.800000\n75% 22.000000 19.700000\nmax 29.000000 23.300000\n```\n\n\n:::\n:::\n\n\n\n\nFrom the `describe()` output we can see that the mean and median of the `length` variable are quite close together. The `id` column also has minimum, maximum, mean etc. values - these are not useful! The numbers in the `id` column have no numerical value, but are just to ensure each observation can be traced back, if needed.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(fishlength_py,\n aes(x = 'river',\n y = 'length')) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-12-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nThe data do not appear to contain any obvious errors, and whilst both the mean and median are less than 20 (18.3 and 18.8 respectively) it is not absolutely certain that the sample mean is sufficiently different from this value to be \"statistically significant\", although we may anticipate such a result.\n\n## Assumptions\n\nWhen it comes to one-sample tests, we have two options:\n\na. t-test\nb. Wilcoxon signed-rank test\n\nFor us to use a t-test for this analysis (and for the results to be valid) we have to make two assumptions:\n\n1. The parent distribution from which the sample is taken is normally distributed (and as such the sample data are normally distributed themselves).\n\n::: callout-note\nIt is worth noting though that the t-test is actually pretty robust in situations where the sample data are not normal. For sufficiently large sample sizes (your guess is as good as mine, but conventionally this means about 30 data points), you can use a t-test without worrying about whether the underlying population is normally distributed or not.\n:::\n\n2. Each data point in the sample is independent of the others. This is in general not something that can be tested for and instead has to be considered from the sampling procedure. For example, taking repeated measurements from the same individual would generate data that are not independent.\n\nThe second point we know nothing about and so we ignore it here (this is an issue that needs to be considered from the experimental design), whereas the first assumption can be checked. There are three ways of checking for normality:\n\nIn increasing order of rigour, we have\n\n1. Histogram\n2. Quantile-quantile plot\n3. Shapiro-Wilk test\n\n### Histogram of the data\n\nPlot a histogram of the data, which gives:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(fishlengthDF,\n aes(x = length)) +\n geom_histogram(bins = 15)\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-13-3.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(fishlength_py,\n aes(x = \"length\")) +\n geom_histogram(bins = 15))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-14-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nThe distribution appears to be uni-modal and symmetric, and so it isn't obviously non-normal. However, there are a lot of distributions that have these simple properties but which aren't normal, so this isn't exactly rigorous. Thankfully there are other, more rigorous tests.\n\nNB. By even looking at this distribution to assess the assumption of normality we are already going far beyond what anyone else ever does. Nevertheless, we will continue.\n\n### Q-Q plot of the data\n\nQ-Q plot is the short for quantile-quantile plot. This **diagnostic** plot (as it is sometimes called) is a way of comparing two distributions. How Q-Q plots work won't be explained here but will be addressed in the next session.\n\nConstruct a Q-Q Plot of the quantiles of the data against the quantiles of a normal distribution:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(fishlengthDF,\n aes(sample = length)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-15-3.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(fishlength_py,\n aes(sample = \"length\")) +\n stat_qq() +\n stat_qq_line(colour = \"blue\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-16-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nWhat is important to know is that if the data were normally distributed then all of the points should lie on (or close to) the diagonal line in this graph.\n\nIn this case, the points lie quite close to the line for the most part but the sample quantiles (points) from either end of the sample distribution are either smaller (below the line on the left) or larger (above the line on the right) than expected if they were supposed to be normally distributed. This suggests that the sample distribution is a bit more spread out than would be expected if it came from a normal distribution.\n\nIt is important to recognise that there isn't a simple unambiguous answer when interpreting these types of graph, in terms of whether the assumption of normality has been well met or not and instead it often boils down to a matter of experience.\n\nIt is a very rare situation indeed where the assumptions necessary for a test will be met unequivocally and a certain degree of personal interpretation is always needed. Here you have to ask yourself whether the data are normal \"enough\" for you to be confident in the validity of the test.\n\nBelow are four examples of QQ plots for different types of distributions:\n\n\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-18-1.png){width=672}\n:::\n:::\n\n\n\n\nThese two graphs relate to 200 data points that have been drawn from a normal distribution. Even here you can see that the points do not all lie perfectly on the diagonal line in the QQ plot, and a certain amount of deviation at the top and bottom of the graph can happen just by chance (if I were to draw a different set of point then the graph would look slightly different).\n\n------------------------------------------------------------------------\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-19-1.png){width=672}\n:::\n:::\n\n\n\n\nThese two graphs relate to 200 data points that have been drawn from a uniform distribution. Uniform distributions are more condensed than normal distributions, and this is reflected in the QQ plot having a very pronounced S-shaped pattern to it (this is colloquially known as **snaking**).\n\n------------------------------------------------------------------------\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n\n\n\nThese two graphs relate to 200 data points that have been drawn from a t distribution. t distributions are more spread out than normal distributions, and this is reflected in the QQ plot again having a very pronounced S-shaped pattern to it, but this time the snaking is a reflection of that observed for the uniform distribution.\n\n------------------------------------------------------------------------\n\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-21-1.png){width=672}\n:::\n:::\n\n\n\n\nThese two graphs relate to 200 data points that have been drawn from an exponential distribution. Exponential distributions are not symmetric and are very skewed compared with normal distributions. The significant right-skew in this distribution is reflected in the QQ plot again having points that curve away above the diagonal line at both ends (a left-skew would have the points being below the line at both ends).\n\nIn all four cases it is worth noting that the deviations are only at the ends of the plot.\n\n### Shapiro-Wilk test\n\nThis is one of a number of formal statistical test that assess whether a given sample of numbers come from a normal distribution. It calculates the probability of getting the sample data if the underlying distribution is in fact normal. It is very easy to carry out in R.\n\nPerform a Shapiro-Wilk test on the data:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nThe `shapiro.test()` function needs a numerical vector as input. We get this by extracting the `length` column.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nshapiro.test(fishlengthDF$length)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: fishlengthDF$length\nW = 0.94938, p-value = 0.1764\n```\n\n\n:::\n:::\n\n\n\n\n- The 1st line gives the name of the test and the `data:` tells you which data are used.\n- The 3rd line contains the two key outputs from the test:\n- The calculated W-statistic is 0.9494 (we don't need to know this)\n- The p-value is 0.1764\n\n## Python\n\nWe take the `length` values from the `fishlength_py` data frame and pass that to the `normality()` function in `pingouin`:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.normality(fishlength_py.length)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval normal\nlength 0.949384 0.176423 True\n```\n\n\n:::\n:::\n\n\n\n\n- the `W` column gives us the W-statistic\n- the `pval` column gives us the p-value\n- the `normal` column gives us the outcome of the test in `True/False`\n:::\n\nAs the p-value is bigger than 0.05 (say) then we can say that there is insufficient evidence to reject the null hypothesis that the sample came from a normal distribution.\n\nIt is important to recognise that the Shapiro-Wilk test is not without limitations. It is rather sensitive to the sample size being considered. In general, for small sample sizes, the test is very relaxed about normality (and nearly all data sets are considered normal), whereas for large sample sizes the test can be overly strict, and it can fail to recognise data sets that are very nearly normal indeed.\n\n### Assumptions overview\n\n::: callout-important\nIn terms of assessing the assumptions of a test it is always worth considering several methods, both graphical and analytic, and not just relying on a single method.\n:::\n\nIn the `fishlength` example, the graphical Q-Q plot analysis was not especially conclusive as there was some suggestion of snaking in the plots, but the Shapiro-Wilk test gave a non-significant p-value (0.1764). Putting these two together, along with the original histogram and the recognition that there were only 30 data points in the data set I personally would be happy that the assumptions of the t-test were met well enough to trust the result of the t-test, but you may not be...\n\nIn which case we would consider an alternative test that has less stringent assumptions (but is less powerful): the [one-sample Wilcoxon signed-rank test](#cs1-onesample-wilcoxon-signed-rank).\n\n## Implement and interpret the test\n\nPerform a one-sample, two-tailed t-test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nt_test(length ~ 1,\n mu = 20, alternative = \"two.sided\",\n data = fishlengthDF)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n .y. group1 group2 n statistic df p\n* \n1 length 1 null model 29 -3.55 28 0.00139\n```\n\n\n:::\n:::\n\n\n\n\n- The first argument must be a numerical column of data values. In our case it's the `length` values. We add `~ 1` to indicate that we're going to compare these to a \"null model\". More on this later, but for now it's just a quirky syntax thing in R.\n- The `mu` argument must be a number and is the mean to be tested under the null hypothesis.\n- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`. We have no prior assumptions on whether the alternative fish length would be greater or less than 20, so we choose `two.sided`.\n\nIn the output:\n\n- `.y.` is the variable of interest\n- `n` is the number of observations\n- ignore the `group` columns for now\n- `statistic` is the calculated t-value (-3.549184)\n- `df` gives us the number of degrees of freedom (28). Again, more on this later.\n- `p` gives us the p-value of 0.00139\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(x = fishlength_py.length,\n y = 20,\n alternative = \"two-sided\").round(3)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T dof alternative p-val CI95% cohen-d BF10 power\nT-test -3.549 28 two-sided 0.001 [17.31, 19.28] 0.659 25.071 0.929\n```\n\n\n:::\n:::\n\n\n\n\n- the `x` argument must be a numerical series of data values\n- the `y` argument must be a number and is the mean to be tested under the null hypothesis\n- the `alternative` argument defines the alternative hypothesis (we have no expectation that the fish length is smaller or larger than 20, if the null hypothesis does not hold up)\n- with `.round(3)` we're rounding the outcome to 3 digits\n\nIn the output:\n\nWe're not focussing on *all* of the output just yet, but\n\n- `T` gives us the value of the t-statistic\n- `dof` gives us the degrees of freedom (we'll need this for reporting)\n- `pval` gives us the p-value\n:::\n\nThe p-value is what we're mostly interested in. It gives the probability of us getting a sample such as ours if the null hypothesis were actually true.\n\n::: callout-important\n## p-value interpretation\n\n- a high p-value means that there is a high probability of observing a sample such as ours and the null hypothesis is probably true whereas\n- a low p-value means that there is a low probability of observing a sample such as ours and the null hypothesis is probably not true.\n\nIt is important to realise that the p-value is just an indication and there is no absolute certainty here in this interpretation.\n\nPeople, however like more definite answers and so we pick an artificial probability threshold (called a significance level) in order to be able to say something more decisive. The standard significance level is 0.05 and since our p-value is smaller than this we choose to say that \"it is very unlikely that we would have this particular sample if the null hypothesis were true\".\n:::\n\nIn this case the p-value is much smaller than 0.05, so we reject our null hypothesis and state that:\n\n> A one-sample t-test indicated that the mean body length of male guppies ($\\bar{x}$ = 18.29mm) differs significantly from 20 mm (p = 0.0014).\n\nThe above sentence is an adequate concluding statement for this test and is what we would write in any paper or report. Note that we have included (in brackets) information on the actual mean value of our group($\\bar{x}$ = 18.29mm) and the p-value (p = 0.0014). In some journals you are only required to report whether the p-value is less than the critical value (e.g. p \\< 0.05) but I would always recommend reporting the actual p-value obtained.\n\n::: callout-important\nAdditional information, such as the test statistic and degrees of freedom, are sometimes also reported. This is more of a legacy from the time where people did the calculations by hand and used tables. I personally find it much more useful to report as above and supply the data and analysis, so other people can see what I've done and why!\n:::\n\n## Dealing with non-normal data\n\nYour data might not always be normally distributed. That's not a huge issue and there are statistical tests that can deal with this. For a one-sample data set there is the Wilcoxon signed rank test. This test, in contrast to the one-sample t-test does not assume that the parent distribution is normally distributed. We do still need the parent distribution (and consequently the sample) to be the same shape and scale.\n\nThe Wilcoxon signed rank test checks if the rank-transformed values are symmetric around the median. As such, using this test we look to see if the *median* of the parent distributions differs significantly from a given hypothesised value (in contrast with the t-test that looks at the *mean*).\n\n### Data and hypotheses\n\nAgain, we use the `fishlength` data set. The one-sample Wilcoxon signed rank test allows to see if the *median* body length is different from a specified value. Here we want to test whether the data support the hypothesis that the median body is actually 20 mm. The following null and alternative hypotheses are very similar to those used for the one sample t-test:\n\n- $H_0$: The median body length is equal to 20 mm ($\\mu =$ 20).\n- $H_1$: The median body length is not equal to 20 mm ($\\mu \\neq$ 20).\n\nWe will use a one-sample, two-tailed Wilcoxon signed rank test to see if we should reject the null hypothesis or not.\n\n### Summarise and visualise\n\nWe did this before in the previous section, nothing really should have changed between now and then (if it has then you're not off to a good start on this practical!)\n\n### Assumptions\n\nIn order to use a one-sample Wilcoxon signed rank test for this analysis (and for the results to be strictly valid) we have to make two assumptions:\n\n1. The data are distributed symmetrically around the median\n2. Each data point in the sample is independent of the others. This is the same as for the t-test and is a common feature of nearly all statistical tests. Lack of independence in your data is really tough to deal with (if not impossible) and a large part of proper experimental design is ensuring this.\n\nWhilst there are formal statistical tests for symmetry we will opt for a simple visual inspection using both a box plot and a histogram.\n\nPlot a histogram and a box plot of the data:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nLet's first determine the median, so we can use that to compare our data to. We'll also store the value in an object called `median_fishlength`.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# determine the median\nmedian_fishlength <- median(fishlengthDF$length)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a histogram\nfishlengthDF %>% \n ggplot(aes(x = length)) +\n geom_histogram(bins = 10) +\n geom_vline(xintercept = median_fishlength,\n colour = \"red\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-27-1.png){width=672}\n:::\n\n```{.r .cell-code}\n# create box plot\nfishlengthDF %>% \n ggplot(aes(y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-27-2.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\nLet's first determine the median, so we can use that to compare our data to.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nmedian_fishlength = fishlength_py.length.median()\n```\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# create a histogram\n(ggplot(fishlength_py,\n aes(x = \"length\")) +\n geom_histogram(bins = 10) +\n geom_vline(xintercept = median_fishlength,\n colour = \"red\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-29-1.png){width=614}\n:::\n\n```{.python .cell-code}\n# create box plot\n(ggplot(fishlength_py,\n aes(x = 1,\n y = \"length\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-29-2.png){width=614}\n:::\n:::\n\n\n\n:::\n\nHere we can see that whilst the distribution isn't perfectly symmetric, neither is it heavily skewed to the left or right and we can make the call that the distribution is *symmetric enough* for us to be happy with the results of the test.\n\n### Implement and interpret the test\n\nPerform a one-sample, two-tailed Wilcoxon signed rank test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox_test(length ~ 1,\n mu = 20, alternative = \"two.sided\",\n data = fishlengthDF)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 6\n .y. group1 group2 n statistic p\n* \n1 length 1 null model 29 67.5 0.00122\n```\n\n\n:::\n:::\n\n\n\n\nThe syntax is identical to the one-sample t-test we carried out earlier.\n\n- The first argument contains the column with data values.\n- The `mu` argument must be a number and is the median to be tested under the null hypothesis.\n- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less.`\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.wilcoxon(fishlength_py.length - 20,\n alternative = \"two-sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W-val alternative p-val RBC CLES\nWilcoxon 67.5 two-sided 0.000736 -0.689655 NaN\n```\n\n\n:::\n:::\n\n\n\n\nThe syntax is similar to what we did earlier:\n\n- The 1st argument we give to the `wilcoxon()` function is an array of the differences between our data points and the median to be tested under the null hypothesis, i.e. our data points (`fishlength_py.length`) minus the test median (20, in this case).\n- The 2nd argument gives us the type of alternative hypothesis and must be one of \"two-sided\", \"larger\", or \"smaller\".\n:::\n\nAgain, the p-value is what we're most interested in. It gives the probability of us getting a sample such as ours if the null hypothesis were actually true. So, in this case since our p-value is less than 0.05 we can reject our null hypothesis and state that:\n\n> A one-sample Wilcoxon signed rank test indicated that the median body length of male guppies ($\\tilde{x}$ = 18.8 mm) differs significantly from 20 mm (p = 0.0012).\n\nThe above sentence is an adequate concluding statement for this test and is what we would write in any paper or report. Note that we have included (in brackets) information on the median value of the group ($\\tilde{x}$ = 18.8 mm) and the p-value (p = 0.0012). Keep in mind that, when publishing, you'd also submit your data and scripts, so people can follow your analysis.\n\n## Exercises\n\n### Gastric juices {#sec-exr_gastric}\n\n:::{.callout-exercise}\n\n\n\n{{< level 2 >}}\n\n\n\n\n\n\nThe following data are the dissolving times (in seconds) of a drug in agitated gastric juice:\n\n`42.7, 43.4, 44.6, 45.1, 45.6, 45.9, 46.8, 47.6`\n\nThese data are stored in `data/CS1-gastric_juices.csv`.\n\nDo these results provide any evidence to suggest that dissolving time for this drug is different from 45 seconds?\n\n1. Here the data are already formatted for your convenience into a [tidy](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) format\n2. Load the data from `data/CS1-gastric_juices.csv`\n3. Write down the null and alternative hypotheses.\n4. Summarise and visualise the data and perform an appropriate one-sample t-test.\n - What can you say about the dissolving time? (what sentence would you use to report this)\n5. Check the assumptions for the test.\n - Was the test valid?\n\n::: {.callout-answer collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : mean $=$ 45s\n\n$H_1$ : mean $\\neq$ 45s\n\n#### Data, summarise & visualise\n\nWe read in the data from `CS1-gastric_juices.csv`. It contains two columns, an `id` column and a `dissolving_time` column with the measured values.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\ndissolving <- read_csv(\"data/CS1-gastric_juices.csv\")\n\n# have a look at the data\ndissolving\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 8 × 2\n id dissolving_time\n \n1 1 42.7\n2 2 43.4\n3 3 44.6\n4 4 45.1\n5 5 45.6\n6 6 45.9\n7 7 46.8\n8 8 47.6\n```\n\n\n:::\n\n```{.r .cell-code}\n# summarise the data\nsummary(dissolving)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id dissolving_time\n Min. :1.00 Min. :42.70 \n 1st Qu.:2.75 1st Qu.:44.30 \n Median :4.50 Median :45.35 \n Mean :4.50 Mean :45.21 \n 3rd Qu.:6.25 3rd Qu.:46.12 \n Max. :8.00 Max. :47.60 \n```\n\n\n:::\n:::\n\n\n\n\nWe can look at the histogram and box plot of the data:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a histogram\nggplot(dissolving,\n aes(x = dissolving_time)) +\n geom_histogram(bins = 4)\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-33-1.png){width=672}\n:::\n\n```{.r .cell-code}\n# create a boxplot\nggplot(dissolving,\n aes(y = dissolving_time)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-33-2.