`, etc.) that has more than one element. Defaults to 1 (for ``).
+ getTopLevel: function($scope) {
+ for (var i = 1; i <= 6; i++) {
+ var $headings = this.findOrFilter($scope, 'h' + i);
+ if ($headings.length > 1) {
+ return i;
+ }
+ }
+
+ return 1;
+ },
+
+ // returns the elements for the top level, and the next below it
+ getHeadings: function($scope, topLevel) {
+ var topSelector = 'h' + topLevel;
+
+ var secondaryLevel = topLevel + 1;
+ var secondarySelector = 'h' + secondaryLevel;
+
+ return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
+ },
+
+ getNavLevel: function(el) {
+ return parseInt(el.tagName.charAt(1), 10);
+ },
+
+ populateNav: function($topContext, topLevel, $headings) {
+ var $context = $topContext;
+ var $prevNav;
+
+ var helpers = this;
+ $headings.each(function(i, el) {
+ var $newNav = helpers.generateNavItem(el);
+ var navLevel = helpers.getNavLevel(el);
+
+ // determine the proper $context
+ if (navLevel === topLevel) {
+ // use top level
+ $context = $topContext;
+ } else if ($prevNav && $context === $topContext) {
+ // create a new level of the tree and switch to it
+ $context = helpers.createChildNavList($prevNav);
+ } // else use the current $context
+
+ $context.append($newNav);
+
+ $prevNav = $newNav;
+ });
+ },
+
+ parseOps: function(arg) {
+ var opts;
+ if (arg.jquery) {
+ opts = {
+ $nav: arg
+ };
+ } else {
+ opts = arg;
+ }
+ opts.$scope = opts.$scope || $(document.body);
+ return opts;
+ }
+ },
+
+ // accepts a jQuery object, or an options object
+ init: function(opts) {
+ opts = this.helpers.parseOps(opts);
+
+ // ensure that the data attribute is in place for styling
+ opts.$nav.attr('data-toggle', 'toc');
+
+ var $topContext = this.helpers.createChildNavList(opts.$nav);
+ var topLevel = this.helpers.getTopLevel(opts.$scope);
+ var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
+ this.helpers.populateNav($topContext, topLevel, $headings);
+ }
+ };
+
+ $(function() {
+ $('nav[data-toggle="toc"]').each(function(i, el) {
+ var $nav = $(el);
+ Toc.init($nav);
+ });
+ });
+})();
diff --git a/docs/docsearch.json b/docs/docsearch.json
index f06be93e..52a7aa83 100644
--- a/docs/docsearch.json
+++ b/docs/docsearch.json
@@ -74,7 +74,7 @@
"selector": ".contents h2, .contents h3",
"default_value": "Context"
},
- "text": ".contents p, .contents li, .tempate-article .contents .pre"
+ "text": ".contents p, .contents li"
}
},
"selectors_exclude": [
diff --git a/docs/index.html b/docs/index.html
index e9e0ac5e..6d141f5f 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -6,10 +6,11 @@
ggplot2 Based Publication Ready Plots • ggpubr
-
-
-
-
+
+
+
+
+
-
-
+
@@ -81,93 +88,93 @@
- Install from CRAN as follow:
-
+install.packages("ggpubr")
- Or, install the latest version from GitHub as follow:
-# Install
-if(!require(devtools)) install.packages("devtools")
-devtools::install_github("kassambara/ggpubr")
+# Install
+if(!require(devtools)) install.packages("devtools")
+devtools::install_github("kassambara/ggpubr")
Distribution
-library(ggpubr)
-#> Loading required package: ggplot2
-#> Loading required package: magrittr
-# Create some data format
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-set.seed(1234)
-wdata = data.frame(
- sex = factor(rep(c("F", "M"), each=200)),
- weight = c(rnorm(200, 55), rnorm(200, 58)))
-head(wdata, 4)
-#> sex weight
-#> 1 F 53.79293
-#> 2 F 55.27743
-#> 3 F 56.08444
-#> 4 F 52.65430
-
-# Density plot with mean lines and marginal rug
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline and fill colors by groups ("sex")
-# Use custom palette
-ggdensity(wdata, x = "weight",
- add = "mean", rug = TRUE,
- color = "sex", fill = "sex",
- palette = c("#00AFBB", "#E7B800"))
+library(ggpubr)
+#> Loading required package: ggplot2
+#> Loading required package: magrittr
+# Create some data format
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+set.seed(1234)
+wdata = data.frame(
+ sex = factor(rep(c("F", "M"), each=200)),
+ weight = c(rnorm(200, 55), rnorm(200, 58)))
+head(wdata, 4)
+#> sex weight
+#> 1 F 53.79293
+#> 2 F 55.27743
+#> 3 F 56.08444
+#> 4 F 52.65430
+
+# Density plot with mean lines and marginal rug
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change outline and fill colors by groups ("sex")
+# Use custom palette
+ggdensity(wdata, x = "weight",
