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supervisor-summarize.R
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# data loading external file formats
library(R.matlab); library(openxlsx); library(xml2); library(zip);
# data manipulation
library(tidyverse); library(dplyr); library(tidyr); library(rlang); library(stringr); library(purrr); library(data.table); library(glue)
InitializeWriter <- function() {
options(warn=1) # we want to display warnings as they occur, so that it's clear which file caused which warnings
source(paste0(projects_folder, "settings.R")) # user variables
experiment_config_df <<- read.csv(paste0(projects_folder, "experiment_details.csv"), na.strings = "N/A")
experiment_config_df <<- Filter(function(x)!all(is.na(x)), experiment_config_df) # remove NA columns
rat_archive <<- fread(paste0(projects_folder, "rat_archive.csv"), na.strings = c("N/A","NA"))
load(paste0(projects_folder, "run_archive.Rdata"), .GlobalEnv)
}
Workbook_Writer <- function() {
Define_Styles <- function() {
r = list(
rat_name = createStyle(fontSize = 22, textDecoration = "bold"),
rat_header = createStyle(valign = "center", wrapText = TRUE),
date = createStyle(halign = "right"),
mandatory_input_reject = createStyle(bgFill = "#FFE699", fontColour = "#9C5700"), #conditional uses bg
mandatory_input_accept = createStyle(bgFill = "#C6EFCE", fontColour = "#006100"), #conditional uses bg
optional_input = createStyle(fgFill = "#FFFFCC", fontColour = "#9C5700"), #regular uses fg
warning = createStyle(bgFill = "#FFC7CE", fontColour = "#9C0006"), #conditional uses bg
halign_center = createStyle(halign = "center"),
table_header = createStyle(fgFill = "darkgray", fontColour = "white", textDecoration = "bold"), #regular uses fg
key = createStyle(textDecoration = "bold"),
key_center = createStyle(textDecoration = "bold", halign = "center"),
key_merged = createStyle(fgFill = "#D9D9D9", textDecoration = "bold", halign = "left"), #regular uses fg
averages = createStyle(textDecoration = "italic"),
today = createStyle(fontColour = "#ED7D31"),
percent = createStyle(numFmt = "0%")
)
}
Setup_Workbook <- function() {
wb <<- createWorkbook()
rowCurrent <<- 1 #persistent, index of the next unwritten rowCurrent
options("openxlsx.borderColour" = "#4F80BD")
options("openxlsx.borderStyle" = "thin")
modifyBaseFont(wb, fontSize = 10, fontName = "Calibri")
addWorksheet(wb, sheetName = "Summary", tabColour = "limegreen")
setColWidths(wb, 1, cols = "A", widths = 20)
setColWidths(wb, 1, cols = "B", widths = 9)
setColWidths(wb, 1, cols = "C", widths = 10)
setColWidths(wb, 1, cols = "D", widths = 34)
setColWidths(wb, 1, cols = 5:27, widths = 5)
setColWidths(wb, 1, cols = 18:27, widths = 5, hidden = FALSE)
setColWidths(wb, 1, cols = "AB", widths = 5, hidden = FALSE)
setColWidths(wb, 1, cols = "AC", widths = 53.71)
# add experimental configurations table from external file
max_search_rows <<- nrow(experiment_config_df)
writeData(wb, 1, experiment_config_df, startRow = user_settings$config_row, startCol = user_settings$config_col, colNames = FALSE, rowNames = FALSE)
return(wb)
}
Add_Rat_To_Workbook <- function(ratID) {
Write_Header <- function() {
Calculate_Next_Run_Text <- function () {
# TODO this should be even smarter and determine if there are assignments for today
# If not, and today is not sunday, then assign for today
# Basically it should advance to the first non-sunday that's unassigned
# But to do any of that I need to know where I'm putting assignment data. Probably rat_archive.
nextrun = Sys.Date() + 1
nextrun_text = "Tomorrow"
if (nextrun %>% format("%A") == "Sunday") {
nextrun = nextrun + 1
nextrun_text = "Monday"
}
nextrun_text = paste0(nextrun_text, " - ", nextrun %>% format("%m/%d/%Y"))
return(nextrun_text)
}
Write_Dynamic_Lists <- function() {
Build_List <- function(i) {
#build the excel formula that will display the items from the output range that correspond to the query cell for the input range
dynamic_list_formula = paste0("=IFERROR(INDEX(", output_range, ",SMALL(IF(", query_cell, "=", input_range, ",ROW(", input_range, ")-ROW(", range_start, ")+1),ROW(", i, ":", i, "))),\"\")")
writeFormula(wb, 1, dynamic_list_formula, startRow = user_settings$config_row+i+rowCurrent-1, startCol = user_settings$dynamic_col + col_offset, array = TRUE)
}
r = user_settings$config_row
c = user_settings$config_col
# phases list lives at c+1 (experiment) and +2 (phase), querying off experiment in F
range_start = getCellRefs(data.frame(r, c + 2))
range_end = getCellRefs(data.frame(r + max_search_rows, c + 2))
output_range = paste0(range_start, ":", range_end)
range_start = getCellRefs(data.frame(r, c + 1)) # going to use this when we build formula, so it has to be second
range_end = getCellRefs(data.frame(r + max_search_rows, c + 1))
input_range = paste0(range_start, ":", range_end)
query_cell = getCellRefs(data.frame(rowCurrent, 6))
col_offset = 0
sapply(c(1:user_settings$dynamic_list_length), Build_List)
#task list lives at c+3 (phase) and +4 (task), querying from phase in I
range_start = getCellRefs(data.frame(r, c + 4))
range_end = getCellRefs(data.frame(r + max_search_rows, c + 4))
output_range = paste0(range_start, ":", range_end)
range_start = getCellRefs(data.frame(r, c + 3)) # going to use this when we build formula, so it has to be second
range_end = getCellRefs(data.frame(r + max_search_rows, c + 3))
input_range = paste0(range_start, ":", range_end)
query_cell = getCellRefs(data.frame(rowCurrent, 9))
col_offset = col_offset + 1
sapply(c(1:user_settings$dynamic_list_length), Build_List)
#detail list lives at c+5 (phase again) and +6 (detail), querying from phase again in I