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\ndissolving_py = pd.read_csv(\"data/CS1-gastric_juices.csv\")\n\n# have a look at the data\ndissolving_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id dissolving_time\n0 1 42.7\n1 2 43.4\n2 3 44.6\n3 4 45.1\n4 5 45.6\n```\n\n\n:::\n\n```{.python .cell-code}\n# summarise the data\ndissolving_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id dissolving_time\ncount 8.00000 8.000000\nmean 4.50000 45.212500\nstd 2.44949 1.640068\nmin 1.00000 42.700000\n25% 2.75000 44.300000\n50% 4.50000 45.350000\n75% 6.25000 46.125000\nmax 8.00000 47.600000\n```\n\n\n:::\n:::\n\n\n\n\nWe can look at the histogram and box plot of the data:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create a histogram\n(ggplot(dissolving_py,\n aes(x = \"dissolving_time\")) +\n geom_histogram(bins = 4))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-35-1.png){width=614}\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# create a box plot\n(ggplot(dissolving_py,\n aes(x = 1,\n y = \"dissolving_time\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-36-3.png){width=614}\n:::\n:::\n\n\n\n\nPython (or `plotnine` in particular) gets a bit cranky if you try to create a `geom_boxplot` but do not define the `x` aesthetic. Hence us putting it as `1`. The value has no numerical meaning, however.\n:::\n\nThere are only 8 data points, so the histogram is rather uninformative. Thankfully the box plot is a bit more useful here. We can see:\n\n1. There don't appear to be any major errors in data entry and there aren't any huge outliers\n2. The median value in the box-plot (the thick black line) is pretty close to 45 and so I wouldn't be surprised if the mean of the data isn't significantly different from 45. We can confirm that by looking at the mean and median values that we calculated using the summary command from earlier.\n3. The data appear to be symmetric, and so whilst we can't tell if they're normal they're a least not massively skewed.\n\n#### Assumptions\n\nNormality:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test\nshapiro.test(dissolving$dissolving_time)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: dissolving$dissolving_time\nW = 0.98023, p-value = 0.9641\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a Q-Q plot\nggplot(dissolving,\n aes(sample = dissolving_time)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-38-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# Perform Shapiro-Wilk test to check normality\npg.normality(dissolving_py.dissolving_time)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval normal\ndissolving_time 0.980234 0.964054 True\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# Create a Q-Q plot\n(ggplot(dissolving_py,\n aes(sample = \"dissolving_time\")) +\n stat_qq() +\n stat_qq_line(colour = \"blue\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_one-sample_files/figure-html/unnamed-chunk-40-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\n- The Shapiro test has a p-value of 0.964 which (given that it is bigger than 0.05) suggests that the data are normal enough.\n- The Q-Q plot isn't perfect, with some deviation of the points away from the line but since the points aren't accelerating away from the line and, since we only have 8 points, we can claim, with some slight reservations, that the assumption of normality appears to be adequately well met.\n\nOverall, we are somewhat confident that the assumption of normality is well-enough met for the t-test to be an appropriate method for analysing the data. Note the ridiculous number of caveats here and the slightly political/slippery language I'm using. This is intentional and reflects the ambiguous nature of assumption checking. This is an important approach to doing statistics that you need to embrace.\n\nIn reality, if I found myself in this situation I would also try doing a non-parametric test on the data (Wilcoxon signed-rank test) and see whether I get the same conclusion about whether the median dissolving time differs from 45s. Technically, you don't know about the Wilcoxon test yet as you haven't done that section of the materials. Anyway, if I get the same conclusion then my confidence in the result of the test goes up considerably; it doesn't matter how well an assumption has been met, I get the same result. If on the other hand I get a completely different conclusion from carrying out the non-parametric test then all bets are off; I now have very little confidence in my test result as I don't know which one to believe (in the case that the assumptions of the test are a bit unclear). In this example a Wilcoxon test also gives us a non-significant result and so all is good.\n\n#### Implement test\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform one-sample t-test\ndissolving\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 8 × 2\n id dissolving_time\n \n1 1 42.7\n2 2 43.4\n3 3 44.6\n4 4 45.1\n5 5 45.6\n6 6 45.9\n7 7 46.8\n8 8 47.6\n```\n\n\n:::\n\n```{.r .cell-code}\nt_test(dissolving_time ~ 1,\n mu = 45, alternative = \"two.sided\",\n data = dissolving)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n .y. group1 group2 n statistic df p\n* \n1 dissolving_time 1 null model 8 0.366 7 0.725\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(x = dissolving_py.dissolving_time,\n y = 45,\n alternative = \"two-sided\").round(3)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T dof alternative p-val CI95% cohen-d BF10 power\nT-test 0.366 7 two-sided 0.725 [43.84, 46.58] 0.13 0.356 0.062\n```\n\n\n:::\n:::\n\n\n\n:::\n\n> A one-sample t-test indicated that the mean dissolving time of the drug is not significantly different from 45s ($\\bar{x}$ = 45.2, p = 0.725).\n\nAnd that, is that.\n:::\n:::\n\n### Gastric juices (revisited) {#sec-exr_gastricrevisit}\n\n:::{.callout-exercise}\n\n\n\n\n{{< level 2 >}}\n\n\n\n\n\n\nWhat if we were unsure if we could assume normality here? In that case we'd have to perform a Wilcoxon signed rank test.\n\n1. Analyse the drug data set from before using a one-sample Wilcoxon signed rank test\n2. Discuss with a (virtual) neighbour which of the two tests you feel is best suited to the data.\n3. Does it matter in this case?\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : median $=$ 45s\n\n$H_1$ : median $\\neq$ 45s\n\n#### Assumptions\n\nFrom the box plot from the previous exercise we already know that the data are symmetric enough for the test to be valid.\n\n#### Wilcoxon signed rank test\n\nCalculating the median and performing the test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmedian(dissolving$dissolving_time)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] 45.35\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox_test(dissolving_time ~ 1,\n mu = 45, alternative = \"two.sided\",\n data = dissolving)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 6\n .y. group1 group2 n statistic p\n* \n1 dissolving_time 1 null model 8 22 0.641\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndissolving_py.dissolving_time.median()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nnp.float64(45.35)\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\npg.wilcoxon(dissolving_py.dissolving_time - 45,\n alternative = \"two-sided\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W-val alternative p-val RBC CLES\nWilcoxon 14.0 two-sided 0.640625 0.222222 NaN\n```\n\n\n:::\n:::\n\n\n\n:::\n\n> A one-sample Wilcoxon-signed rank test indicated that the median dissolving time of the drug is not significantly different from 45 s ($\\tilde{x}$ = 45.35 , p = 0.64)\n\n#### Discussion\n\nIn terms of choosing between the two test we can see that both meet their respective assumptions and so both tests are valid. In this case both tests also agree in terms of their conclusions i.e. that the average dissolving time (either mean or median) doesn't differ significantly from the proposed value of 45 s.\n\n- So one answer would be that it doesn't matter which test you use.\n- Another answer would be that you should pick the test that measures the quantity you're interested in *i.e.* if you care about medians then use the Wilcoxon test, whereas if you care about means then use the t-test.\n- A final answer would be that, since both test are valid we would prefer to use the test with greater **power**. t-tests always have more power than Wilcoxon tests (as long as they're valid) and so we could report that one. (We'll talk about this in the last session but power is effectively the capacity of a test to detect a significant difference - so more power is better).\n:::\n:::\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- One-sample tests are used when you have a single sample of continuous data\n- The t-test assumes that the data are normally distributed and independent of each other\n- A good way of assessing the assumption of normality is by checking the data against a Q-Q plot\n- The Wilcoxon signed rank test is used when you have a single sample of continuous data, which is not normally distributed\n:::\n", "supporting": [ "cs1_practical_one-sample_files" ], diff --git a/_freeze/materials/cs1_practical_one-sample/figure-html/unnamed-chunk-12-1.png b/_freeze/materials/cs1_practical_one-sample/figure-html/unnamed-chunk-12-1.png index c74fbe2051f581a391e2ab25b82633f55201ac38..518a37e238c0013eb47ce95462fd98a34645ac9b 100644 GIT binary patch literal 17122 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index 4914827..ab850d0 100644 --- a/_freeze/materials/cs1_practical_two-samples/execute-results/html.json +++ b/_freeze/materials/cs1_practical_two-samples/execute-results/html.json @@ -1,7 +1,8 @@ { - "hash": "4b18903bdba20a996936bc3da833e6c1", + "hash": "9b88278f9f9447f3ef5ac3efc080f25c", "result": { - "markdown": "---\ntitle: \"Two-sample data\"\n---\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n::: {.callout-tip}\n#### Learning outcomes\n\n**Questions**\n\n- When do I perform a two-sample test?\n- What are the assumptions?\n- How do I interpret and present the results of the test?\n- How do I deal with non-normal data?\n\n**Objectives**\n\n- Set out your hypothesis for comparing two samples of continuous data\n- Be able to summarise and visualise the data\n- Understand and assess the underlying assumptions of the test\n- Perform a two-sample t-test\n- Be able to interpret and report the results\n- Be able to do these steps on non-normal data\n\n:::\n\n## Libraries and functions\n\n::: {.callout-note collapse=\"true\"}\n## Click to expand\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n### Libraries\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# A collection of R packages designed for data science\nlibrary(tidyverse)\n\n# Converts stats functions to a tidyverse-friendly format\nlibrary(rstatix)\n```\n:::\n\n\n### Functions\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Computes summary statistics \nrstatix::get_summary_stats() \n\n# Performs Levene's test for equality of variance\n# (non-normally distributed data)\nrstatix::levene_test()\n\n# Performs Bartlett's test for equality of variance\n# (normally distributed data)\nstats::bartlett.test()\n\n# Performs Shapiro Wilk test\nstats::shapiro.test()\n\n# Performs one- and two-sample Wilcoxon tests\n# the latter is also known as 'Mann-Whitney U' test\nstats::wilcox.test()\n\n# Plots a Q-Q plot for comparison with a normal distribution\nggplot2::stat_qq()\n\n# Adds a comparison line to the Q-Q plot\nggplot2::stat_qq_line()\n```\n:::\n\n\n## Python\n\n| Libraries | Description |\n|:---------------------|:-------------------------------------------------|\n| `plotnine` | The Python equivalent of `ggplot2`. |\n| `pandas` | A Python data analysis and manipulation tool. |\n| `pingouin` | A Python module developed to have simple yet exhaustive stats functions. |\n\n| Functions | Description |\n|:-----------------------------------|:-----------------------------------|\n| `pandas.DataFrame.read_csv` | Reads in a `.csv` file |\n| `pandas.DataFrame.head()` | Plots the first few rows |\n| `pandas.DataFrame.describe()` | Gives summary statistics |\n| `pandas.DataFrame.groupby()` | Group DataFrame using a mapper or by a Series of columns |\n| `pandas.DataFrame.pivot()` | Return reshaped DataFrame organised by given index / column values. |\n| `pandas.DataFrame.query()` | Query the columns of a DataFrame with a boolean expression |\n| [`pingouin.normality()`](https://pingouin-stats.org/generated/pingouin.normality.html) | Performs the Shapiro-Wilk test for normality. |\n| [`pingouin.homoscedasticity()`](https://pingouin-stats.org/generated/pingouin.homoscedasticity.html#pingouin.homoscedasticity) | Checks for equality of variance. |\n| [`pingouin.ttest()`](https://pingouin-stats.org/generated/pingouin.ttest.html#pingouin.ttest) | Performs a t-test |\n| [`pingouin.mwu()`](https://pingouin-stats.org/generated/pingouin.mwu.html#pingouin.mwu) | Performs the Mann-Whitney U test. |\n| `plotnine.stats.stat_qq()` | Plots a Q-Q plot for comparison with a normal distribution. |\n| `plotnine.stats.stat_qq_line()` | Adds a comparison line to the Q-Q plot. |\n:::\n:::\n\n## Purpose and aim\nThese two-sample Student's t-test is used when we have two samples of continuous data where we are trying to find out if the samples came from the same parent distribution or not. This essentially boils down to finding out if there is a difference in the means of the two samples.\n\n## Data and hypotheses\n\nFor example, suppose we now measure the body lengths of male guppies (in mm) collected from two rivers in Trinidad; the Aripo and the Guanapo. We want to test whether the mean body length differs between samples. We form the following null and alternative hypotheses:\n\n- $H_0$: The mean body length does not differ between the two groups $(\\mu A = \\mu G)$\n- $H_1$: The mean body length does differ between the two groups $(\\mu A \\neq \\mu G)$\n\nWe use a two-sample, two-tailed t-test to see if we can reject the null hypothesis.\n\n- We use a two-sample test because we now have two samples.\n- We use a two-tailed t-test because we want to know if our data suggest that the true (population) means are different from one another rather than that one mean is specifically bigger or smaller than the other.\n- We're using Student's t-test because the sample sizes are big and because we're assuming that the parent populations have equal variance (We can check this later).\n\nThe data are stored in the file `data/CS1-twosample.csv`.\n\nLet's read in the data and have a quick look at the first rows to see how the data is structured.\n\nMake sure you have downloaded the data and placed it within your working directory.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nFirst we load the relevant libraries:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load tidyverse\nlibrary(tidyverse)\n\n# load rstatix, a tidyverse-friendly stats package\nlibrary(rstatix)\n```\n:::\n\n\nWe then read in the data and create a table containing the data.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrivers <- read_csv(\"data/CS1-twosample.csv\")\n\nrivers\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 68 × 2\n river length\n \n 1 Guanapo 19.1\n 2 Guanapo 23.3\n 3 Guanapo 18.2\n 4 Guanapo 16.4\n 5 Guanapo 19.7\n 6 Guanapo 16.6\n 7 Guanapo 17.5\n 8 Guanapo 19.9\n 9 Guanapo 19.1\n10 Guanapo 18.8\n# ℹ 58 more rows\n```\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py = pd.read_csv(\"data/CS1-twosample.csv\")\n\nrivers_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n river length\n0 Guanapo 19.1\n1 Guanapo 23.3\n2 Guanapo 18.2\n3 Guanapo 16.4\n4 Guanapo 19.7\n```\n:::\n:::\n\n:::\n\n## Summarise and visualise {#cs1-students-sumvisual}\n\nLet's first summarise the data.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n river length \n Length:68 Min. :11.20 \n Class :character 1st Qu.:18.40 \n Mode :character Median :19.30 \n Mean :19.46 \n 3rd Qu.:20.93 \n Max. :26.40 \n```\n:::\n:::\n\n\nThis gives us the standard summary statistics, but in this case we have more than one group (Aripo and Guanapo), so it might be helpful to get summary statistics *per group*. One way of doing this is by using the `get_summary_stats()` function from the `rstatix` library.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get common summary stats for the length column\nrivers %>% \n group_by(river) %>% \n get_summary_stats(type = \"common\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 × 11\n river variable n min max median iqr mean sd se ci\n \n1 Aripo length 39 17.5 26.4 20.1 2.2 20.3 1.78 0.285 0.577\n2 Guanapo length 29 11.2 23.3 18.8 2.2 18.3 2.58 0.48 0.983\n```\n:::\n:::\n\n\nNumbers might not always give you the best insight into your data, so we also visualise our data:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(rivers,\n aes(x = river, y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n length\ncount 68.000000\nmean 19.463235\nstd 2.370081\nmin 11.200000\n25% 18.400000\n50% 19.300000\n75% 20.925000\nmax 26.400000\n```\n:::\n:::\n\n\nThis gives us the standard summary statistics, but in this case we have more than one group (Aripo and Guanapo), so it might be helpful to get summary statistics *per group*. Here we use the `pd.groupby()` function to group by `river`. We only want to have summary statistics for the `length` variable, so we specify that as well:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py.groupby(\"river\")[\"length\"].describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n count mean std min 25% 50% 75% max\nriver \nAripo 39.0 20.330769 1.780620 17.5 19.1 20.1 21.3 26.4\nGuanapo 29.0 18.296552 2.584636 11.2 17.5 18.8 19.7 23.3\n```\n:::\n:::\n\n\nNumbers might not always give you the best insight into your data, so we also visualise our data:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(rivers_py,\n aes(x = \"river\", y = \"length\")) + \n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-13-1.png){width=614}\n:::\n:::\n\n:::\n\nThe box plot does appear to suggest that the two samples have different means, and moreover that the guppies in Guanapo may be smaller than the guppies in Aripo. It isn't immediately obvious that the two populations don't have equal variances though (box plots are not quite the right tool for this), so we plough on. Who ever said statistics would be glamorous?\n\n## Assumptions\n\nIn order to use a Student's t-test (and for the results to be strictly valid) we have to make three assumptions:\n\n1. The parent distributions from which the samples are taken are both normally distributed (which would lead to the sample data being normally distributed too).\n2. Each data point in the samples is independent of the others.\n3. The parent distributions should have the same variance.\n\nIn this example the first assumption can be ignored as the sample sizes are large enough (because of maths, with Aripo containing 39 and Guanapo 29 samples). If the samples were smaller then we would use the tests from the previous section.\n\nThe second point we can do nothing about unless we know how the data were collected, so again we ignore it.\n\nThe third point regarding equality of variance can be tested using either Bartlett's test (if the samples are normally distributed) or Levene's test (if the samples are not normally distributed).\n\nThis is where it gets a bit trickier. Although we don't care if the samples are normally distributed for the t-test to be valid (because the sample size is big enough to compensate), we do need to know if they are normally distributed in order to decide which variance test to use.\n\nSo we perform a [Shapiro-Wilk test](#shapiro-wilk-test) on both samples separately.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nWe can use the `filter()` function to filter the data by `river`, then we perform the Shapiro-Wilk test on the `length` measurement. The `shapiro.test()` function needs the data in a vector format. We get these by using the `pull()` function.\n\n::: {.callout-tip}\nIt's good practice to check what kind of data is going into these functions. Run the code line-by-line to see what data is passed on from the `filter()` and `pull()` functions.\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# filter data by river and perform test\nrivers %>% \n filter(river == \"Aripo\") %>% \n pull(length) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.93596, p-value = 0.02802\n```\n:::\n\n```{.r .cell-code}\nrivers %>% \n filter(river == \"Guanapo\") %>% \n pull(length) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.94938, p-value = 0.1764\n```\n:::\n:::\n\n\n## Python\n\nTo perform a Shapiro-Wilk test we can use the `normality()` function from `pingouin`. We can give it the data in the original 'long' format, where we specify:\n\n- `dv` = dependent variable, `length`\n- `group` = grouping variable, `river`\n- `data` = data frame\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.normality(dv = \"length\",\n group = \"river\",\n data = rivers_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval normal\nGuanapo 0.949384 0.176418 True\nAripo 0.935958 0.028022 False\n```\n:::\n:::\n\n:::\n\nWe can see that whilst the Guanapo data is probably normally distributed (p = 0.1764 \\> 0.05), the Aripo data is unlikely to be normally distributed (p = 0.02802 \\< 0.05). Remember that the p-value gives the probability of observing each sample if the parent population is actually normally distributed.\n\nThe Shapiro-Wilk test is quite sensitive to sample size. This means that if you have a large sample then even small deviations from normality will cause the sample to fail the test, whereas smaller samples are allowed to pass with much larger deviations. Here the Aripo data has nearly 40 points in it compared with the Guanapo data and so it is much easier for the Aripo sample to fail compared with the Guanapo data.\n\n:::{.callout-important}\nComplete [Exercise -@sec-exr_rivers].\n:::\n\nThe Q-Q plots show the opposite of what we found with the Shapiro-Wilk tests: the data for Aripo look pretty normally distributed apart from one data point, whereas the assumption of normality for the Guanapo data is less certain.\n\nWhat to do? Well, you could be conservative and state that you are not confident that the data in either group are normally distributed. That would be a perfectly reasonable conclusion.\n\nI would personally not have issues with stating that the Aripo data are probably normally distributed *enough*.\n\n## Equality of variance\n\n:::{.callout-tip}\nRemember that statistical tests do not provide answers, they merely suggest patterns. Human interpretation is still a crucial aspect to what we do.\n:::\n\nThe reason why we're checking for equality of variance (also referred to as **homogeneity of variance**) is because many statistical tests assume that the spread of the data within different parental populations (in this case, two) is the same.\n\nIf that is indeed the case, then the data themselves should have equal spread as well.\n\nThe Shapiro-Wilk test and the Q-Q plots have shown that some of the data might not be normal *enough* (although in opposite directions!) and so in order to test for equality of variance we will use Levene's test.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nThe function we use is `levene_test()` from the `rstatix` library.\n\nIt takes the data in the form of a formula as follows:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlevene_test(data = rivers,\n formula = length ~ river)\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nWarning in leveneTest.default(y = y, group = group, ...): group coerced to\nfactor.\n```\n:::\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 × 4\n df1 df2 statistic p\n \n1 1 66 1.77 0.188\n```\n:::\n:::\n\n\nOr shortened:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlevene_test(rivers,\n length ~ river)\n```\n:::\n\n\nThe key bit of information is the `p` column. This is the p-value 0.1876 for this test.