+ add = "mean", rug = TRUE,
+ color = "sex", fill = "sex",
+ palette = c("#00AFBB", "#E7B800"))
-# Histogram plot with mean lines and marginal rug
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline and fill colors by groups ("sex")
-# Use custom color palette
-gghistogram(wdata, x = "weight",
- add = "mean", rug = TRUE,
- color = "sex", fill = "sex",
- palette = c("#00AFBB", "#E7B800"))
+# Histogram plot with mean lines and marginal rug
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change outline and fill colors by groups ("sex")
+# Use custom color palette
+gghistogram(wdata, x = "weight",
+ add = "mean", rug = TRUE,
+ color = "sex", fill = "sex",
+ palette = c("#00AFBB", "#E7B800"))
Box plots and violin plots
-# Load data
-data("ToothGrowth")
-df <- ToothGrowth
-head(df, 4)
-#> len supp dose
-#> 1 4.2 VC 0.5
-#> 2 11.5 VC 0.5
-#> 3 7.3 VC 0.5
-#> 4 5.8 VC 0.5
-
-# Box plots with jittered points
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline colors by groups: dose
-# Use custom color palette
-# Add jitter points and change the shape by groups
- p <- ggboxplot(df, x = "dose", y = "len",
- color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
- add = "jitter", shape = "dose")
- p
+# Load data
+data("ToothGrowth")
+df <- ToothGrowth
+head(df, 4)
+#> len supp dose
+#> 1 4.2 VC 0.5
+#> 2 11.5 VC 0.5
+#> 3 7.3 VC 0.5
+#> 4 5.8 VC 0.5
+
+# Box plots with jittered points
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change outline colors by groups: dose
+# Use custom color palette
+# Add jitter points and change the shape by groups
+ p <- ggboxplot(df, x = "dose", y = "len",
+ color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
+ add = "jitter", shape = "dose")
+ p
-
- # Add p-values comparing groups
- # Specify the comparisons you want
-my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
-p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
- stat_compare_means(label.y = 50) # Add global p-value
+
+ # Add p-values comparing groups
+ # Specify the comparisons you want
+my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
+p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
+ stat_compare_means(label.y = 50) # Add global p-value
-
-
-# Violin plots with box plots inside
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change fill color by groups: dose
-# add boxplot with white fill color
-ggviolin(df, x = "dose", y = "len", fill = "dose",
- palette = c("#00AFBB", "#E7B800", "#FC4E07"),
- add = "boxplot", add.params = list(fill = "white"))+
- stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
- stat_compare_means(label.y = 50) # Add global the p-value
+
+
+# Violin plots with box plots inside
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change fill color by groups: dose
+# add boxplot with white fill color
+ggviolin(df, x = "dose", y = "len", fill = "dose",
+ palette = c("#00AFBB", "#E7B800", "#FC4E07"),
+ add = "boxplot", add.params = list(fill = "white"))+
+ stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
+ stat_compare_means(label.y = 50) # Add global the p-value
@@ -177,45 +184,45 @@
Demo data set
Load and prepare data:
-# Load data
-data("mtcars")
-dfm <- mtcars
-# Convert the cyl variable to a factor
-dfm$cyl <- as.factor(dfm$cyl)
-# Add the name colums
-dfm$name <- rownames(dfm)
-# Inspect the data
-head(dfm[, c("name", "wt", "mpg", "cyl")])
-#> name wt mpg cyl
-#> Mazda RX4 Mazda RX4 2.620 21.0 6
-#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6
-#> Datsun 710 Datsun 710 2.320 22.8 4
-#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6
-#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8
-#> Valiant Valiant 3.460 18.1 6
+# Load data
+data("mtcars")
+dfm <- mtcars
+# Convert the cyl variable to a factor
+dfm$cyl <- as.factor(dfm$cyl)
+# Add the name colums
+dfm$name <- rownames(dfm)
+# Inspect the data
+head(dfm[, c("name", "wt", "mpg", "cyl")])
+#> name wt mpg cyl
+#> Mazda RX4 Mazda RX4 2.620 21.0 6
+#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6
+#> Datsun 710 Datsun 710 2.320 22.8 4
+#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6
+#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8
+#> Valiant Valiant 3.460 18.1 6
Ordered bar plots
Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups.