range_start = getCellRefs(data.frame(r, c + 6))
range_end = getCellRefs(data.frame(r + max_search_rows, c + 6))
output_range = paste0(range_start, ":", range_end)
range_start = getCellRefs(data.frame(r, c + 5)) # going to use this when we build formula, so it has to be second
range_end = getCellRefs(data.frame(r + max_search_rows, c + 5))
input_range = paste0(range_start, ":", range_end)
query_cell = getCellRefs(data.frame(rowCurrent, 9))
col_offset = col_offset + 1
sapply(c(1:user_settings$dynamic_list_length), Build_List)
}
# Header Workflow ---------------------------------------------------------
Write_Dynamic_Lists()
#Rat Name & ID
rat_name = run_today$rat_name
#rat_name = stringr::str_to_upper(string = rat_name)
digit_index = str_locate(string = rat_name, pattern = "[:digit:]")[1]
rat_name = paste0(stringr::str_sub(rat_name, 1, digit_index - 1),
" ",
stringr::str_sub(rat_name, digit_index, stringr::str_length(rat_name))
)
addStyle(wb, 1, style[["rat_name"]], rows = rowCurrent, cols = 1) # Name
addStyle(wb, 1, style[["rat_name"]], rows = rowCurrent, cols = 30) # ID
#Date
addStyle(wb, 1, style[["date"]], rows = rowCurrent, cols = 2:3)
mergeCells(wb, 1, cols = 2:3, rows = rowCurrent)
nextrun_text = Calculate_Next_Run_Text()
#Tomorrow's Filename
conditionalFormatting(wb, 1, type = "contains", rule = "[", style = style[["mandatory_input_reject"]], rows = rowCurrent, cols = 4)
conditionalFormatting(wb, 1, type = "notcontains", rule = "[", style = style[["mandatory_input_accept"]], rows = rowCurrent, cols = 4)
#Experiment
range_start = getCellRefs(data.frame(user_settings$config_row, user_settings$config_col))
range_end = getCellRefs(data.frame(user_settings$config_row + user_settings$dynamic_list_length, user_settings$config_col))
range_string = paste0(range_start, ":", range_end)
suppressWarnings(dataValidation(wb, 1, rows = rowCurrent, cols = 6, type = "list", value = range_string, operator = ""))
mergeCells(wb, 1, cols = 6:7, rows = rowCurrent)
rule_string = paste0("COUNTIF(", range_string, ",F", rowCurrent, ")>0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_accept"]], rows = rowCurrent, cols = 6)
rule_string = paste0("COUNTIF(", range_string, ",F", rowCurrent, ")<=0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_reject"]], rows = rowCurrent, cols = 6)
#Experimental Phase
range_start = getCellRefs(data.frame(rowCurrent + 1, user_settings$dynamic_col))
range_end = getCellRefs(data.frame(rowCurrent + 1 + user_settings$dynamic_list_length, user_settings$dynamic_col))
range_string = paste0(range_start, ":", range_end)
suppressWarnings(dataValidation(wb, 1, rows = rowCurrent, cols = 9, type = "list", value = range_string, operator = ""))
rule_string = paste0("COUNTIF(", range_string, ",I", rowCurrent, ")>0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_accept"]], rows = rowCurrent, cols = 9)
rule_string = paste0("COUNTIF(", range_string, ",I", rowCurrent, ")<=0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_reject"]], rows = rowCurrent, cols = 9)
mergeCells(wb, 1, cols = 9:10, rows = rowCurrent)
#Task
range_start = getCellRefs(data.frame(rowCurrent + 1, user_settings$dynamic_col + 1))
range_end = getCellRefs(data.frame(rowCurrent + 1 + user_settings$dynamic_list_length, user_settings$dynamic_col + 1))
range_string = paste0(range_start, ":", range_end)
suppressWarnings(dataValidation(wb, 1, rows = rowCurrent, cols = 12, type = "list", value = range_string, operator = ""))
rule_string = paste0("COUNTIF(", range_string, ",L", rowCurrent, ")>0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_accept"]], rows = rowCurrent, cols = 12)
rule_string = paste0("COUNTIF(", range_string, ",L", rowCurrent, ")<=0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_reject"]], rows = rowCurrent, cols = 12)
mergeCells(wb, 1, cols = 12:13, rows = rowCurrent)
#Detail
range_start = getCellRefs(data.frame(rowCurrent + 1, user_settings$dynamic_col + 2))
range_end = getCellRefs(data.frame(rowCurrent + 1 + user_settings$dynamic_list_length, user_settings$dynamic_col + 2))
range_string = paste0(range_start, ":", range_end)
suppressWarnings(dataValidation(wb, 1, rows = rowCurrent, cols = 15, type = "list", value = range_string, operator = ""))
rule_string = paste0("COUNTIF(", range_string, ",O", rowCurrent, ")>0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_accept"]], rows = rowCurrent, cols = 15)
rule_string = paste0("COUNTIF(", range_string, ",O", rowCurrent, ")<=0")
conditionalFormatting(wb, 1, rule = rule_string, style = style[["mandatory_input_reject"]], rows = rowCurrent, cols = 15)
mergeCells(wb, 1, cols = 15:16, rows = rowCurrent)
#Persistent Comment Field
addStyle(wb, 1, rows = rowCurrent, cols = 18:27, style = style[["optional_input"]])
mergeCells(wb, 1, cols = 18:27, rows = rowCurrent) # rat persistent comment merge
#Warnings
conditionalFormatting(wb, 1, type = "expression", rule = "==\"Warnings: none\"", style = style[["mandatory_input_accept"]], rows = rowCurrent, cols = 29)
conditionalFormatting(wb, 1, type = "expression", rule = "!=\"Warnings: none\"", style = style[["warning"]], rows = rowCurrent, cols = 29)
addStyle(wb, 1, rows = rowCurrent, cols = 29, style = style[["halign_center"]], stack = TRUE) # center the warning text
#Entire Main rowCurrent
addStyle(wb, 1, rows = rowCurrent, cols = 1:30, style = style[["rat_header"]], stack = TRUE) # vertically center & wrap the main rowCurrent
#Set_Height_Main_Row()
#Retrieve persistent comment if there is one
comment = rat_archive %>% filter(Rat_ID == ratID) %>% .$Persistent_Comment
