########
#### Current file: /Users/au301842/freesearcheR/inst/apps/freesearcheR/functions.R
########
########
#### Current file: R//app_version.R
########
app_version <- function()'250307_1453'
########
#### Current file: R//baseline_table.R
########
#' Print a flexible baseline characteristics table
#'
#' @param data data set
#' @param fun.args list of arguments passed to
#' @param fun function to
#' @param vars character vector of variables to include
#'
#' @return object of standard class for fun
#' @export
#'
#' @examples
#' mtcars |> baseline_table()
#' mtcars |> baseline_table(fun.args = list(by = "gear"))
baseline_table <- function(data, fun.args = NULL, fun = gtsummary::tbl_summary, vars = NULL) {
if (!is.null(vars)) {
data <- data |> dplyr::select(dplyr::all_of(vars))
}
out <- do.call(fun, c(list(data = data), fun.args))
return(out)
}
########
#### Current file: R//contrast_text.R
########
#' @title Contrast Text Color
#' @description Calculates the best contrast text color for a given
#' background color.
#' @param background A hex/named color value that represents the background.
#' @param light_text A hex/named color value that represents the light text
#' color.
#' @param dark_text A hex/named color value that represents the dark text color.
#' @param threshold A numeric value between 0 and 1 that is used to determine
#' the luminance threshold of the background color for text color.
#' @param method A character string that specifies the method for calculating
#' the luminance. Three different methods are available:
#' c("relative","perceived","perceived_2")
#' @param ... parameter overflow. Ignored.
#' @details
#' This function aids in deciding the font color to print on a given background.
#' The function is based on the example provided by teppo:
#' https://stackoverflow.com/a/66669838/21019325.
#' The different methods provided are based on the methods outlined in the
#' StackOverflow thread:
#' https://stackoverflow.com/questions/596216/formula-to-determine-perceived-brightness-of-rgb-color
#' @return A character string that contains the best contrast text color.
#' @examples
#' contrast_text(c("#F2F2F2", "blue"))
#'
#' contrast_text(c("#F2F2F2", "blue"), method="relative")
#' @export
#'
#' @importFrom grDevices col2rgb
#'
contrast_text <- function(background,
light_text = 'white',
dark_text = 'black',
threshold = 0.5,
method = "perceived_2",
...) {
if (method == "relative") {
luminance <-
c(c(.2126, .7152, .0722) %*% grDevices::col2rgb(background) / 255)
} else if (method == "perceived") {
luminance <-
c(c(.299, .587, .114) %*% grDevices::col2rgb(background) / 255)
} else if (method == "perceived_2") {
luminance <- c(sqrt(colSums((
c(.299, .587, .114) * grDevices::col2rgb(background)
) ^ 2)) / 255)
}
ifelse(luminance < threshold,
light_text,
dark_text)
}
########
#### Current file: R//correlations-module.R
########
#' Data correlations evaluation module
#'
#' @param id Module id. (Use 'ns("id")')
#'
#' @name data-correlations
#' @returns Shiny ui module
#' @export
data_correlations_ui <- function(id, ...) {
ns <- shiny::NS(id)
shiny::tagList(
shiny::textOutput(outputId = ns("suggest")),
shiny::plotOutput(outputId = ns("correlation_plot"), ...)
)
}
#'
#' @param data data
#' @param color.main main color
#' @param color.sec secondary color
#' @param ... arguments passed to toastui::datagrid
#'
#' @name data-correlations
#' @returns shiny server module
#' @export
data_correlations_server <- function(id,
data,
include.class = NULL,
cutoff = .7,
...) {
shiny::moduleServer(
id = id,
module = function(input, output, session) {
# ns <- session$ns
rv <- shiny::reactiveValues(
data = NULL
)
rv$data <- shiny::reactive({
shiny::req(data)
if (!is.null(include.class)) {
filter <- sapply(data(), class) %in% include.class
out <- data()[filter]
} else {
out <- data()
}
out
})
# rv <- list()
# rv$data <- mtcars
output$suggest <- shiny::renderPrint({
shiny::req(rv$data)
shiny::req(cutoff)
pairs <- correlation_pairs(rv$data(), threshold = cutoff())
more <- ifelse(nrow(pairs) > 1, "from each pair ", "")
if (nrow(pairs) == 0) {
out <- glue::glue("No variables have a correlation measure above the threshold.")
} else {
out <- pairs |>
apply(1, \(.x){
glue::glue("'{.x[1]}'x'{.x[2]}'({round(as.numeric(.x[3]),2)})")
}) |>
(\(.x){
glue::glue("The following variable pairs are highly correlated: {sentence_paste(.x)}.\nConsider excluding one {more}from the dataset to ensure variables are independent.")
})()
}
out
})
output$correlation_plot <- shiny::renderPlot({
psych::pairs.panels(rv$data())
})
}
)
}
correlation_pairs <- function(data, threshold = .8) {
data <- data[!sapply(data, is.character)]
data <- data |> dplyr::mutate(dplyr::across(dplyr::where(is.factor), as.numeric))
cor <- Hmisc::rcorr(as.matrix(data))
r <- cor$r %>% as.table()
d <- r |>
as.data.frame() |>
dplyr::filter(abs(Freq) > threshold, Freq != 1)
d[1:2] |>
apply(1, \(.x){
sort(unname(.x))
},
simplify = logical(1)
) |>
duplicated() |>
(\(.x){
d[!.x, ]
})() |>
setNames(c("var1", "var2", "cor"))
}
sentence_paste <- function(data, and.str = "and") {
and.str <- gsub(" ", "", and.str)
if (length(data) < 2) {
data
} else if (length(data) == 2) {
paste(data, collapse = glue::glue(" {and.str} "))
} else if (length(data) > 2) {
paste(paste(data[-length(data)], collapse = ", "), data[length(data)], collapse = glue::glue(" {and.str} "))
}
}
cor_app <- function() {
ui <- shiny::fluidPage(
shiny::sliderInput(
inputId = "cor_cutoff",
label = "Correlation cut-off",
min = 0,
max = 1,
step = .1,
value = .7,
ticks = FALSE
),
data_correlations_ui("data", height = 600)
)
server <- function(input, output, session) {
data_correlations_server("data", data = shiny::reactive(mtcars), cutoff = shiny::reactive(input$cor_cutoff))
}
shiny::shinyApp(ui, server)
}
cor_app()
########
#### Current file: R//custom_SelectInput.R
########
#' A selectizeInput customized for data frames with column labels
#'
#' @description
#' Copied and modified from the IDEAFilter package
#' Adds the option to select "none" which is handled later
#'
#' @param inputId passed to \code{\link[shiny]{selectizeInput}}
#' @param label passed to \code{\link[shiny]{selectizeInput}}
#' @param data \code{data.frame} object from which fields should be populated
#' @param selected default selection
#' @param ... passed to \code{\link[shiny]{selectizeInput}}
#' @param col_subset a \code{vector} containing the list of allowable columns to select
#' @param placeholder passed to \code{\link[shiny]{selectizeInput}} options
#' @param onInitialize passed to \code{\link[shiny]{selectizeInput}} options
#' @param none_label label for "none" item
#'
#' @return a \code{\link[shiny]{selectizeInput}} dropdown element
#'
#' @importFrom shiny selectizeInput
#' @export
#'
columnSelectInput <- function(inputId, label, data, selected = "", ...,
col_subset = NULL, placeholder = "", onInitialize, none_label="No variable selected") {
datar <- if (is.reactive(data)) data else reactive(data)
col_subsetr <- if (is.reactive(col_subset)) col_subset else reactive(col_subset)
labels <- Map(function(col) {
json <- sprintf(
IDEAFilter:::strip_leading_ws('
{
"name": "%s",
"label": "%s",
"datatype": "%s"
}'),
col,
attr(datar()[[col]], "label") %||% "",
IDEAFilter:::get_dataFilter_class(datar()[[col]])
)
}, col = names(datar()))
if (!"none" %in% names(datar())){
labels <- c("none"=list(sprintf('\n {\n \"name\": \"none\",\n \"label\": \"%s\",\n \"datatype\": \"\"\n }',none_label)),labels)
choices <- setNames(names(labels), labels)
choices <- choices[match(if (length(col_subsetr()) == 0 || isTRUE(col_subsetr() == "")) names(datar()) else col_subsetr(), choices)]
} else {
choices <- setNames(names(datar()), labels)
choices <- choices[match(if (length(col_subsetr()) == 0 || isTRUE(col_subsetr() == "")) choices else col_subsetr(), choices)]
}
shiny::selectizeInput(
inputId = inputId,
label = label,
choices = choices,
selected = selected,
...,
options = c(
list(render = I("{
// format the way that options are rendered
option: function(item, escape) {
item.data = JSON.parse(item.label);
return '' +
'
' +
escape(item.data.name) + ' ' +
' ' +
item.data.datatype +
'' +
'
' +
(item.data.label != '' ? '
' + escape(item.data.label) + '
' : '') +
'
' +
escape(item.data.name) +
'
';
}
}"))
)
)
}
#' A selectizeInput customized for named vectors
#'
#' @param inputId passed to \code{\link[shiny]{selectizeInput}}
#' @param label passed to \code{\link[shiny]{selectizeInput}}
#' @param data A named \code{vector} object from which fields should be populated
#' @param selected default selection
#' @param ... passed to \code{\link[shiny]{selectizeInput}}
#' @param placeholder passed to \code{\link[shiny]{selectizeInput}} options
#' @param onInitialize passed to \code{\link[shiny]{selectizeInput}} options
#'
#' @returns a \code{\link[shiny]{selectizeInput}} dropdown element
#' @export
#'
#' @examples
#' if (shiny::interactive()) {
#' shinyApp(
#' ui = fluidPage(
#' shiny::uiOutput("select"),
#' tableOutput("data")
#' ),
#' server = function(input, output) {
#' output$select <- shiny::renderUI({
#' vectorSelectInput(
#' inputId = "variable", label = "Variable:",
#' data = c(
#' "Cylinders" = "cyl",
#' "Transmission" = "am",
#' "Gears" = "gear"
#' )
#' )
#' })
#'
#' output$data <- renderTable(
#' {
#' mtcars[, c("mpg", input$variable), drop = FALSE]
#' },
#' rownames = TRUE
#' )
#' }
#' )
#' }
vectorSelectInput <- function(inputId,
label,
data,
selected = "",
...,
placeholder = "",
onInitialize) {
datar <- if (shiny::is.reactive(data)) data else shiny::reactive(data)
labels <- sprintf(
IDEAFilter:::strip_leading_ws('
{
"name": "%s",
"label": "%s"
}'),
datar(),
names(datar()) %||% ""
)
choices <- stats::setNames(datar(), labels)
shiny::selectizeInput(
inputId = inputId,
label = label,
choices = choices,
selected = selected,
...,
options = c(
list(render = I("{
// format the way that options are rendered
option: function(item, escape) {
item.data = JSON.parse(item.label);
return '' +
'
' +
escape(item.data.name) + ' ' +
'
' +
(item.data.label != '' ? '
' + escape(item.data.label) + '
' : '') +
'
' +
escape(item.data.name) +
'
';
}
}"))
)
)
}
########
#### Current file: R//cut-variable-dates.R
########
library(datamods)
library(toastui)
library(phosphoricons)
library(rlang)
library(shiny)
# old_deprecated_cut.hms <- function(x, breaks = "hour", ...) {
# # For now, this function will allways try to cut to hours
# # This limits time cutting to only do hour-binning, no matter the
#
# breaks_o <- breaks
#
# if (identical(breaks, "hour")) {
# # splitter <- match(
# # num,
# # levels(factor(num))
# # )
# breaks <- hms::as_hms(paste0(1:23, ":00:00"))
# }
#
# # if (identical(breaks, "daynight")) {
# # # splitter <- num %in% 8:20 + 1
# # breaks <- hms::as_hms(c("08:00:00","20:00:00"))
# # }
#
# if (length(breaks) != 1) {
# if ("hms" %in% class(breaks)) {
# splitter <- seq_along(breaks) |>
# purrr::map(\(.x){
# # browser()
# out <- x %in% x[x >= breaks[.x] & x < breaks[.x + 1]]
# if (.x == length(breaks)) {
# out[match(breaks[length(breaks)], x)] <- TRUE
# }
# ifelse(out, .x, 0)
# }) |>
# dplyr::bind_cols(.name_repair = "unique_quiet") |>
# rowSums()
# splitter[splitter == 0] <- NA
# } else {
# breaks <- "hour"
# }
# }
#
# if (is.numeric(breaks)) {
# breaks_n <- quantile(x, probs = seq(0, 1, 1 / breaks))
# ## Use lapply or similar to go through levels two at a time
# splitter <- seq(breaks) |>
# purrr::map(\(.x){
# # browser()
# out <- x %in% x[x >= breaks_n[.x] & x < breaks_n[.x + 1]]
# if (.x == breaks) {
# out[match(breaks_n[length(breaks_n)], x)] <- TRUE
# }
# ifelse(out, .x, 0)
# }) |>
# dplyr::bind_cols(.name_repair = "unique_quiet") |>
# rowSums()
# }
#
# # browser()
#
# num <- strsplit(as.character(x), ":") |>
# lapply(\(.x).x[[1]]) |>
# unlist() |>
# as.numeric()
#
# # browser()
# labs <- split(x, splitter) |>
# purrr::imap(\(.x, .i){
# # if (identical(breaks_o, "daynight") && .i == 1) {
# # h <- hms::as_hms(hms::hms(hours = 24) - abs(.x - hms::hms(hours = 8)))
# #
# # paste0("[", .x[match(sort(h)[1], h)], ",", .x[match(sort(h)[length(h)], h)], "]")
# # } else {
# .x <- sort(.x)
# paste0("[", .x[1], ",", .x[length(.x)], "]")
# # }
# }) |>
# unlist()
#
# structure(match(splitter, names(labs)), levels = labs, class = "factor")
# }
#' Extended cutting function
#'
#' @param x an object inheriting from class "hms"
#' @param ... passed on
#'
#' @rdname cut
#'
#' @return factor
#' @export
#'
#' @examples
#' readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "08:20:20", "21:20:20", "03:02:20")) |> cut(2)
#' readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "08:20:20", "21:20:20", "03:02:20")) |> cut("min")
#' readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "08:20:20", "21:20:20", "03:02:20")) |> cut(breaks = "hour")
#' readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "08:20:20", "21:20:20", "03:02:20")) |> cut(breaks = hms::as_hms(c("01:00:00", "03:01:20", "9:20:20")))
#' d_t <- readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "03:02:20", NA))
#' f <- d_t |> cut(2)
#' readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "03:02:20", NA)) |> cut(breaks = lubridate::as_datetime(c(hms::as_hms(levels(f)), hms::as_hms(max(d_t, na.rm = TRUE) + 1))), right = FALSE)
cut.hms <- function(x, breaks, ...) {
if (hms::is_hms(breaks)) {
breaks <- lubridate::as_datetime(breaks, tz = "UTC")
}
x <- lubridate::as_datetime(x, tz = "UTC")
out <- cut.POSIXt(x, breaks = breaks, ...)