-ggbarplot(dfm, x = "name", y = "mpg",
- fill = "cyl", # change fill color by cyl
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "desc", # Sort the value in dscending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90 # Rotate vertically x axis texts
- )
+ggbarplot(dfm, x = "name", y = "mpg",
+ fill = "cyl", # change fill color by cyl
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "desc", # Sort the value in dscending order
+ sort.by.groups = FALSE, # Don't sort inside each group
+ x.text.angle = 90 # Rotate vertically x axis texts
+ )
Sort bars inside each group. Use the argument sort.by.groups = TRUE.
-ggbarplot(dfm, x = "name", y = "mpg",
- fill = "cyl", # change fill color by cyl
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "asc", # Sort the value in dscending order
- sort.by.groups = TRUE, # Sort inside each group
- x.text.angle = 90 # Rotate vertically x axis texts
- )
+ggbarplot(dfm, x = "name", y = "mpg",
+ fill = "cyl", # change fill color by cyl
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "asc", # Sort the value in dscending order
+ sort.by.groups = TRUE, # Sort inside each group
+ x.text.angle = 90 # Rotate vertically x axis texts
+ )
@@ -223,45 +230,45 @@
Deviation graphs
The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
-# Calculate the z-score of the mpg data
-dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
-dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
- levels = c("low", "high"))
-# Inspect the data
-head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
-#> name wt mpg mpg_z mpg_grp cyl
-#> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6
-#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6
-#> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4
-#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6
-#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8
-#> Valiant Valiant 3.460 18.1 -0.3302874 low 6
+# Calculate the z-score of the mpg data
+dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
+dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
+ levels = c("low", "high"))
+# Inspect the data
+head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
+#> name wt mpg mpg_z mpg_grp cyl
+#> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6
+#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6
+#> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4
+#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6
+#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8
+#> Valiant Valiant 3.460 18.1 -0.3302874 low 6
Create an ordered barplot, colored according to the level of mpg:
-ggbarplot(dfm, x = "name", y = "mpg_z",
- fill = "mpg_grp", # change fill color by mpg_level
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "asc", # Sort the value in ascending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90, # Rotate vertically x axis texts
- ylab = "MPG z-score",
- xlab = FALSE,
- legend.title = "MPG Group"
- )
+ggbarplot(dfm, x = "name", y = "mpg_z",
+ fill = "mpg_grp", # change fill color by mpg_level
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "asc", # Sort the value in ascending order
+ sort.by.groups = FALSE, # Don't sort inside each group
+ x.text.angle = 90, # Rotate vertically x axis texts
+ ylab = "MPG z-score",
+ xlab = FALSE,
+ legend.title = "MPG Group"
+ )
Rotate the plot: use rotate = TRUE and sort.val = “desc”
-ggbarplot(dfm, x = "name", y = "mpg_z",
- fill = "mpg_grp", # change fill color by mpg_level
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "desc", # Sort the value in descending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90, # Rotate vertically x axis texts