\n\n## Python\n\nTo test for equality of variance, we can use the `homoscedasticity()` function from `pingouin`.\n\nNote that, contrary to R, we specify the type of test in the `method` argument. The default is `\"levene\"`, assuming that data are *not* normally distributed.\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"length\",\n group = \"river\",\n method = \"levene\",\n data = rivers_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval equal_var\nlevene 1.773184 0.187569 True\n```\n:::\n:::\n\n:::\n\nThe p-value tells us the probability of observing these two samples if they come from distributions with the same variance. As this probability is greater than our arbitrary significance level of 0.05 then we can be somewhat confident that the necessary assumptions for carrying out Student's t-test on these two samples was valid. (Once again woohoo!)\n\n### Bartlett's test\n\nIf we had wanted to carry out Bartlett's test (i.e. if the data *had* been sufficiently normally distributed) then we would have done:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nHere we use `bartlett.test()` function.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbartlett.test(length ~ river, data = rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tBartlett test of homogeneity of variances\n\ndata: length by river\nBartlett's K-squared = 4.4734, df = 1, p-value = 0.03443\n```\n:::\n:::\n\n\nThe relevant p-value is given on the 3rd line.\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"length\",\n group = \"river\",\n method = \"bartlett\",\n data = rivers_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T pval equal_var\nbartlett 4.473437 0.034426 False\n```\n:::\n:::\n\n:::\n\n## Implement and interpret the test\n\nIn this case we're ignoring the fact that the data are not normal enough, according to the Shapiro-Wilk test. However, this is not entirely naughty, because the sample sizes are pretty large and the t-test is also pretty robust in this case, we can perform a t-test. Remember, this is only allowed because the variances of the two groups (Aripo and Guanapo) are equal.\n\nPerform a two-sample, two-tailed, t-test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# two-sample, two-tailed t-test\nt.test(length ~ river,\n alternative = \"two.sided\",\n var.equal = TRUE,\n data = rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tTwo Sample t-test\n\ndata: length by river\nt = 3.8433, df = 66, p-value = 0.0002754\nalternative hypothesis: true difference in means between group Aripo and group Guanapo is not equal to 0\n95 percent confidence interval:\n 0.9774482 3.0909868\nsample estimates:\n mean in group Aripo mean in group Guanapo \n 20.33077 18.29655 \n```\n:::\n:::\n\n\nHere we do the following:\n\n- The first argument must be in the formula format: `variables ~ category`\n- The second argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n- The third argument says whether the variance of the two samples can be assumed to be equal (Student's t-test) or unequal (Welch's t-test)\n\nSo, how do we interpret these results?\n\n- The 1st line gives the name of the test and the 2nd line reminds you what the data set was called, and what variables were used.\n- The 3rd line contains the three key outputs from the test:\n - The calculated t-value is 3.8433\n - There are 66 degrees of freedom\n - The p-value is 0.0002754.\n- The 4th line simply states the alternative hypothesis in terms of the difference between the two sample means (testing if the two sample means are different is equivalent to testing whether the difference in the means is equal to zero).\n- The 5th and 6th lines give the 95th confidence interval (we don't need to know this here).\n- The 7th, 8th and 9th lines give the sample means for each group (20.33077 in Aripo and 18.29655 in Guanapo) which we found earlier.\n\n## Python\n\nThe `ttest()` function in `pingouin` needs two vectors as input, so we split the data as follows:\n\n\n::: {.cell}\n\n```{.python .cell-code}\naripo = rivers_py.query('river == \"Aripo\"')[\"length\"]\nguanapo = rivers_py.query('river == \"Guanapo\"')[\"length\"]\n```\n:::\n\n\nNext, we perform the t-test. We specify that the variance are equal by setting `correction = False`. We also `transpose()` the data, so we can actually see the entire output.\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(aripo, guanapo,\n correction = False).transpose()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T-test\nT 3.843267\ndof 66\nalternative two-sided\np-val 0.000275\nCI95% [0.98, 3.09]\ncohen-d 0.942375\nBF10 92.191\npower 0.966135\n```\n:::\n:::\n\n:::\n\nAgain, the p-value is what we're most interested in. Since the p-value is very small (much smaller than the standard significance level) we choose to say \"that it is very unlikely that these two samples came from the same parent distribution and as such we can reject our null hypothesis\" and state that:\n\n> A Student's t-test indicated that the mean body length of male guppies in the Guanapo river ($\\bar{x}$ = 18.29 mm) differs significantly from the mean body length of male guppies in the Aripo river ($\\bar{x}$ = 20.33 mm, p = 0.0003).\n\nNow there's a conversation starter.\n\n::: {.callout-important}\nComplete [Exercise -@sec-exr_turtles].\n:::\n\n## Dealing with non-normal data\n\nIf we're not sure that the data we are dealing with may come from a parent distribution that is normal, then we can't use a Student's t-test. Instead we use the **Mann-Whitney U test**. This test does not assume that the parent distributions are normally distributed. It does however assume that both have the same shape and variance. With this test we check if the medians of the two parent distributions differ significantly from each other.\n\n### Data and hypotheses\n\nAgain, we use the `rivers` data set. We want to test whether the median body length of male guppies differs between samples. We form the following null and alternative hypotheses:\n\n- $H_0$: The difference in median body length between the two groups is 0 $(\\mu A - \\mu G = 0)$\n- $H_1$: The difference in median body length between the two groups is not 0 $(\\mu A - \\mu G \\neq 0)$\n\nWe use a two-tailed Mann-Whitney U test to see if we can reject the null hypothesis.\n\n### Summarise and visualise\n\nWe did this in the [previous section](#cs1-students-sumvisual).\n\n### Assumptions\n\nWe have checked these previously.\n\n### Implement and interpret the test\n\nCalculate the median for each group (for reference) and perform a two-tailed, Mann-Whitney U test:\n\n::: {.panel-tabset group=\"language\"}\n## R\nWe group the data using `group_by()` for each `river` and then use the `summarise()` the data.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrivers %>% \n group_by(river) %>% \n summarise(median_length = median(length))\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 × 2\n river median_length\n \n1 Aripo 20.1\n2 Guanapo 18.8\n```\n:::\n:::\n\n\nPerform the Mann-Whitney U test:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox.test(length ~ river,\n alternative = \"two.sided\",\n data = rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tWilcoxon rank sum test with continuity correction\n\ndata: length by river\nW = 841, p-value = 0.0006464\nalternative hypothesis: true location shift is not equal to 0\n```\n:::\n:::\n\n\n- The first argument must be in the formula format: `variable ~ category`\n- The second argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n\nYou *may* get a warning message in the console stating `cannot compute exact p-value with ties`. This just means that some of the data points have exactly the same value which affects the internal mathematics slightly. However, given that the p-value is so very small, this is not something that we need to worry about.\n\nAfter the warning message:\n\n- The 1st line gives the name of the test and the 2nd line reminds you what the dataset was called, and what variables were used\n- The 3rd line contains the two key outputs from the test:\n - The calculated W-value is 841 (we'll use this in reporting)\n - The p-value is 0.0006464.\n- The 4th line simply states the alternative hypothesis in terms of the difference between the two sample medians in that if there were a difference then one distribution would be shifted relative to the other.\n\n## Python\n\nBefore we can implement the Mann-Whitney U test, we need to reformat our data a bit.\n\nThe `pg.mwu()` function requires the numerical input for the two groups it needs to compare.\n\nThe easiest way is to reformat our data from the *long* format where all the data are stacked on top of one another to the *wide* format, where the `length` values are in separate columns for the two rivers.\n\nWe can do this with the `pd.pivot()` function. We save the output in a new object and then access the values as required. It keeps all the data separate, meaning that there will be missing values `NaN` in this format. The `pg.mwu()` function ignores missing values by default.\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\nrivers_py_wide = pd.pivot(rivers_py,\n columns = 'river',\n values = 'length')\n \n# have a look at the format\nrivers_py_wide.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nriver Aripo Guanapo\n0 NaN 19.1\n1 NaN 23.3\n2 NaN 18.2\n3 NaN 16.4\n4 NaN 19.7\n```\n:::\n:::\n\n\nNext, we can calculate the median values for each river:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py_wide['Aripo'].median()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n20.1\n```\n:::\n\n```{.python .cell-code}\nrivers_py_wide['Guanapo'].median()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n18.8\n```\n:::\n:::\n\n\nFinally, we can perform the Mann-Whitney U test:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform the Mann-Whitney U test\n# ignoring the missing values\npg.mwu(rivers_py_wide['Aripo'],\n rivers_py_wide['Guanapo'])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n U-val alternative p-val RBC CLES\nMWU 841.0 two-sided 0.000646 -0.487179 0.74359\n```\n:::\n:::\n\n:::\n\nGiven that the p-value is less than 0.05 we can reject the null hypothesis at this confidence level. Again, the p-value on the 3rd line is what we're most interested in. Since the p-value is very small (much smaller than the standard significance level) we choose to say \"that it is very unlikely that these two samples came from the same parent distribution and as such we can reject our null hypothesis\".\n\nTo put it more completely, we can state that:\n\n> A Mann-Whitney test indicated that the median body length of male guppies in the Guanapo river ($\\tilde{x}$ = 18.8 mm) differs significantly from the median body length of male guppies in the Aripo river ($\\tilde{x}$ = 20.1 mm, p = 0.0006).\n\n\n## Exercises\n\n### Q-Q plots rivers {#sec-exr_rivers}\n\n:::{.callout-exercise}\n\n\n{{< level 1 >}}\n\n\n\nCreate the Q-Q plots for the two rivers in the `data/CS1-twosample.csv` file and discuss with your neighbour what you see in light of the results from the above Shapiro-Wilk test.\n\n::: {.callout-answer collapse=true}\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# we group the data by river\n# then create a panel per river\n# containing the Q-Q plot for that river\nggplot(rivers,\n aes(sample = length)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\") +\n facet_wrap(facets = vars(river))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-29-1.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(rivers_py,\n aes(sample = \"length\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\") +\n facet_wrap(\"river\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-30-1.png){width=614}\n:::\n:::\n\n:::\n:::\n:::\n\n### Turtles {#sec-exr_turtles}\n::: {.callout-exercise}\n\n\n{{< level 2 >}}\n\n\n\nThis exercise explores serum cholesterol concentrations in turtles.\n\nUsing the data in `data/CS1-turtle.csv`, test the null hypothesis that male and female turtles have the same mean serum cholesterol concentrations.\n\n1. Load the data\n2. Write down the null and alternative hypotheses\n3. Import the data\n4. Summarise and visualise the data\n5. Check your assumptions (normality and variance) using appropriate tests and plots\n6. Perform a two-sample t-test\n7. Write down a sentence that summarises the results that you have found\n\n::: {.callout-answer collapse=true}\n## Answer\n\n#### Data\n\nOnce you've loaded the data, have a look at the structure. The data is in a tidy data format, where each variable (the thing that you measure) is in its own column. Each observation has its own row.\n\n#### Hypotheses\n\n$H_0$ : male mean $=$ female mean\n\n$H_1$ : male mean $\\neq$ female mean\n\n#### Load, summarise and visualise data\n\nLet's load the data and explore our data a bit more before we dive into the statistics.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\nturtle <- read_csv(\"data/CS1-turtle.csv\")\n\n# and have a look\nturtle\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 13 × 2\n serum sex \n \n 1 220. Male \n 2 219. Male \n 3 230. Male \n 4 229. Male \n 5 222 Male \n 6 224. Male \n 7 226. Male \n 8 223. Female\n 9 222. Female\n10 230. Female\n11 224. Female\n12 224. Female\n13 231. Female\n```\n:::\n:::\n\n\nLet's summarise the data (although a visualisation is probably much easier to work with):\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create summary statistics for each group\nturtle %>% \n group_by(sex) %>% \n get_summary_stats(type = \"common\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 × 11\n sex variable n min max median iqr mean sd se ci\n \n1 Female serum 6 222. 231. 224. 5.22 226. 3.87 1.58 4.06\n2 Male serum 7 219. 230. 224. 6.6 224. 4.26 1.61 3.94\n```\n:::\n:::\n\n\nand visualise the data:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# visualise the data\nggplot(turtle,\n aes(x = sex, y = serum)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-33-1.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\nturtle_py = pd.read_csv(\"data/CS1-turtle.csv\")\n\nturtle_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n serum\ncount 13.000000\nmean 224.900000\nstd 3.978274\nmin 218.600000\n25% 222.000000\n50% 224.100000\n75% 228.800000\nmax 230.800000\n```\n:::\n:::\n\n\nand visualise the data:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(turtle_py,\n aes(x = \"sex\", y = \"serum\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-35-1.png){width=614}\n:::\n:::\n\n:::\n\nAs always we use the plot and summary to assess three things:\n\n1. Does it look like we've loaded the data in correctly?\n - We have two groups and the extreme values of our plots seem to match with our data set, so I'm happy that we haven't done anything massively wrong here.\n2. Do we think that there is a difference between the two groups?\n - We need the result of the formal test to make sense given the data, so it's important to develop a sense of what we think is going to happen here. Whilst the ranges of the two groups suggests that the Female serum levels might be higher than the males when we look at things more closely we realise that isn't the case. The box plot shows that the median values of the two groups is virtually identical and this is backed up by the summary statistics we calculated: the medians are both about 224.1, and the means are fairly close too (225.7 vs 224.2). Based on this, and the fact that there are only 13 observations in total I would be very surprised if any test came back showing that there was a difference between the groups.\n3. What do we think about assumptions?\n - Normality looks a bit worrying: whilst the `Male` group appears nice and symmetric (and so might be normal), the `Female` group appears to be quite skewed (since the median is much closer to the bottom than the top). We'll have to look carefully at the more formal checks to decided whether we think the data are normal enough for us to use a t-test.\n - Homogeneity of variance. At this stage the spread of the data within each group looks similar, but because of the potential skew in the `Female` group we'll again want to check the assumptions carefully.\n\n#### Assumptions\n\n**Normality**\n\nLet's look at the normality of each of the groups separately. There are several ways of getting at the `serum` values for `Male` and `Female` groups separately. All of them come down to splitting the data. Afterwards we use the Shapiro-Wilk ('formal' test), followed by Q-Q plots (much more informative).\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test on each group\nturtle %>% \n filter(sex == \"Female\") %>% \n pull(serum) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.84178, p-value = 0.1349\n```\n:::\n\n```{.r .cell-code}\nturtle %>% \n filter(sex == \"Male\") %>% \n pull(serum) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.94392, p-value = 0.6743\n```\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.normality(dv = \"serum\",\n group = \"sex\",\n data = turtle_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval normal\nMale 0.943924 0.674276 True\nFemale 0.841784 0.134869 True\n```\n:::\n:::\n\n:::\n\nThe p-values for both Shapiro-Wilk tests are non-significant which suggests that the data are normal enough. This is a bit surprising given what we saw in the box plot but there are two bits of information that we can use to reassure us.\n\n1. The p-value for the `Female` group is smaller than for the `Male` group (suggesting that the `Female` group is closer to being non-normal than the `Male` group) which makes sense based on our visual observations.\n2. The Shapiro-Wilk test is generally quite relaxed about normality for small sample sizes (and notoriously strict for very large sample sizes). For a group with only 6 data points in it, the data would actually have to have a really, really skewed distribution. Given that the Female group only has 6 data points in it, it's not too surprising that the Shapiro-Wilk test came back saying everything is OK.\n\nGiven these caveats of the Shapiro-Wilk test (I'll stop mentioning them now, I think I've made my opinion clear ;)), let's look at the Q-Q plots.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create Q-Q plots for both groups\nggplot(turtle,\n aes(sample = serum)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\") +\n facet_wrap(facets = vars(sex))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-38-1.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create Q-Q plots for both groups\n(ggplot(turtle_py,\n aes(sample = \"serum\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\") +\n facet_wrap(\"sex\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-39-1.png){width=614}\n:::\n:::\n\n:::\n\nThe results from the Q-Q plots echo what we've already seen from the Shapiro-Wilk analyses. The normality of the data in the `Male` group doesn't look too bad whereas the those in the `Female` group looks somewhat dodgy.\n\nOverall, the assumption of normality of the data doesn't appear to be very well met at all, but we do have to bear in mind that there are only a few data points in each group and we might just be seeing this pattern in the data due to random chance rather than because the underlying populations are actually not normally distributed. Personally, though I'd edge towards non-normal here.\n\n**Homogeneity of Variance**\n\nIt's not clear whether the data are normal or not, so it isn't clear which test to use here. The sensible approach is to do both and hope that they agree (fingers crossed!). Or err on the side of caution and assume they are not normal, but potentially throwing away statistical power (more on that later).\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nBartlett's test gives us:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Bartlett's test\nbartlett.test(serum ~ sex,\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tBartlett test of homogeneity of variances\n\ndata: serum by sex\nBartlett's K-squared = 0.045377, df = 1, p-value = 0.8313\n```\n:::\n:::\n\n\nand Levene's test gives us:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Levene's test\nlevene_test(serum ~ sex,\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 1 × 4\n df1 df2 statistic p\n \n1 1 11 0.243 0.631\n```\n:::\n:::\n\n\n## Python\n\nBartlett's test gives us:\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"serum\",\n group = \"sex\",\n method = \"bartlett\",\n data = turtle_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T pval equal_var\nbartlett 0.045377 0.831312 True\n```\n:::\n:::\n\n\nand Levene's test gives us:\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"serum\",\n group = \"sex\",\n method = \"levene\",\n data = turtle_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval equal_var\nlevene 0.243418 0.63145 True\n```\n:::\n:::\n\n:::\n\nThe good news is that both Levene and Bartlett agree that there is homogeneity of variance between the two groups (thank goodness, that's one less thing to worry about!).\n\nOverall, what this means is that we're not too sure about normality, but that homogeneity of variance is pretty good.\n\n#### Implement two-sample t-test\n\nBecause of the result of the Bartlett test I know that I can carry out a two-sample Student's t-test. If the variances between the two groups were not equal, then we'd have to perform Welch's t-test.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n::: {.callout-note}\nIn the `t.test()` function the default option for dealing with equality of variance is to assume there there isn't any. If you look at the help page with `?t.test()` then you'll see that the default for `var.equal = FALSE`. Here we *do* assume equality of variance, so we set it to `var.equal = TRUE`.\n:::\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform two-sample t-test\nt.test(serum ~ sex,\n alternative = \"two.sided\",\n var.equal = TRUE,\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tTwo Sample t-test\n\ndata: serum by sex\nt = 0.62681, df = 11, p-value = 0.5436\nalternative hypothesis: true difference in means between group Female and group Male is not equal to 0\n95 percent confidence interval:\n -3.575759 6.423378\nsample estimates:\nmean in group Female mean in group Male \n 225.6667 224.2429 \n```\n:::\n:::\n\n\n## Python\n\nTo test if the two groups are different from one another, we can use the `pg.ttest()` function.\n\nThis function requires the data to be separated into individual groups, so we need to split the `serum` data by `sex`. One of the ways we can do this is:\n\n\n::: {.cell}\n\n```{.python .cell-code}\nturtle_male = turtle_py.query('sex == \"Male\"')[\"serum\"]\nturtle_female = turtle_py.query('sex == \"Female\"')[\"serum\"]\n```\n:::\n\n\nNext, we use these data to test for differences:\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(turtle_female, turtle_male,\n alternative = \"two-sided\",\n correction = False).transpose()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T-test\nT 0.626811\ndof 11\nalternative two-sided\np-val 0.543573\nCI95% [-3.58, 6.42]\ncohen-d 0.348725\nBF10 0.519\npower 0.088495\n```\n:::\n:::\n\n:::\n\nWith a p-value of 0.544, this test tells us that there is insufficient evidence to suggest that the means of the two groups are different. A suitable summary sentence would be:\n\n> A Student's two-sample t-test indicated that the mean serum cholesterol level did not differ significantly between male and female turtles (p = 0.544).