attr(out, which = "brks") <- hms::as_hms(lubridate::as_datetime(attr(out, which = "brks")))
attr(out, which = "levels") <- as.character(hms::as_hms(lubridate::as_datetime(attr(out, which = "levels"))))
out
}
#' @rdname cut
#' @param x an object inheriting from class "POSIXt" or "Date"
#'
#' @examples
#' readr::parse_datetime(c("1992-02-01 01:00:20", "1992-02-06 03:00:20", "1992-05-01 01:20:20", "1992-09-01 08:20:20", "1999-02-01 21:20:20", "1992-12-01 03:02:20")) |> cut(2)
#' readr::parse_datetime(c("1992-02-01 01:00:20", "1992-02-06 03:00:20", "1992-05-01 01:20:20", "1992-09-01 08:20:20", "1999-02-01 21:20:20", "1992-12-01 03:02:20")) |> cut(breaks="weekday")
#' readr::parse_datetime(c("1992-02-01 01:00:20", "1992-02-06 03:00:20", "1992-05-01 01:20:20", "1992-09-01 08:20:20", "1999-02-01 21:20:20", "1992-12-01 03:02:20")) |> cut(breaks="month_only")
cut.POSIXt <- function(x, breaks, right = FALSE, include.lowest = TRUE, start.on.monday=TRUE, ...) {
breaks_o <- breaks
# browser()
if (is.numeric(breaks)) {
breaks <- quantile(
x,
probs = seq(0, 1, 1 / breaks),
right = right,
include.lowest = include.lowest,
na.rm=TRUE
)
}
if(identical(breaks,"weekday")){
days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday")
if (!start.on.monday){
days <- days[c(7,1:6)]
}
out <- factor(weekdays(x),levels=days) |> forcats::fct_drop()
} else if (identical(breaks,"month_only")){
ms <- paste0("1970-",1:12,"-01") |> as.Date() |> months()
out <- factor(months(x),levels=ms) |> forcats::fct_drop()
} else {
## Doesn't really work very well for breaks other than the special character cases as right border is excluded
out <- base::cut.POSIXt(x, breaks=breaks,right=right,...) |> forcats::fct_drop()
# browser()
}
l <- levels(out)
if (is.numeric(breaks_o)) {
l <- breaks
} else if (is.character(breaks) && length(breaks) == 1 && !(identical(breaks,"weekday") | identical(breaks,"month_only"))) {
if (include.lowest) {
if (right) {
l <- c(l, min(as.character(x)))
} else {
l <- c(l, max(as.character(x)))
}
}
} else if (length(l) < length(breaks_o)) {
l <- breaks_o
}
attr(out, which = "brks") <- l
out
}
#' @rdname cut
#' @param x an object inheriting from class "POSIXct"
cut.POSIXct <- cut.POSIXt
#' @rdname cut
#' @param x an object inheriting from class "POSIXct"
#'
#' @examples
#' as.Date(c("1992-02-01 01:00:20", "1992-02-06 03:00:20", "1992-05-01 01:20:20", "1992-09-01 08:20:20", "1999-02-01 21:20:20", "1992-12-01 03:02:20")) |> cut(2)
#' as.Date(c("1992-02-01 01:00:20", "1992-02-06 03:00:20", "1992-05-01 01:20:20", "1992-09-01 08:20:20", "1999-02-01 21:20:20", "1992-12-01 03:02:20")) |> cut(breaks="weekday")
cut.Date <- function(x,breaks,start.on.monday=TRUE,...){
if(identical(breaks,"weekday")){
days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
"Sunday")
if (!start.on.monday){
days <- days[c(7,1:6)]
}
out <- factor(weekdays(x),levels=days) |> forcats::fct_drop()
} else if (identical(breaks,"month_only")){
ms <- paste0("1970-",1:12,"-01") |> as.Date() |> months()
out <- factor(months(x),levels=ms) |> forcats::fct_drop()
} else {
## Doesn't really work very well for breaks other than the special character cases as right border is excluded
out <- base::cut.Date(x, breaks=breaks,...) |> forcats::fct_drop()
# browser()
}
out
}
#' Test class
#'
#' @param data data
#' @param class.vec vector of class names to test
#'
#' @return factor
#' @export
#'
#' @examples
#' \dontrun{
#' vapply(REDCapCAST::redcapcast_data, \(.x){
#' is_any_class(.x, c("hms", "Date", "POSIXct", "POSIXt"))
#' }, logical(1))
#' }
is_any_class <- function(data, class.vec) {
any(class(data) %in% class.vec)
}
#' Test is date/datetime/time
#'
#' @param data data
#'
#' @return factor
#' @export
#'
#' @examples
#' vapply(REDCapCAST::redcapcast_data, is_datetime, logical(1))
is_datetime <- function(data) {
is_any_class(data, class.vec = c("hms", "Date", "POSIXct", "POSIXt"))
}
#' @title Module to Convert Numeric to Factor
#'
#' @description
#' This module contain an interface to cut a numeric into several intervals.
#'
#'
#' @param id Module ID.
#'
#' @return A [shiny::reactive()] function returning the data.
#' @export
#'
#' @importFrom shiny NS fluidRow column numericInput checkboxInput checkboxInput plotOutput uiOutput
#' @importFrom shinyWidgets virtualSelectInput
#' @importFrom toastui datagridOutput2
#'
#' @name cut-variable
#'
cut_variable_ui <- function(id) {
ns <- NS(id)
tagList(
shiny::fluidRow(
column(
width = 3,
virtualSelectInput(
inputId = ns("variable"),
label = i18n("Variable to cut:"),
choices = NULL,
width = "100%"
)
),
column(
width = 3,
shiny::uiOutput(ns("cut_method"))
),
column(
width = 3,
numericInput(
inputId = ns("n_breaks"),
label = i18n("Number of breaks:"),
value = 3,
min = 2,
max = 12,
width = "100%"
)
),
column(
width = 3,
checkboxInput(
inputId = ns("right"),
label = i18n("Close intervals on the right"),
value = TRUE
),
checkboxInput(
inputId = ns("include_lowest"),
label = i18n("Include lowest value"),
value = TRUE
)
)
),
conditionalPanel(
condition = "input.method == 'fixed'",
ns = ns,
uiOutput(outputId = ns("slider_fixed"))
),
plotOutput(outputId = ns("plot"), width = "100%", height = "270px"),
datagridOutput2(outputId = ns("count")),
actionButton(
inputId = ns("create"),
label = tagList(ph("scissors"), i18n("Create factor variable")),
class = "btn-outline-primary float-end"
),
tags$div(class = "clearfix")
)
}
#' @param data_r A [shiny::reactive()] function returning a `data.frame`.