if (is.na(comment)) comment = "[Persistent comment field e.g. week-ahead informal plan for this rat]"
rat_header_df = data.frame(
rat_name, #A
nextrun_text, #B
"", #C - merge with previous
"[Tomorrow's Filename]", #D
"", #E
experiment_current, #F
"", #G - merge with previous
"", #H
phase_current, #I
"", #J - merge with previous
"", #K
"[Task]", #L,
"", #M - merge with previous
"", #N
detail_current, #O,
"", #P - merge with previous
"", #Q
comment, #R
"", "", "", "", "", #S T U V W
"", "", "", "", "", #X Y Z AA AB
"", #AC (Warnings will be filled in dynamically later)
paste0("#", ratID), #AD, column 30 -- offscreen, but used for readback
check.names = FALSE, fix.empty.names = FALSE
)
writeData(wb, 1, x = rat_header_df, startRow = rowCurrent, colNames = FALSE, rowNames = FALSE)
}
Write_Table <- function() {
Build_Counts <- function() {
# if experiment_current != Oddball
# get current date and compare to rat_archive 'HL induced' column's date to determine post-HL or not
pre_HL = is.na(dplyr::filter(rat_archive, Rat_ID == ratID)$HL_date) #(boolean)
if (!pre_HL) HL_date = dplyr::filter(rat_archive, Rat_ID == ratID)$HL_date
# BBN Rxn/TH PreHL Alone
if (phase_current == "BBN" & ! task_current %in% c("Training", "Reset") & pre_HL & detail_current == "Alone") {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "BBN" & task %in% c("Rxn", "TH") & detail == "Alone") %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# BBN Rxn/TH PreHL Mixed
#TODO Untested as not in current dataset
if (phase_current == "BBN" & task_current %in% c("Rxn", "TH") & pre_HL & detail_current == "Mixed") {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "BBN" & task %in% c("Rxn", "TH") & detail == "Mixed") %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# BBN Training/Reset PreHL
#TODO Untested as not in current dataset
if (phase_current == "BBN" & task_current %in% c("Training", "Reset") & pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "BBN" & detail == detail_current) %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# Gap Detection Training/Reset PreHL
if (phase_current == "Gap Detection" & task_current %in% c("Training", "Reset") & pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Gap Detection") %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# Gap Detection Rxn/TH PreHL
if (phase_current == "Gap Detection" & task_current %in% c("Rxn", "TH") & pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Gap Detection") %>%
filter(! task %in% c("Training", "Reset")) %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# Tones Rxn/TH PreHL
if (phase_current == "Tones" & task_current %in% c("Rxn", "TH") & pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Tones" & task %in% c("Rxn", "TH")) %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop") %>%
dplyr::arrange(dplyr::desc(detail), task)
}
# Tones Training/Reset PreHL
#TODO Untested as not in current dataset
if (phase_current == "Tones" & task_current %in% c("Training", "Reset") & pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Tones") %>% # keeping all tasks
group_by(task, detail) %>%
summarise(task = unique(task), detail = NA,
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# BBN/Tones Rxn/TH PostHL
if (phase_current %in% c("BBN", "Tones") & task_current %in% c("Rxn", "TH") & !pre_HL) {
df = rat_runs %>%
mutate(condition = dplyr::if_else(date <= HL_date, "baseline", "post-HL")) %>%
tidyr::unnest_wider(assignment) %>%
mutate(duration = dplyr::if_else(
rapply(.$summary, length, how="unlist") %>% .[seq(7, length(.), 7)] == 1,
.$summary %>% modify_depth(1, "duration") %>% as.character %>% str_extract("[:digit:]+"),
"Mixed"
))
# get the duration that was used in today's run
duration_current = df %>% arrange(desc(date)) %>% head(1) %>% .$duration
# count number of pre-HL TH runs, and number of post-HL runs by detail
BBN_counts = df %>%
dplyr::filter(phase == "BBN") %>%
dplyr::filter((task == "TH" & condition == "baseline" & detail == detail_current & duration == duration_current)
| (condition == "post-HL" & detail == detail_current)) %>%
group_by(task, detail, condition) %>%
summarise(task = paste("BBN", unique(task)), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = unique(condition),
.groups = "drop") %>%
dplyr::arrange(condition, task)
Tones_counts = df %>%
dplyr::filter(phase == "Tones") %>%
dplyr::filter(task %in% c("Rxn", "TH")) %>%
group_by(task, detail, condition) %>%
summarise(task = paste("Tones", unique(task)), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = unique(condition),
.groups = "drop") %>%
dplyr::arrange(condition, task)
count_df = rbind(BBN_counts, Tones_counts) %>%
relocate(condition, .after = n)
}
# BBN Training/Reset PostHL
#TODO Partially tested as not in current dataset
if (phase_current == "BBN" & task_current %in% c("Training", "Reset") & !pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "BBN" & date <= HL_date) %>%
group_by(task, detail) %>%
summarise(task = unique(task), detail = NA,
date = tail(date, 1), n = n(),
condition = "post-HL",
.groups = "drop")
}
# Tones Training/Reset PostHL
#TODO Partially tested as not in current dataset
if (phase_current == "Tones" & task_current %in% c("Training", "Reset") & !pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Tones") %>% # keeping all tasks
group_by(task, detail) %>%
summarise(task = unique(task), detail = NA,
date = tail(date, 1), n = n(),
condition = "post-HL",
.groups = "drop")
}
# Octave
if (phase_current == "Octave") {
if (pre_HL) {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Octave") %>% # keeping all tasks
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