\n\n#### Discussion\n\nIn reality, because of the ambiguous normality assumption assessment, for this data set I would actually carry out two different tests; the two-sample t-test with equal variance and the Mann-Whitney U test. If both of them agreed then it wouldn't matter too much which one I reported (I'd personally report both with a short sentence to say that I'm doing that because it wasn't clear whether the assumption of normality had or had not been met), but it would be acceptable to report just one.\n:::\n:::\n\n### Turtles (revisited) {#sec-exr_turtlesrevisit}\n\n:::{.callout-exercise}\n\n\n{{< level 2 >}}\n\n\n\nAnalyse the turtle data set from before using a Mann-Whitney U test.\n\nWe follow the same process as with Student's t-test.\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : male median $=$ female median\n\n$H_1$ : male median $\\neq$ female median\n\n#### Summarise and visualise\n\nThis is the same as before.\n\n#### Assumptions\n\nWe've already checked that the variances of the two groups are similar, so we're OK there. Whilst the Mann-Whitney U test doesn't require normality or symmetry of distributions it does require that the distributions have the same shape. In this example, with just a handful of data points in each group, it's quite hard to make this call one way or another. My advice in this case would be say that unless it's obvious that the distributions are very different we can just allow this assumption to pass, and you're only going see obvious differences in distribution shape when you have considerably more data points than we have here.\n\n#### Carry out a Mann-Whitney U test\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox.test(serum ~ sex,\n alternative = \"two.sided\",\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tWilcoxon rank sum exact test\n\ndata: serum by sex\nW = 26, p-value = 0.5338\nalternative hypothesis: true location shift is not equal to 0\n```\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\nturtle_py_wide = pd.pivot(turtle_py,\n columns = 'sex',\n values = 'serum')\n \n# have a look at the format\nturtle_py_wide.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nsex Female Male\n0 NaN 220.1\n1 NaN 218.6\n2 NaN 229.6\n3 NaN 228.8\n4 NaN 222.0\n```\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform the Mann-Whitney U test\n# ignoring the missing values\npg.mwu(turtle_py_wide['Male'],\n turtle_py_wide['Female'])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n U-val alternative p-val RBC CLES\nMWU 16.0 two-sided 0.5338 0.238095 0.380952\n```\n:::\n:::\n\n:::\n\nThis gives us exactly the same conclusion that we got from the two-sample t-test _i.e_. that there isn't any significant difference between the two groups.\n\n> A Mann-Whitney U test indicated that there wasn't a significant difference in the median serum cholesterol levels between male and female turtles (p = 0.534)\n\n:::\n:::\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- Student's t tests are used when you have two samples of continuous data, which are normally distributed, independent of each other and have equal variance\n- A good way of assessing the assumption of normality is by checking the data against a Q-Q plot\n- We can check equality of variance (homoscedasticity) with Bartlett's (normal data) or Levene's (non-normal data) test\n- The Mann-Whitney U test is used when you have two samples of continuous data, which are not normally distributed, but are independent of each other, have equal variance and similar distributional shape\n:::\n", + "engine": "knitr", + "markdown": "---\ntitle: \"Two-sample data\"\n---\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n\n\n::: {.callout-tip}\n#### Learning outcomes\n\n**Questions**\n\n- When do I perform a two-sample test?\n- What are the assumptions?\n- How do I interpret and present the results of the test?\n- How do I deal with non-normal data?\n\n**Objectives**\n\n- Set out your hypothesis for comparing two samples of continuous data\n- Be able to summarise and visualise the data\n- Understand and assess the underlying assumptions of the test\n- Perform a two-sample t-test\n- Be able to interpret and report the results\n- Be able to do these steps on non-normal data\n\n:::\n\n## Libraries and functions\n\n::: {.callout-note collapse=\"true\"}\n## Click to expand\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n### Libraries\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# A collection of R packages designed for data science\nlibrary(tidyverse)\n\n# Converts stats functions to a tidyverse-friendly format\nlibrary(rstatix)\n```\n:::\n\n\n\n\n### Functions\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Computes summary statistics \nrstatix::get_summary_stats() \n\n# Performs Levene's test for equality of variance\n# (non-normally distributed data)\nrstatix::levene_test()\n\n# Performs Bartlett's test for equality of variance\n# (normally distributed data)\nstats::bartlett.test()\n\n# Performs Shapiro Wilk test\nstats::shapiro.test()\n\n# Performs one- and two-sample Wilcoxon tests\n# the latter is also known as 'Mann-Whitney U' test\nrstatix::wilcox_test()\n\n# Plots a Q-Q plot for comparison with a normal distribution\nggplot2::stat_qq()\n\n# Adds a comparison line to the Q-Q plot\nggplot2::stat_qq_line()\n```\n:::\n\n\n\n\n## Python\n\n| Libraries | Description |\n|:---------------------|:-------------------------------------------------|\n| `plotnine` | The Python equivalent of `ggplot2`. |\n| `pandas` | A Python data analysis and manipulation tool. |\n| `pingouin` | A Python module developed to have simple yet exhaustive stats functions. |\n\n| Functions | Description |\n|:-----------------------------------|:-----------------------------------|\n| `pandas.DataFrame.read_csv` | Reads in a `.csv` file |\n| `pandas.DataFrame.head()` | Plots the first few rows |\n| `pandas.DataFrame.describe()` | Gives summary statistics |\n| `pandas.DataFrame.groupby()` | Group DataFrame using a mapper or by a Series of columns |\n| `pandas.DataFrame.pivot()` | Return reshaped DataFrame organised by given index / column values. |\n| `pandas.DataFrame.query()` | Query the columns of a DataFrame with a boolean expression |\n| [`pingouin.normality()`](https://pingouin-stats.org/generated/pingouin.normality.html) | Performs the Shapiro-Wilk test for normality. |\n| [`pingouin.homoscedasticity()`](https://pingouin-stats.org/generated/pingouin.homoscedasticity.html#pingouin.homoscedasticity) | Checks for equality of variance. |\n| [`pingouin.ttest()`](https://pingouin-stats.org/generated/pingouin.ttest.html#pingouin.ttest) | Performs a t-test |\n| [`pingouin.mwu()`](https://pingouin-stats.org/generated/pingouin.mwu.html#pingouin.mwu) | Performs the Mann-Whitney U test. |\n| `plotnine.stats.stat_qq()` | Plots a Q-Q plot for comparison with a normal distribution. |\n| `plotnine.stats.stat_qq_line()` | Adds a comparison line to the Q-Q plot. |\n:::\n:::\n\n## Purpose and aim\nThese two-sample Student's t-test is used when we have two samples of continuous data where we are trying to find out if the samples came from the same parent distribution or not. This essentially boils down to finding out if there is a difference in the means of the two samples.\n\n## Data and hypotheses\n\nFor example, suppose we now measure the body lengths of male guppies (in mm) collected from two rivers in Trinidad; the Aripo and the Guanapo. We want to test whether the mean body length differs between samples. We form the following null and alternative hypotheses:\n\n- $H_0$: The mean body length does not differ between the two groups $(\\mu A = \\mu G)$\n- $H_1$: The mean body length does differ between the two groups $(\\mu A \\neq \\mu G)$\n\nWe use a two-sample, two-tailed t-test to see if we can reject the null hypothesis.\n\n- We use a two-sample test because we now have two samples.\n- We use a two-tailed t-test because we want to know if our data suggest that the true (population) means are different from one another rather than that one mean is specifically bigger or smaller than the other.\n- We're using Student's t-test because the sample sizes are big and because we're assuming that the parent populations have equal variance (We can check this later).\n\nThe data are stored in the file `data/CS1-twosample.csv`.\n\nLet's read in the data and have a quick look at the first rows to see how the data is structured.\n\nMake sure you have downloaded the data and placed it within your working directory.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nFirst we load the relevant libraries:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load tidyverse\nlibrary(tidyverse)\n\n# load rstatix, a tidyverse-friendly stats package\nlibrary(rstatix)\n```\n:::\n\n\n\n\nWe then read in the data and create a table containing the data.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrivers <- read_csv(\"data/CS1-twosample.csv\")\n\nrivers\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 68 × 2\n river length\n \n 1 Guanapo 19.1\n 2 Guanapo 23.3\n 3 Guanapo 18.2\n 4 Guanapo 16.4\n 5 Guanapo 19.7\n 6 Guanapo 16.6\n 7 Guanapo 17.5\n 8 Guanapo 19.9\n 9 Guanapo 19.1\n10 Guanapo 18.8\n# ℹ 58 more rows\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py = pd.read_csv(\"data/CS1-twosample.csv\")\n\nrivers_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n river length\n0 Guanapo 19.1\n1 Guanapo 23.3\n2 Guanapo 18.2\n3 Guanapo 16.4\n4 Guanapo 19.7\n```\n\n\n:::\n:::\n\n\n\n:::\n\n## Summarise and visualise {#cs1-students-sumvisual}\n\nLet's first summarise the data.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n river length \n Length:68 Min. :11.20 \n Class :character 1st Qu.:18.40 \n Mode :character Median :19.30 \n Mean :19.46 \n 3rd Qu.:20.93 \n Max. :26.40 \n```\n\n\n:::\n:::\n\n\n\n\nThis gives us the standard summary statistics, but in this case we have more than one group (Aripo and Guanapo), so it might be helpful to get summary statistics *per group*. One way of doing this is by using the `get_summary_stats()` function from the `rstatix` library.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get common summary stats for the length column\nrivers %>% \n group_by(river) %>% \n get_summary_stats(type = \"common\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 11\n river variable n min max median iqr mean sd se ci\n \n1 Aripo length 39 17.5 26.4 20.1 2.2 20.3 1.78 0.285 0.577\n2 Guanapo length 29 11.2 23.3 18.8 2.2 18.3 2.58 0.48 0.983\n```\n\n\n:::\n:::\n\n\n\n\nNumbers might not always give you the best insight into your data, so we also visualise our data:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nggplot(rivers,\n aes(x = river, y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n length\ncount 68.000000\nmean 19.463235\nstd 2.370081\nmin 11.200000\n25% 18.400000\n50% 19.300000\n75% 20.925000\nmax 26.400000\n```\n\n\n:::\n:::\n\n\n\n\nThis gives us the standard summary statistics, but in this case we have more than one group (Aripo and Guanapo), so it might be helpful to get summary statistics *per group*. Here we use the `pd.groupby()` function to group by `river`. We only want to have summary statistics for the `length` variable, so we specify that as well:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py.groupby(\"river\")[\"length\"].describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n count mean std min 25% 50% 75% max\nriver \nAripo 39.0 20.330769 1.780620 17.5 19.1 20.1 21.3 26.4\nGuanapo 29.0 18.296552 2.584636 11.2 17.5 18.8 19.7 23.3\n```\n\n\n:::\n:::\n\n\n\n\nNumbers might not always give you the best insight into your data, so we also visualise our data:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(rivers_py,\n aes(x = \"river\", y = \"length\")) + \n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-13-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nThe box plot does appear to suggest that the two samples have different means, and moreover that the guppies in Guanapo may be smaller than the guppies in Aripo. It isn't immediately obvious that the two populations don't have equal variances though (box plots are not quite the right tool for this), so we plough on. Who ever said statistics would be glamorous?\n\n## Assumptions\n\nIn order to use a Student's t-test (and for the results to be strictly valid) we have to make three assumptions:\n\n1. The parent distributions from which the samples are taken are both normally distributed (which would lead to the sample data being normally distributed too).\n2. Each data point in the samples is independent of the others.\n3. The parent distributions should have the same variance.\n\nIn this example the first assumption can be ignored as the sample sizes are large enough (because of maths, with Aripo containing 39 and Guanapo 29 samples). If the samples were smaller then we would use the tests from the previous section.\n\nThe second point we can do nothing about unless we know how the data were collected, so again we ignore it.\n\nThe third point regarding equality of variance can be tested using either Bartlett's test (if the samples are normally distributed) or Levene's test (if the samples are not normally distributed).\n\nThis is where it gets a bit trickier. Although we don't care if the samples are normally distributed for the t-test to be valid (because the sample size is big enough to compensate), we do need to know if they are normally distributed in order to decide which variance test to use.\n\nSo we perform a [Shapiro-Wilk test](#shapiro-wilk-test) on both samples separately.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nWe can use the `filter()` function to filter the data by `river`, then we perform the Shapiro-Wilk test on the `length` measurement. The `shapiro.test()` function needs the data in a vector format. We get these by using the `pull()` function.\n\n::: {.callout-tip}\nIt's good practice to check what kind of data is going into these functions. Run the code line-by-line to see what data is passed on from the `filter()` and `pull()` functions.\n:::\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# filter data by river and perform test\nrivers %>% \n filter(river == \"Aripo\") %>% \n pull(length) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.93596, p-value = 0.02802\n```\n\n\n:::\n\n```{.r .cell-code}\nrivers %>% \n filter(river == \"Guanapo\") %>% \n pull(length) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.94938, p-value = 0.1764\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\nTo perform a Shapiro-Wilk test we can use the `normality()` function from `pingouin`. We can give it the data in the original 'long' format, where we specify:\n\n- `dv` = dependent variable, `length`\n- `group` = grouping variable, `river`\n- `data` = data frame\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.normality(dv = \"length\",\n group = \"river\",\n data = rivers_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval normal\nriver \nGuanapo 0.949384 0.176423 True\nAripo 0.935958 0.028023 False\n```\n\n\n:::\n:::\n\n\n\n:::\n\nWe can see that whilst the Guanapo data is probably normally distributed (p = 0.1764 \\> 0.05), the Aripo data is unlikely to be normally distributed (p = 0.02802 \\< 0.05). Remember that the p-value gives the probability of observing each sample if the parent population is actually normally distributed.\n\nThe Shapiro-Wilk test is quite sensitive to sample size. This means that if you have a large sample then even small deviations from normality will cause the sample to fail the test, whereas smaller samples are allowed to pass with much larger deviations. Here the Aripo data has nearly 40 points in it compared with the Guanapo data and so it is much easier for the Aripo sample to fail compared with the Guanapo data.\n\n:::{.callout-important}\nComplete [Exercise -@sec-exr_rivers].\n:::\n\nThe Q-Q plots show the opposite of what we found with the Shapiro-Wilk tests: the data for Aripo look pretty normally distributed apart from one data point, whereas the assumption of normality for the Guanapo data is less certain.\n\nWhat to do? Well, you could be conservative and state that you are not confident that the data in either group are normally distributed. That would be a perfectly reasonable conclusion.\n\nI would personally not have issues with stating that the Aripo data are probably normally distributed *enough*.\n\n## Equality of variance\n\n:::{.callout-tip}\nRemember that statistical tests do not provide answers, they merely suggest patterns. Human interpretation is still a crucial aspect to what we do.\n:::\n\nThe reason why we're checking for equality of variance (also referred to as **homogeneity of variance**) is because many statistical tests assume that the spread of the data within different parental populations (in this case, two) is the same.\n\nIf that is indeed the case, then the data themselves should have equal spread as well.\n\nThe Shapiro-Wilk test and the Q-Q plots have shown that some of the data might not be normal *enough* (although in opposite directions!) and so in order to test for equality of variance we will use Levene's test.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nThe function we use is `levene_test()` from the `rstatix` library.\n\nIt takes the data in the form of a formula as follows:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlevene_test(data = rivers,\n formula = length ~ river)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning in leveneTest.default(y = y, group = group, ...): group coerced to\nfactor.\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 4\n df1 df2 statistic p\n \n1 1 66 1.77 0.188\n```\n\n\n:::\n:::\n\n\n\n\nOr shortened:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlevene_test(rivers,\n length ~ river)\n```\n:::\n\n\n\n\nThe key bit of information is the `p` column. This is the p-value 0.1876 for this test.\n\n## Python\n\nTo test for equality of variance, we can use the `homoscedasticity()` function from `pingouin`.\n\nNote that, contrary to R, we specify the type of test in the `method` argument. The default is `\"levene\"`, assuming that data are *not* normally distributed.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"length\",\n group = \"river\",\n method = \"levene\",\n data = rivers_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval equal_var\nlevene 1.773184 0.187569 True\n```\n\n\n:::\n:::\n\n\n\n:::\n\nThe p-value tells us the probability of observing these two samples if they come from distributions with the same variance. As this probability is greater than our arbitrary significance level of 0.05 then we can be somewhat confident that the necessary assumptions for carrying out Student's t-test on these two samples was valid. (Once again woohoo!)\n\n### Bartlett's test\n\nIf we had wanted to carry out Bartlett's test (i.e. if the data *had* been sufficiently normally distributed) then we would have done:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nHere we use `bartlett.test()` function.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nbartlett.test(length ~ river, data = rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tBartlett test of homogeneity of variances\n\ndata: length by river\nBartlett's K-squared = 4.4734, df = 1, p-value = 0.03443\n```\n\n\n:::\n:::\n\n\n\n\nThe relevant p-value is given on the 3rd line.\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"length\",\n group = \"river\",\n method = \"bartlett\",\n data = rivers_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T pval equal_var\nbartlett 4.473437 0.034426 False\n```\n\n\n:::\n:::\n\n\n\n:::\n\n## Implement and interpret the test\n\nIn this case we're ignoring the fact that the data are not normal enough, according to the Shapiro-Wilk test. However, this is not entirely naughty, because the sample sizes are pretty large and the t-test is also pretty robust in this case, we can perform a t-test. Remember, this is only allowed because the variances of the two groups (Aripo and Guanapo) are equal.\n\nPerform a two-sample, two-tailed, t-test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# two-sample, two-tailed t-test\nt_test(length ~ river,\n alternative = \"two.sided\",\n var.equal = TRUE,\n data = rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 8\n .y. group1 group2 n1 n2 statistic df p\n* \n1 length Aripo Guanapo 39 29 3.84 66 0.000275\n```\n\n\n:::\n:::\n\n\n\n\nHere we do the following:\n\n- The first argument must be in the formula format: `response ~ predictor`\n- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n- The `var.equal` argument says whether the variance of the two samples can be assumed to be equal (Student's t-test) or unequal (Welch's t-test)\n\nSo, how do we interpret these results?\n\n- `.y.` gives you the name of the response variable, or variable of interest (`length` in our case)\n- `group1` and `group2` gives you the names of the groups being compared\n- `n1` and `n2` gives you the number of observations in each group\n- `statistic` gives you the value of the statistic (t = 3.8432667)\n- `df` gives you the number of degrees of freedom (66)\n- `p` gives you the p-value (2.75\\times 10^{-4})\n\n## Python\n\nThe `ttest()` function in `pingouin` needs two vectors as input, so we split the data as follows:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\naripo = rivers_py.query('river == \"Aripo\"')[\"length\"]\nguanapo = rivers_py.query('river == \"Guanapo\"')[\"length\"]\n```\n:::\n\n\n\n\nNext, we perform the t-test. We specify that the variance are equal by setting `correction = False`. We also `transpose()` the data, so we can actually see the entire output.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(aripo, guanapo,\n correction = False).transpose()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T-test\nT 3.843267\ndof 66\nalternative two-sided\np-val 0.000275\nCI95% [0.98, 3.09]\ncohen-d 0.942375\nBF10 92.191\npower 0.966135\n```\n\n\n:::\n:::\n\n\n\n:::\n\nAgain, the p-value is what we're most interested in. Since the p-value is very small (much smaller than the standard significance level) we choose to say \"that it is very unlikely that these two samples came from the same parent distribution and as such we can reject our null hypothesis\" and state that:\n\n> A Student's t-test indicated that the mean body length of male guppies in the Guanapo river ($\\bar{x}$ = 18.29 mm) differs significantly from the mean body length of male guppies in the Aripo river ($\\bar{x}$ = 20.33 mm, p = 0.0003).\n\nNow there's a conversation starter.\n\n::: {.callout-important}\nComplete [Exercise -@sec-exr_turtles].\n:::\n\n## Dealing with non-normal data\n\nIf we're not sure that the data we are dealing with may come from a parent distribution that is normal, then we can't use a Student's t-test. Instead we use the **Mann-Whitney U test**. This test does not assume that the parent distributions are normally distributed. It does however assume that both have the same shape and variance. With this test we check if the medians of the two parent distributions differ significantly from each other.