#'
#' @export
#'
#' @importFrom shiny moduleServer observeEvent reactive req bindEvent renderPlot
#' @importFrom shinyWidgets updateVirtualSelect noUiSliderInput
#' @importFrom toastui renderDatagrid2 datagrid grid_colorbar
#' @importFrom rlang %||% call2 set_names expr syms
#' @importFrom classInt classIntervals
#'
#' @rdname cut-variable
cut_variable_server <- function(id, data_r = reactive(NULL)) {
moduleServer(
id,
function(input, output, session) {
rv <- reactiveValues(data = NULL)
bindEvent(observe({
data <- data_r()
rv$data <- data
vars_num <- vapply(data, \(.x){
is.numeric(.x) || is_datetime(.x)
}, logical(1))
vars_num <- names(vars_num)[vars_num]
updateVirtualSelect(
inputId = "variable",
choices = vars_num,
selected = if (isTruthy(input$variable)) input$variable else vars_num[1]
)
}), data_r(), input$hidden)
output$slider_fixed <- renderUI({
data <- req(data_r())
variable <- req(input$variable)
req(hasName(data, variable))
if (is_datetime(data[[variable]])) {
brks <- cut(data[[variable]],
breaks = input$n_breaks
)$brks
} else {
brks <- classInt::classIntervals(
var = data[[variable]],
n = input$n_breaks,
style = "quantile"
)$brks
}
if (is_datetime(data[[variable]])) {
lower <- min(data[[variable]], na.rm = TRUE)
} else {
lower <- floor(min(data[[variable]], na.rm = TRUE))
}
if (is_datetime(data[[variable]])) {
upper <- max(data[[variable]], na.rm = TRUE)
} else {
upper <- ceiling(max(data[[variable]], na.rm = TRUE))
}
noUiSliderInput(
inputId = session$ns("fixed_brks"),
label = i18n("Fixed breaks:"),
min = lower,
max = upper,
value = brks,
color = datamods:::get_primary_color(),
width = "100%"
)
})
output$cut_method <- renderUI({
data <- req(data_r())
variable <- req(input$variable)
choices <- c(
# "quantile"
)
if ("hms" %in% class(data[[variable]])) {
choices <- c(choices, "hour")
} else if (any(c("POSIXt","Date") %in% class(data[[variable]]))) {
choices <- c(
choices,
"day",
"weekday",
"week",
"month",
"month_only",
"quarter",
"year"
)
} else {
choices <- c(
choices,
"fixed",
"quantile",
# "sd",
# "equal",
# "pretty",
# "kmeans",
# "hclust",
# "bclust",
# "fisher",
# "jenks",
"headtails" # ,
# "maximum",
# "box"
)
}
shinyWidgets::virtualSelectInput(
inputId = session$ns("method"),
label = i18n("Method:"),
choices = choices,
selected = NULL,
width = "100%"
)
})
breaks_r <- reactive({
data <- req(data_r())
variable <- req(input$variable)
req(hasName(data, variable))
req(input$n_breaks, input$method)
if (input$method == "fixed") {
req(input$fixed_brks)
if (any(c("hms", "POSIXt") %in% class(data[[variable]]))) {
cut.POSIXct <- cut.POSIXt
f <- cut(data[[variable]], breaks = input$fixed_brks)
list(var = f, brks = levels(f))
} else {
classInt::classIntervals(
var = as.numeric(data[[variable]]),
n = input$n_breaks,
style = "fixed",
fixedBreaks = input$fixed_brks
)
}
} else if (input$method == "quantile") {
req(input$fixed_brks)
if (any(c("hms", "POSIXt") %in% class(data[[variable]]))) {
cut.POSIXct <- cut.POSIXt
f <- cut(data[[variable]], breaks = input$n_breaks)
list(var = f, brks = levels(f))
} else {
classInt::classIntervals(
var = as.numeric(data[[variable]]),
n = input$n_breaks,
style = "quantile"
)
}
} else if (input$method %in% c(
"day",
"weekday",
"week",
"month",
"month_only",
"quarter",
"year"
)) {
# To enable datetime cutting
cut.POSIXct <- cut.POSIXt
f <- cut(data[[variable]], breaks = input$method)
list(var = f, brks = levels(f))
} else if (input$method %in% c("hour")) {
# To enable datetime cutting
cut.POSIXct <- cut.POSIXt
f <- cut(data[[variable]], breaks = "hour")
list(var = f, brks = levels(f))
} else {
classInt::classIntervals(
var = as.numeric(data[[variable]]),
n = input$n_breaks,
style = input$method
)
}
})
output$plot <- renderPlot({
data <- req(data_r())
variable <- req(input$variable)
plot_histogram(data, variable, breaks = breaks_r()$brks, color = datamods:::get_primary_color())
})
data_cutted_r <- reactive({
data <- req(data_r())
variable <- req(input$variable)
data[[paste0(variable, "_cut")]] <- cut(
x = data[[variable]],
breaks = if (input$method %in% c("day", "weekday", "week", "month", "month_only", "quarter", "year", "hour")) input$method else breaks_r()$brks,
include.lowest = input$include_lowest,
right = input$right
)
code <- call2(
"mutate",
!!!set_names(
list(
expr(cut(
!!!syms(list(x = variable)),
!!!list(breaks = breaks_r()$brks, include.lowest = input$include_lowest, right = input$right)
))
),
paste0(variable, "_cut")
)
)
attr(data, "code") <- Reduce(
f = function(x, y) expr(!!x %>% !!y),
x = c(attr(data, "code"), code)
)
data
})
output$count <- renderDatagrid2({
data <- req(data_cutted_r())
variable <- req(input$variable)
count_data <- as.data.frame(
table(
breaks = data[[paste0(variable, "_cut")]],
useNA = "ifany"
),
responseName = "count"
)
gridTheme <- getOption("datagrid.theme")
if (length(gridTheme) < 1) {
datamods:::apply_grid_theme()
}
on.exit(toastui::reset_grid_theme())
grid <- datagrid(
data = count_data,
colwidths = "guess",
theme = "default",
bodyHeight = "auto"
)
grid <- toastui::grid_columns(grid, className = "font-monospace")
grid_colorbar(
grid,
column = "count",
label_outside = TRUE,
label_width = "40px",
bar_bg = datamods:::get_primary_color(),
from = c(0, max(count_data$count) + 1)
)
})
data_returned_r <- observeEvent(input$create, {
rv$data <- data_cutted_r()
})
return(reactive(rv$data))
}
)
}
#' @inheritParams shiny::modalDialog
#' @export
#'
#' @importFrom shiny showModal modalDialog textInput
#' @importFrom htmltools tagList
#'
#' @rdname cut-variable
modal_cut_variable <- function(id,
title = i18n("Convert Numeric to Factor"),
easyClose = TRUE,
size = "l",
footer = NULL) {
ns <- NS(id)
showModal(modalDialog(
title = tagList(title, datamods:::button_close_modal()),
cut_variable_ui(id),
tags$div(
style = "display: none;",
textInput(inputId = ns("hidden"), label = NULL, value = datamods:::genId())
),
easyClose = easyClose,
size = size,
footer = footer
))
}
#' @inheritParams shinyWidgets::WinBox
#' @export
#'
#' @importFrom shinyWidgets WinBox wbOptions wbControls
#' @importFrom htmltools tagList
#' @rdname cut-variable
winbox_cut_variable <- function(id,
title = i18n("Convert Numeric to Factor"),
options = shinyWidgets::wbOptions(),
controls = shinyWidgets::wbControls()) {
ns <- NS(id)
WinBox(
title = title,
ui = tagList(
cut_variable_ui(id),
tags$div(
style = "display: none;",
textInput(inputId = ns("hidden"), label = NULL, value = genId())
)
),
options = modifyList(
shinyWidgets::wbOptions(height = "750px", modal = TRUE),
options
),
controls = controls,
auto_height = FALSE
)
}
#' @importFrom graphics abline axis hist par plot.new plot.window
plot_histogram <- function(data, column, bins = 30, breaks = NULL, color = "#112466") {
x <- data[[column]]
x <- as.numeric(x)
op <- par(mar = rep(1.5, 4))
on.exit(par(op))
plot.new()
plot.window(xlim = range(pretty(x)), ylim = range(pretty(hist(x, breaks = bins, plot = FALSE)$counts)))
abline(v = pretty(x), col = "#D8D8D8")
abline(h = pretty(hist(x, breaks = bins, plot = FALSE)$counts), col = "#D8D8D8")
hist(x, breaks = bins, xlim = range(pretty(x)), xaxs = "i", yaxs = "i", col = color, add = TRUE)
axis(side = 1, at = pretty(x), pos = 0)
axis(side = 2, at = pretty(hist(x, breaks = bins, plot = FALSE)$counts), pos = min(pretty(x)))
abline(v = breaks, col = "#FFFFFF", lty = 1, lwd = 1.5)
abline(v = breaks, col = "#2E2E2E", lty = 2, lwd = 1.5)
}
########
#### Current file: R//data_plots.R
########
# source(here::here("functions.R"))
#' Data correlations evaluation module
#'
#' @param id Module id. (Use 'ns("id")')
#'
#' @name data-correlations
#' @returns Shiny ui module
#' @export
#'
data_visuals_ui <- function(id, tab_title = "Plots", ...) {
ns <- shiny::NS(id)
# bslib::navset_bar(
list(
# Sidebar with a slider input
sidebar = bslib::sidebar(
bslib::accordion(
multiple = FALSE,
bslib::accordion_panel(
title = "Creating plot",
icon = bsicons::bs_icon("graph-up"),
shiny::uiOutput(outputId = ns("primary")),
shiny::uiOutput(outputId = ns("type")),
shiny::uiOutput(outputId = ns("secondary")),
shiny::uiOutput(outputId = ns("tertiary"))
),
bslib::accordion_panel(
title = "Advanced",
icon = bsicons::bs_icon("gear")
),
bslib::accordion_panel(
title = "Download",
icon = bsicons::bs_icon("download"),
shinyWidgets::noUiSliderInput(
inputId = ns("height"),
label = "Plot height (mm)",
min = 50,
max = 300,
value = 100,
step = 1,
format = shinyWidgets::wNumbFormat(decimals = 0),
color = datamods:::get_primary_color()
),
shinyWidgets::noUiSliderInput(
inputId = ns("width"),
label = "Plot width (mm)",
min = 50,
max = 300,
value = 100,
step = 1,
format = shinyWidgets::wNumbFormat(decimals = 0),
color = datamods:::get_primary_color()
),
shiny::selectInput(
inputId = ns("plot_type"),
label = "File format",
choices = list(
"png",
"tiff",
"eps",
"pdf",
"jpeg",
"svg"
)
),
shiny::br(),
# Button
shiny::downloadButton(
outputId = ns("download_plot"),
label = "Download plot",
icon = shiny::icon("download")
)
)
)
),
bslib::nav_panel(
title = tab_title,
shiny::plotOutput(ns("plot"))
)
)
}
#'
#' @param data data
#' @param ... ignored
#'
#' @name data-correlations
#' @returns shiny server module
#' @export
data_visuals_server <- function(id,
data,
...) {
shiny::moduleServer(
id = id,
module = function(input, output, session) {
ns <- session$ns
rv <- shiny::reactiveValues(
plot.params = NULL,
plot = NULL
)
output$primary <- shiny::renderUI({
columnSelectInput(
inputId = ns("primary"),
data = data,
placeholder = "Select variable",
label = "Response variable",
multiple = FALSE
)
})
output$type <- shiny::renderUI({
shiny::req(input$primary)
# browser()
if (!input$primary %in% names(data())) {
plot_data <- data()[1]
} else {
plot_data <- data()[input$primary]
}
plots <- possible_plots(
data = plot_data
)
shiny::selectizeInput(
inputId = ns("type"),
selected = NULL,
label = shiny::h4("Plot type"),
choices = plots,
multiple = FALSE
)
})
rv$plot.params <- shiny::reactive({
get_plot_options(input$type)
})
output$secondary <- shiny::renderUI({
shiny::req(input$type)
# browser()
columnSelectInput(
inputId = ns("secondary"),
data = data,
placeholder = "Select variable",
label = "Secondary/group variable",
multiple = FALSE,
col_subset = c(
purrr::pluck(rv$plot.params(), 1)[["secondary.extra"]],
all_but(
colnames(subset_types(
data(),
purrr::pluck(rv$plot.params(), 1)[["secondary.type"]]
)),
input$primary
)
),
none_label = "No variable"
)
})
output$tertiary <- shiny::renderUI({
shiny::req(input$type)
columnSelectInput(
inputId = ns("tertiary"),
data = data,
placeholder = "Select variable",
label = "Strata variable",
multiple = FALSE,
col_subset = c(
"none",
all_but(
colnames(subset_types(
data(),
purrr::pluck(rv$plot.params(), 1)[["tertiary.type"]]
)),
input$primary,
input$secondary
)
),
none_label = "No stratification"
)
})
rv$plot <- shiny::reactive({
shiny::req(input$primary)
shiny::req(input$type)
shiny::req(input$secondary)
shiny::req(input$tertiary)
create_plot(
data = data(),
type = names(rv$plot.params()),
x = input$primary,
y = input$secondary,
z = input$tertiary
)
})
output$plot <- shiny::renderPlot({
rv$plot()
})
output$download_plot <- shiny::downloadHandler(
filename = shiny::reactive({
paste0("plot.", input$plot_type)
}),
content = function(file) {
shiny::withProgress(message = "Drawing the plot. Hold on for a moment..", {
ggplot2::ggsave(
filename = file,
plot = rv$plot(),
width = input$width,
height = input$height,
dpi = 300,
units = "mm", scale = 2
)
})
}
)
shiny::observe(
return(rv$plot)
)
}
)
}
#' Select all from vector but
#'
#' @param data vector
#' @param ... exclude
#'
#' @returns vector
#' @export
#'
#' @examples
#' all_but(1:10, c(2, 3), 11, 5)
all_but <- function(data, ...) {
data[!data %in% c(...)]
}
#' Easily subset by data type function
#'
#' @param data data
#' @param types desired types
#' @param type.fun function to get type. Default is outcome_type
#'
#' @returns vector
#' @export
#'
#' @examples
#' default_parsing(mtcars) |> subset_types("ordinal")
#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal"))
#' #' default_parsing(mtcars) |> subset_types("factor",class)
subset_types <- function(data, types, type.fun = outcome_type) {
data[sapply(data, type.fun) %in% types]
}
#' Implemented functions
#'
#' @description
#' Library of supported functions. The list name and "descr" element should be
#' unique for each element on list.
#'
#' - descr: Plot description
#'
#' - primary.type: Primary variable data type (continuous, dichotomous or ordinal)
#'
#' - secondary.type: Secondary variable data type (continuous, dichotomous or ordinal)
#'
#' - secondary.extra: "none" or NULL to have option to choose none.
#'
#' - tertiary.type: Tertiary variable data type (continuous, dichotomous or ordinal)
#'
#'
#' @returns list
#' @export
#'
#' @examples
#' supported_plots() |> str()
supported_plots <- function() {
list(
plot_hbars = list(
descr = "Stacked horizontal bars",
note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
primary.type = c("dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = "none"
),
plot_violin = list(
descr = "Violin plot",
note = "A modern alternative to the classic boxplot to visualise data distribution",
primary.type = c("continuous", "dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = "none"
),
plot_ridge = list(
descr = "Ridge plot",
note = "An alternative option to visualise data distribution",
primary.type = "continuous",
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
),
plot_sankey = list(
descr = "Sankey plot",
note = "A way of visualising change between groups",
primary.type = c("dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
),
plot_scatter = list(
descr = "Scatter plot",
note = "A classic way of showing the association between to variables",
primary.type = "continuous",
secondary.type = c("continuous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
)
)
}
#' Title
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' plot_ridge(x = "mpg", y = "cyl")
#' mtcars |> plot_ridge(x = "mpg", y = "cyl", z = "gear")
plot_ridge <- function(data, x, y, z = NULL, ...) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
ggplot2::ggplot(.ds, ggplot2::aes(x = !!dplyr::sym(x), y = !!dplyr::sym(y), fill = !!dplyr::sym(y))) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggplot2::theme(legend.position = "none") |> rempsyc:::theme_apa()
})
patchwork::wrap_plots(out)
}
#' Get possible regression models
#'
#' @param data data
#'
#' @returns character vector
#' @export
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' dplyr::pull("cyl") |>
#' possible_plots()
#'
#' mtcars |>
#' default_parsing() |>
#' dplyr::select("mpg") |>
#' possible_plots()
possible_plots <- function(data) {
# browser()
if (is.data.frame(data)) {
data <- data[[1]]
}
type <- outcome_type(data)
if (type == "unknown") {
out <- type
} else {
out <- supported_plots() |>
lapply(\(.x){
if (type %in% .x$primary.type) {
.x$descr
}
}) |>
unlist()
}
unname(out)
}
#' Get the function options based on the selected function description
#'
#' @param data vector
#'
#' @returns list
#' @export
#'
#' @examples
#' ls <- mtcars |>
#' default_parsing() |>
#' dplyr::pull(mpg) |>
#' possible_plots() |>
#' (\(.x){
#' .x[[1]]
#' })() |>
#' get_plot_options()
get_plot_options <- function(data) {
descrs <- supported_plots() |>
lapply(\(.x){
.x$descr
}) |>
unlist()
supported_plots() |>
(\(.x){
.x[match(data, descrs)]
})()
}
#' Wrapper to create plot based on provided type
#'
#' @param type plot type (derived from possible_plots() and matches custom function)
#' @param ... ignored for now
#'
#' @name data-plots
#'
#' @returns ggplot2 object
#' @export
#'
#' @examples
#' create_plot(mtcars, "plot_violin", "mpg", "cyl")
create_plot <- function(data, type, x, y, z = NULL, ...) {
if (!y %in% names(data)) {
y <- NULL
}
if (!z %in% names(data)) {
z <- NULL
}
do.call(
type,
list(data, x, y, z, ...)