- ylab = "MPG z-score",
- legend.title = "MPG Group",
- rotate = TRUE,
- ggtheme = theme_minimal()
- )
+ggbarplot(dfm, x = "name", y = "mpg_z",
+ fill = "mpg_grp", # change fill color by mpg_level
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "desc", # Sort the value in descending order
+ sort.by.groups = FALSE, # Don't sort inside each group
+ x.text.angle = 90, # Rotate vertically x axis texts
+ ylab = "MPG z-score",
+ legend.title = "MPG Group",
+ rotate = TRUE,
+ ggtheme = theme_minimal()
+ )
@@ -273,13 +280,13 @@
Lollipop chart
- Install from CRAN as follow:
-
+install.packages("ggpubr")
- Or, install the latest version from GitHub as follow:
-# Install
-if(!require(devtools)) install.packages("devtools")
-devtools::install_github("kassambara/ggpubr")
+# Install
+if(!require(devtools)) install.packages("devtools")
+devtools::install_github("kassambara/ggpubr")
Distribution
-library(ggpubr)
-#> Loading required package: ggplot2
-#> Loading required package: magrittr
-# Create some data format
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-set.seed(1234)
-wdata = data.frame(
- sex = factor(rep(c("F", "M"), each=200)),
- weight = c(rnorm(200, 55), rnorm(200, 58)))
-head(wdata, 4)
-#> sex weight
-#> 1 F 53.79293
-#> 2 F 55.27743
-#> 3 F 56.08444
-#> 4 F 52.65430
-
-# Density plot with mean lines and marginal rug
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline and fill colors by groups ("sex")
-# Use custom palette
-ggdensity(wdata, x = "weight",
- add = "mean", rug = TRUE,
- color = "sex", fill = "sex",
- palette = c("#00AFBB", "#E7B800"))
+library(ggpubr)
+#> Loading required package: ggplot2
+#> Loading required package: magrittr
+# Create some data format
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+set.seed(1234)
+wdata = data.frame(
+ sex = factor(rep(c("F", "M"), each=200)),
+ weight = c(rnorm(200, 55), rnorm(200, 58)))
+head(wdata, 4)
+#> sex weight
+#> 1 F 53.79293
+#> 2 F 55.27743
+#> 3 F 56.08444
+#> 4 F 52.65430
+
+# Density plot with mean lines and marginal rug
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change outline and fill colors by groups ("sex")
+# Use custom palette
+ggdensity(wdata, x = "weight",
+ add = "mean", rug = TRUE,
+ color = "sex", fill = "sex",
+ palette = c("#00AFBB", "#E7B800"))
-# Histogram plot with mean lines and marginal rug
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline and fill colors by groups ("sex")
-# Use custom color palette
-gghistogram(wdata, x = "weight",
- add = "mean", rug = TRUE,
- color = "sex", fill = "sex",
- palette = c("#00AFBB", "#E7B800"))
+# Histogram plot with mean lines and marginal rug
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change outline and fill colors by groups ("sex")
+# Use custom color palette
+gghistogram(wdata, x = "weight",
+ add = "mean", rug = TRUE,
+ color = "sex", fill = "sex",
+ palette = c("#00AFBB", "#E7B800"))
Box plots and violin plots
-# Load data
-data("ToothGrowth")
-df <- ToothGrowth
-head(df, 4)
-#> len supp dose
-#> 1 4.2 VC 0.5
-#> 2 11.5 VC 0.5
-#> 3 7.3 VC 0.5
-#> 4 5.8 VC 0.5
-
-# Box plots with jittered points
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline colors by groups: dose
-# Use custom color palette
-# Add jitter points and change the shape by groups
- p <- ggboxplot(df, x = "dose", y = "len",
- color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
- add = "jitter", shape = "dose")
- p
+# Load data
+data("ToothGrowth")
+df <- ToothGrowth
+head(df, 4)
+#> len supp dose
+#> 1 4.2 VC 0.5