} else {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == "Octave" & date >= HL_date) %>% # keeping all tasks
group_by(task, detail) %>%
summarise(task = unique(task), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = "post-HL",
.groups = "drop")
}
}
# Oddball Training/Reset PreHL
if (phase_current == "Tones" & task_current %in% c("Training", "Reset") & pre_HL & detail_current == "Oddball") {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(detail == "Oddball") %>% # keeping all tasks
group_by(task, detail) %>%
summarise(task = unique(task), detail = detail_current,
date = tail(date, 1), n = n(),
condition = "baseline",
.groups = "drop")
}
# Oddball
if (experiment_current == "Oddball") {
count_df = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
dplyr::filter(phase == phase_current & task == task_current & invalid != TRUE) %>%
mutate(frequency = str_extract(file_name, pattern = "^[:digit:]+")) %>%
group_by(task, detail, frequency) %>%
summarise(task = unique(task), frequency = unique(frequency), detail = unique(detail),
date = tail(date, 1), n = n(),
condition = NA,
.groups = "drop") %>%
arrange(task, frequency) %>%
mutate(task = paste(task, frequency)) %>%
select(-frequency)
}
# # OLD
# df_basecase = rat_runs %>%
# tidyr::unnest_wider(assignment) %>%
# dplyr::filter(phase == phase_current & task == "Base case") %>% # note that this is agnostic of the most recent detail and will return any recent base case streak
# mutate(groupid = data.table::rleid(task, detail) ) %>%
# filter(groupid == suppressWarnings(max(groupid))) %>%
# summarise(task = unique(task), detail = unique(detail),
# date = tail(date, 1), n = n(),
# condition = NA,
# .groups = "drop")
#
# # df_task = count of today's task
# if (task_current != "Base case") {
# df_task = rat_runs %>%
# tidyr::unnest_wider(assignment) %>%
# dplyr::filter(phase == phase_current & task == task_current) %>%
# summarise(task = unique(task), detail = unique(detail),
# date = tail(date, 1), n = n(),
# condition = NA,
# .groups = "drop")
#
# # This ensures that the Base case is always at the top
# count_df = rbind(df_basecase, df_task)
# } else count_df = df_basecase
# }
#format date correctly
date = count_df$date
date = paste0(stringr::str_sub(date, 5, 6), "/", stringr::str_sub(date, 7, 8), "/", stringr::str_sub(date, 1, 4))
count_df$date = date
count_df = count_df %>% add_column(blank = NA, .after = "n") # col 5 should be blank
count_df[, (length(count_df) + 1):29] = NA # add columns to reach 29
return(count_df)
}
Build_Table <- function() {
# Common Columns ----------------------------------------------------------
columns = c("task", "detail", "date", "time", "file_name", "weight",
"trial_count", "hit_percent", "FA_percent", "mean_attempts_per_trial",
"threshold", "reaction", "FA_detailed", "warnings_list", "comments", "analysis_type")
r = rat_runs %>%
dplyr::filter(map_lgl(assignment, ~ .x$experiment == experiment_current)) %>%
dplyr::filter(map_lgl(assignment, ~ .x$phase == phase_current)) %>%
# highlight file names that are invalid with stars on both sides or wrong file with star at the end
mutate(file_name = case_when(invalid == TRUE ~ glue("* {file_name} *"),
str_detect(paste(warnings_list), pattern = "wrong file") ~ glue("{file_name} *"),
TRUE ~ file_name)) %>%
tidyr::unnest_wider(assignment) %>%
# omit Oddball rows with <1 complete block - here to save time as its one less row to unnest
dplyr::filter(! (stim_type == "train" & complete_block_count < 2)) %>%
tidyr::unnest_wider(stats) %>%
tidyr::unnest_wider(summary) %>%
dplyr::select(all_of(columns)) %>%
arrange(desc(date), .by_group = F)
weight_max = max(rat_runs$weight) # Rat_runs not r because we want all history, not just days corresponding to this experiment/phase
# today's duration
# if it's a single duration, we want the below dfs limited to runs that INCLUDE (not necc. perfectmatch) today's duration
# if it's a multiple duration, we want the below dfs limited to runs that INCLUDE (not necc. perfectmatch) the minimum duration used today
# suppress warnings to not complain on oddball, which have no duration
min_duration = pluck(run_today, "summary", 1, "duration") %>% unlist() %>% suppressWarnings(min(.))
#min_duration = r %>% unnest(reaction) %>% .$`Dur (ms)` %>% unique() %>% min()
#min_duration = r %>% unnest(reaction) %>% dplyr::filter(task == task_current & detail == detail_current) %>% .$`Dur (ms)` %>% unique() %>% min()
# Needed for gap detection
current_frequency = r %>% arrange(desc(date)) %>% head(1) %>% pluck("reaction", 1, "Inten (dB)")
# Needed to deal with the initial training
analysis_type = r %>% arrange(desc(date)) %>% head(1) %>% .$analysis_type
# Task-specific RXN column ------------------------------------------------
if (experiment_current == "" | is.na(experiment_current)) {
# I don't currently know which will match but neither should be true
r = r
} else if (experiment_current == "Oddball") {
r = r %>% mutate(reaction1 = reaction) %>%
unnest(reaction) %>%
group_by(date) %>%
mutate(Rxn = mean(Rxn)) %>%
select(-`Freq (kHz)`, -`Dur (ms)`, -`Inten (dB)`) %>% #TODO check that Frequency is actually go tone positional data
distinct()
} else if(phase_current == "Gap Detection") { #Experiment is Not blank, Not Oddball
if(detail_current == "None"| analysis_type == "Training - Gap") {
r = r %>%
unnest(reaction) %>%
group_by(date) %>%
mutate(Rxn = mean(Rxn))
} else {
r = r %>%
unnest(reaction) %>%
dplyr::filter(`Dur (ms)` == 10) %>% # not equal TH
group_by(date) %>%
mutate(Rxn = mean(Rxn))
}
r = r %>% select(-`Freq (kHz)`, -`Dur (ms)`, -`Inten (dB)`)
} else { # Experiment is none of Blank, Oddball, Gapdetection.