)
}
#' Nice horizontal stacked bars (Grotta bars)
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_hbars(x = "carb", y = "cyl")
#' mtcars |> plot_hbars(x = "carb", y = NULL)
plot_hbars <- function(data, x, y, z = NULL) {
out <- vertical_stacked_bars(data = data, score = x, group = y, strata = z)
out
}
#' Vertical stacked bar plot wrapper
#'
#' @param data
#' @param score
#' @param group
#' @param strata
#' @param t.size
#'
#' @return
#' @export
#'
vertical_stacked_bars <- function(data,
score = "full_score",
group = "pase_0_q",
strata = NULL,
t.size = 10,
l.color = "black",
l.size = .5,
draw.lines = TRUE) {
if (is.null(group)) {
df.table <- data[c(score, group, strata)] |>
dplyr::mutate("All" = 1) |>
table()
group <- "All"
draw.lines <- FALSE
} else {
df.table <- data[c(score, group, strata)] |>
table()
}
p <- df.table |>
rankinPlot::grottaBar(
scoreName = score,
groupName = group,
textColor = c("black", "white"),
strataName = strata,
textCut = 6,
textSize = 20,
printNumbers = "none",
lineSize = l.size,
returnData = TRUE
)
colors <- viridisLite::viridis(nrow(df.table))
contrast_cut <-
sum(contrast_text(colors, threshold = .3) == "white")
score_label <- ifelse(is.na(REDCapCAST::get_attr(data$score, "label")), score, REDCapCAST::get_attr(data$score, "label"))
group_label <- ifelse(is.na(REDCapCAST::get_attr(data$group, "label")), group, REDCapCAST::get_attr(data$group, "label"))
p |>
(\(.x){
.x$plot +
ggplot2::geom_text(
data = .x$rectData[which(.x$rectData$n >
0), ],
size = t.size,
fontface = "plain",
ggplot2::aes(
x = group,
y = p_prev + 0.49 * p,
color = as.numeric(score) > contrast_cut,
# label = paste0(sprintf("%2.0f", 100 * p),"%"),
label = sprintf("%2.0f", 100 * p)
)
) +
ggplot2::labs(fill = score_label) +
ggplot2::scale_fill_manual(values = rev(colors)) +
ggplot2::theme(
legend.position = "bottom",
axis.title = ggplot2::element_text(),
) +
ggplot2::xlab(group_label) +
ggplot2::ylab(NULL)
# viridis::scale_fill_viridis(discrete = TRUE, direction = -1, option = "D")
})()
}
#' Print label, and if missing print variable name
#'
#' @param data vector or data frame
#'
#' @returns character string
#' @export
#'
#' @examples
#' mtcars |> get_label(var = "mpg")
#' mtcars |> get_label()
#' mtcars$mpg |> get_label()
#' gtsummary::trial |> get_label(var = "trt")
#' 1:10 |> get_label()
get_label <- function(data, var = NULL) {
if (!is.null(var)) {
data <- data[[var]]
}
out <- REDCapCAST::get_attr(data = data, attr = "label")
if (is.na(out)) {
if (is.null(var)) {
out <- deparse(substitute(data))
} else {
if (is.symbol(var)) {
out <- gsub('\"', "", deparse(substitute(var)))
} else {
out <- var
}
}
}
out
}
#' Beatiful violin plot
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_violin(x = "mpg", y = "cyl", z = "gear")
plot_violin <- function(data, x, y, z = NULL) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
rempsyc::nice_violin(
data = .ds,
group = y,
response = x, xtitle = get_label(data, var = x), ytitle = get_label(data, var = y)
)
})
patchwork::wrap_plots(out)
}
#' Beautiful violin plot
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_scatter(x = "mpg", y = "wt")
plot_scatter <- function(data, x, y, z = NULL) {
if (is.null(z)) {
rempsyc::nice_scatter(
data = data,
predictor = y,
response = x, xtitle = get_label(data, var = x), ytitle = get_label(data, var = y)
)
} else {
rempsyc::nice_scatter(
data = data,
predictor = y,
response = x,
group = z
)
}
}
#' Readying data for sankey plot
#'
#' @param data
#' @param x
#' @param y
#' @param z
#'
#' @returns
#' @export
#'
#' @examples
#' ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = sample(c(letters[1:4], NA), 100, TRUE, prob = c(rep(.23, 4), .08)))
#' ds |> sankey_ready("first", "last")
#' ds |> sankey_ready("first", "last", numbers = "percentage")
sankey_ready <- function(data, x, y, z = NULL, numbers = "count") {
## TODO: Ensure ordering x and y
if (is.null(z)) {
out <- dplyr::count(data, !!dplyr::sym(x), !!dplyr::sym(y))
} else {
out <- dplyr::count(data, !!dplyr::sym(x), !!dplyr::sym(y), !!dplyr::sym(z))
}
out <- out |>
dplyr::group_by(!!dplyr::sym(x)) |>
dplyr::mutate(gx.sum = sum(n)) |>
dplyr::ungroup() |>
dplyr::group_by(!!dplyr::sym(y)) |>
dplyr::mutate(gy.sum = sum(n)) |>
dplyr::ungroup()
if (numbers == "count") {
out <- out |> dplyr::mutate(
lx = factor(paste0(!!dplyr::sym(x), "\n(n=", gx.sum, ")")),
ly = factor(paste0(!!dplyr::sym(y), "\n(n=", gy.sum, ")"))
)
} else if (numbers == "percentage") {
out <- out |> dplyr::mutate(
lx = factor(paste0(!!dplyr::sym(x), "\n(", round((gx.sum / sum(n)) * 100, 1), "%)")),
ly = factor(paste0(!!dplyr::sym(y), "\n(", round((gy.sum / sum(n)) * 100, 1), "%)"))
)
}
if (is.factor(data[[x]])){
index <- match(levels(data[[x]]),str_remove_last(levels(out$lx),"\n"))
out$lx <- factor(out$lx,levels=levels(out$lx)[index])
}
if (is.factor(data[[y]])){
index <- match(levels(data[[y]]),str_remove_last(levels(out$ly),"\n"))
out$ly <- factor(out$ly,levels=levels(out$ly)[index])
}
out
}
str_remove_last <- function(data,pattern="\n"){
strsplit(data,split = pattern) |>
lapply(\(.x)paste(unlist(.x[[-length(.x)]]),collapse=pattern)) |>
unlist()
}
#' Line breaking at given number of characters for nicely plotting labels
#'
#' @param data
#' @param lineLength
#'
#' @returns
#' @export
#'
#' @examples
line_break <- function(data, lineLength = 20) {
# gsub(paste0('(.{1,',lineLength,'})(\\s)'), '\\1\n', data)
paste(strwrap(data, lineLength), collapse = "\n")
## https://stackoverflow.com/a/29847221
}
#' Beautiful sankey plot with option to split by a tertiary group
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)))
#' ds |> plot_sankey("first", "last")
#' ds |> plot_sankey("first", "last", color.group = "y")
#' ds |> plot_sankey("first", "last", z = "g", color.group = "y")
plot_sankey <- function(data, x, y, z = NULL, color.group = "x", colors = NULL) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
plot_sankey_single(.ds,x = x, y = y,color.group = color.group, colors = colors)
})
patchwork::wrap_plots(out)
}
default_theme <- function() {
theme_void()
}
#' Beautiful sankey plot
#'
#' @param color.group
#' @param colors
#' @param ... passed to sankey_ready()
#'
#' @returns ggplot2 object
#' @export
#'
#' @examples
#' ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)))
#' ds |> plot_sankey_single("first", "last")
#' ds |> plot_sankey_single("first", "last", color.group = "y")
plot_sankey_single <- function(data,x,y, color.group = "x", colors = NULL,...){
data <- data |> sankey_ready(x = x, y = y,...)