\n\n### Data and hypotheses\n\nAgain, we use the `rivers` data set. We want to test whether the median body length of male guppies differs between samples. We form the following null and alternative hypotheses:\n\n- $H_0$: The difference in median body length between the two groups is 0 $(\\mu A - \\mu G = 0)$\n- $H_1$: The difference in median body length between the two groups is not 0 $(\\mu A - \\mu G \\neq 0)$\n\nWe use a two-tailed Mann-Whitney U test to see if we can reject the null hypothesis.\n\n### Summarise and visualise\n\nWe did this in the [previous section](#cs1-students-sumvisual).\n\n### Assumptions\n\nWe have checked these previously.\n\n### Implement and interpret the test\n\nCalculate the median for each group (for reference) and perform a two-tailed, Mann-Whitney U test:\n\n::: {.panel-tabset group=\"language\"}\n## R\nWe group the data using `group_by()` for each `river` and then use the `summarise()` the data.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrivers %>% \n group_by(river) %>% \n summarise(median_length = median(length))\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 2\n river median_length\n \n1 Aripo 20.1\n2 Guanapo 18.8\n```\n\n\n:::\n:::\n\n\n\n\nPerform the Mann-Whitney U test:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox_test(length ~ river,\n alternative = \"two.sided\",\n data = rivers)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n .y. group1 group2 n1 n2 statistic p\n* \n1 length Aripo Guanapo 39 29 841 0.000646\n```\n\n\n:::\n:::\n\n\n\n\n- The first argument must be in the formula format: `response ~ predictor`\n- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n\nThe output:\n\n- `.y.` gives you the name of the response variable, or variable of interest (`length` in our case)\n- `group1` and `group2` gives you the names of the groups being compared\n- `n1` and `n2` gives you the number of observations in each group\n- `statistic` gives you the value of the statistic (t = 841)\n- `p` gives you the p-value (6.46\\times 10^{-4})\n\n## Python\n\nBefore we can implement the Mann-Whitney U test, we need to reformat our data a bit.\n\nThe `pg.mwu()` function requires the numerical input for the two groups it needs to compare.\n\nThe easiest way is to reformat our data from the *long* format where all the data are stacked on top of one another to the *wide* format, where the `length` values are in separate columns for the two rivers.\n\nWe can do this with the `pd.pivot()` function. We save the output in a new object and then access the values as required. It keeps all the data separate, meaning that there will be missing values `NaN` in this format. The `pg.mwu()` function ignores missing values by default.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\nrivers_py_wide = pd.pivot(rivers_py,\n columns = 'river',\n values = 'length')\n \n# have a look at the format\nrivers_py_wide.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nriver Aripo Guanapo\n0 NaN 19.1\n1 NaN 23.3\n2 NaN 18.2\n3 NaN 16.4\n4 NaN 19.7\n```\n\n\n:::\n:::\n\n\n\n\nNext, we can calculate the median values for each river:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nrivers_py_wide['Aripo'].median()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nnp.float64(20.1)\n```\n\n\n:::\n\n```{.python .cell-code}\nrivers_py_wide['Guanapo'].median()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nnp.float64(18.8)\n```\n\n\n:::\n:::\n\n\n\n\nFinally, we can perform the Mann-Whitney U test:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform the Mann-Whitney U test\n# ignoring the missing values\npg.mwu(rivers_py_wide['Aripo'],\n rivers_py_wide['Guanapo'])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n U-val alternative p-val RBC CLES\nMWU 841.0 two-sided 0.000646 0.487179 0.74359\n```\n\n\n:::\n:::\n\n\n\n:::\n\nGiven that the p-value is less than 0.05 we can reject the null hypothesis at this confidence level. Again, the p-value on the 3rd line is what we're most interested in. Since the p-value is very small (much smaller than the standard significance level) we choose to say \"that it is very unlikely that these two samples came from the same parent distribution and as such we can reject our null hypothesis\".\n\nTo put it more completely, we can state that:\n\n> A Mann-Whitney test indicated that the median body length of male guppies in the Guanapo river ($\\tilde{x}$ = 18.8 mm) differs significantly from the median body length of male guppies in the Aripo river ($\\tilde{x}$ = 20.1 mm, p = 0.0006).\n\n\n## Exercises\n\n### Q-Q plots rivers {#sec-exr_rivers}\n\n:::{.callout-exercise}\n\n\n\n\n{{< level 1 >}}\n\n\n\n\n\n\nCreate the Q-Q plots for the two rivers in the `data/CS1-twosample.csv` file and discuss with your neighbour what you see in light of the results from the above Shapiro-Wilk test.\n\n::: {.callout-answer collapse=true}\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# we group the data by river\n# then create a panel per river\n# containing the Q-Q plot for that river\nggplot(rivers,\n aes(sample = length)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\") +\n facet_wrap(facets = vars(river))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-29-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(rivers_py,\n aes(sample = \"length\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\") +\n facet_wrap(\"river\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-30-1.png){width=614}\n:::\n:::\n\n\n\n:::\n:::\n:::\n\n### Turtles {#sec-exr_turtles}\n::: {.callout-exercise}\n\n\n\n\n{{< level 2 >}}\n\n\n\n\n\n\nThis exercise explores serum cholesterol concentrations in turtles.\n\nUsing the data in `data/CS1-turtle.csv`, test the null hypothesis that male and female turtles have the same mean serum cholesterol concentrations.\n\n1. Load the data\n2. Write down the null and alternative hypotheses\n3. Import the data\n4. Summarise and visualise the data\n5. Check your assumptions (normality and variance) using appropriate tests and plots\n6. Perform a two-sample t-test\n7. Write down a sentence that summarises the results that you have found\n\n::: {.callout-answer collapse=true}\n## Answer\n\n#### Data\n\nOnce you've loaded the data, have a look at the structure. The data is in a tidy data format, where each variable (the thing that you measure) is in its own column. Each observation has its own row.\n\n#### Hypotheses\n\n$H_0$ : male mean $=$ female mean\n\n$H_1$ : male mean $\\neq$ female mean\n\n#### Load, summarise and visualise data\n\nLet's load the data and explore our data a bit more before we dive into the statistics.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\nturtle <- read_csv(\"data/CS1-turtle.csv\")\n\n# and have a look\nturtle\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 13 × 2\n serum sex \n \n 1 220. Male \n 2 219. Male \n 3 230. Male \n 4 229. Male \n 5 222 Male \n 6 224. Male \n 7 226. Male \n 8 223. Female\n 9 222. Female\n10 230. Female\n11 224. Female\n12 224. Female\n13 231. Female\n```\n\n\n:::\n:::\n\n\n\n\nLet's summarise the data (although a visualisation is probably much easier to work with):\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create summary statistics for each group\nturtle %>% \n group_by(sex) %>% \n get_summary_stats(type = \"common\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 11\n sex variable n min max median iqr mean sd se ci\n \n1 Female serum 6 222. 231. 224. 5.22 226. 3.87 1.58 4.06\n2 Male serum 7 219. 230. 224. 6.6 224. 4.26 1.61 3.94\n```\n\n\n:::\n:::\n\n\n\n\nand visualise the data:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# visualise the data\nggplot(turtle,\n aes(x = sex, y = serum)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-33-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nturtle_py = pd.read_csv(\"data/CS1-turtle.csv\")\n\nturtle_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n serum\ncount 13.000000\nmean 224.900000\nstd 3.978274\nmin 218.600000\n25% 222.000000\n50% 224.100000\n75% 228.800000\nmax 230.800000\n```\n\n\n:::\n:::\n\n\n\n\nand visualise the data:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(turtle_py,\n aes(x = \"sex\", y = \"serum\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-35-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nAs always we use the plot and summary to assess three things:\n\n1. Does it look like we've loaded the data in correctly?\n - We have two groups and the extreme values of our plots seem to match with our data set, so I'm happy that we haven't done anything massively wrong here.\n2. Do we think that there is a difference between the two groups?\n - We need the result of the formal test to make sense given the data, so it's important to develop a sense of what we think is going to happen here. Whilst the ranges of the two groups suggests that the Female serum levels might be higher than the males when we look at things more closely we realise that isn't the case. The box plot shows that the median values of the two groups is virtually identical and this is backed up by the summary statistics we calculated: the medians are both about 224.1, and the means are fairly close too (225.7 vs 224.2). Based on this, and the fact that there are only 13 observations in total I would be very surprised if any test came back showing that there was a difference between the groups.\n3. What do we think about assumptions?\n - Normality looks a bit worrying: whilst the `Male` group appears nice and symmetric (and so might be normal), the `Female` group appears to be quite skewed (since the median is much closer to the bottom than the top). We'll have to look carefully at the more formal checks to decided whether we think the data are normal enough for us to use a t-test.\n - Homogeneity of variance. At this stage the spread of the data within each group looks similar, but because of the potential skew in the `Female` group we'll again want to check the assumptions carefully.\n\n#### Assumptions\n\n**Normality**\n\nLet's look at the normality of each of the groups separately. There are several ways of getting at the `serum` values for `Male` and `Female` groups separately. All of them come down to splitting the data. Afterwards we use the Shapiro-Wilk ('formal' test), followed by Q-Q plots (much more informative).\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test on each group\nturtle %>% \n filter(sex == \"Female\") %>% \n pull(serum) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.84178, p-value = 0.1349\n```\n\n\n:::\n\n```{.r .cell-code}\nturtle %>% \n filter(sex == \"Male\") %>% \n pull(serum) %>% \n shapiro.test()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: .\nW = 0.94392, p-value = 0.6743\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.normality(dv = \"serum\",\n group = \"sex\",\n data = turtle_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval normal\nsex \nMale 0.943924 0.674275 True\nFemale 0.841785 0.134869 True\n```\n\n\n:::\n:::\n\n\n\n:::\n\nThe p-values for both Shapiro-Wilk tests are non-significant which suggests that the data are normal enough. This is a bit surprising given what we saw in the box plot but there are two bits of information that we can use to reassure us.\n\n1. The p-value for the `Female` group is smaller than for the `Male` group (suggesting that the `Female` group is closer to being non-normal than the `Male` group) which makes sense based on our visual observations.\n2. The Shapiro-Wilk test is generally quite relaxed about normality for small sample sizes (and notoriously strict for very large sample sizes). For a group with only 6 data points in it, the data would actually have to have a really, really skewed distribution. Given that the Female group only has 6 data points in it, it's not too surprising that the Shapiro-Wilk test came back saying everything is OK.\n\nGiven these caveats of the Shapiro-Wilk test (I'll stop mentioning them now, I think I've made my opinion clear ;)), let's look at the Q-Q plots.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create Q-Q plots for both groups\nggplot(turtle,\n aes(sample = serum)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\") +\n facet_wrap(facets = vars(sex))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-38-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create Q-Q plots for both groups\n(ggplot(turtle_py,\n aes(sample = \"serum\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\") +\n facet_wrap(\"sex\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples_files/figure-html/unnamed-chunk-39-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nThe results from the Q-Q plots echo what we've already seen from the Shapiro-Wilk analyses. The normality of the data in the `Male` group doesn't look too bad whereas the those in the `Female` group looks somewhat dodgy.\n\nOverall, the assumption of normality of the data doesn't appear to be very well met at all, but we do have to bear in mind that there are only a few data points in each group and we might just be seeing this pattern in the data due to random chance rather than because the underlying populations are actually not normally distributed. Personally, though I'd edge towards non-normal here.\n\n**Homogeneity of Variance**\n\nIt's not clear whether the data are normal or not, so it isn't clear which test to use here. The sensible approach is to do both and hope that they agree (fingers crossed!). Or err on the side of caution and assume they are not normal, but potentially throwing away statistical power (more on that later).\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nBartlett's test gives us:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Bartlett's test\nbartlett.test(serum ~ sex,\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tBartlett test of homogeneity of variances\n\ndata: serum by sex\nBartlett's K-squared = 0.045377, df = 1, p-value = 0.8313\n```\n\n\n:::\n:::\n\n\n\n\nand Levene's test gives us:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Levene's test\nlevene_test(serum ~ sex,\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 4\n df1 df2 statistic p\n \n1 1 11 0.243 0.631\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\nBartlett's test gives us:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"serum\",\n group = \"sex\",\n method = \"bartlett\",\n data = turtle_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T pval equal_var\nbartlett 0.045377 0.831312 True\n```\n\n\n:::\n:::\n\n\n\n\nand Levene's test gives us:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.homoscedasticity(dv = \"serum\",\n group = \"sex\",\n method = \"levene\",\n data = turtle_py)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval equal_var\nlevene 0.243418 0.63145 True\n```\n\n\n:::\n:::\n\n\n\n:::\n\nThe good news is that both Levene and Bartlett agree that there is homogeneity of variance between the two groups (thank goodness, that's one less thing to worry about!).\n\nOverall, what this means is that we're not too sure about normality, but that homogeneity of variance is pretty good.\n\n#### Implement two-sample t-test\n\nBecause of the result of the Bartlett test I know that I can carry out a two-sample Student's t-test. If the variances between the two groups were not equal, then we'd have to perform Welch's t-test.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n::: {.callout-note}\nIn the `t_test()` function the default option for dealing with equality of variance is to assume there there isn't any. If you look at the help page with `?t_test()` then you'll see that the default for `var.equal = FALSE`. Here we *do* assume equality of variance, so we set it to `var.equal = TRUE`.\n:::\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform two-sample t-test\nt_test(serum ~ sex,\n alternative = \"two.sided\",\n var.equal = TRUE,\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 8\n .y. group1 group2 n1 n2 statistic df p\n* \n1 serum Female Male 6 7 0.627 11 0.544\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\nTo test if the two groups are different from one another, we can use the `pg.ttest()` function.\n\nThis function requires the data to be separated into individual groups, so we need to split the `serum` data by `sex`. One of the ways we can do this is:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\nturtle_male = turtle_py.query('sex == \"Male\"')[\"serum\"]\nturtle_female = turtle_py.query('sex == \"Female\"')[\"serum\"]\n```\n:::\n\n\n\n\nNext, we use these data to test for differences:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(turtle_female, turtle_male,\n alternative = \"two-sided\",\n correction = False).transpose()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T-test\nT 0.626811\ndof 11\nalternative two-sided\np-val 0.543573\nCI95% [-3.58, 6.42]\ncohen-d 0.348725\nBF10 0.519\npower 0.088495\n```\n\n\n:::\n:::\n\n\n\n:::\n\nWith a p-value of 0.544, this test tells us that there is insufficient evidence to suggest that the means of the two groups are different. A suitable summary sentence would be:\n\n> A Student's two-sample t-test indicated that the mean serum cholesterol level did not differ significantly between male and female turtles (p = 0.544).\n\n#### Discussion\n\nIn reality, because of the ambiguous normality assumption assessment, for this data set I would actually carry out two different tests; the two-sample t-test with equal variance and the Mann-Whitney U test. If both of them agreed then it wouldn't matter too much which one I reported (I'd personally report both with a short sentence to say that I'm doing that because it wasn't clear whether the assumption of normality had or had not been met), but it would be acceptable to report just one.\n:::\n:::\n\n### Turtles (revisited) {#sec-exr_turtlesrevisit}\n\n:::{.callout-exercise}\n\n\n\n\n{{< level 2 >}}\n\n\n\n\n\n\nAnalyse the turtle data set from before using a Mann-Whitney U test.\n\nWe follow the same process as with Student's t-test.\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : male median $=$ female median\n\n$H_1$ : male median $\\neq$ female median\n\n#### Summarise and visualise\n\nThis is the same as before.\n\n#### Assumptions\n\nWe've already checked that the variances of the two groups are similar, so we're OK there. Whilst the Mann-Whitney U test doesn't require normality or symmetry of distributions it does require that the distributions have the same shape. In this example, with just a handful of data points in each group, it's quite hard to make this call one way or another. My advice in this case would be say that unless it's obvious that the distributions are very different we can just allow this assumption to pass, and you're only going see obvious differences in distribution shape when you have considerably more data points than we have here.\n\n#### Carry out a Mann-Whitney U test\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox_test(serum ~ sex,\n alternative = \"two.sided\",\n data = turtle)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n .y. group1 group2 n1 n2 statistic p\n* \n1 serum Female Male 6 7 26 0.534\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\nturtle_py_wide = pd.pivot(turtle_py,\n columns = 'sex',\n values = 'serum')\n \n# have a look at the format\nturtle_py_wide.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nsex Female Male\n0 NaN 220.1\n1 NaN 218.6\n2 NaN 229.6\n3 NaN 228.8\n4 NaN 222.0\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform the Mann-Whitney U test\n# ignoring the missing values\npg.mwu(turtle_py_wide['Male'],\n turtle_py_wide['Female'])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n U-val alternative p-val RBC CLES\nMWU 16.0 two-sided 0.5338 -0.238095 0.380952\n```\n\n\n:::\n:::\n\n\n\n:::\n\nThis gives us exactly the same conclusion that we got from the two-sample t-test _i.e_. that there isn't any significant difference between the two groups.\n\n> A Mann-Whitney U test indicated that there wasn't a significant difference in the median serum cholesterol levels between male and female turtles (p = 0.534)\n\n:::\n:::\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- Student's t tests are used when you have two samples of continuous data, which are normally distributed, independent of each other and have equal variance\n- A good way of assessing the assumption of normality is by checking 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b/materials/cs1_practical_one-sample.qmd @@ -50,6 +50,9 @@ exec(open('setup_files/setup.py').read()) #| eval: false # A collection of R packages designed for data science library(tidyverse) + +# Converts stats functions to a tidyverse-friendly format +library(rstatix) ``` ### Functions @@ -58,13 +61,13 @@ library(tidyverse) #| eval: false #| warning: false # Performs a one-sample t-test, Student's t-test and Welch's t-test in later sections -stats::t.test() +rstatix::t_test() # Performs a Shapiro-Wilk test for normality stats::shapiro.test() # Performs one and two sample Wilcoxon tests -stats::wilcox.test() +rstatix::wilcox_test() # Plots a Q-Q plot for comparison with a normal distribution ggplot2::stat_qq() @@ -473,24 +476,23 @@ Perform a one-sample, two-tailed t-test: ## R ```{r} -t.test(fishlengthDF$length, - mu = 20, alternative = "two.sided") +t_test(length ~ 1, + mu = 20, alternative = "two.sided", + data = fishlengthDF) ``` -- The first argument must be a numerical vector of data values. In our case it's the `length` values. -- The second argument must be a number and is the mean to be tested under the null hypothesis. -- The third argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`. We have no prior assumptions on whether the alternative fish length would be greater or less than 20, so we choose `two.sided`. +- The first argument must be a numerical column of data values. In our case it's the `length` values. We add `~ 1` to indicate that we're going to compare these to a "null model". More on this later, but for now it's just a quirky syntax thing in R. +- The `mu` argument must be a number and is the mean to be tested under the null hypothesis. +- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`. We have no prior assumptions on whether the alternative fish length would be greater or less than 20, so we choose `two.sided`. In the output: -- The 1st line gives the name of the test and the 2nd line reminds you what the dataset was called -- The 3rd line contains the three key outputs from the test: - - The calculated t-value is -3.5492 (we'll need this for reporting) - - There are 28 degrees of freedom (again we'll need this for reporting) - - The p-value is 0.001387. -- The 4th line simply states the alternative hypothesis -- The 5th and 6th lines give the 95th confidence interval (we don't need to know this) -- The 7th, 8th and 9th lines give the sample mean again (18.29655). +- `.y.` is the variable of interest +- `n` is the number of observations +- ignore the `group` columns for now +- `statistic` is the calculated t-value (`r t_test(length ~ 1, mu = 20, alternative = "two.sided", data = fishlengthDF) |> pull(statistic)`) +- `df` gives us the number of degrees of freedom (`r t_test(length ~ 1, mu = 20, alternative = "two.sided", data = fishlengthDF) |> pull(df)`). Again, more on this later. +- `p` gives us the p-value of `r t_test(length ~ 1, mu = 20, alternative = "two.sided", data = fishlengthDF) |> pull(p)` ## Python @@ -626,28 +628,16 @@ Perform a one-sample, two-tailed Wilcoxon signed rank test: ## R ```{r} -wilcox.test(fishlengthDF$length, - mu = 20, alternative = "two.sided") +wilcox_test(length ~ 1, + mu = 20, alternative = "two.sided", + data = fishlengthDF) ``` The syntax is identical to the one-sample t-test we carried out earlier. -- The first argument must be a numerical vector of data values. - -- The second argument must be a number and is the median to be tested under the null hypothesis. - -- The third argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less.