if (phase_current == "Octave" | detail_current == "Oddball") { # Oddball detail implies phase=Tones
r = r %>% unnest(reaction) %>%
select(-`Freq (kHz)`, -`Dur (ms)`, -`Inten (dB)`)
} else {
# Experiment is none of Blank, Oddball, Gapdetection (therefore experiment is TTS, Fmr1-LE, or Tsc2-LE)
# Phase is not Octave (therefore Phase is either Tones or BBN).
# If phase is Tones, detail is not Oddball.
# TH and Rxn refer to Task not the column
df_TH_BBN = NULL
df_TH_tones = NULL
df_Rxn = NULL
r = r %>% unnest(reaction)
# build entries of runs where we were doing BBN thresholding
# specifically, filter by type and then duration matching today's.
# Matching, perfect match, because we have ~20 rows per run that enumerate the various combinations from list elements including dur
df_TH_BBN = r %>%
dplyr::filter(task == "TH" & `Dur (ms)` == min_duration & `Freq (kHz)` == 0) %>%
group_by(date, time) %>%
slice(which.min(`Inten (dB)`))
# build entries of runs where we were doing Tones thresholding, which will be filtered to desired dB
intensity = r %>%
dplyr::filter(task == "TH" & `Dur (ms)` == min_duration & `Freq (kHz)` != 0) %>% #select(-threshold, -file_name, - weight, -mean_attempts_per_trial) %>% View
group_by(date, time) %>%
count(`Inten (dB)`) %>% arrange(desc(`Inten (dB)`)) %>% slice(which.max(n)) %>%
rename(desired_dB = `Inten (dB)`) %>% select(-n)
df_TH_tones = r %>%
dplyr::filter(task == "TH" & `Dur (ms)` == min_duration & `Freq (kHz)` != 0) %>% #select(-threshold, -file_name, - weight, -mean_attempts_per_trial) %>% View
right_join(intensity, by = c("date", "time")) %>%
filter(`Inten (dB)` == desired_dB) %>%
group_by(date) %>%
mutate(Rxn = mean(Rxn)) %>%
select(-desired_dB)
# now non-thresholding days (task!=TH)
# if we have a 60db entry for a date, great
df_Temp = r %>%
dplyr::filter(task != "TH" & `Dur (ms)` == min_duration & `Inten (dB)` == 60) %>% # not equal TH
group_by(date, time) %>%
mutate(Rxn = mean(Rxn))
# for all dates, take their 55 and 65 entries (stepsize 5 will have potentially all of 55, 60, 65, stepsize 10 will have either 60 or both 55 and 65)
# take the average Rxn from the 55 and 65 and only keep dates that aren't already in df_Temp
df_Rxn = r %>%
dplyr::filter(task != "TH" & `Dur (ms)` == min_duration & `Inten (dB)` %in% c(55,65)) %>% # not equal TH
group_by(date) %>%
summarise(Rxn = mean(Rxn), `Inten (dB)` = mean(`Inten (dB)`), across(), .groups = "drop") %>%
distinct() %>%
filter(! date %in% df_Temp$date) %>%
rbind(df_Temp)
r = rbind(df_TH_BBN, df_TH_tones, df_Rxn)
if (analysis_type %in% c("Training - Gap", "Training - BBN")) {
r = r %>% rename(Dur = `Dur (ms)`, Freq = `Freq (kHz)`) %>% select(-`Inten (dB)`)
} else {
r = r %>% select(-`Freq (kHz)`, -`Dur (ms)`, -`Inten (dB)`)
}
r = distinct(r)
}
}
r = r %>% select(-analysis_type) %>% relocate(Rxn, .before = mean_attempts_per_trial) %>%
mutate(Spacer1 = NA)
# Phase-specific Columns --------------------------------------------------------
if (analysis_type %in% c("Training - Gap")) {
# Training has no TH
r = r %>%
# unnesting reaction has caused an amplification of rows - this is to remove that
select(-threshold) %>% unique() %>%
group_by(task, detail) %>%
relocate(Spacer1, .after = mean_attempts_per_trial) %>%
select(-FA_detailed)
} else if (phase_current %in% c("Gap Detection")) {
r = r %>% unnest(threshold) %>% select(-Freq, -dB, -Dur) %>%
group_by(task, detail) %>%
mutate(THrange = paste0(suppressWarnings(min(TH, na.rm = TRUE)) %>% round(digits = 0), "-", suppressWarnings(max(TH, na.rm = TRUE)) %>% round(digits = 0))) %>%
relocate(THrange, .after = TH) %>%
relocate(Spacer1, .after = mean_attempts_per_trial) %>%
select(-FA_detailed) %>%
unique
} else if (analysis_type %in% c("Training - BBN")) {
# Training has no TH
r = r %>% unnest(threshold) %>% filter(Dur == min_duration) %>% select(-Freq, -Dur) %>%
group_by(task, detail) %>%
relocate(Spacer1, .after = mean_attempts_per_trial) %>%
select(-FA_detailed)
} else if (phase_current == "BBN") {
r = r %>% unnest(threshold) %>% filter(Dur == min_duration) %>% select(-Freq, -Dur) %>%
group_by(task, detail) %>%
mutate(THrange = paste0(suppressWarnings(min(TH, na.rm = TRUE)) %>% round(digits = 0), "-", suppressWarnings(max(TH, na.rm = TRUE)) %>% round(digits = 0))) %>%
relocate(THrange, .after = TH) %>%
relocate(Spacer1, .after = mean_attempts_per_trial) %>%
select(-FA_detailed)
} else if (phase_current == "Tones" & detail_current != "Oddball") {
r = r %>% unnest(threshold) %>%
filter(Freq != 0 & Dur == min_duration) %>%
group_by(task, detail, Freq) %>%