+#> 2 11.5 VC 0.5
+#> 3 7.3 VC 0.5
+#> 4 5.8 VC 0.5
+
+# Box plots with jittered points
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change outline colors by groups: dose
+# Use custom color palette
+# Add jitter points and change the shape by groups
+ p <- ggboxplot(df, x = "dose", y = "len",
+ color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
+ add = "jitter", shape = "dose")
+ p
-
- # Add p-values comparing groups
- # Specify the comparisons you want
-my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
-p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
- stat_compare_means(label.y = 50) # Add global p-value
+
+ # Add p-values comparing groups
+ # Specify the comparisons you want
+my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
+p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
+ stat_compare_means(label.y = 50) # Add global p-value
-
-
-# Violin plots with box plots inside
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change fill color by groups: dose
-# add boxplot with white fill color
-ggviolin(df, x = "dose", y = "len", fill = "dose",
- palette = c("#00AFBB", "#E7B800", "#FC4E07"),
- add = "boxplot", add.params = list(fill = "white"))+
- stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
- stat_compare_means(label.y = 50) # Add global the p-value
+
+
+# Violin plots with box plots inside
+# :::::::::::::::::::::::::::::::::::::::::::::::::::
+# Change fill color by groups: dose
+# add boxplot with white fill color
+ggviolin(df, x = "dose", y = "len", fill = "dose",
+ palette = c("#00AFBB", "#E7B800", "#FC4E07"),
+ add = "boxplot", add.params = list(fill = "white"))+
+ stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
+ stat_compare_means(label.y = 50) # Add global the p-value
@@ -177,45 +184,45 @@
Demo data set
Load and prepare data:
-# Load data
-data("mtcars")
-dfm <- mtcars
-# Convert the cyl variable to a factor
-dfm$cyl <- as.factor(dfm$cyl)
-# Add the name colums
-dfm$name <- rownames(dfm)
-# Inspect the data
-head(dfm[, c("name", "wt", "mpg", "cyl")])
-#> name wt mpg cyl
-#> Mazda RX4 Mazda RX4 2.620 21.0 6
-#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6
-#> Datsun 710 Datsun 710 2.320 22.8 4
-#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6
-#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8
-#> Valiant Valiant 3.460 18.1 6
+# Load data
+data("mtcars")
+dfm <- mtcars
+# Convert the cyl variable to a factor
+dfm$cyl <- as.factor(dfm$cyl)
+# Add the name colums
+dfm$name <- rownames(dfm)
+# Inspect the data
+head(dfm[, c("name", "wt", "mpg", "cyl")])
+#> name wt mpg cyl
+#> Mazda RX4 Mazda RX4 2.620 21.0 6
+#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6
+#> Datsun 710 Datsun 710 2.320 22.8 4
+#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6
+#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8
+#> Valiant Valiant 3.460 18.1 6
Ordered bar plots
Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups.
-ggbarplot(dfm, x = "name", y = "mpg",
- fill = "cyl", # change fill color by cyl
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "desc", # Sort the value in dscending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90 # Rotate vertically x axis texts
- )
+ggbarplot(dfm, x = "name", y = "mpg",
+ fill = "cyl", # change fill color by cyl
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "desc", # Sort the value in dscending order
+ sort.by.groups = FALSE, # Don't sort inside each group
+ x.text.angle = 90 # Rotate vertically x axis texts
+ )
Sort bars inside each group. Use the argument sort.by.groups = TRUE.