` - -- The first line gives a warning (not an error) message regarding the implementation of this test. This can be safely ignored in this case as the p-value is so small, but essentially, it's letting you know that some of the data values are identical to each other. This is not supposed to happen as we should be dealing with continuous data for this test, but in practice it's not something that we need to worry ourselves with. - -- It then gives you the name of the test and `data:` reminds you what the data set was called (here it's the numerical `length` values coming from the pipe) - -- Next are the two key outputs from the test: - - - The calculated V statistic is 67.5 - - The p-value is 0.001222. - -- The last line simply states the alternative hypothesis +- The first argument contains the column with data values. +- The `mu` argument must be a number and is the median to be tested under the null hypothesis. +- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less.` ## Python @@ -825,8 +815,10 @@ In reality, if I found myself in this situation I would also try doing a non-par ```{r} # perform one-sample t-test -t.test(dissolving$dissolving_time, - mu = 45, alternative = "two.sided") +dissolving +t_test(dissolving_time ~ 1, + mu = 45, alternative = "two.sided", + data = dissolving) ``` ## Python @@ -881,8 +873,9 @@ median(dissolving$dissolving_time) ``` ```{r} -wilcox.test(dissolving$dissolving_time, - mu = 45, alternative = "two.sided") +wilcox_test(dissolving_time ~ 1, + mu = 45, alternative = "two.sided", + data = dissolving) ``` ## Python diff --git a/materials/cs1_practical_two-samples-paired.qmd b/materials/cs1_practical_two-samples-paired.qmd index 1d8bd70..b1e86b8 100644 --- a/materials/cs1_practical_two-samples-paired.qmd +++ b/materials/cs1_practical_two-samples-paired.qmd @@ -53,6 +53,9 @@ A paired t-test is used when we have two samples of continuous data that can be #| eval: false # A collection of R packages designed for data science library(tidyverse) + +# Converts stats functions to a tidyverse-friendly format +library(rstatix) ``` ### Functions @@ -62,13 +65,13 @@ library(tidyverse) #| warning: false # Performs a one-sample t-test, Student's t-test # and Welch's t-test in later sections -stats::t.test() +rstatix::t_test() # Performs a Shapiro-Wilk test for normality stats::shapiro.test() # Performs one and two sample Wilcoxon tests -stats::wilcox.test() +rstatix::wilcox_test() # Plots a Q-Q plot for comparison with a normal distribution ggplot2::stat_qq() @@ -295,17 +298,17 @@ Perform a two-sample, two-tailed, paired t-test: ```{r} #| results: hide # perform the test -t.test(cortisol ~ time, +t_test(cortisol ~ time, alternative = "two.sided", paired = TRUE, data = cortisol) ``` -- The first two arguments define the formula -- The third argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` -- The fourth argument says that the data are paired +- The first argument must be in the formula format: `response ~ predictor` +- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` +- The `paired = TRUE` argument indicates that the data are paired -From our perspective the value of interest is the `p-value`. +From our perspective the value of interest in the output is the `p` (`r t_test(cortisol ~ time, alternative = "two.sided", paired = TRUE, data = cortisol) |> pull(p)`). ## Python @@ -358,16 +361,15 @@ Perform a two-tailed, Wilcoxon signed rank test: ## R ```{r} -wilcox.test(cortisol ~ time, +wilcox_test(cortisol ~ time, alternative = "two.sided", paired = TRUE, data = cortisol) ``` -- The first argument gives the formula -- The second argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` -- The third argument indicates that the test is paired -- The last argument is the data set +- The first argument must be in the formula format: `response ~ predictor` +- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` +- The `paired = TRUE` argument indicates that the data are paired ## Python diff --git a/materials/cs1_practical_two-samples.qmd b/materials/cs1_practical_two-samples.qmd index 6b8c97b..798025d 100644 --- a/materials/cs1_practical_two-samples.qmd +++ b/materials/cs1_practical_two-samples.qmd @@ -78,7 +78,7 @@ stats::shapiro.test() # Performs one- and two-sample Wilcoxon tests # the latter is also known as 'Mann-Whitney U' test -stats::wilcox.test() +rstatix::wilcox_test() # Plots a Q-Q plot for comparison with a normal distribution ggplot2::stat_qq() @@ -375,7 +375,7 @@ Perform a two-sample, two-tailed, t-test: ```{r} # two-sample, two-tailed t-test -t.test(length ~ river, +t_test(length ~ river, alternative = "two.sided", var.equal = TRUE, data = rivers) @@ -383,20 +383,18 @@ t.test(length ~ river, Here we do the following: -- The first argument must be in the formula format: `variables ~ category` -- The second argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` -- The third argument says whether the variance of the two samples can be assumed to be equal (Student's t-test) or unequal (Welch's t-test) +- The first argument must be in the formula format: `response ~ predictor` +- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` +- The `var.equal` argument says whether the variance of the two samples can be assumed to be equal (Student's t-test) or unequal (Welch's t-test) So, how do we interpret these results? -- The 1st line gives the name of the test and the 2nd line reminds you what the data set was called, and what variables were used. -- The 3rd line contains the three key outputs from the test: - - The calculated t-value is 3.8433 - - There are 66 degrees of freedom - - The p-value is 0.0002754. -- The 4th line simply states the alternative hypothesis in terms of the difference between the two sample means (testing if the two sample means are different is equivalent to testing whether the difference in the means is equal to zero). -- The 5th and 6th lines give the 95th confidence interval (we don't need to know this here). -- The 7th, 8th and 9th lines give the sample means for each group (20.33077 in Aripo and 18.29655 in Guanapo) which we found earlier. +- `.y.` gives you the name of the response variable, or variable of interest (`length` in our case) +- `group1` and `group2` gives you the names of the groups being compared +- `n1` and `n2` gives you the number of observations in each group +- `statistic` gives you the value of the statistic (t = `r t_test(length ~ river, alternative = "two.sided", var.equal = TRUE, data = rivers) |> pull(statistic)`) +- `df` gives you the number of degrees of freedom (`r t_test(length ~ river, alternative = "two.sided", var.equal = TRUE, data = rivers) |> pull(df)`) +- `p` gives you the p-value (`r t_test(length ~ river, alternative = "two.sided", var.equal = TRUE, data = rivers) |> pull(p)`) ## Python @@ -464,23 +462,21 @@ Perform the Mann-Whitney U test: ```{r} #| warning: false -wilcox.test(length ~ river, +wilcox_test(length ~ river, alternative = "two.sided", data = rivers) ``` -- The first argument must be in the formula format: `variable ~ category` -- The second argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` +- The first argument must be in the formula format: `response ~ predictor` +- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less` -You *may* get a warning message in the console stating `cannot compute exact p-value with ties`. This just means that some of the data points have exactly the same value which affects the internal mathematics slightly. However, given that the p-value is so very small, this is not something that we need to worry about. +The output: -After the warning message: - -- The 1st line gives the name of the test and the 2nd line reminds you what the dataset was called, and what variables were used -- The 3rd line contains the two key outputs from the test: - - The calculated W-value is 841 (we'll use this in reporting) - - The p-value is 0.0006464. -- The 4th line simply states the alternative hypothesis in terms of the difference between the two sample medians in that if there were a difference then one distribution would be shifted relative to the other. +- `.y.` gives you the name of the response variable, or variable of interest (`length` in our case) +- `group1` and `group2` gives you the names of the groups being compared +- `n1` and `n2` gives you the number of observations in each group +- `statistic` gives you the value of the statistic (t = `r wilcox_test(length ~ river, alternative = "two.sided", data = rivers) |> pull(statistic)`) +- `p` gives you the p-value (`r wilcox_test(length ~ river, alternative = "two.sided", data = rivers) |> pull(p)`) ## Python @@ -782,12 +778,12 @@ Because of the result of the Bartlett test I know that I can carry out a two-sam ## R ::: {.callout-note} -In the `t.test()` function the default option for dealing with equality of variance is to assume there there isn't any. If you look at the help page with `?t.test()` then you'll see that the default for `var.equal = FALSE`. Here we *do* assume equality of variance, so we set it to `var.equal = TRUE`. +In the `t_test()` function the default option for dealing with equality of variance is to assume there there isn't any. If you look at the help page with `?t_test()` then you'll see that the default for `var.equal = FALSE`. Here we *do* assume equality of variance, so we set it to `var.equal = TRUE`. ::: ```{r} # perform two-sample t-test -t.test(serum ~ sex, +t_test(serum ~ sex, alternative = "two.sided", var.equal = TRUE, data = turtle) @@ -856,7 +852,7 @@ We've already checked that the variances of the two groups are similar, so we're ## R ```{r} -wilcox.test(serum ~ sex, +wilcox_test(serum ~ sex, alternative = "two.sided", data = turtle) ```

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zlq2>zmKm>wNkqgCx2o-R>y|nIc65w$QQ^M>5VxLeRy(Sus@koylS1Jez9dh)qjv)$ zMeHvc9U;EnKn-v=;vzO}D#!|T1r;wY;}k&kiSYJqTF+i*Syc~l{GoCiD60Z3-MY!O z%q$&=Be~t81q5AVXj7rq>lvq< zdl`pcErM1-n=>qA5+k~?VNW-A{T4UJbU8d>6?VTMl~FPu`PnktFB^P0SPVj8gwuF} zNT0@y-6Dlk;AjL&(2kCo64|ol5IhO(bc6o-6)TQ+Ya>Awp#q=!NTAdo=iySTV4=X5 z3$)ftqD^f?$}LbFI(K|xpknbpUeMcta?!vq&o`j;?>nB7RGf*@@-gu0-=<--ysnVi zPV-)i>TKka84Q*avFCJ=QPQQEv)*vAX622yP2NXj;gCp91T_2By8T|yn zl!uBFjQ#}H7SJwb;pPhvBpDTe$AIk&hU;*Ibl^|}Qm`&e#=C*QQmADg+?Mn6DkPz} zU$b$gpZ#zU(OL+1S%j1hruSJy06+o&8N_vw-?STx<1>E&%Q1X}gqt?(NJvwajv#$i zhpvG;eD3LxVZ2lt=t(@v%E|vsBnqRdV!Woto2ENStoU;RVa!(vna!IIkiZ&ifgm?<%p{P({wlZCIemWD z+4uYJ?6$5#U}7Q`M&K{_zjH8rD8mNAun_J5MiD!AQ`0CW^fOL-Aiyt@?c$~;nTl)6 z6QU4U651mPil|!(=Gj=tD5A28x4aQyVrb)p3= zO^adpvj3Gpl6F30) or, if the number of points is small, then this test also works when the parent distributions are normally distributed.\n\n## Libraries and functions\n\n::: {.callout-note collapse=\"true\"}\n## Click to expand\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n### Libraries\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# A collection of R packages designed for data science\nlibrary(tidyverse)\n```\n:::\n\n\n### Functions\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Performs a one-sample t-test, Student's t-test\n# and Welch's t-test in later sections\nstats::t.test()\n\n# Performs a Shapiro-Wilk test for normality\nstats::shapiro.test()\n\n# Performs one and two sample Wilcoxon tests\nstats::wilcox.test()\n\n# Plots a Q-Q plot for comparison with a normal distribution\nggplot2::stat_qq()\n\n# Adds a comparison line to the Q-Q plot\nggplot2::stat_qq_line()\n\n# Plots jittered points by adding a small amount of random\n# variation to each point, to handle overplotting\nggplot2::geom_jitter()\n\n# Computes summary statistics \nrstatix::get_summary_stats() \n\n# \"Widens\" the data, increasing the number of columns\ntidyr::pivot_wider()\n```\n:::\n\n\n## Python\n\n| Libraries | Description |\n|:----------------------|:------------------------------------------------|\n| `plotnine` | The Python equivalent of `ggplot2`. |\n| `pandas` | A Python data analysis and manipulation tool. |\n| `pingouin` | A Python module developed to have simple yet exhaustive stats functions |\n\n| Functions | Description |\n|:-----------------------------------|:-----------------------------------|\n| `pandas.DataFrame.read_csv` | Reads in a `.csv` file |\n| `pandas.DataFrame.pivot()` | Return reshaped DataFrame organised by given index / column values. |\n| [`pingouin.normality()`](https://pingouin-stats.org/generated/pingouin.normality.html) | Performs the Shapiro-Wilk test for normality. |\n| [`pingouin.ttest()`](https://pingouin-stats.org/generated/pingouin.ttest.html#pingouin.ttest) | Performs a t-test |\n| [`plotnine.stats.stat_qq()`](https://plotnine.readthedocs.io/en/stable/generated/plotnine.stats.stat_qq.html) | Plots a Q-Q plot for comparison with a normal distribution. |\n| [`plotnine.stats.stat_qq_line()`](https://plotnine.readthedocs.io/en/stable/generated/plotnine.stats.stat_qq_line.html) | Adds a comparison line to the Q-Q plot. |\n:::\n:::\n\n## Data and hypotheses\n\nFor example, suppose we measure the cortisol levels in 20 adult females (nmol/l) first thing in the morning and again in the evening. We want to test whether the cortisol levels differs between the two measurement times. We will initially form the following null and alternative hypotheses:\n\n- $H_0$: There is no difference in cortisol level between times ($\\mu M = \\mu E$)\n- $H_1$: There is a difference in cortisol levels between times ($\\mu M \\neq \\mu E$)\n\nWe use a two-sample, two-tailed paired t-test to see if we can reject the null hypothesis.\n\n- We use a **two-sample** test because we now have two samples\n- We use a **two-tailed** t-test because we want to know if our data suggest that the true (population) means are different from one another rather than that one mean is specifically bigger or smaller than the other\n- We use a **paired** test because each data point in the first sample can be linked to another data point in the second sample by a connecting factor\n- We're using a **t-test** because we're assuming that the parent populations are normal and have equal variance (We'll check this in a bit)\n\nThe data are stored in a tidy format in the file `data/CS1-twopaired.csv`.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\ncortisol <- read_csv(\"data/CS1-twopaired.csv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n```\nRows: 40 Columns: 3\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (1): time\ndbl (2): patient_id, cortisol\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n:::\n\n```{.r .cell-code}\n# have a look at the data\ncortisol\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 40 × 3\n patient_id time cortisol\n \n 1 1 morning 311.\n 2 2 morning 146.\n 3 3 morning 297 \n 4 4 morning 271.\n 5 5 morning 268.\n 6 6 morning 264.\n 7 7 morning 358.\n 8 8 morning 316.\n 9 9 morning 336.\n10 10 morning 221.\n# ℹ 30 more rows\n```\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\ncortisol_py = pd.read_csv('data/CS1-twopaired.csv')\n\n# inspect the data\ncortisol_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n patient_id time cortisol\n0 1 morning 310.6\n1 2 morning 146.1\n2 3 morning 297.0\n3 4 morning 270.9\n4 5 morning 267.5\n```\n:::\n:::\n\n:::\n\nWe can see that the data frame consists of three columns:\n\n1. `patient_id`, a unique ID for each patient\n2. `time` when the cortisol level was measured\n3. `cortisol`, which contains the measured value.\n\nFor each `patient_id` there are two measurements: one in the morning and one in the afternoon.\n\n## Summarise and visualise\n\nIt's always a good idea to visualise your data, so let's do that.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a boxplot\nggplot(cortisol,\n aes(x = time, y = cortisol)) +\n geom_boxplot() +\n geom_jitter(width = 0.05) +\n ylab(\"Cortisol level (nmol/l)\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n:::\n\n\nHere we use also visualise the actual data points, to get a sense of how these data are spread out. To avoid overlapping the data points (try using `geom_point()` instead of `geom_jitter()`), we jitter the data points. What `geom_jitter()` does is add a small amount of variation to each point.\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(cortisol_py,\n aes(x = \"time\",\n y = \"cortisol\")) +\n geom_boxplot() +\n geom_jitter(width = 0.05) +\n ylab(\"Cortisol level (nmol/l)\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-8-1.png){width=614}\n:::\n:::\n\n:::\n\nHowever, this plot does not capture how the cortisol level of each *individual* subject has changed though. We can explore the individual changes between morning and evening by looking at the *differences* between the two times of measurement for each patient.\n\nTo do this, we need to put our data into a wide format, so we can calculate the change in cortisol level for each patient.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nIn `tidyverse` we can use the `pivot_wider()` function.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# calculate the difference between evening and morning values\ncortisol_diff <- cortisol %>%\n pivot_wider(id_cols = patient_id,\n names_from = time,\n values_from = cortisol) %>% \n mutate(cortisol_change = evening - morning)\n\ncortisol_diff\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 20 × 4\n patient_id morning evening cortisol_change\n \n 1 1 311. 273. -37.4\n 2 2 146. 65.7 -80.4\n 3 3 297 257. -40.4\n 4 4 271. 321 50.1\n 5 5 268. 80.3 -187. \n 6 6 264. 379. 116. \n 7 7 358. 163. -195. \n 8 8 316. 294. -22 \n 9 9 336. 140. -196. \n10 10 221. 231. 10.4\n11 11 366 131. -235. \n12 12 256. 114. -142. \n13 13 432. 217. -215. \n14 14 208. 60.1 -148. \n15 15 324. 199. -125. \n16 16 388. 170. -218. \n17 17 332 160. -172. \n18 18 414. 179. -235. \n19 19 405. 286 -119. \n20 20 356. 226. -130. \n```\n:::\n:::\n\n\nThere are three arguments in `pivot_wider()`:\n\n1. `id_cols = patient_id` tells it that each observational unit is determined by `patient_id`\n2. `names_from = time` says that there will be new columns, with names from the `time` column (in this case, there are two values in there, `morning` and `evening`)\n3. `values_from = cortisol` populates the new columns with the values coming from the `cortisol` column\n\nLastly, we create a new column `cortisol_change` that contains the difference between the `evening` and `morning` measurements.\n\nAfter this we can plot our data:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot the data\nggplot(cortisol_diff,\n aes(y = cortisol_change)) +\n geom_boxplot() +\n ylab(\"Change in cortisol (nmol/l)\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\nThe differences in cortisol levels appear to be very much less than zero, meaning that the evening cortisol levels appear to be much lower than the morning ones. As such we would expect that the test would give a pretty significant result.\n\nAn alternative representation would be to plot the data points for both evening and morning and connect them by patient:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot cortisol levels by patient\nggplot(cortisol,\n aes(x = time,\n y = cortisol,\n group = patient_id)) +\n geom_point() +\n geom_line()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-11-1.png){width=672}\n:::\n:::\n\n\nThis gives a similar picture to what the boxplot was telling us, that for most patients the cortisol levels are higher in the morning than in the evening.\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\ncortisol_diff_py = pd.pivot(cortisol_py, index = \"patient_id\", columns = \"time\", values = \"cortisol\")\n\n# add a new column with difference between\n# evening and morning cortisol levels\ncortisol_diff_py[\"cortisol_change\"] = cortisol_diff_py[\"evening\"].subtract(cortisol_diff_py[\"morning\"])\n \n# have a look at the format\ncortisol_diff_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\ntime evening morning cortisol_change\npatient_id \n1 273.2 310.6 -37.4\n2 65.7 146.1 -80.4\n3 256.6 297.0 -40.4\n4 321.0 270.9 50.1\n5 80.3 267.5 -187.2\n```\n:::\n:::\n\n\nAfter this we can plot our data:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# plot the data\n(ggplot(cortisol_diff_py,\n aes(x = \"1\",\n y = \"cortisol_change\")) +\n geom_boxplot() +\n ylab(\"Change in cortisol (nmol/l)\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-13-1.png){width=614}\n:::\n:::\n\n\nThe differences in cortisol levels appear to be very much less than zero, meaning that the evening cortisol levels appear to be much lower than the morning ones. As such we would expect that the test would give a pretty significant result.\n\nAn alternative representation would be to plot the data points for both evening and morning and connect them by patient:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# plot cortisol levels by patient\n(ggplot(cortisol_py,\n aes(x = \"time\",\n y = \"cortisol\",\n group = \"patient_id\")) +\n geom_point() +\n geom_line())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-14-3.png){width=614}\n:::\n:::\n\n\nThis gives a similar picture to what the boxplot was telling us, that for most patients the cortisol levels are higher in the morning than in the evening.\n:::\n\n## Assumptions\n\nYou will do this in the exercise!\n\n## Implement and interpret the test\n\nPerform a two-sample, two-tailed, paired t-test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform the test\nt.test(cortisol ~ time,\n alternative = \"two.sided\",\n paired = TRUE,\n data = cortisol)\n```\n:::\n\n\n- The first two arguments define the formula\n- The third argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n- The fourth argument says that the data are paired\n\nFrom our perspective the value of interest is the `p-value`.\n\n## Python\n\nTo perform a paired t-test we can use the same `pg.ttest()` as before, but set the argument `paired = True`.\n\nAnnoyingly, the output is not entirely visible because the data frame is too wide. To deal with that, we can simply transpose it with `transpose()`\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(cortisol_diff_py[\"evening\"],\n cortisol_diff_py[\"morning\"],\n alternative = \"two-sided\",\n paired = True).transpose()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n T-test\nT -5.18329\ndof 19\nalternative two-sided\np-val 0.000053\nCI95% [-162.96, -69.21]\ncohen-d 1.434359\nBF10 491.599\npower 0.99998\n```\n:::\n:::\n\n\nFrom our perspective the value of interest is the `p-val`.\n:::\n\nSince the p-value = 5.29 $\\times$ 10-5) and thus substantially less than 0.05 we can reject the null hypothesis and state:\n\n> A two-tailed, paired t-test indicated that the average cortisol level in adult females differed significantly between the morning (313.5 nmol/l) and the evening (197.4 nmol/l, p = 5.3 \\* 10-5).\n\n## Dealing with non-normal data\n\nThe example above assumes that the paired data come from parent distributions that are normal. As we've seen before, we may have data where we can't rely on that assumption. Fortunately, there is very little that we need to change in our approach if we want to analyse paired data that violate the assumption of normality.