mutate(THrange = paste0(suppressWarnings(min(TH, na.rm = TRUE)) %>% round(digits = 0), "-", suppressWarnings(max(TH, na.rm = TRUE)) %>% round(digits = 0))) %>%
relocate(THrange, .after = TH) %>%
gather(variable, value, (TH:THrange)) %>%
unite(temp, variable, Freq) %>%
pivot_wider(names_from = temp, values_from = value)
r = r %>%
select(-Dur, -FA_detailed) %>%
relocate(warnings_list, comments, .after = last_col())
# Adding missing columns without overwriting extant THs and THranges
df = tibble(TH_4 = NA, TH_8 = NA, TH_16 = NA, TH_32 = NA,
THrange_4 = NA, THrange_8 = NA, THrange_16 = NA, THrange_32 = NA)
r = add_column(r, !!!df[setdiff(names(df), names(r))]) %>%
relocate(TH_4, TH_8, TH_16, TH_32, THrange_4, THrange_8, THrange_16, THrange_32, .after = Spacer1) %>%
mutate(Spacer2 = NA) %>%
relocate(Spacer2, .after = TH_32) %>%
mutate_at(vars(starts_with("TH_")), as.numeric)
x = rat_runs %>%
dplyr::filter(map_lgl(assignment, ~ .x$experiment == experiment_current)) %>%
dplyr::filter(map_lgl(assignment, ~ .x$phase == phase_current)) %>%
tidyr::unnest_wider(assignment) %>%
tidyr::unnest(summary) %>%
select(task, detail, date, time, `Freq (kHz)`, dB_min, dB_max) %>%
group_by(date, task, detail, `Freq (kHz)`) %>% #do(print(.))
mutate(Spacer3 = NA,
Stimrange = paste0(unique(dB_min), "-", unique(dB_max))) %>%
select(task, detail, date, time, Spacer3, `Freq (kHz)`, Stimrange)
r = left_join(r, x, by = c("task", "detail", "date", "time")) %>%
unique() %>%
pivot_wider(names_from = `Freq (kHz)`, values_from = Stimrange)
# Adding missing columns without overwriting extant THs and THranges
df = tibble(`4` = NA, `8` = NA, `16` = NA, `32` = NA)
r = add_column(r, !!!df[setdiff(names(df), names(r))]) %>%
relocate(`4`, `8`, `16`, `32`, .after = Spacer3)
} else if (phase_current == "Tones" & detail_current == "Oddball") {
r = r %>% filter(detail == "Oddball") %>%
select(-threshold) %>%
select(-FA_detailed)
x = rat_runs %>%
tidyr::unnest_wider(assignment) %>%
filter(detail == detail_current) %>%
tidyr::unnest_wider(stats) %>%
unnest(dprime) %>%
select(task, detail, date, time, dprime)
r = left_join(r, x, by = c("task", "detail", "date", "time"))
} else if (phase_current == "Octave") {
r = r %>% select(-threshold)
#TODO NOT MVP convert to 1/12 of octaves based on summary kHz range
df_discrimination = r %>% filter(! task %in% c("Training", "Holding")) %>%
unnest(FA_detailed)
if(nrow(df_discrimination) > 0){
# get a cononical no_go value
# no_go = r %>%
# # easiest access is the 2nd value in the training/holding file names
# filter(task %in% c("Training", "Holding")) %>%
# # to guard against bad files, select the last 5 and pick the most common no_go value
# arrange(desc(date)) %>% head(n = 5) %>% transmute(no_go = as.numeric(str_extract(file_name, pattern = "[:digit:]+?(?=kHz_[:digit:]+?dB_300ms)"))) %>%
# group_by(no_go) %>% summarize(n = n()) %>% arrange(n) %>% head(n = 1) %>% .$no_go
df_discrimination =
df_discrimination %>%
group_by(date) %>%
# copy the max d' to every value because its the only one we want outputted at the end
do(mutate(.,dprime = max(dprime))) %>%
# calculate fraction of the octave by pulling the go frequency from the file_name (only source we have at this point)
mutate(octave_fraction = log(as.numeric(str_extract(file_name, pattern = "[:digit:]+?(?=-.+?kHz)"))/`Freq (kHz)`)/log(2),
Oct = abs(round(octave_fraction * 12))) %>%
select(-octave_fraction, -FA, -trials, -`Freq (kHz)`) %>%
pivot_wider(names_from = Oct, values_from = FA_percent_detailed)
#create columns for Octave fractions that are missing:
column_holder =
#make a list containing the common column names up through spacer1 and append columns named 1 through 12
c(names(select(df_discrimination, task:Spacer1)), seq(1:12))
# add the missing columns as dbls
df_discrimination[column_holder[!(column_holder %in% colnames(df_discrimination))]] = na_dbl
# reorder columns
df_discrimination = select(df_discrimination, task:Spacer1, "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12")
}
df_training = r %>% filter(task %in% c("Training", "Holding")) %>% select(-FA_detailed)
x = rat_runs %>%
dplyr::filter(map_lgl(assignment, ~ .x$experiment == experiment_current)) %>%
dplyr::filter(map_lgl(assignment, ~ .x$phase == phase_current)) %>%
tidyr::unnest_wider(assignment) %>%
tidyr::unnest_wider(stats) %>%
unnest(dprime) %>%
select(task, detail, date, time, dprime)
df_training = left_join(df_training, x, by = c("task", "detail", "date", "time"))
r = rbind(df_discrimination, df_training) %>%
relocate(dprime, .after = Spacer1) %>%
mutate(Spacer2 = NA) %>% relocate(Spacer2, .after = dprime)