-ggbarplot(dfm, x = "name", y = "mpg",
- fill = "cyl", # change fill color by cyl
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "asc", # Sort the value in dscending order
- sort.by.groups = TRUE, # Sort inside each group
- x.text.angle = 90 # Rotate vertically x axis texts
- )
+ggbarplot(dfm, x = "name", y = "mpg",
+ fill = "cyl", # change fill color by cyl
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "asc", # Sort the value in dscending order
+ sort.by.groups = TRUE, # Sort inside each group
+ x.text.angle = 90 # Rotate vertically x axis texts
+ )
@@ -223,45 +230,45 @@
Deviation graphs
The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
-# Calculate the z-score of the mpg data
-dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
-dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
- levels = c("low", "high"))
-# Inspect the data
-head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
-#> name wt mpg mpg_z mpg_grp cyl
-#> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6
-#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6
-#> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4
-#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6
-#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8
-#> Valiant Valiant 3.460 18.1 -0.3302874 low 6
+# Calculate the z-score of the mpg data
+dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
+dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
+ levels = c("low", "high"))
+# Inspect the data
+head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
+#> name wt mpg mpg_z mpg_grp cyl
+#> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6
+#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6
+#> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4
+#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6
+#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8
+#> Valiant Valiant 3.460 18.1 -0.3302874 low 6
Create an ordered barplot, colored according to the level of mpg:
-ggbarplot(dfm, x = "name", y = "mpg_z",
- fill = "mpg_grp", # change fill color by mpg_level
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "asc", # Sort the value in ascending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90, # Rotate vertically x axis texts
- ylab = "MPG z-score",
- xlab = FALSE,
- legend.title = "MPG Group"
- )
+ggbarplot(dfm, x = "name", y = "mpg_z",
+ fill = "mpg_grp", # change fill color by mpg_level
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "asc", # Sort the value in ascending order
+ sort.by.groups = FALSE, # Don't sort inside each group
+ x.text.angle = 90, # Rotate vertically x axis texts
+ ylab = "MPG z-score",
+ xlab = FALSE,
+ legend.title = "MPG Group"
+ )
Rotate the plot: use rotate = TRUE and sort.val = “desc”
-ggbarplot(dfm, x = "name", y = "mpg_z",
- fill = "mpg_grp", # change fill color by mpg_level
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "desc", # Sort the value in descending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90, # Rotate vertically x axis texts
- ylab = "MPG z-score",
- legend.title = "MPG Group",
- rotate = TRUE,
- ggtheme = theme_minimal()
- )
+ggbarplot(dfm, x = "name", y = "mpg_z",
+ fill = "mpg_grp", # change fill color by mpg_level
+ color = "white", # Set bar border colors to white
+ palette = "jco", # jco journal color palett. see ?ggpar
+ sort.val = "desc", # Sort the value in descending order
+ sort.by.groups = FALSE, # Don't sort inside each group
+ x.text.angle = 90, # Rotate vertically x axis texts
+ ylab = "MPG z-score",
+ legend.title = "MPG Group",
+ rotate = TRUE,
+ ggtheme = theme_minimal()
+ )
@@ -273,13 +280,13 @@
Lollipop chart
install.packages("ggpubr")
# Install
-if(!require(devtools)) install.packages("devtools")
-devtools::install_github("kassambara/ggpubr")# Install +if(!require(devtools)) install.packages("devtools") +devtools::install_github("kassambara/ggpubr")
Distribution
-library(ggpubr)
-#> Loading required package: ggplot2
-#> Loading required package: magrittr
-# Create some data format
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-set.seed(1234)
-wdata = data.frame(
- sex = factor(rep(c("F", "M"), each=200)),
- weight = c(rnorm(200, 55), rnorm(200, 58)))
-head(wdata, 4)
-#> sex weight
-#> 1 F 53.79293
-#> 2 F 55.27743
-#> 3 F 56.08444
-#> 4 F 52.65430
-
-# Density plot with mean lines and marginal rug
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline and fill colors by groups ("sex")
-# Use custom palette
-ggdensity(wdata, x = "weight",
- add = "mean", rug = TRUE,
- color = "sex", fill = "sex",