# browser()
library(ggalluvial)
na.color <- "#2986cc"
box.color <- "#1E4B66"
if (is.null(colors)) {
if (color.group == "y") {
main.colors <- viridisLite::viridis(n = length(levels(data[[y]])))
secondary.colors <- rep(na.color, length(levels(data[[x]])))
label.colors <- Reduce(c, lapply(list(secondary.colors, rev(main.colors)), contrast_text))
} else {
main.colors <- viridisLite::viridis(n = length(levels(data[[x]])))
secondary.colors <- rep(na.color, length(levels(data[[y]])))
label.colors <- Reduce(c, lapply(list(rev(main.colors), secondary.colors), contrast_text))
}
colors <- c(na.color, main.colors, secondary.colors)
} else {
label.colors <- contrast_text(colors)
}
group_labels <- c(get_label(data, x), get_label(data, y)) |>
sapply(line_break) |>
unname()
p <- ggplot2::ggplot(data, ggplot2::aes(y = n, axis1 = lx, axis2 = ly))
if (color.group == "y") {
p <- p +
ggalluvial::geom_alluvium(
ggplot2::aes(fill = !!dplyr::sym(y), color = !!dplyr::sym(y)),
width = 1 / 16,
alpha = .8,
knot.pos = 0.4,
curve_type = "sigmoid"
) + ggalluvial::geom_stratum(ggplot2::aes(fill = !!dplyr::sym(y)),
size = 2,
width = 1 / 3.4
)
} else {
p <- p +
ggalluvial::geom_alluvium(
ggplot2::aes(fill = !!dplyr::sym(x), color = !!dplyr::sym(x)),
width = 1 / 16,
alpha = .8,
knot.pos = 0.4,
curve_type = "sigmoid"
) + ggalluvial::geom_stratum(ggplot2::aes(fill = !!dplyr::sym(x)),
size = 2,
width = 1 / 3.4
)
}
p +
ggplot2::geom_text(
stat = "stratum",
ggplot2::aes(label = after_stat(stratum)),
colour = label.colors,
size = 8,
lineheight = 1
) +
ggplot2::scale_x_continuous(
breaks = 1:2,
labels = group_labels
) +
ggplot2::scale_fill_manual(values = colors[-1], na.value = colors[1]) +
ggplot2::scale_color_manual(values = main.colors) +
ggplot2::theme_void() +
ggplot2::theme(
legend.position = "none",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.y = element_blank(),
# axis.title.y = element_blank(),
axis.text.x = ggplot2::element_text(size = 20),
# text = element_text(size = 5),
# plot.title = element_blank(),
# panel.background = ggplot2::element_rect(fill = "white"),
plot.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_blank()
)
}
########
#### Current file: R//data-summary.R
########
#' Data summary module
#'
#' @param id Module id. (Use 'ns("id")')
#'
#' @name data-summary
#' @returns Shiny ui module
#' @export
data_summary_ui <- function(id) {
ns <- NS(id)
toastui::datagridOutput(outputId = ns("tbl_summary"))
}
#'
#' @param data data
#' @param color.main main color
#' @param color.sec secondary color
#' @param ... arguments passed to toastui::datagrid
#'
#' @name data-summary
#' @returns shiny server module
#' @export
data_summary_server <- function(id,
data,
color.main,
color.sec,
...) {
shiny::moduleServer(
id = id,
module = function(input, output, session) {
ns <- session$ns
# data_r <- shiny::reactive({
# if (shiny::is.reactive(data)) {
# data()
# } else {
# data
# }
# })
output$tbl_summary <-
toastui::renderDatagrid(
{
shiny::req(data())
data() |>
overview_vars() |>
create_overview_datagrid() |>
add_sparkline(
column = "vals",
color.main = color.main,
color.sec = color.sec
)
}
)
}
)
}
#' Add sparkline to datagrid
#'
#' @param grid grid
#' @param column clumn to transform
#'
#' @returns datagrid
#' @export
#'
#' @examples
#' grid <- mtcars |>
#' default_parsing() |>
#' overview_vars() |>
#' toastui::datagrid() |>
#' add_sparkline()
#' grid
add_sparkline <- function(grid, column = "vals", color.main = "#2a8484", color.sec = "#84EF84") {
out <- toastui::grid_sparkline(
grid = grid,
column = column,
renderer = function(data) {
data_cl <- class(data)
if (identical(data_cl, "factor")) {
type <- "column"
s <- summary(data)
ds <- data.frame(x = names(s), y = s)
horizontal <- FALSE
} else if (any(c("numeric", "integer") %in% data_cl)) {
if (is_consecutive(data)) {
type <- "line"
ds <- data.frame(x = NA, y = NA)
horizontal <- FALSE
} else {
type <- "box"
ds <- data.frame(x = 1, y = data)
horizontal <- TRUE
}
} else if (any(c("Date", "POSIXct", "POSIXt", "hms", "difftime") %in% data_cl)) {
type <- "line"
ds <- data.frame(x = seq_along(data), y = data)
horizontal <- FALSE
} else {
type <- "line"
ds <- data.frame(x = NA, y = NA)
horizontal <- FALSE
}
apexcharter::apex(
ds,
apexcharter::aes(x, y),
type = type,
auto_update = TRUE
) |>
apexcharter::ax_chart(sparkline = list(enabled = TRUE)) |>
apexcharter::ax_plotOptions(
boxPlot = apexcharter::boxplot_opts(color.upper = color.sec, color.lower = color.main),
bar = apexcharter::bar_opts(horizontal = horizontal)
) |>
apexcharter::ax_colors(
c(color.main, color.sec)
)
}
)
toastui::grid_columns(
grid = out,
columns = column,
minWidth = 200
)
}
#' Checks if elements in vector are equally spaced as indication of ID
#'
#' @param data vector
#'
#' @returns logical
#' @export
#'
#' @examples
#' 1:10 |> is_consecutive()
#' sample(1:100,40) |> is_consecutive()
is_consecutive <- function(data){
suppressWarnings(length(unique(diff(as.numeric(data))))==1)
}
#' Create a data overview data.frame ready for sparklines
#'
#' @param data data
#'
#' @returns data.frame
#' @export
#'
#' @examples
#' mtcars |> overview_vars()
overview_vars <- function(data) {
data <- as.data.frame(data)
dplyr::tibble(
class = get_classes(data),
type = get_classes(data),
name = names(data),
n_missing = unname(colSums(is.na(data))),
p_complete = 1 - n_missing / nrow(data),
n_unique = get_n_unique(data),
vals = as.list(data)
)
}
#' Create a data overview datagrid
#'
#' @param data data
#'
#' @returns datagrid
#' @export
#'
#' @examples
#' mtcars |>
#' overview_vars() |>
#' create_overview_datagrid()
create_overview_datagrid <- function(data) {
# browser()
gridTheme <- getOption("datagrid.theme")
if (length(gridTheme) < 1) {
datamods:::apply_grid_theme()
}
on.exit(toastui::reset_grid_theme())
col.names <- names(data)
std_names <- c(
"Name" = "name",
"Class" = "class",
"Type" = "type",
"Missings" = "n_missing",
"Complete" = "p_complete",
"Unique" = "n_unique",
"Distribution" = "vals"
)
headers <- lapply(col.names, \(.x){
if (.x %in% std_names) {
names(std_names)[match(.x, std_names)]
} else {
.x
}
}) |> unlist()
grid <- toastui::datagrid(
data = data,
theme = "default",
colwidths = "fit"
)
grid <- toastui::grid_columns(
grid = grid,
columns = col.names,
header = headers,
resizable = TRUE
)
grid <- toastui::grid_columns(
grid = grid,
columns = "vals",
width = 120
)
grid <- toastui::grid_columns(
grid = grid,
columns = "class",
header = " ",
align = "center",sortable = FALSE,
width = 40
)
grid <- add_class_icon(
grid = grid,
column = "class"
)
grid <- toastui::grid_format(
grid = grid,
"p_complete",
formatter = toastui::JS("function(obj) {return (obj.value*100).toFixed(0) + '%';}")
)
## This could obviously be extended, which will added even more complexity.
grid <- toastui::grid_filters(
grid = grid,
column = "name",
# columns = unname(std_names[std_names!="vals"]),
showApplyBtn = FALSE,
showClearBtn = TRUE,
type = "text"
)
return(grid)
}
#' Convert class grid column to icon
#'
#' @param grid grid
#' @param column column
#'
#' @returns datagrid
#' @export
#'
#' @examples
#' mtcars |>
#' overview_vars() |>
#' toastui::datagrid() |>
#' add_class_icon()
add_class_icon <- function(grid, column = "class") {
out <- toastui::grid_format(
grid = grid,
column = column,
formatter = function(value) {
lapply(
X = value,
FUN = function(x) {
if (identical(x, "numeric")) {
shiny::icon("calculator")
} else if (identical(x, "factor")) {
shiny::icon("chart-simple")
} else if (identical(x, "integer")) {
shiny::icon("arrow-down-1-9")
} else if (identical(x, "character")) {
shiny::icon("arrow-down-a-z")
} else if (any(c("Date", "POSIXct", "POSIXt") %in% x)) {
shiny::icon("calendar-days")
} else if ("hms" %in% x) {
shiny::icon("clock")
} else {
shiny::icon("table")
}
}
)
}
)
toastui::grid_columns(
grid = out,
header = NULL,
columns = column,
width = 60
)
}
########
#### Current file: R//file-import-module.R
########
#' Shiny UI module to load a data file
#'
#' @param id id
#'
#' @return shiny UI
#' @export
#'
m_datafileUI <- function(id) {
ns <- shiny::NS(id)
shiny::tagList(
shiny::fileInput(
inputId = ns("file"),
label = "Upload a file",
multiple = FALSE,
accept = c(
".csv",
".xlsx",
".xls",
".dta",
".ods",
".rds"
)
),
shiny::h4("Parameter specifications"),
shiny::helpText(shiny::em("Select the desired variables and press 'Submit'")),
shiny::uiOutput(ns("include_vars")),
DT::DTOutput(ns("data_input")),
shiny::actionButton(ns("submit"), "Submit")
)
}
m_datafileServer <- function(id, output.format = "df") {
shiny::moduleServer(id, function(input, output, session, ...) {
ns <- shiny::NS(id)
ds <- shiny::reactive({
REDCapCAST::read_input(input$file$datapath) |> REDCapCAST::parse_data()
})
output$include_vars <- shiny::renderUI({
shiny::req(input$file)
shiny::selectizeInput(
inputId = ns("include_vars"),
selected = NULL,
label = "Covariables to include",
choices = colnames(ds()),
multiple = TRUE
)
})
base_vars <- shiny::reactive({
if (is.null(input$include_vars)) {
out <- colnames(ds())
} else {
out <- input$include_vars
}
out
})
output$data_input <-
DT::renderDT({
shiny::req(input$file)
ds()[base_vars()]
})
shiny::eventReactive(input$submit, {
# shiny::req(input$file)
data <- shiny::isolate({
ds()[base_vars()]
})
file_export(data,
output.format = output.format,
tools::file_path_sans_ext(input$file$name)
)
})
})
}
file_app <- function() {
ui <- shiny::fluidPage(
m_datafileUI("data"),
# DT::DTOutput(outputId = "redcap_prev")
toastui::datagridOutput2(outputId = "redcap_prev")
)
server <- function(input, output, session) {
m_datafileServer("data", output.format = "list")
}
shiny::shinyApp(ui, server)
}
file_app()
# tdm_data_upload <- teal::teal_data_module(
# ui <- function(id) {
# shiny::fluidPage(
# m_datafileUI(id)
# )
# },
# server = function(id) {
# m_datafileServer(id, output.format = "teal")
# }
# )
#
# tdm_data_read <- teal::teal_data_module(
# ui <- function(id) {
# shiny::fluidPage(
# m_redcap_readUI(id = "redcap")
# )
# },
# server = function(id) {
# moduleServer(
# id,
# function(input, output, session) {
# ns <- session$ns
#
# m_redcap_readServer(id = "redcap", output.format = "teal")
# }
# )
# }
# )
########
#### Current file: R//helpers.R
########
#' Wrapper function to get function from character vector referring to function from namespace. Passed to 'do.call()'
#'
#' @description
#' This function follows the idea from this comment: https://stackoverflow.com/questions/38983179/do-call-a-function-in-r-without-loading-the-package
#' @param x function or function name
#'
#' @return function or character vector
#' @export
#'
#' @examples
#' getfun("stats::lm")
getfun <- function(x) {
if ("character" %in% class(x)) {
if (length(grep("::", x)) > 0) {
parts <- strsplit(x, "::")[[1]]
requireNamespace(parts[1])
getExportedValue(parts[1], parts[2])
}
} else {
x
}
}
#' Wrapper to save data in RDS, load into specified qmd and render
#'
#' @param data list to pass to qmd
#' @param ... Passed to `quarto::quarto_render()`
#'
#' @return output file name
#' @export
#'
write_quarto <- function(data,...) {
# Exports data to temporary location
#
# I assume this is more secure than putting it in the www folder and deleting
# on session end
# temp <- base::tempfile(fileext = ".rds")
# readr::write_rds(data, file = here)
readr::write_rds(data, file = "www/web_data.rds")
## Specifying a output path will make the rendering fail
## Ref: https://github.com/quarto-dev/quarto-cli/discussions/4041
## Outputs to the same as the .qmd file
quarto::quarto_render(
execute_params = list(data.file = "web_data.rds"),
# execute_params = list(data.file = temp),
...
)
}
write_rmd <- function(data,...) {
# Exports data to temporary location
#
# I assume this is more secure than putting it in the www folder and deleting
# on session end
# temp <- base::tempfile(fileext = ".rds")
# readr::write_rds(data, file = here)
readr::write_rds(data, file = "www/web_data.rds")
## Specifying a output path will make the rendering fail
## Ref: https://github.com/quarto-dev/quarto-cli/discussions/4041
## Outputs to the same as the .qmd file
rmarkdown::render(
params = list(data.file = "web_data.rds"),
# execute_params = list(data.file = temp),
...
)
}
#' Flexible file import based on extension
#'
#' @param file file name
#' @param consider.na character vector of strings to consider as NAs
#'
#' @return tibble
#' @export
#'
#' @examples
#' read_input("https://raw.githubusercontent.com/agdamsbo/cognitive.index.lookup/main/data/sample.csv")
read_input <- function(file, consider.na = c("NA", '""', "")) {
ext <- tools::file_ext(file)
if (ext == "csv") {
df <- readr::read_csv(file = file, na = consider.na)
} else if (ext %in% c("xls", "xlsx")) {
df <- openxlsx2::read_xlsx(file = file, na.strings = consider.na)
} else if (ext == "dta") {
df <- haven::read_dta(file = file)
} else if (ext == "ods") {
df <- readODS::read_ods(path = file)
} else if (ext == "rds") {
df <- readr::read_rds(file = file)
} else {
stop("Input file format has to be on of:
'.csv', '.xls', '.xlsx', '.dta', '.ods' or '.rds'")
}
df
}
#' Convert string of arguments to list of arguments
#'
#' @description
#' Idea from the answer: https://stackoverflow.com/a/62979238
#'
#' @param string string to convert to list to use with do.call
#'
#' @return list
#' @export
#'
argsstring2list <- function(string) {
eval(parse(text = paste0("list(", string, ")")))
}
#' Factorize variables in data.frame
#'
#' @param data data.frame
#' @param vars variables to force factorize
#'
#' @return data.frame
#' @export
factorize <- function(data, vars) {
if (!is.null(vars)) {
data |>
dplyr::mutate(
dplyr::across(
dplyr::all_of(vars),
REDCapCAST::as_factor
)
)
} else {
data
}
}
dummy_Imports <- function() {
list(
MASS::as.fractions(),
broom::augment(),
broom.helpers::all_categorical(),
here::here(),
cardx::all_of(),
parameters::ci(),
DT::addRow(),
bslib::accordion()
)
# https://github.com/hadley/r-pkgs/issues/828
}
#' Title
#'
#' @param data data
#' @param output.format output
#' @param filename filename
#' @param ... passed on
#'
#' @returns data
#' @export
#'
file_export <- function(data, output.format = c("df", "teal", "list"), filename, ...) {
output.format <- match.arg(output.format)
filename <- gsub("-", "_", filename)
if (output.format == "teal") {
out <- within(
teal_data(),
{
assign(name, value |>
dplyr::bind_cols(.name_repair = "unique_quiet") |>
default_parsing())
},
value = data,
name = filename
)
datanames(out) <- filename
} else if (output.format == "df") {
out <- data |>
default_parsing()
} else if (output.format == "list") {
out <- list(
data = data,
name = filename
)
out <- c(out, ...)