} else if (experiment_current == "Oddball") {
# handled as 2 separate tables to deal with edge case of missing all of a specific type of trial
FA_table = r %>%
select(-threshold) %>%
rename(Rxn_avg = Rxn) %>%
unnest(FA_detailed) %>%
# drop the duplicate column that is being used in the next (Rxn) table
select(-reaction1)
Rxn_table = r %>%
select(-threshold) %>%
rename(Rxn_avg = Rxn) %>%
unnest(reaction1) %>%
# only keep necessary columns
select(date, time, Rxn, `Inten (dB)`)
# use a full_join so that if somehow Rxn_table has more rows (shouldn't happen), they aren't silently dropped
r = full_join(FA_table, Rxn_table, by = c("date", "time", "position" = "Inten (dB)"))
r = r %>%
group_by(date, time) %>%
do(filter(., position %in% c(min(position), median(position), max(position))) %>%
mutate(position = c("early", "mid", "late"))) %>%
select(-FA, -trials) %>%
pivot_wider(names_from = position, values_from = c(Rxn, FA_percent_detailed)) %>%
mutate(Spacer2 = NA) %>% relocate(Spacer2, .after = Rxn_late)
} else if (experiment_current == "GD") {
r = r %>%
select(-FA_detailed) %>%
unnest(threshold) %>%
select(-Freq, -dB) %>%
mutate(THrange = paste0(suppressWarnings(min(TH, na.rm = TRUE)) %>% round(digits = 0), "-", suppressWarnings(max(TH, na.rm = TRUE)) %>% round(digits = 0))) %>%
relocate(Spacer1, .before = TH) %>%
relocate(THrange, .after = TH)
}
averages = r %>%
dplyr::group_by(task, detail) %>%
dplyr::summarise_if(.predicate = is.numeric, .funs = mean, na.rm = TRUE) %>%
dplyr::mutate(date = "Overall", file_name = "Averages", warnings_list = NA, comments = NA) %>%
dplyr::relocate(date, file_name, .before = weight) %>%
ungroup() %>%
mutate_all(~ifelse(is.nan(.), NA, .))
order = r %>% arrange(desc(date)) %>% group_by(task) %>% do(head(., 1)) %>% arrange(desc(date)) %>% .$task
r = r %>%
# sort by date and time descending
arrange(desc(date), desc(time)) %>% group_by(task) %>%
# remove time column as its only for merges caused by multi-run days
select(-time) %>%
do(if (unique(.$task) %in% c("TH", "CNO 3mg/kg", "Discrimination")) head(., 10)
else head(., 5)) %>%
arrange(match(task, order)) %>%
dplyr::mutate(date = paste0(stringr::str_sub(date, 5, 6), "/", stringr::str_sub(date, 7, 8), "/", stringr::str_sub(date, 1, 4))) %>%
mutate(date = as.character(date))
columns = names(r)
r = bind_rows(averages, r) %>%
select(all_of(columns)) %>%
mutate(weight = (weight - weight_max)/weight_max)
if(length(r) < 29) r[, (length(r) + 1):29] = NA # add columns to reach 29
r = r %>% relocate(warnings_list, comments, .after = last_col())
return(as.data.frame(r)) #shed the grouping that prevents rbinding later on
}
Build_Table_Key <- function() {
r = c("", "", " ", " ", " ", " ", " ", " ", "___", "_____")
r = rbind(r, c("", "", "Date", "Filename", "Wt d%", "Trials", "Hit %", "FA %", "Rxn", "Atmpt")) %>% as.data.frame()
r = cbind(r, NA) #spacer
if (phase_current == "BBN" | phase_current == "Gap Detection") {
r = cbind(r, c("", "TH"))
r = cbind(r, c("", "{TH}"))
}
else if (phase_current == "Tones" & detail_current != "Oddball") {
r = cbind(r, c(" TH", "4"))
r = cbind(r, c(" TH", "8"))
r = cbind(r, c(" TH", "16"))
r = cbind(r, c(" TH", "32"))
r = cbind(r, NA)
r = cbind(r, c(" {TH} Range", "4"))
r = cbind(r, c(" {TH} Range", "8"))
r = cbind(r, c(" {TH} Range", "16"))
r = cbind(r, c(" {TH} Range", "32"))
r = cbind(r, NA)
r = cbind(r, c(" {Stim} Range", "4"))
r = cbind(r, c(" {Stim} Range", "8"))
r = cbind(r, c(" {Stim} Range", "16"))
r = cbind(r, c(" {Stim} Range", "32"))
}
else if (phase_current == "Tones" & detail_current == "Oddball") {
r = cbind(r, c("", "d'"))
r = cbind(r, NA)
}
else if (phase_current == "Octave") {
r = cbind(r, c("", "d'"))
r = cbind(r, NA)
r = cbind(r, t(data.frame(c(" No-Go False Alarm % (by octave steps)") %>% rep_len(12), c(1:12))))
}
else if (phase_current == "Tone-BBN" || phase_current == "Tone-Tone") {
r = cbind(r, c(" Rxn Time", "Early"))
r = cbind(r, c(" Rxn Time", "Mid"))
r = cbind(r, c(" Rxn Time", "Late"))
r = cbind(r, NA)
r = cbind(r, c(" False Alarm %", "Early"))
r = cbind(r, c(" False Alarm %", "Mid"))
r = cbind(r, c(" False Alarm %", "Late"))
r = cbind(r, NA)
}
else {
stop("ERROR: unrecognized phase: ", phase_current)
}
r[, (length(r) + 1):27] = NA # add columns to reach 27
r = cbind(r, c("", "Warn"))
r = cbind(r, c("", "Observations"))
return(r)
}
# Write Table Workflow ----------------------------------------------------
row_table_start = rowCurrent + 1 #save for later
addStyle(wb, 1, style[["table_header"]], rows = row_table_start, cols = 1:29, gridExpand = TRUE)
df_table = Build_Table()
df_key = Build_Table_Key()