- palette = c("#00AFBB", "#E7B800"))library(ggpubr) +#> Loading required package: ggplot2 +#> Loading required package: magrittr +# Create some data format +# ::::::::::::::::::::::::::::::::::::::::::::::::::: +set.seed(1234) +wdata = data.frame( + sex = factor(rep(c("F", "M"), each=200)), + weight = c(rnorm(200, 55), rnorm(200, 58))) +head(wdata, 4) +#> sex weight +#> 1 F 53.79293 +#> 2 F 55.27743 +#> 3 F 56.08444 +#> 4 F 52.65430 + +# Density plot with mean lines and marginal rug +# ::::::::::::::::::::::::::::::::::::::::::::::::::: +# Change outline and fill colors by groups ("sex") +# Use custom palette +ggdensity(wdata, x = "weight", + add = "mean", rug = TRUE, + color = "sex", fill = "sex", + palette = c("#00AFBB", "#E7B800"))
# Histogram plot with mean lines and marginal rug
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline and fill colors by groups ("sex")
-# Use custom color palette
-gghistogram(wdata, x = "weight",
- add = "mean", rug = TRUE,
- color = "sex", fill = "sex",
- palette = c("#00AFBB", "#E7B800"))# Histogram plot with mean lines and marginal rug +# ::::::::::::::::::::::::::::::::::::::::::::::::::: +# Change outline and fill colors by groups ("sex") +# Use custom color palette +gghistogram(wdata, x = "weight", + add = "mean", rug = TRUE, + color = "sex", fill = "sex", + palette = c("#00AFBB", "#E7B800"))
Box plots and violin plots
-# Load data
-data("ToothGrowth")
-df <- ToothGrowth
-head(df, 4)
-#> len supp dose
-#> 1 4.2 VC 0.5
-#> 2 11.5 VC 0.5
-#> 3 7.3 VC 0.5
-#> 4 5.8 VC 0.5
-
-# Box plots with jittered points
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change outline colors by groups: dose
-# Use custom color palette
-# Add jitter points and change the shape by groups
- p <- ggboxplot(df, x = "dose", y = "len",
- color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
- add = "jitter", shape = "dose")
- p# Load data +data("ToothGrowth") +df <- ToothGrowth +head(df, 4) +#> len supp dose +#> 1 4.2 VC 0.5 +#> 2 11.5 VC 0.5 +#> 3 7.3 VC 0.5 +#> 4 5.8 VC 0.5 + +# Box plots with jittered points +# ::::::::::::::::::::::::::::::::::::::::::::::::::: +# Change outline colors by groups: dose +# Use custom color palette +# Add jitter points and change the shape by groups + p <- ggboxplot(df, x = "dose", y = "len", + color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"), + add = "jitter", shape = "dose") + p
- # Add p-values comparing groups
- # Specify the comparisons you want
-my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
-p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
- stat_compare_means(label.y = 50) # Add global p-value+ # Add p-values comparing groups + # Specify the comparisons you want +my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) +p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value + stat_compare_means(label.y = 50) # Add global p-value
-
-# Violin plots with box plots inside
-# :::::::::::::::::::::::::::::::::::::::::::::::::::
-# Change fill color by groups: dose
-# add boxplot with white fill color
-ggviolin(df, x = "dose", y = "len", fill = "dose",
- palette = c("#00AFBB", "#E7B800", "#FC4E07"),
- add = "boxplot", add.params = list(fill = "white"))+
- stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
- stat_compare_means(label.y = 50) # Add global the p-value + +# Violin plots with box plots inside +# ::::::::::::::::::::::::::::::::::::::::::::::::::: +# Change fill color by groups: dose +# add boxplot with white fill color +ggviolin(df, x = "dose", y = "len", fill = "dose", + palette = c("#00AFBB", "#E7B800", "#FC4E07"), + add = "boxplot", add.params = list(fill = "white"))+ + stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels + stat_compare_means(label.y = 50) # Add global the p-value
Demo data set
Load and prepare data:
-# Load data
-data("mtcars")
-dfm <- mtcars
-# Convert the cyl variable to a factor
-dfm$cyl <- as.factor(dfm$cyl)
-# Add the name colums
-dfm$name <- rownames(dfm)
-# Inspect the data
-head(dfm[, c("name", "wt", "mpg", "cyl")])
-#> name wt mpg cyl
-#> Mazda RX4 Mazda RX4 2.620 21.0 6
-#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6
-#> Datsun 710 Datsun 710 2.320 22.8 4
-#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6
-#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8
-#> Valiant Valiant 3.460 18.1 6# Load data +data("mtcars") +dfm <- mtcars +# Convert the cyl variable to a factor +dfm$cyl <- as.factor(dfm$cyl) +# Add the name colums +dfm$name <- rownames(dfm) +# Inspect the data +head(dfm[, c("name", "wt", "mpg", "cyl")]) +#> name wt mpg cyl +#> Mazda RX4 Mazda RX4 2.620 21.0 6 +#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6 +#> Datsun 710 Datsun 710 2.320 22.8 4 +#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6 +#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8 +#> Valiant Valiant 3.460 18.1 6
Ordered bar plots
Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups.