}
out
}
#' Default data parsing
#'
#' @param data data
#'
#' @returns data.frame or tibble
#' @export
#'
#' @examples
#' mtcars |> str()
#' mtcars |>
#' default_parsing() |>
#' str()
default_parsing <- function(data) {
name_labels <- lapply(data,\(.x) REDCapCAST::get_attr(.x,attr = "label"))
out <- data |>
REDCapCAST::parse_data() |>
REDCapCAST::as_factor() |>
REDCapCAST::numchar2fct()
purrr::map2(out,name_labels,\(.x,.l){
if (!(is.na(.l) | .l=="")) {
REDCapCAST::set_attr(.x, .l, attr = "label")
} else {
attr(x = .x, which = "label") <- NULL
.x
}
# REDCapCAST::set_attr(data = .x, label = .l,attr = "label", overwrite = FALSE)
}) |> dplyr::bind_cols()
}
#' Remove NA labels
#'
#' @param data data
#'
#' @returns data.frame
#' @export
#'
#' @examples
#' ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x,label=NA,attr = "label"))
#' ds |> remove_na_attr() |> str()
remove_na_attr <- function(data,attr="label"){
out <- data |> lapply(\(.x){
ls <- REDCapCAST::get_attr(data = .x,attr = attr)
if (is.na(ls) | ls == ""){
attr(x = .x, which = attr) <- NULL
}
.x
})
dplyr::bind_cols(out)
}
#' Removes columns with completenes below cutoff
#'
#' @param data data frame
#' @param cutoff numeric
#'
#' @returns data frame
#' @export
#'
#' @examples
#'data.frame(a=1:10,b=NA, c=c(2,NA)) |> remove_empty_cols(cutoff=.5)
remove_empty_cols <- function(data,cutoff=.7){
filter <- apply(X = data,MARGIN = 2,FUN = \(.x){
sum(as.numeric(!is.na(.x)))/length(.x)
}) >= cutoff
data[filter]
}
#' Append list with named index
#'
#' @param data data to add to list
#' @param list list
#' @param index index name
#'
#' @returns list
#'
#' @examples
#' ls_d <- list(test=c(1:20))
#' ls_d <- list()
#' data.frame(letters[1:20],1:20) |> append_list(ls_d,"letters")
#' letters[1:20]|> append_list(ls_d,"letters")
append_list <- function(data,list,index){
## This will overwrite and not warn
## Not very safe, but convenient to append code to list
if (index %in% names(list)){
list[[index]] <- data
out <- list
} else {
out <- setNames(c(list,list(data)),c(names(list),index))
}
out
}
########
#### Current file: R//redcap_read_shiny_module.R
########
#' Shiny module to browser and export REDCap data
#'
#' @param id Namespace id
#' @param include_title logical to include title
#'
#' @rdname redcap_read_shiny_module
#'
#' @return shiny ui element
#' @export
m_redcap_readUI <- function(id, include_title = TRUE) {
ns <- shiny::NS(id)
server_ui <- shiny::tagList(
# width = 6,
shiny::tags$h4("REDCap server"),
shiny::textInput(
inputId = ns("uri"),
label = "Web address",
value = "https://redcap.your.institution/"
),
shiny::helpText("Format should be either 'https://redcap.your.institution/' or 'https://your.institution/redcap/'"),
shiny::textInput(
inputId = ns("api"),
label = "API token",
value = ""
),
shiny::helpText("The token is a string of 32 numbers and letters."),
shiny::actionButton(
inputId = ns("data_connect"),
label = "Connect",
icon = shiny::icon("link", lib = "glyphicon"),
# width = NULL,
disabled = TRUE
),
shiny::br(),
shiny::br(),
tags$div(
id = ns("connect-placeholder"),
shinyWidgets::alert(
id = ns("connect-result"),
status = "info",
tags$p(phosphoricons::ph("info", weight = "bold"), "Please fill in server address (URI) and API token, then press 'Connect'.")
),
dismissible = TRUE
),
shiny::br()
)
params_ui <-
shiny::tagList(
# width = 6,
shiny::tags$h4("Data import parameters"),
shiny::helpText("Options here will show, when API and uri are typed"),
shiny::uiOutput(outputId = ns("fields")),
shinyWidgets::switchInput(
inputId = "do_filter",
label = "Apply filter?",
value = FALSE,
inline = FALSE,
onLabel = "YES",
offLabel = "NO"
),
shiny::conditionalPanel(
condition = "input.do_filter",
shiny::uiOutput(outputId = ns("arms")),
shiny::textInput(
inputId = ns("filter"),
label = "Optional filter logic (e.g., [gender] = 'female')"
)
)
)
shiny::fluidPage(
if (include_title) shiny::tags$h3("Import data from REDCap"),
bslib::layout_columns(
server_ui,
params_ui,
col_widths = bslib::breakpoints(
sm = c(12, 12),
md = c(12, 12)
)
),
shiny::column(
width = 12,
# shiny::actionButton(inputId = ns("import"), label = "Import"),
## TODO: Use busy indicator like on download to have button activate/deactivate
shiny::actionButton(
inputId = ns("data_import"),
label = "Import",
icon = shiny::icon("download", lib = "glyphicon"),
width = "100%",
disabled = TRUE
),
# bslib::input_task_button(
# id = ns("data_import"),
# label = "Import",
# icon = shiny::icon("download", lib = "glyphicon"),
# label_busy = "Just a minute...",
# icon_busy = fontawesome::fa_i("arrows-rotate",
# class = "fa-spin",
# "aria-hidden" = "true"
# ),
# type = "primary",
# auto_reset = TRUE#,state="busy"
# ),
shiny::br(),
shiny::br(),
shiny::helpText("Press 'Import' to get data from the REDCap server. Check the preview below before proceeding."),
shiny::br(),
shiny::br()
)
)
}
#' @rdname redcap_read_shiny_module
#'
#' @return shiny server module
#' @export
#'
m_redcap_readServer <- function(id) {
module <- function(input, output, session) {
ns <- session$ns
data_rv <- shiny::reactiveValues(
dd_status = NULL,
data_status = NULL,
uri = NULL,
project_name = NULL,
info = NULL,
arms = NULL,
dd_list = NULL,
data = NULL
)
shiny::observeEvent(list(input$api, input$uri), {
uri <- paste0(ifelse(endsWith(input$uri, "/"), input$uri, paste0(input$uri, "/")), "api/")
if (is_valid_redcap_url(uri) & is_valid_token(input$api)) {
data_rv$uri <- uri
shiny::updateActionButton(inputId = "data_connect", disabled = FALSE)
} else {
shiny::updateActionButton(inputId = "data_connect", disabled = TRUE)
}
})
tryCatch(
{
shiny::observeEvent(
list(
input$data_connect
),
{
shiny::req(input$api)
shiny::req(data_rv$uri)
parameters <- list(
redcap_uri = data_rv$uri,
token = input$api
)
# browser()
imported <- try(rlang::exec(REDCapR::redcap_metadata_read, !!!parameters), silent = TRUE)
## TODO: Simplify error messages
if (inherits(imported, "try-error") || NROW(imported) < 1 || ifelse(is.list(imported), !isTRUE(imported$success), FALSE)) {
if (ifelse(is.list(imported), !isTRUE(imported$success), FALSE)) {
mssg <- imported$raw_text
} else {
mssg <- attr(imported, "condition")$message
}
datamods:::insert_error(mssg = mssg, selector = "connect")
data_rv$dd_status <- "error"
data_rv$dd_list <- NULL
} else if (isTRUE(imported$success)) {
data_rv$dd_status <- "success"
data_rv$project_name <- REDCapR::redcap_project_info_read(
redcap_uri = data_rv$uri,
token = input$api
)$data$project_title
datamods:::insert_alert(
selector = ns("connect"),
status = "success",
include_data_alert(
dataIdName = "see_data",
extra = tags$p(tags$b(phosphoricons::ph("check", weight = "bold"), "Connected to server!"), tags$p(paste0(data_rv$project_name, " loaded."))),
btn_show_data = TRUE
)
)
data_rv$dd_list <- imported
}
},
ignoreInit = TRUE
)
},
warning = function(warn) {
showNotification(paste0(warn), type = "warning")
},
error = function(err) {
showNotification(paste0(err), type = "err")
}
)
shiny::observeEvent(input$see_data, {
datamods::show_data(
purrr::pluck(data_rv$dd_list, "data"),
title = "Data dictionary",
type = "modal",
show_classes = FALSE,
tags$b("Preview:")
)
})
arms <- shiny::reactive({
shiny::req(input$api)
shiny::req(data_rv$uri)
REDCapR::redcap_event_read(
redcap_uri = data_rv$uri,
token = input$api
)$data
})
output$fields <- shiny::renderUI({
shiny::req(data_rv$dd_list)
shinyWidgets::virtualSelectInput(
inputId = ns("fields"),
label = "Select variables to import:",
choices = purrr::pluck(data_rv$dd_list, "data") |>
dplyr::select(field_name, form_name) |>
(\(.x){
split(.x$field_name, .x$form_name)
})(),
updateOn = "change",
multiple = TRUE,
search = TRUE,
showValueAsTags = TRUE
)
})
shiny::observeEvent(input$fields, {
if (is.null(input$fields) | length(input$fields) == 0) {
shiny::updateActionButton(inputId = "data_import", disabled = TRUE)
} else {
shiny::updateActionButton(inputId = "data_import", disabled = FALSE)
}
})
output$arms <- shiny::renderUI({
vectorSelectInput(
inputId = ns("arms"),
selected = NULL,
label = "Filter by events/arms",
data = stats::setNames(arms()[[3]],arms()[[1]]),
multiple = TRUE
)
})
shiny::observeEvent(input$data_import, {
shiny::req(input$fields)
record_id <- purrr::pluck(data_rv$dd_list, "data")[[1]][1]
parameters <- list(
uri = data_rv$uri,
token = input$api,
fields = unique(c(record_id, input$fields)),
events = input$arms,
raw_or_label = "both",
filter_logic = input$filter
)
shiny::withProgress(message = "Downloading REDCap data. Hold on for a moment..", {
imported <- try(rlang::exec(REDCapCAST::read_redcap_tables, !!!parameters), silent = TRUE)
})
code <- rlang::call2(REDCapCAST::read_redcap_tables, !!!parameters)
if (inherits(imported, "try-error") || NROW(imported) < 1) {
data_rv$data_status <- "error"
data_rv$data_list <- NULL
} else {
data_rv$data_status <- "success"
data_rv$data <- imported |>
REDCapCAST::redcap_wider() |>
dplyr::select(-dplyr::ends_with("_complete")) |>
dplyr::select(-dplyr::any_of(record_id)) |>
REDCapCAST::suffix2label()
}
})
return(shiny::reactive(data_rv$data))
}
shiny::moduleServer(
id = id,
module = module
)
}
#' @importFrom htmltools tagList tags
#' @importFrom shiny icon getDefaultReactiveDomain
include_data_alert <- function(dataIdName = "see_data",
btn_show_data,
see_data_text = "Click to see data",
extra = NULL,
session = shiny::getDefaultReactiveDomain()) {
if (isTRUE(btn_show_data)) {
success_message <- tagList(
extra,
tags$br(),
shiny::actionLink(
inputId = session$ns(dataIdName),
label = tagList(phosphoricons::ph("table"), see_data_text)
)
)
}
return(success_message)
}
# #' REDCap import teal data module
# #'
# #' @rdname redcap_read_shiny_module
# tdm_redcap_read <- teal::teal_data_module(
# ui <- function(id) {
# shiny::fluidPage(
# m_redcap_readUI(id)
# )
# },
# server = function(id) {
# m_redcap_readServer(id, output.format = "teal")
# }
# )
#' Title
#'
#' @param url
#'
#' @returns
#' @export
#'
#' @examples
#' url <- c(
#' "www.example.com",
#' "http://example.com",
#' "https://redcap.your.inst/api/"
#' )
#' is_valid_redcap_url(url)
is_valid_redcap_url <- function(url) {
pattern <- "https://[^ /$.?#].[^\\s]*/api/$"
stringr::str_detect(url, pattern)
}
#' Validate REDCap token
#'
#' @param token token
#' @param pattern_env pattern
#'
#' @returns
#' @export
#'
#' @examples
#' token <- paste(sample(c(1:9, LETTERS[1:6]), 32, TRUE), collapse = "")
#' is_valid_token(token)
is_valid_token <- function(token, pattern_env = NULL, nchar = 32) {
checkmate::assert_character(token, any.missing = TRUE, len = 1)
if (!is.null(pattern_env)) {
checkmate::assert_character(pattern_env,
any.missing = FALSE,
len = 1
)
pattern <- pattern_env
} else {
pattern <- glue::glue("^([0-9A-Fa-f]{})(?:\\n)?$",
.open = "<",
.close = ">"
)
}
if (is.na(token)) {
out <- FALSE
} else if (is.null(token)) {
out <- FALSE
} else if (nchar(token) == 0L) {
out <- FALSE
} else if (!grepl(pattern, token, perl = TRUE)) {
out <- FALSE
} else {
out <- TRUE
}
out
}
#' Test app for the redcap_read_shiny_module
#'
#' @rdname redcap_read_shiny_module
#'
#' @examples
#' \dontrun{
#' redcap_demo_app()
#' }
redcap_demo_app <- function() {
ui <- shiny::fluidPage(
m_redcap_readUI("data"),
toastui::datagridOutput2(outputId = "redcap_prev"),
DT::DTOutput("data_summary")
)
server <- function(input, output, session) {
data_val <- shiny::reactiveValues(data = NULL)
data_val$data <- m_redcap_readServer(id = "data")
output$data_summary <- DT::renderDataTable(
{
shiny::req(data_val$data)
data_val$data()
},
options = list(
scrollX = TRUE,
pageLength = 5
),
)
}
shiny::shinyApp(ui, server)
}
########
#### Current file: R//redcap.R
########
########
#### Current file: R//regression_model.R
########
#' Create a regression model programatically
#'
#' @param data data set
#' @param fun Name of function as character vector or function to use for model creation.