\n\nAs with the non-normal two-sample data, there is the underlying assumption that the parent distributions of the samples are comparable in shape and variance.\n\n### Data and hypotheses\n\nUsing the `cortisol` data from before we form the following null and alternative hypotheses:\n\n- $H_0$: The median of the difference in cortisol levels between the two groups is 0 $(\\mu M = \\mu E)$\n- $H_1$: The median of the difference in cortisol levels between the two groups is not 0 $(\\mu M \\neq \\mu E)$\n\nWe use a two-tailed Wilcoxon signed rank test to see if we can reject the null hypothesis.\n\n### Summarise and visualise\n\nAlready implemented previously.\n\n### Assumptions\n\nThese have been checked previously.\n\n### Implement and interpret the test\n\nPerform a two-tailed, Wilcoxon signed rank test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox.test(cortisol ~ time,\n alternative = \"two.sided\",\n paired = TRUE,\n data = cortisol)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tWilcoxon signed rank exact test\n\ndata: cortisol by time\nV = 13, p-value = 0.0001678\nalternative hypothesis: true location shift is not equal to 0\n```\n:::\n:::\n\n\n- The first argument gives the formula\n- The second argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n- The third argument indicates that the test is paired\n- The last argument is the data set\n\n## Python\n\nWe'll use the wide format data set that we created previously:\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.wilcoxon(x = cortisol_diff_py[\"evening\"],\n y = cortisol_diff_py[\"morning\"],\n alternative = \"two-sided\",\n correction = True)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W-val alternative p-val RBC CLES\nWilcoxon 13.0 two-sided 0.000168 -0.87619 0.16\n```\n:::\n:::\n\n:::\n\nThe p-value is given in the `p` column (p-value = 0.000168). Given that this is less than 0.05 we can still reject the null hypothesis.\n\n> A two-tailed, Wilcoxon signed rank test indicated that the median cortisol level in adult females differed significantly between the morning (320.5 nmol/l) and the evening (188.9 nmol/l, p = 0.00017).\n\n## Exercises\n\n### Cortisol levels {#sec-exr_cortisol}\n\n:::{.callout-exercise}\n\n\n{{< level 2 >}}\n\n\n\nCheck the assumptions necessary for this this paired t-test. Was a paired t-test an appropriate test?\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\nWe actually don't care too much about the distributions of the individual groups. Instead we care about the properties of the **differences**. So for a paired t-test to be valid for this data set, we need the differences between the morning and evening values to be normally distributed.\n\nLet's check this with the Shapiro-Wilk test and Q-Q plots, using the wide data frames we created earlier.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nPerform Shapiro-Wilk test:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test on cortisol differences\nshapiro.test(cortisol_diff$cortisol_change)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: cortisol_diff$cortisol_change\nW = 0.92362, p-value = 0.1164\n```\n:::\n:::\n\n\nCreate Q-Q plot:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create the Q-Q plot\nggplot(cortisol_diff,\n aes(sample = cortisol_change)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n\n## Python\n\nPerform Shapiro-Wilk test:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform Shapiro-Wilk test on cortisol differences\npg.normality(cortisol_diff_py[\"cortisol_change\"])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval normal\ncortisol_change 0.923622 0.116356 True\n```\n:::\n:::\n\n\nCreate Q-Q plot:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create the Q-Q plot\n(ggplot(cortisol_diff_py,\n aes(sample = \"cortisol_change\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-22-1.png){width=614}\n:::\n:::\n\n:::\n\nThe Shapiro-Wilk test says that the data are normal enough and whilst the Q-Q plot is mostly fine, there is some suggestion of snaking at the bottom left. I'm actually OK with this because the suggestion of snaking is actually only due to a single point (the last point on the left). If you cover that point up with your thumb (or finger of your choice) then the remaining points in the Q-Q plot look pretty darn good, and so the suggestion of snaking is actually driven by only a single point (which can happen by chance). As such I'm happy that the assumption of normality is well-met in this case. This **single point** check is a useful thing to remember when assessing diagnostic plots.\n\nSo, yep, a paired t-test is appropriate for this data set.\n:::\n:::\n\n### Deer legs {#sec-exr_deerlegs}\n\n:::{.callout-exercise}\n\n\n{{< level 2 >}}\n\n\n\nUsing the following data on deer legs (yes, really!), test the null hypothesis that the fore and hind legs of the deer in this data set are the same length.\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 10 × 2\n hindleg foreleg\n \n 1 142 138\n 2 140 136\n 3 144 147\n 4 144 139\n 5 142 143\n 6 146 141\n 7 149 143\n 8 150 145\n 9 142 136\n10 148 146\n```\n:::\n:::\n\n\nDo these results provide any evidence to suggest that fore- and hind-leg length differ in deer?\n\n1. Write down the null and alternative hypotheses\n2. Import the data from `data/CS1-deer.csv`\n3. Summarise and visualise the data\n4. Check your assumptions (normality and variance) using appropriate tests\n5. Discuss with your (virtual) neighbour which test is most appropriate?\n6. Perform the test\n7. Write down a sentence that summarises the results that you have found\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : foreleg average (mean or median) $=$ hindleg average (mean or median)\n\n$H_1$ : foreleg average $\\neq$ hindleg average\n\n#### Import data, summarise and visualise\n\nFirst of all, we need to load in the data.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\ndeer <- read_csv(\"data/CS1-deer.csv\")\n\n# have a look\ndeer\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 20 × 3\n id leg length\n \n 1 1 hindleg 142\n 2 2 hindleg 140\n 3 3 hindleg 144\n 4 4 hindleg 144\n 5 5 hindleg 142\n 6 6 hindleg 146\n 7 7 hindleg 149\n 8 8 hindleg 150\n 9 9 hindleg 142\n10 10 hindleg 148\n11 1 foreleg 138\n12 2 foreleg 136\n13 3 foreleg 147\n14 4 foreleg 139\n15 5 foreleg 143\n16 6 foreleg 141\n17 7 foreleg 143\n18 8 foreleg 145\n19 9 foreleg 136\n20 10 foreleg 146\n```\n:::\n:::\n\n\nThe ordering of the data is important here; the first hind leg row corresponds to the first fore leg row, the second to the second and so on. To indicate this we use an `id` column, where each observation has a unique ID.\n\nLet's look at the data and see what it tells us.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# summarise the data\nsummary(deer)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id leg length \n Min. : 1.0 Length:20 Min. :136.0 \n 1st Qu.: 3.0 Class :character 1st Qu.:140.8 \n Median : 5.5 Mode :character Median :143.0 \n Mean : 5.5 Mean :143.1 \n 3rd Qu.: 8.0 3rd Qu.:146.0 \n Max. :10.0 Max. :150.0 \n```\n:::\n:::\n\n\nWe can also summarise some of the main summary statistics for each type of `leg`. We don't need summary statistics for the `id` column, so we unselect it with `select(-id)`.\n\nTo make life easy we use the `get_summary_stats()` function from the `rstatix` package. Have a look at the help function to see what kind of summary statistics it can produce. In this case I'm using the `type = \"common\"` option to specify that I want to find commonly used statistics (e.g. sample number, min, max, median, mean etc.)\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# or even summarise by leg type\ndeer %>% \n select(-id) %>% \n group_by(leg) %>% \n get_summary_stats(type = \"common\")\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 2 × 11\n leg variable n min max median iqr mean sd se ci\n \n1 foreleg length 10 136 147 142 6.25 141. 4.03 1.27 2.88\n2 hindleg length 10 140 150 144 5.5 145. 3.40 1.08 2.43\n```\n:::\n:::\n\n\nVisualising the data is often more useful:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# we can also visualise the data\nggplot(deer,\n aes(x = leg, y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-27-1.png){width=672}\n:::\n:::\n\n\nAll of this suggests that there might be a difference between the legs, with hind legs being longer than forelegs. However, this representation obscures the fact that we have *paired* data. What we really need to look at is the _difference_ in leg length for each observation:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a data set that contains the difference in leg length\nleg_diff <- deer %>% \n pivot_wider(id_cols = id,\n names_from = leg,\n values_from = length) %>% \n mutate(leg_diff = hindleg - foreleg)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot the difference in leg length\nggplot(leg_diff,\n aes(y = leg_diff)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-29-1.png){width=672}\n:::\n:::\n\n\nAdditionally, we can also plot the data by observation:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot the data by observation\nggplot(deer,\n aes(x = leg, y = length, group = id)) +\n geom_point() +\n geom_line()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-30-1.png){width=672}\n:::\n:::\n\n\n## Python\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\ndeer_py = pd.read_csv(\"data/CS1-deer.csv\")\n\n# have a look\ndeer_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id leg length\n0 1 hindleg 142\n1 2 hindleg 140\n2 3 hindleg 144\n3 4 hindleg 144\n4 5 hindleg 142\n```\n:::\n:::\n\n\nThe ordering of the data is important here; the first hind leg row corresponds to the first fore leg row, the second to the second and so on. To indicate this we use an `id` column, where each observation has a unique ID.\n\nLet's look at the data and see what we can see.\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# summarise the data\ndeer_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n id length\ncount 20.000000 20.000000\nmean 5.500000 143.050000\nstd 2.946898 4.006245\nmin 1.000000 136.000000\n25% 3.000000 140.750000\n50% 5.500000 143.000000\n75% 8.000000 146.000000\nmax 10.000000 150.000000\n```\n:::\n:::\n\n\nWe can also summarise by leg type:\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndeer_py.groupby(\"leg\")[\"length\"].describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n count mean std min 25% 50% 75% max\nleg \nforeleg 10.0 141.4 4.033196 136.0 138.25 142.0 144.5 147.0\nhindleg 10.0 144.7 3.400980 140.0 142.00 144.0 147.5 150.0\n```\n:::\n:::\n\n\nIt might be more helpful to look at the *difference* in leg length. In order to calculate that, we need to reformat our data into a 'wide' format first:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\nleg_diff_py = pd.pivot(deer_py,\n index = \"id\",\n columns = \"leg\",\n values = \"length\")\n\n# have a look at the format\nleg_diff_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nleg foreleg hindleg\nid \n1 138 142\n2 136 140\n3 147 144\n4 139 144\n5 143 142\n```\n:::\n:::\n\n\nNext, we can add a new column `leg_diff` that contains the leg difference:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# add a new column with difference between\n# hind and fore leg length\nleg_diff_py[\"leg_diff\"] = leg_diff_py[\"hindleg\"].subtract(leg_diff_py[\"foreleg\"])\n \n```\n:::\n\n\nFinally, we can visualise this:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# we can also visualise the data\n(ggplot(leg_diff_py,\n aes(x = \"1\",\n y = \"leg_diff\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-36-1.png){width=614}\n:::\n:::\n\n\nAll of this suggests that there might be a difference between the legs, with hind legs being longer than forelegs. However, this representation obscures the fact that we have *paired* data. What we really need to look at is the difference in leg length for each observation:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# plot paired observations\n(ggplot(deer_py,\n aes(x = \"leg\",\n y = \"length\",\n group = \"id\")) +\n geom_point() +\n geom_line())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-37-3.png){width=614}\n:::\n:::\n\n:::\n\nAll of this gives us a much clearer picture. It looks as though the hindlegs are about 4 cm longer than the forelegs, on average. It also suggests that our leg differences might not be normally distributed (the data look a bit skewed in the boxplot).\n\n#### Assumptions\n\nWe need to consider the distribution of the *difference* in leg lengths rather than the individual distributions.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nShapiro-Wilk test:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test on leg differences\nshapiro.test(leg_diff$leg_diff)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n\n\tShapiro-Wilk normality test\n\ndata: leg_diff$leg_diff\nW = 0.81366, p-value = 0.02123\n```\n:::\n:::\n\n\nQ-Q plot:\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a Q-Q plot\nggplot(leg_diff,\n aes(sample = leg_diff)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-39-1.png){width=672}\n:::\n:::\n\n\n## Python\n\nShapiro-Wilk test:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform Shapiro-Wilk test on leg length differences\npg.normality(leg_diff_py[\"leg_diff\"])\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n W pval normal\nleg_diff 0.813656 0.021235 False\n```\n:::\n:::\n\n\nCreate the Q-Q plot:\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create the Q-Q plot\n(ggplot(leg_diff_py,\n aes(sample = \"leg_diff\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-41-1.png){width=614}\n:::\n:::\n\n:::\n\nBoth our Shapiro-Wilk test and our Q-Q plot suggest that the difference data aren't normally distributed, which rules out a paired t-test. We should therefore consider a paired Wilcoxon signed rank test next. Remember that this test requires that the distribution of differences be of a similar shape, whereas our box plot from before suggested that the data were very much skewed.\n\nThis means that we're not able to perform a paired Wilcoxon signed rank test either!\n\n#### Conclusions\n\nSo, frustratingly, neither of the tests at our disposal are appropriate for this data set. The differences in fore leg and hind leg lengths are neither normal enough for a paired t-test nor are they symmetric enough for a Wilcoxon signed rank test. We also don't have enough data to just use the t-test (we'd need more than 30 points or so). So what do we do in this situation? Well, the answer is that there aren't actually any traditional statistical tests that are valid for this data set as it stands!\n\nThere are two options available to someone:\n\n1. try transforming the raw data (take logs, square root, reciprocals) and hope that one of them leads to a modified data set that satisfies the assumptions of one of the tests we've covered, or\n2. use a permutation test approach (which would work but is beyond the scope of this course).\n\nThe reason I included this example in the first practical is purely to illustrate how a very simple data set with an apparently clear message (leg lengths differ within deer) can be intractable. You don't need to have very complex data sets before you go beyond the capabilities of classical statistics.\n\nAs Jeremy Clarkson [would put it](https://www.quotes.net/mquote/941330):\n\n> And on that bombshell, it's time to end. Goodnight!\n\n:::\n:::\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- Paired t-tests are used when you have two paired samples of continuous data, which are normally distributed and have equal variance\n- A good way of assessing the assumption of normality is by checking the data against a Q-Q plot\n- We can check equality of variance (homoscedasticity) with Bartlett's (normal data) or Levene's (non-normal data) test\n- The Wilcoxon signed rank test is used when you have two paired samples of continuous data, which are not normally distributed (but have comparable distributional shapes), and have equal variance.\n:::\n\n", + "engine": "knitr", + "markdown": "---\ntitle: \"Paired data\"\n---\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n\n\n::: {.callout-tip}\n#### Learning outcomes\n\n**Questions**\n\n- When do I perform a paired two-sample test?\n- What are the assumptions?\n- How do I interpret and present the results of the test?\n- How do I deal with paired non-normal data?\n\n**Objectives**\n\n- Set out your hypothesis for comparing two paired samples of continuous data\n- Be able to summarise and visualise the data\n- Understand and assess the underlying assumptions of the test\n- Perform a paired two-sample t-test\n- Be able to interpret and report the results\n- Be able to do these steps on non-normal data\n\n:::\n\nA paired t-test is used when we have two samples of continuous data that can be paired (examples of these sort of data would be weights of individuals before and after a diet). This test is applicable if the number of paired points within the samples is large (\\>30) or, if the number of points is small, then this test also works when the parent distributions are normally distributed.\n\n## Libraries and functions\n\n::: {.callout-note collapse=\"true\"}\n## Click to expand\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n### Libraries\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# A collection of R packages designed for data science\nlibrary(tidyverse)\n\n# Converts stats functions to a tidyverse-friendly format\nlibrary(rstatix)\n```\n:::\n\n\n\n\n### Functions\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# Performs a one-sample t-test, Student's t-test\n# and Welch's t-test in later sections\nrstatix::t_test()\n\n# Performs a Shapiro-Wilk test for normality\nstats::shapiro.test()\n\n# Performs one and two sample Wilcoxon tests\nrstatix::wilcox_test()\n\n# Plots a Q-Q plot for comparison with a normal distribution\nggplot2::stat_qq()\n\n# Adds a comparison line to the Q-Q plot\nggplot2::stat_qq_line()\n\n# Plots jittered points by adding a small amount of random\n# variation to each point, to handle overplotting\nggplot2::geom_jitter()\n\n# Computes summary statistics \nrstatix::get_summary_stats() \n\n# \"Widens\" the data, increasing the number of columns\ntidyr::pivot_wider()\n```\n:::\n\n\n\n\n## Python\n\n| Libraries | Description |\n|:----------------------|:------------------------------------------------|\n| `plotnine` | The Python equivalent of `ggplot2`. |\n| `pandas` | A Python data analysis and manipulation tool. |\n| `pingouin` | A Python module developed to have simple yet exhaustive stats functions |\n\n| Functions | Description |\n|:-----------------------------------|:-----------------------------------|\n| `pandas.DataFrame.read_csv` | Reads in a `.csv` file |\n| `pandas.DataFrame.pivot()` | Return reshaped DataFrame organised by given index / column values. |\n| [`pingouin.normality()`](https://pingouin-stats.org/generated/pingouin.normality.html) | Performs the Shapiro-Wilk test for normality. |\n| [`pingouin.ttest()`](https://pingouin-stats.org/generated/pingouin.ttest.html#pingouin.ttest) | Performs a t-test |\n| [`plotnine.stats.stat_qq()`](https://plotnine.readthedocs.io/en/stable/generated/plotnine.stats.stat_qq.html) | Plots a Q-Q plot for comparison with a normal distribution. |\n| [`plotnine.stats.stat_qq_line()`](https://plotnine.readthedocs.io/en/stable/generated/plotnine.stats.stat_qq_line.html) | Adds a comparison line to the Q-Q plot. |\n:::\n:::\n\n## Data and hypotheses\n\nFor example, suppose we measure the cortisol levels in 20 adult females (nmol/l) first thing in the morning and again in the evening. We want to test whether the cortisol levels differs between the two measurement times. We will initially form the following null and alternative hypotheses:\n\n- $H_0$: There is no difference in cortisol level between times ($\\mu M = \\mu E$)\n- $H_1$: There is a difference in cortisol levels between times ($\\mu M \\neq \\mu E$)\n\nWe use a two-sample, two-tailed paired t-test to see if we can reject the null hypothesis.\n\n- We use a **two-sample** test because we now have two samples\n- We use a **two-tailed** t-test because we want to know if our data suggest that the true (population) means are different from one another rather than that one mean is specifically bigger or smaller than the other\n- We use a **paired** test because each data point in the first sample can be linked to another data point in the second sample by a connecting factor\n- We're using a **t-test** because we're assuming that the parent populations are normal and have equal variance (We'll check this in a bit)\n\nThe data are stored in a tidy format in the file `data/CS1-twopaired.csv`.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\ncortisol <- read_csv(\"data/CS1-twopaired.csv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nRows: 40 Columns: 3\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (1): time\ndbl (2): patient_id, cortisol\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n\n\n:::\n\n```{.r .cell-code}\n# have a look at the data\ncortisol\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 40 × 3\n patient_id time cortisol\n \n 1 1 morning 311.\n 2 2 morning 146.\n 3 3 morning 297 \n 4 4 morning 271.\n 5 5 morning 268.\n 6 6 morning 264.\n 7 7 morning 358.\n 8 8 morning 316.\n 9 9 morning 336.\n10 10 morning 221.\n# ℹ 30 more rows\n```\n\n\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\ncortisol_py = pd.read_csv('data/CS1-twopaired.csv')\n\n# inspect the data\ncortisol_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n patient_id time cortisol\n0 1 morning 310.6\n1 2 morning 146.1\n2 3 morning 297.0\n3 4 morning 270.9\n4 5 morning 267.5\n```\n\n\n:::\n:::\n\n\n\n:::\n\nWe can see that the data frame consists of three columns:\n\n1. `patient_id`, a unique ID for each patient\n2. `time` when the cortisol level was measured\n3. `cortisol`, which contains the measured value.\n\nFor each `patient_id` there are two measurements: one in the morning and one in the afternoon.\n\n## Summarise and visualise\n\nIt's always a good idea to visualise your data, so let's do that.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a boxplot\nggplot(cortisol,\n aes(x = time, y = cortisol)) +\n geom_boxplot() +\n geom_jitter(width = 0.05) +\n ylab(\"Cortisol level (nmol/l)\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-7-1.png){width=672}\n:::\n:::\n\n\n\n\nHere we use also visualise the actual data points, to get a sense of how these data are spread out. To avoid overlapping the data points (try using `geom_point()` instead of `geom_jitter()`), we jitter the data points. What `geom_jitter()` does is add a small amount of variation to each point.\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n(ggplot(cortisol_py,\n aes(x = \"time\",\n y = \"cortisol\")) +\n geom_boxplot() +\n geom_jitter(width = 0.05) +\n ylab(\"Cortisol level (nmol/l)\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-8-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nHowever, this plot does not capture how the cortisol level of each *individual* subject has changed though. We can explore the individual changes between morning and evening by looking at the *differences* between the two times of measurement for each patient.\n\nTo do this, we need to put our data into a wide format, so we can calculate the change in cortisol level for each patient.