-ggbarplot(dfm, x = "name", y = "mpg",
- fill = "cyl", # change fill color by cyl
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "desc", # Sort the value in dscending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90 # Rotate vertically x axis texts
- )ggbarplot(dfm, x = "name", y = "mpg", + fill = "cyl", # change fill color by cyl + color = "white", # Set bar border colors to white + palette = "jco", # jco journal color palett. see ?ggpar + sort.val = "desc", # Sort the value in dscending order + sort.by.groups = FALSE, # Don't sort inside each group + x.text.angle = 90 # Rotate vertically x axis texts + )
Sort bars inside each group. Use the argument sort.by.groups = TRUE.
-ggbarplot(dfm, x = "name", y = "mpg",
- fill = "cyl", # change fill color by cyl
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "asc", # Sort the value in dscending order
- sort.by.groups = TRUE, # Sort inside each group
- x.text.angle = 90 # Rotate vertically x axis texts
- )ggbarplot(dfm, x = "name", y = "mpg", + fill = "cyl", # change fill color by cyl + color = "white", # Set bar border colors to white + palette = "jco", # jco journal color palett. see ?ggpar + sort.val = "asc", # Sort the value in dscending order + sort.by.groups = TRUE, # Sort inside each group + x.text.angle = 90 # Rotate vertically x axis texts + )
Deviation graphs
The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
-# Calculate the z-score of the mpg data
-dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
-dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"),
- levels = c("low", "high"))
-# Inspect the data
-head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
-#> name wt mpg mpg_z mpg_grp cyl
-#> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6
-#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6
-#> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4
-#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6
-#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8
-#> Valiant Valiant 3.460 18.1 -0.3302874 low 6# Calculate the z-score of the mpg data +dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg) +dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"), + levels = c("low", "high")) +# Inspect the data +head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")]) +#> name wt mpg mpg_z mpg_grp cyl +#> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6 +#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6 +#> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4 +#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6 +#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8 +#> Valiant Valiant 3.460 18.1 -0.3302874 low 6
Create an ordered barplot, colored according to the level of mpg:
-ggbarplot(dfm, x = "name", y = "mpg_z",
- fill = "mpg_grp", # change fill color by mpg_level
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "asc", # Sort the value in ascending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90, # Rotate vertically x axis texts
- ylab = "MPG z-score",
- xlab = FALSE,
- legend.title = "MPG Group"
- )ggbarplot(dfm, x = "name", y = "mpg_z", + fill = "mpg_grp", # change fill color by mpg_level + color = "white", # Set bar border colors to white + palette = "jco", # jco journal color palett. see ?ggpar + sort.val = "asc", # Sort the value in ascending order + sort.by.groups = FALSE, # Don't sort inside each group + x.text.angle = 90, # Rotate vertically x axis texts + ylab = "MPG z-score", + xlab = FALSE, + legend.title = "MPG Group" + )
Rotate the plot: use rotate = TRUE and sort.val = “desc”
-ggbarplot(dfm, x = "name", y = "mpg_z",
- fill = "mpg_grp", # change fill color by mpg_level
- color = "white", # Set bar border colors to white
- palette = "jco", # jco journal color palett. see ?ggpar
- sort.val = "desc", # Sort the value in descending order
- sort.by.groups = FALSE, # Don't sort inside each group
- x.text.angle = 90, # Rotate vertically x axis texts
- ylab = "MPG z-score",
- legend.title = "MPG Group",
- rotate = TRUE,
- ggtheme = theme_minimal()
- )ggbarplot(dfm, x = "name", y = "mpg_z", + fill = "mpg_grp", # change fill color by mpg_level + color = "white", # Set bar border colors to white + palette = "jco", # jco journal color palett. see ?ggpar + sort.val = "desc", # Sort the value in descending order + sort.by.groups = FALSE, # Don't sort inside each group + x.text.angle = 90, # Rotate vertically x axis texts + ylab = "MPG z-score", + legend.title = "MPG Group", + rotate = TRUE, + ggtheme = theme_minimal() + )