#' @param vars character vector of variables to include
#' @param outcome.str Name of outcome variable. Character vector.
#' @param auto.mode Make assumptions on function dependent on outcome data format. Overwrites other arguments.
#' @param formula.str Formula as string. Passed through 'glue::glue'. If given, 'outcome.str' and 'vars' are ignored. Optional.
#' @param args.list List of arguments passed to 'fun' with 'do.call'.
#' @param ... ignored for now
#'
#' @importFrom stats as.formula
#'
#' @return object of standard class for fun
#' @export
#' @rdname regression_model
#'
#' @examples
#' gtsummary::trial |>
#' regression_model(outcome.str = "age")
#' gtsummary::trial |>
#' regression_model(
#' outcome.str = "age",
#' auto.mode = FALSE,
#' fun = "stats::lm",
#' formula.str = "{outcome.str}~.",
#' args.list = NULL
#' )
#' gtsummary::trial |>
#' default_parsing() |>
#' regression_model(
#' outcome.str = "trt",
#' auto.mode = FALSE,
#' fun = "stats::glm",
#' args.list = list(family = binomial(link = "logit"))
#' )
#' m <- mtcars |>
#' default_parsing() |>
#' regression_model(
#' outcome.str = "mpg",
#' auto.mode = FALSE,
#' fun = "stats::lm",
#' formula.str = "{outcome.str}~{paste(vars,collapse='+')}",
#' args.list = NULL,
#' vars = c("mpg", "cyl")
#' )
#' broom::tidy(m)
regression_model <- function(data,
outcome.str,
auto.mode = FALSE,
formula.str = NULL,
args.list = NULL,
fun = NULL,
vars = NULL,
...) {
if (!is.null(formula.str)) {
if (formula.str == "") {
formula.str <- NULL
}
}
## This will handle if outcome is not in data for nicer shiny behavior
if (!outcome.str %in% names(data)){
outcome.str <- names(data)[1]
print("outcome is not in data, first column is used")
}
if (is.null(vars)) {
vars <- names(data)[!names(data) %in% outcome.str]
} else {
if (outcome.str %in% vars) {
vars <- vars[!vars %in% outcome.str]
}
data <- data |> dplyr::select(dplyr::all_of(c(vars, outcome.str)))
}
if (!is.null(formula.str)) {
formula.glue <- glue::glue(formula.str)
} else {
assertthat::assert_that(outcome.str %in% names(data),
msg = "Outcome variable is not present in the provided dataset"
)
formula.glue <- glue::glue("{outcome.str}~{paste(vars,collapse='+')}")
}
# Formatting character variables as factor
# Improvement should add a missing vector to format as NA
data <- data |>
purrr::map(\(.x){
if (is.character(.x)) {
suppressWarnings(REDCapCAST::as_factor(.x))
} else {
.x
}
}) |>
dplyr::bind_cols(.name_repair = "unique_quiet")
if (is.null(fun)) auto.mode <- TRUE
if (auto.mode) {
if (is.numeric(data[[outcome.str]])) {
fun <- "stats::lm"
} else if (is.factor(data[[outcome.str]])) {
if (length(levels(data[[outcome.str]])) == 2) {
fun <- "stats::glm"
args.list <- list(family = stats::binomial(link = "logit"))
} else if (length(levels(data[[outcome.str]])) > 2) {
fun <- "MASS::polr"
args.list <- list(
Hess = TRUE,
method = "logistic"
)
} else {
stop("The provided output variable only has one level")
}
} else {
stop("Output variable should be either numeric or factor for auto.mode")
}
}
assertthat::assert_that("character" %in% class(fun),
msg = "Please provide the function as a character vector."
)
# browser()
out <- do.call(
getfun(fun),
c(
list(
data = data,
formula = as.formula(formula.glue)
),
args.list
)
)
# Recreating the call
# out$call <- match.call(definition=eval(parse(text=fun)), call(fun, data = 'data',formula = as.formula(formula.str),args.list))
return(out)
}
#' Create a regression model programatically
#'
#' @param data data set
#' @param fun Name of function as character vector or function to use for model creation.
#' @param vars character vector of variables to include
#' @param outcome.str Name of outcome variable. Character vector.
#' @param args.list List of arguments passed to 'fun' with 'do.call'.
#' @param ... ignored for now
#'
#' @importFrom stats as.formula
#' @rdname regression_model
#'
#' @return object of standard class for fun
#' @export
#'
#' @examples
#' \dontrun{
#' gtsummary::trial |>
#' regression_model_uv(outcome.str = "age")
#' gtsummary::trial |>
#' regression_model_uv(
#' outcome.str = "age",
#' fun = "stats::lm",
#' args.list = NULL
#' )
#' m <- gtsummary::trial |> regression_model_uv(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' args.list = list(family = stats::binomial(link = "logit"))
#' )
#' lapply(m,broom::tidy) |> dplyr::bind_rows()
#' }
regression_model_uv <- function(data,
outcome.str,
args.list = NULL,
fun = NULL,
vars = NULL,
...) {
## This will handle if outcome is not in data for nicer shiny behavior
if (!outcome.str %in% names(data)){
outcome.str <- names(data)[1]
print("outcome is not in data, first column is used")
}
if (!is.null(vars)) {
data <- data |>
dplyr::select(dplyr::all_of(
unique(c(outcome.str, vars))
))
}
if (is.null(args.list)) {
args.list <- list()
}
if (is.null(fun)) {
if (is.numeric(data[[outcome.str]])) {
fun <- "stats::lm"
} else if (is.factor(data[[outcome.str]])) {
if (length(levels(data[[outcome.str]])) == 2) {
fun <- "stats::glm"
args.list <- list(family = stats::binomial(link = "logit"))
} else if (length(levels(data[[outcome.str]])) > 2) {
fun <- "MASS::polr"
args.list <- list(
Hess = TRUE,
method = "logistic"
)
} else {
stop("The provided output variable only has one level")
}
} else {
stop("Output variable should be either numeric or factor for auto.mode")
}
}
assertthat::assert_that("character" %in% class(fun),
msg = "Please provide the function as a character vector."
)
out <- names(data)[!names(data) %in% outcome.str] |>
purrr::map(\(.var){
do.call(
regression_model,
c(
list(
data = data[match(c(outcome.str, .var), names(data))],
outcome.str = outcome.str
),
args.list
)
)
})
return(out)
}
### HELPERS
#' Outcome data type assessment
#'
#' @param data data
#'
#' @returns outcome type
#' @export
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' lapply(outcome_type)
outcome_type <- function(data) {
cl_d <- class(data)
if (any(c("numeric", "integer") %in% cl_d)) {
out <- "continuous"
} else if (identical("factor", cl_d)) {
if (length(levels(data)) == 2) {
out <- "dichotomous"
} else if (length(levels(data)) > 2) {
out <- "ordinal"
}
} else {
out <- "unknown"
}
out
}
#' Implemented functions
#'
#' @description
#' Library of supported functions. The list name and "descr" element should be
#' unique for each element on list.
#'
#'
#' @returns list
#' @export
#'
#' @examples
#' supported_functions()
supported_functions <- function() {
list(
lm = list(
descr = "Linear regression model",
design = "cross-sectional",
out.type = "continuous",
fun = "stats::lm",
args.list = NULL,
formula.str = "{outcome.str}~{paste(vars,collapse='+')}",
table.fun = "gtsummary::tbl_regression",
table.args.list = list(exponentiate = FALSE)
),
glm = list(
descr = "Logistic regression model",
design = "cross-sectional",
out.type = "dichotomous",
fun = "stats::glm",
args.list = list(family = stats::binomial(link = "logit")),
formula.str = "{outcome.str}~{paste(vars,collapse='+')}",
table.fun = "gtsummary::tbl_regression",
table.args.list = list()
),
polr = list(
descr = "Ordinal logistic regression model",
design = "cross-sectional",
out.type = "ordinal",
fun = "MASS::polr",
args.list = list(
Hess = TRUE,
method = "logistic"
),
formula.str = "{outcome.str}~{paste(vars,collapse='+')}",
table.fun = "gtsummary::tbl_regression",
table.args.list = list()
)
)
}
#' Get possible regression models
#'
#' @param data data
#'
#' @returns character vector
#' @export
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' dplyr::pull("cyl") |>
#' possible_functions(design = "cross-sectional")
#'
#' mtcars |>
#' default_parsing() |>
#' dplyr::select("cyl") |>
#' possible_functions(design = "cross-sectional")
possible_functions <- function(data, design = c("cross-sectional")) {
# browser()
if (is.data.frame(data)) {
data <- data[[1]]
}
design <- match.arg(design)
type <- outcome_type(data)
design_ls <- supported_functions() |>
lapply(\(.x){
if (design %in% .x$design) {
.x
}
})
if (type == "unknown") {
out <- type
} else {
out <- design_ls |>
lapply(\(.x){
if (type %in% .x$out.type) {
.x$descr
}
}) |>
unlist()
}
unname(out)
}
#' Get the function options based on the selected function description
#'
#' @param data vector
#'
#' @returns list
#' @export
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' dplyr::pull(mpg) |>
#' possible_functions(design = "cross-sectional") |>
#' (\(.x){
#' .x[[1]]
#' })() |>
#' get_fun_options()
get_fun_options <- function(data) {
descrs <- supported_functions() |>
lapply(\(.x){
.x$descr
}) |>
unlist()
supported_functions() |>
(\(.x){
.x[match(data, descrs)]
})()
}
#' Wrapper to create regression model based on supported models
#'
#' @description
#' Output is a concatenated list of model information and model
#'
#'
#' @param data data
#' @param outcome.str name of outcome variable
#' @param fun.descr Description of chosen function matching description in
#' "supported_functions()"
#' @param fun name of custom function. Default is NULL.
#' @param formula.str custom formula glue string. Default is NULL.
#' @param args.list custom character string to be converted using
#' argsstring2list() or list of arguments. Default is NULL.