df_counts = Build_Counts()
# need blank rows inserted into df_table to give space for df_key
df_blank <- data.frame(matrix(ncol = 29 , nrow = nrow(df_key) + nrow(df_counts)))
# the obnoxious blank strings are because every column in a table has to have a unique header,
# and because we want those headers to be blank for all but column A
names = c("Task (Filter)", "Detail (Filter)", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ", " ")
colnames(df_table) = names
colnames(df_blank) = names
df_table = rbind(df_blank, df_table)
row_counts = row_table_start + 1
row_key_start = row_table_start + nrow(df_counts) + 1
row_key_end = row_table_start + nrow(df_counts) + nrow(df_key)
row_table_end = row_table_start + nrow(df_table)
writeDataTable(wb, 1, x = df_table, startRow = row_table_start, colNames = TRUE, rowNames = FALSE, bandedRows = FALSE, tableStyle = "TableStyleMedium18", na.string = "")
writeData(wb, 1, x = df_counts, startRow = row_counts, colNames = FALSE, rowNames = FALSE)
writeData(wb, 1, x = df_key, startRow = row_key_start, colNames = FALSE, rowNames = FALSE)
# style the averages rows
averages_last_row = row_table_start + max(which(df_table[,3] == "Overall"))
addStyle(wb, 1, style[["averages"]], rows = row_key_end:averages_last_row, cols = 1:29, gridExpand = TRUE, stack = TRUE)
# style the 'today' rowCurrent
today_offset = min(which(df_table[,3] != "Overall"))
row_today = row_table_start + today_offset
addStyle(wb, 1, style[["today"]], rows = row_today, cols = 1:29)
# style the '%' columns
tk = t(tail(df_key))
row.names(tk) <- 1:nrow(tk)
pc1 = tk %>% as.data.frame() %>% filter(str_detect(.[,1], "%")) %>% rownames() %>% as.numeric
pc2 = tk %>% as.data.frame() %>% filter(str_detect(.[,2], "%")) %>% rownames() %>% as.numeric
percentage_columns = c(pc1, pc2)
addStyle(wb, 1, style[["percent"]], rows = row_key_end:row_table_end, cols = percentage_columns, gridExpand = TRUE, stack = TRUE)
# copy today's warnings
warns = df_table[today_offset, 28] %>% unlist() %>% stringr::str_c(collapse = "\n")
if (warns == "") warns = "Warnings: none"
writeData(wb, 1, x = warns, startRow = rowCurrent, startCol = 29)
# style the key
addStyle(wb, 1, style[["key"]], rows = row_key_start:row_key_end, cols = 1:29, gridExpand = TRUE)
addStyle(wb, 1, style[["key_center"]], rows = row_key_start:row_key_end, cols = 5:28, gridExpand = TRUE) # don't center observations
# detect and merge common header cells -- nope, merges can't be done inside a table object, so just style it
matching_cells = (df_key[1,12] == df_key[1,13] && df_key[1,13] == df_key[1,14])
if (!is.na(matching_cells) && matching_cells) { # Rxn Time
deleteData(wb, 1, rows = row_key_start, cols = 13:14, gridExpand = TRUE) # delete all but first
addStyle(wb, 1, style[["key_merged"]], rows = row_key_start, cols = 12:14)
}
matching_cells = (df_key[1,12] == df_key[1,15])
if (!is.na(matching_cells) && matching_cells) { # TH; rxn above will also fire but we just overwrite what it does
deleteData(wb, 1, rows = row_key_start, cols = 15)
addStyle(wb, 1, style[["key_merged"]], rows = row_key_start, cols = 12:15)
}
matching_cells = (df_key[1,17] == df_key[1,18] && df_key[1,17] == df_key[1,19] && df_key[1,17] == df_key[1,20])
if (!is.na(matching_cells) && matching_cells) { # TH Range
deleteData(wb, 1, rows = row_key_start, cols = 18:20, gridExpand = TRUE)
addStyle(wb, 1, style[["key_merged"]], rows = row_key_start, cols = 17:20)
}
matching_cells = (df_key[1,22] == df_key[1,23] && df_key[1,22] == df_key[1,24] && df_key[1,22] == df_key[1,25])
if (!is.na(matching_cells) && matching_cells) { # Stim range
deleteData(wb, 1, rows = row_key_start, cols = 23:25, gridExpand = TRUE)
addStyle(wb, 1, style[["key_merged"]], rows = row_key_start, cols = 22:25)
}
matching_cells = (df_key[1,16] == df_key[1,17] && df_key[1,17] == df_key[1,18])
if (!is.na(matching_cells) && matching_cells) { # FA %
deleteData(wb, 1, rows = row_key_start, cols = 17:18, gridExpand = TRUE)
addStyle(wb, 1, style[["key_merged"]], rows = row_key_start, cols = 16:18)
}
matching_cells = (df_key[1,14] == df_key[1,25]) # No Go False Alarm % (by octave steps)
if (!is.na(matching_cells) && matching_cells) {
deleteData(wb, 1, rows = row_key_start, cols = 15:25, gridExpand = TRUE)
addStyle(wb, 1, style[["key_merged"]], rows = row_key_start, cols = 14:25)
}
# advance rowCurrent for next table
rowCurrent <<- row_table_end + 2
}
# Add Rat To Workbook Workflow --------------------------------------------
rat_runs <<- run_archive %>% dplyr::filter(rat_ID == ratID) %>% dplyr::arrange(date)
if (nrow(rat_runs) == 0) {