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nIn `tidyverse` we can use the `pivot_wider()` function.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# calculate the difference between evening and morning values\ncortisol_diff <- cortisol %>%\n pivot_wider(id_cols = patient_id,\n names_from = time,\n values_from = cortisol) %>% \n mutate(cortisol_change = evening - morning)\n\ncortisol_diff\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 20 × 4\n patient_id morning evening cortisol_change\n \n 1 1 311. 273. -37.4\n 2 2 146. 65.7 -80.4\n 3 3 297 257. -40.4\n 4 4 271. 321 50.1\n 5 5 268. 80.3 -187. \n 6 6 264. 379. 116. \n 7 7 358. 163. -195. \n 8 8 316. 294. -22 \n 9 9 336. 140. -196. \n10 10 221. 231. 10.4\n11 11 366 131. -235. \n12 12 256. 114. -142. \n13 13 432. 217. -215. \n14 14 208. 60.1 -148. \n15 15 324. 199. -125. \n16 16 388. 170. -218. \n17 17 332 160. -172. \n18 18 414. 179. -235. \n19 19 405. 286 -119. \n20 20 356. 226. -130. \n```\n\n\n:::\n:::\n\n\n\n\nThere are three arguments in `pivot_wider()`:\n\n1. `id_cols = patient_id` tells it that each observational unit is determined by `patient_id`\n2. `names_from = time` says that there will be new columns, with names from the `time` column (in this case, there are two values in there, `morning` and `evening`)\n3. `values_from = cortisol` populates the new columns with the values coming from the `cortisol` column\n\nLastly, we create a new column `cortisol_change` that contains the difference between the `evening` and `morning` measurements.\n\nAfter this we can plot our data:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot the data\nggplot(cortisol_diff,\n aes(y = cortisol_change)) +\n geom_boxplot() +\n ylab(\"Change in cortisol (nmol/l)\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n\n\nThe differences in cortisol levels appear to be very much less than zero, meaning that the evening cortisol levels appear to be much lower than the morning ones. As such we would expect that the test would give a pretty significant result.\n\nAn alternative representation would be to plot the data points for both evening and morning and connect them by patient:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot cortisol levels by patient\nggplot(cortisol,\n aes(x = time,\n y = cortisol,\n group = patient_id)) +\n geom_point() +\n geom_line()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-11-1.png){width=672}\n:::\n:::\n\n\n\n\nThis gives a similar picture to what the boxplot was telling us, that for most patients the cortisol levels are higher in the morning than in the evening.\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\ncortisol_diff_py = pd.pivot(cortisol_py, index = \"patient_id\", columns = \"time\", values = \"cortisol\")\n\n# add a new column with difference between\n# evening and morning cortisol levels\ncortisol_diff_py[\"cortisol_change\"] = cortisol_diff_py[\"evening\"].subtract(cortisol_diff_py[\"morning\"])\n \n# have a look at the format\ncortisol_diff_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\ntime evening morning cortisol_change\npatient_id \n1 273.2 310.6 -37.4\n2 65.7 146.1 -80.4\n3 256.6 297.0 -40.4\n4 321.0 270.9 50.1\n5 80.3 267.5 -187.2\n```\n\n\n:::\n:::\n\n\n\n\nAfter this we can plot our data:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# plot the data\n(ggplot(cortisol_diff_py,\n aes(x = \"1\",\n y = \"cortisol_change\")) +\n geom_boxplot() +\n ylab(\"Change in cortisol (nmol/l)\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-13-1.png){width=614}\n:::\n:::\n\n\n\n\nThe differences in cortisol levels appear to be very much less than zero, meaning that the evening cortisol levels appear to be much lower than the morning ones. As such we would expect that the test would give a pretty significant result.\n\nAn alternative representation would be to plot the data points for both evening and morning and connect them by patient:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# plot cortisol levels by patient\n(ggplot(cortisol_py,\n aes(x = \"time\",\n y = \"cortisol\",\n group = \"patient_id\")) +\n geom_point() +\n geom_line())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-14-3.png){width=614}\n:::\n:::\n\n\n\n\nThis gives a similar picture to what the boxplot was telling us, that for most patients the cortisol levels are higher in the morning than in the evening.\n:::\n\n## Assumptions\n\nYou will do this in the exercise!\n\n## Implement and interpret the test\n\nPerform a two-sample, two-tailed, paired t-test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform the test\nt_test(cortisol ~ time,\n alternative = \"two.sided\",\n paired = TRUE,\n data = cortisol)\n```\n:::\n\n\n\n\n- The first argument must be in the formula format: `response ~ predictor`\n- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n- The `paired = TRUE` argument indicates that the data are paired\n\nFrom our perspective the value of interest in the output is the `p` (5.29\\times 10^{-5}).\n\n## Python\n\nTo perform a paired t-test we can use the same `pg.ttest()` as before, but set the argument `paired = True`.\n\nAnnoyingly, the output is not entirely visible because the data frame is too wide. To deal with that, we can simply transpose it with `transpose()`\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.ttest(cortisol_diff_py[\"evening\"],\n cortisol_diff_py[\"morning\"],\n alternative = \"two-sided\",\n paired = True).transpose()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n T-test\nT -5.18329\ndof 19\nalternative two-sided\np-val 0.000053\nCI95% [-162.96, -69.21]\ncohen-d 1.434359\nBF10 491.599\npower 0.99998\n```\n\n\n:::\n:::\n\n\n\n\nFrom our perspective the value of interest is the `p-val`.\n:::\n\nSince the p-value = 5.29 $\\times$ 10-5) and thus substantially less than 0.05 we can reject the null hypothesis and state:\n\n> A two-tailed, paired t-test indicated that the average cortisol level in adult females differed significantly between the morning (313.5 nmol/l) and the evening (197.4 nmol/l, p = 5.3 \\* 10-5).\n\n## Dealing with non-normal data\n\nThe example above assumes that the paired data come from parent distributions that are normal. As we've seen before, we may have data where we can't rely on that assumption. Fortunately, there is very little that we need to change in our approach if we want to analyse paired data that violate the assumption of normality.\n\nAs with the non-normal two-sample data, there is the underlying assumption that the parent distributions of the samples are comparable in shape and variance.\n\n### Data and hypotheses\n\nUsing the `cortisol` data from before we form the following null and alternative hypotheses:\n\n- $H_0$: The median of the difference in cortisol levels between the two groups is 0 $(\\mu M = \\mu E)$\n- $H_1$: The median of the difference in cortisol levels between the two groups is not 0 $(\\mu M \\neq \\mu E)$\n\nWe use a two-tailed Wilcoxon signed rank test to see if we can reject the null hypothesis.\n\n### Summarise and visualise\n\nAlready implemented previously.\n\n### Assumptions\n\nThese have been checked previously.\n\n### Implement and interpret the test\n\nPerform a two-tailed, Wilcoxon signed rank test:\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwilcox_test(cortisol ~ time,\n alternative = \"two.sided\",\n paired = TRUE,\n data = cortisol)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 1 × 7\n .y. group1 group2 n1 n2 statistic p\n* \n1 cortisol evening morning 20 20 13 0.000168\n```\n\n\n:::\n:::\n\n\n\n\n- The first argument must be in the formula format: `response ~ predictor`\n- The `alternative` argument gives the type of alternative hypothesis and must be one of `two.sided`, `greater` or `less`\n- The `paired = TRUE` argument indicates that the data are paired\n\n## Python\n\nWe'll use the wide format data set that we created previously:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\npg.wilcoxon(x = cortisol_diff_py[\"evening\"],\n y = cortisol_diff_py[\"morning\"],\n alternative = \"two-sided\",\n correction = True)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W-val alternative p-val RBC CLES\nWilcoxon 13.0 two-sided 0.000168 -0.87619 0.16\n```\n\n\n:::\n:::\n\n\n\n:::\n\nThe p-value is given in the `p` column (p-value = 0.000168). Given that this is less than 0.05 we can still reject the null hypothesis.\n\n> A two-tailed, Wilcoxon signed rank test indicated that the median cortisol level in adult females differed significantly between the morning (320.5 nmol/l) and the evening (188.9 nmol/l, p = 0.00017).\n\n## Exercises\n\n### Cortisol levels {#sec-exr_cortisol}\n\n:::{.callout-exercise}\n\n\n\n\n{{< level 2 >}}\n\n\n\n\n\n\nCheck the assumptions necessary for this this paired t-test. Was a paired t-test an appropriate test?\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\nWe actually don't care too much about the distributions of the individual groups. Instead we care about the properties of the **differences**. So for a paired t-test to be valid for this data set, we need the differences between the morning and evening values to be normally distributed.\n\nLet's check this with the Shapiro-Wilk test and Q-Q plots, using the wide data frames we created earlier.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nPerform Shapiro-Wilk test:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test on cortisol differences\nshapiro.test(cortisol_diff$cortisol_change)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: cortisol_diff$cortisol_change\nW = 0.92362, p-value = 0.1164\n```\n\n\n:::\n:::\n\n\n\n\nCreate Q-Q plot:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create the Q-Q plot\nggplot(cortisol_diff,\n aes(sample = cortisol_change)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\nPerform Shapiro-Wilk test:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform Shapiro-Wilk test on cortisol differences\npg.normality(cortisol_diff_py[\"cortisol_change\"])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval normal\ncortisol_change 0.923622 0.116355 True\n```\n\n\n:::\n:::\n\n\n\n\nCreate Q-Q plot:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create the Q-Q plot\n(ggplot(cortisol_diff_py,\n aes(sample = \"cortisol_change\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-22-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nThe Shapiro-Wilk test says that the data are normal enough and whilst the Q-Q plot is mostly fine, there is some suggestion of snaking at the bottom left. I'm actually OK with this because the suggestion of snaking is actually only due to a single point (the last point on the left). If you cover that point up with your thumb (or finger of your choice) then the remaining points in the Q-Q plot look pretty darn good, and so the suggestion of snaking is actually driven by only a single point (which can happen by chance). As such I'm happy that the assumption of normality is well-met in this case. This **single point** check is a useful thing to remember when assessing diagnostic plots.\n\nSo, yep, a paired t-test is appropriate for this data set.\n:::\n:::\n\n### Deer legs {#sec-exr_deerlegs}\n\n:::{.callout-exercise}\n\n\n\n\n{{< level 2 >}}\n\n\n\n\n\n\nUsing the following data on deer legs (yes, really!), test the null hypothesis that the fore and hind legs of the deer in this data set are the same length.\n\n\n\n\n::: {.cell}\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 10 × 2\n hindleg foreleg\n \n 1 142 138\n 2 140 136\n 3 144 147\n 4 144 139\n 5 142 143\n 6 146 141\n 7 149 143\n 8 150 145\n 9 142 136\n10 148 146\n```\n\n\n:::\n:::\n\n\n\n\nDo these results provide any evidence to suggest that fore- and hind-leg length differ in deer?\n\n1. Write down the null and alternative hypotheses\n2. Import the data from `data/CS1-deer.csv`\n3. Summarise and visualise the data\n4. Check your assumptions (normality and variance) using appropriate tests\n5. Discuss with your (virtual) neighbour which test is most appropriate?\n6. Perform the test\n7. Write down a sentence that summarises the results that you have found\n\n::: {.callout-tip collapse=\"true\"}\n## Answer\n\n#### Hypotheses\n\n$H_0$ : foreleg average (mean or median) $=$ hindleg average (mean or median)\n\n$H_1$ : foreleg average $\\neq$ hindleg average\n\n#### Import data, summarise and visualise\n\nFirst of all, we need to load in the data.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# load the data\ndeer <- read_csv(\"data/CS1-deer.csv\")\n\n# have a look\ndeer\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 20 × 3\n id leg length\n \n 1 1 hindleg 142\n 2 2 hindleg 140\n 3 3 hindleg 144\n 4 4 hindleg 144\n 5 5 hindleg 142\n 6 6 hindleg 146\n 7 7 hindleg 149\n 8 8 hindleg 150\n 9 9 hindleg 142\n10 10 hindleg 148\n11 1 foreleg 138\n12 2 foreleg 136\n13 3 foreleg 147\n14 4 foreleg 139\n15 5 foreleg 143\n16 6 foreleg 141\n17 7 foreleg 143\n18 8 foreleg 145\n19 9 foreleg 136\n20 10 foreleg 146\n```\n\n\n:::\n:::\n\n\n\n\nThe ordering of the data is important here; the first hind leg row corresponds to the first fore leg row, the second to the second and so on. To indicate this we use an `id` column, where each observation has a unique ID.\n\nLet's look at the data and see what it tells us.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# summarise the data\nsummary(deer)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id leg length \n Min. : 1.0 Length:20 Min. :136.0 \n 1st Qu.: 3.0 Class :character 1st Qu.:140.8 \n Median : 5.5 Mode :character Median :143.0 \n Mean : 5.5 Mean :143.1 \n 3rd Qu.: 8.0 3rd Qu.:146.0 \n Max. :10.0 Max. :150.0 \n```\n\n\n:::\n:::\n\n\n\n\nWe can also summarise some of the main summary statistics for each type of `leg`. We don't need summary statistics for the `id` column, so we unselect it with `select(-id)`.\n\nTo make life easy we use the `get_summary_stats()` function from the `rstatix` package. Have a look at the help function to see what kind of summary statistics it can produce. In this case I'm using the `type = \"common\"` option to specify that I want to find commonly used statistics (e.g. sample number, min, max, median, mean etc.)\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# or even summarise by leg type\ndeer %>% \n select(-id) %>% \n group_by(leg) %>% \n get_summary_stats(type = \"common\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tibble: 2 × 11\n leg variable n min max median iqr mean sd se ci\n \n1 foreleg length 10 136 147 142 6.25 141. 4.03 1.27 2.88\n2 hindleg length 10 140 150 144 5.5 145. 3.40 1.08 2.43\n```\n\n\n:::\n:::\n\n\n\n\nVisualising the data is often more useful:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# we can also visualise the data\nggplot(deer,\n aes(x = leg, y = length)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-27-1.png){width=672}\n:::\n:::\n\n\n\n\nAll of this suggests that there might be a difference between the legs, with hind legs being longer than forelegs. However, this representation obscures the fact that we have *paired* data. What we really need to look at is the _difference_ in leg length for each observation:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a data set that contains the difference in leg length\nleg_diff <- deer %>% \n pivot_wider(id_cols = id,\n names_from = leg,\n values_from = length) %>% \n mutate(leg_diff = hindleg - foreleg)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot the difference in leg length\nggplot(leg_diff,\n aes(y = leg_diff)) +\n geom_boxplot()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-29-1.png){width=672}\n:::\n:::\n\n\n\n\nAdditionally, we can also plot the data by observation:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# plot the data by observation\nggplot(deer,\n aes(x = leg, y = length, group = id)) +\n geom_point() +\n geom_line()\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-30-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# load the data\ndeer_py = pd.read_csv(\"data/CS1-deer.csv\")\n\n# have a look\ndeer_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id leg length\n0 1 hindleg 142\n1 2 hindleg 140\n2 3 hindleg 144\n3 4 hindleg 144\n4 5 hindleg 142\n```\n\n\n:::\n:::\n\n\n\n\nThe ordering of the data is important here; the first hind leg row corresponds to the first fore leg row, the second to the second and so on. To indicate this we use an `id` column, where each observation has a unique ID.\n\nLet's look at the data and see what we can see.\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# summarise the data\ndeer_py.describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n id length\ncount 20.000000 20.000000\nmean 5.500000 143.050000\nstd 2.946898 4.006245\nmin 1.000000 136.000000\n25% 3.000000 140.750000\n50% 5.500000 143.000000\n75% 8.000000 146.000000\nmax 10.000000 150.000000\n```\n\n\n:::\n:::\n\n\n\n\nWe can also summarise by leg type:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\ndeer_py.groupby(\"leg\")[\"length\"].describe()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n count mean std min 25% 50% 75% max\nleg \nforeleg 10.0 141.4 4.033196 136.0 138.25 142.0 144.5 147.0\nhindleg 10.0 144.7 3.400980 140.0 142.00 144.0 147.5 150.0\n```\n\n\n:::\n:::\n\n\n\n\nIt might be more helpful to look at the *difference* in leg length. In order to calculate that, we need to reformat our data into a 'wide' format first:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# reformat the data into a 'wide' format\nleg_diff_py = pd.pivot(deer_py,\n index = \"id\",\n columns = \"leg\",\n values = \"length\")\n\n# have a look at the format\nleg_diff_py.head()\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\nleg foreleg hindleg\nid \n1 138 142\n2 136 140\n3 147 144\n4 139 144\n5 143 142\n```\n\n\n:::\n:::\n\n\n\n\nNext, we can add a new column `leg_diff` that contains the leg difference:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# add a new column with difference between\n# hind and fore leg length\nleg_diff_py[\"leg_diff\"] = leg_diff_py[\"hindleg\"].subtract(leg_diff_py[\"foreleg\"])\n \n```\n:::\n\n\n\n\nFinally, we can visualise this:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# we can also visualise the data\n(ggplot(leg_diff_py,\n aes(x = \"1\",\n y = \"leg_diff\")) +\n geom_boxplot())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-36-1.png){width=614}\n:::\n:::\n\n\n\n\nAll of this suggests that there might be a difference between the legs, with hind legs being longer than forelegs. However, this representation obscures the fact that we have *paired* data. What we really need to look at is the difference in leg length for each observation:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# plot paired observations\n(ggplot(deer_py,\n aes(x = \"leg\",\n y = \"length\",\n group = \"id\")) +\n geom_point() +\n geom_line())\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-37-3.png){width=614}\n:::\n:::\n\n\n\n:::\n\nAll of this gives us a much clearer picture. It looks as though the hindlegs are about 4 cm longer than the forelegs, on average. It also suggests that our leg differences might not be normally distributed (the data look a bit skewed in the boxplot).\n\n#### Assumptions\n\nWe need to consider the distribution of the *difference* in leg lengths rather than the individual distributions.\n\n::: {.panel-tabset group=\"language\"}\n## R\n\nShapiro-Wilk test:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# perform Shapiro-Wilk test on leg differences\nshapiro.test(leg_diff$leg_diff)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n\n\tShapiro-Wilk normality test\n\ndata: leg_diff$leg_diff\nW = 0.81366, p-value = 0.02123\n```\n\n\n:::\n:::\n\n\n\n\nQ-Q plot:\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# create a Q-Q plot\nggplot(leg_diff,\n aes(sample = leg_diff)) +\n stat_qq() +\n stat_qq_line(colour = \"blue\")\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-39-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\nShapiro-Wilk test:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# perform Shapiro-Wilk test on leg length differences\npg.normality(leg_diff_py[\"leg_diff\"])\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n W pval normal\nleg_diff 0.813656 0.021235 False\n```\n\n\n:::\n:::\n\n\n\n\nCreate the Q-Q plot:\n\n\n\n\n::: {.cell}\n\n```{.python .cell-code}\n# create the Q-Q plot\n(ggplot(leg_diff_py,\n aes(sample = \"leg_diff\")) +\n stat_qq() +\n stat_qq_line(colour = \"red\"))\n```\n\n::: {.cell-output-display}\n![](cs1_practical_two-samples-paired_files/figure-html/unnamed-chunk-41-1.png){width=614}\n:::\n:::\n\n\n\n:::\n\nBoth our Shapiro-Wilk test and our Q-Q plot suggest that the difference data aren't normally distributed, which rules out a paired t-test. We should therefore consider a paired Wilcoxon signed rank test next. Remember that this test requires that the distribution of differences be of a similar shape, whereas our box plot from before suggested that the data were very much skewed.\n\nThis means that we're not able to perform a paired Wilcoxon signed rank test either!\n\n#### Conclusions\n\nSo, frustratingly, neither of the tests at our disposal are appropriate for this data set. The differences in fore leg and hind leg lengths are neither normal enough for a paired t-test nor are they symmetric enough for a Wilcoxon signed rank test. We also don't have enough data to just use the t-test (we'd need more than 30 points or so). So what do we do in this situation? Well, the answer is that there aren't actually any traditional statistical tests that are valid for this data set as it stands!\n\nThere are two options available to someone:\n\n1. try transforming the raw data (take logs, square root, reciprocals) and hope that one of them leads to a modified data set that satisfies the assumptions of one of the tests we've covered, or\n2. use a permutation test approach (which would work but is beyond the scope of this course).\n\nThe reason I included this example in the first practical is purely to illustrate how a very simple data set with an apparently clear message (leg lengths differ within deer) can be intractable. You don't need to have very complex data sets before you go beyond the capabilities of classical statistics.\n\nAs Jeremy Clarkson [would put it](https://www.quotes.net/mquote/941330):\n\n> And on that bombshell, it's time to end. Goodnight!\n\n:::\n:::\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- Paired t-tests are used when you have two paired samples of continuous data, which are normally distributed and have equal variance\n- A good way of assessing the assumption of normality is by checking the data against a Q-Q plot\n- We can check equality of variance (homoscedasticity) with Bartlett's (normal data) or Levene's (non-normal data) test\n- The Wilcoxon signed rank test is used when you have two paired samples of continuous data, which are not normally distributed (but have comparable distributional shapes), and have equal variance.\n:::\n\n", "supporting": [ "cs1_practical_two-samples-paired_files" ], diff --git a/_freeze/materials/cs1_practical_two-samples-paired/figure-html/unnamed-chunk-13-1.png b/_freeze/materials/cs1_practical_two-samples-paired/figure-html/unnamed-chunk-13-1.png index ec1dd4e274ba6e0ecfde536cdd239c64aa775d90..2d36c957e8fdce998ec7dcc5767370c88262f2a0 100644 GIT binary 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