#' @param ... ignored
#'
#' @returns list
#' @export
#' @rdname regression_model
#'
#' @examples
#' \dontrun{
#' gtsummary::trial |>
#' regression_model(
#' outcome.str = "age",
#' fun = "stats::lm",
#' formula.str = "{outcome.str}~.",
#' args.list = NULL
#' )
#' ls <- regression_model_list(data = default_parsing(mtcars), outcome.str = "cyl", fun.descr = "Ordinal logistic regression model")
#' summary(ls$model)
#'
#' ls <- regression_model_list(data = default_parsing(gtsummary::trial), outcome.str = "trt", fun.descr = "Logistic regression model")
#' tbl <- gtsummary::tbl_regression(ls$model, exponentiate = TRUE)
#' m <- gtsummary::trial |>
#' default_parsing() |>
#' regression_model(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' formula.str = "{outcome.str}~.",
#' args.list = list(family = stats::binomial(link = "logit"))
#' )
#' tbl2 <- gtsummary::tbl_regression(m, exponentiate = TRUE)
#' broom::tidy(ls$model)
#' broom::tidy(m)
#' }
regression_model_list <- function(data,
outcome.str,
fun.descr,
fun = NULL,
formula.str = NULL,
args.list = NULL,
vars = NULL,
...) {
options <- get_fun_options(fun.descr) |>
(\(.x){
.x[[1]]
})()
## Custom, specific fun, args and formula options
if (is.null(formula.str)) {
formula.str.c <- options$formula.str
} else {
formula.str.c <- formula.str
}
if (is.null(fun)) {
fun.c <- options$fun
} else {
fun.c <- fun
}
if (is.null(args.list)) {
args.list.c <- options$args.list
} else {
args.list.c <- args.list
}
if (is.character(args.list.c)) args.list.c <- argsstring2list(args.list.c)
## Handling vars to print code
if (is.null(vars)) {
vars <- names(data)[!names(data) %in% outcome.str]
} else {
if (outcome.str %in% vars) {
vars <- vars[!vars %in% outcome.str]
}
}
model <- do.call(
regression_model,
list(
data = data,
outcome.str = outcome.str,
fun = fun.c,
formula.str = formula.str.c,
args.list = args.list.c
)
)
code <- glue::glue(
"{fun.c}({paste(Filter(length,list(glue::glue(formula.str.c),'data = data',list2str(args.list.c))),collapse=', ')})"
)
list(
options = options,
model = model,
code = code
)
}
list2str <- function(data) {
out <- purrr::imap(data, \(.x, .i){
if (is.logical(.x)) {
arg <- .x
} else {
arg <- glue::glue("'{.x}'")
}
glue::glue("{.i} = {arg}")
}) |>
unlist() |>
paste(collapse = (", "))
if (out == "") {
return(NULL)
} else {
out
}
}
#' @returns list
#' @export
#' @rdname regression_model
#'
#' @examples
#' \dontrun{
#' gtsummary::trial |> regression_model_uv(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' args.list = list(family = stats::binomial(link = "logit"))
#' ) |> lapply(broom::tidy) |> dplyr::bind_rows()
#' ms <- regression_model_uv_list(data = default_parsing(mtcars), outcome.str = "mpg", fun.descr = "Linear regression model")
#' lapply(ms$model,broom::tidy) |> dplyr::bind_rows()
#' }
regression_model_uv_list <- function(data,
outcome.str,
fun.descr,
fun = NULL,
formula.str = NULL,
args.list = NULL,
vars = NULL,
...) {
options <- get_fun_options(fun.descr) |>
(\(.x){
.x[[1]]
})()
## Custom, specific fun, args and formula options
if (is.null(formula.str)) {
formula.str.c <- options$formula.str
} else {
formula.str.c <- formula.str
}
if (is.null(fun)) {
fun.c <- options$fun
} else {
fun.c <- fun
}
if (is.null(args.list)) {
args.list.c <- options$args.list
} else {
args.list.c <- args.list
}
if (is.character(args.list.c)) args.list.c <- argsstring2list(args.list.c)
## Handling vars to print code
if (is.null(vars)) {
vars <- names(data)[!names(data) %in% outcome.str]
} else {
if (outcome.str %in% vars) {
vars <- vars[!vars %in% outcome.str]
}
}
# assertthat::assert_that("character" %in% class(fun),
# msg = "Please provide the function as a character vector."
# )
# model <- do.call(
# regression_model,
# c(
# list(data = data),
# list(outcome.str = outcome.str),
# list(fun = fun.c),
# list(formula.str = formula.str.c),
# args.list.c
# )
# )
model <- vars |>
lapply(\(.var){
do.call(
regression_model,
list(
data = data[c(outcome.str, .var)],
outcome.str = outcome.str,
fun = fun.c,
formula.str = formula.str.c,
args.list = args.list.c
)
)
})
vars <- "."
code_raw <- glue::glue(
"{fun.c}({paste(Filter(length,list(glue::glue(formula.str.c),'data = .d',list2str(args.list.c))),collapse=', ')})"
)
code <- glue::glue("lapply(data,function(.d){code_raw})")
list(
options = options,
model = model,
code = code
)
}
########
#### Current file: R//regression_plot.R
########
#' Regression coef plot from gtsummary. Slightly modified to pass on arguments
#'
#' @param x (`tbl_regression`, `tbl_uvregression`)\cr
#' A 'tbl_regression' or 'tbl_uvregression' object
## #' @param remove_header_rows (scalar `logical`)\cr
## #' logical indicating whether to remove header rows
## #' for categorical variables. Default is `TRUE`
## #' @param remove_reference_rows (scalar `logical`)\cr
## #' logical indicating whether to remove reference rows
## #' for categorical variables. Default is `FALSE`.
#' @param ... arguments passed to `ggstats::ggcoef_plot(...)`
#'
#' @returns ggplot object
#' @export
#'
#' @examples
#' \dontrun{
#' mod <- lm(mpg ~ ., mtcars)
#' p <- mod |>
#' gtsummary::tbl_regression() |>
#' plot(colour = "variable")
#' }
#'
plot.tbl_regression <- function(x,
# remove_header_rows = TRUE,
# remove_reference_rows = FALSE,
...) {
# check_dots_empty()
gtsummary:::check_pkg_installed("ggstats")
gtsummary:::check_not_missing(x)
# gtsummary:::check_scalar_logical(remove_header_rows)
# gtsummary:::check_scalar_logical(remove_reference_rows)
df_coefs <- x$table_body
# if (isTRUE(remove_header_rows)) {
# df_coefs <- df_coefs |> dplyr::filter(!.data$header_row %in% TRUE)
# }
# if (isTRUE(remove_reference_rows)) {
# df_coefs <- df_coefs |> dplyr::filter(!.data$reference_row %in% TRUE)
# }
# browser()
df_coefs$label[df_coefs$row_type == "label"] <- ""
df_coefs %>%
ggstats::ggcoef_plot(exponentiate = x$inputs$exponentiate, ...)
}
# default_parsing(mtcars) |> lapply(class)
#
# purrr::imap(mtcars,\(.x,.i){
# if (.i %in% c("vs","am","gear","carb")){
# as.factor(.x)
# } else .x
# }) |> dplyr::bind_cols()
#
#
#' Wrapper to pivot gtsummary table data to long for plotting
#'
#' @param list a custom regression models list
#' @param model.names names of models to include
#'
#' @returns list
#' @export
#'
merge_long <- function(list, model.names) {
l_subset <- list$tables[model.names]
l_merged <- l_subset |> tbl_merge()
df_body <- l_merged$table_body
sel_list <- lapply(seq_along(l_subset), \(.i){
endsWith(names(df_body), paste0("_", .i))
}) |>
setNames(names(l_subset))
common <- !Reduce(`|`, sel_list)
df_body_long <- sel_list |>
purrr::imap(\(.l, .i){
d <- dplyr::bind_cols(
df_body[common],
df_body[.l],
model = .i
)
setNames(d, gsub("_[0-9]{,}$", "", names(d)))
}) |>
dplyr::bind_rows() |> dplyr::mutate(model=as_factor(model))
l_merged$table_body <- df_body_long
l_merged$inputs$exponentiate <- !identical(class(list$models$Multivariable$model), "lm")
l_merged
}
########
#### Current file: R//regression_table.R
########
#' Create table of regression model
#'
#' @param x regression model
#' @param args.list list of arguments passed to 'fun'.
#' @param fun function to use for table creation. Default is "gtsummary::tbl_regression".
#' @param ... passed to methods
#'
#' @return object of standard class for fun
#' @export
#' @name regression_table
#'
#' @examples
#' \dontrun{
#' tbl <- gtsummary::trial |>
#' regression_model(
#' outcome.str = "stage",
#' fun = "MASS::polr"
#' ) |>
#' regression_table(args.list = list("exponentiate" = TRUE))
#' gtsummary::trial |>
#' regression_model(
#' outcome.str = "age",
#' fun = "stats::lm",
#' formula.str = "{outcome.str}~.",
#' args.list = NULL
#' ) |>
#' regression_table() |> plot()
#' gtsummary::trial |>
#' regression_model(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' args.list = list(family = binomial(link = "logit"))
#' ) |>
#' regression_table()
#' gtsummary::trial |>
#' regression_model_uv(
#' outcome.str = "trt",
#' fun = "stats::glm",
#' args.list = list(family = stats::binomial(link = "logit"))
#' ) |>
#' regression_table()
#' gtsummary::trial |>
#' regression_model_uv(
#' outcome.str = "stage",
#' args.list = list(family = stats::binomial(link = "logit"))
#' ) |>
#' regression_table()
#'
#' list(
#' "Univariable" = regression_model_uv,
#' "Multivariable" = regression_model
#' ) |>
#' lapply(\(.fun){
#' do.call(
#' .fun,
#' c(
#' list(data = gtsummary::trial),
#' list(outcome.str = "stage")
#' )
#' )
#' }) |>
#' purrr::map(regression_table) |>
#' tbl_merge()
#' }
#' regression_table <- function(x, ...) {
#' UseMethod("regression_table")
#' }
#'
#' #' @rdname regression_table
#' #' @export
#' regression_table.list <- function(x, ...) {
#' x |>
#' purrr::map(\(.m){
#' regression_table(x = .m, ...) |>
#' gtsummary::add_n()
#' }) |>
#' gtsummary::tbl_stack()
#' }
#'
#' #' @rdname regression_table
#' #' @export
#' regression_table.default <- function(x, ..., args.list = NULL, fun = "gtsummary::tbl_regression") {
#' # Stripping custom class
#' class(x) <- class(x)[class(x) != "freesearcher_model"]
#'
#' if (any(c(length(class(x)) != 1, class(x) != "lm"))) {
#' if (!"exponentiate" %in% names(args.list)) {
#' args.list <- c(args.list, list(exponentiate = TRUE))
#' }
#' }
#'
#' out <- do.call(getfun(fun), c(list(x = x), args.list))
#' out |>
#' gtsummary::add_glance_source_note() # |>
#' # gtsummary::bold_p()
#' }
regression_table <- function(x, ...) {
if ("list" %in% class(x)){
x |>
purrr::map(\(.m){
regression_table_create(x = .m, ...) |>
gtsummary::add_n()
}) |>
gtsummary::tbl_stack()
} else {
regression_table_create(x,...)
}
}
regression_table_create <- function(x, ..., args.list = NULL, fun = "gtsummary::tbl_regression") {
# Stripping custom class
class(x) <- class(x)[class(x) != "freesearcher_model"]
if (any(c(length(class(x)) != 1, class(x) != "lm"))) {
if (!"exponentiate" %in% names(args.list)) {
args.list <- c(args.list, list(exponentiate = TRUE, p.values = TRUE))
}
}
out <- do.call(getfun(fun), c(list(x = x), args.list))
out |>
gtsummary::add_glance_source_note() # |>
# gtsummary::bold_p()
}
#' A substitue to gtsummary::tbl_merge, that will use list names for the tab
#' spanner names.
#'
#' @param data gtsummary list object
#'
#' @return gt summary list object
#' @export
#'
tbl_merge <- function(data) {
if (is.null(names(data))) {
data |> gtsummary::tbl_merge()
} else {
data |> gtsummary::tbl_merge(tab_spanner = names(data))
}
}
# as_kable(tbl) |> write_lines(file=here::here("inst/apps/data_analysis_modules/www/_table1.md"))
# as_kable_extra(tbl)|> write_lines(file=here::here("inst/apps/data_analysis_modules/www/table1.md"))
########
#### Current file: R//report.R
########
#' Split vector by an index and embed addition
#'
#' @param data vector
#' @param index split index
#' @param add addition
#'
#' @return vector
#' @export
#'
index_embed <- function(data, index, add = NULL) {
start <- seq_len(index)
end <- seq_along(data)[-start]
c(
data[start],
add,
data[end]
)
}
#' Specify format arguments to include in qmd header/frontmatter
#'
#' @param data vector
#' @param fileformat format to include
#'
#' @return vector
#' @export
#'
specify_qmd_format <- function(data, fileformat = c("docx", "odt", "pdf", "all")) {
fileformat <- match.arg(fileformat)
args_list <- default_format_arguments() |> purrr::imap(format_writer)
if (fileformat == "all") {
out <- data |> index_embed(index = 4, add = Reduce(c, args_list))
} else {
out <- data |> index_embed(index = 4, add = args_list[[fileformat]])
}
out
}
#' Merges list of named arguments for qmd header generation
#'
#' @param data vector
#' @param name name
#'
#' @return vector
#' @export
#'
format_writer <- function(data, name) {
if (data == "default") {
glue::glue(" {name}: {data}")
} else {
warning("Not implemented")
}
}
#' Defaults qmd formats
#'
#' @return list
#' @export
#'
default_format_arguments <- function() {
list(
docx = list("default"),
odt = list("default"),
pdf = list("default")
)
}
#' Wrapper to modify quarto file to render specific formats
#'
#' @param file filename
#' @param format desired output
#'
#' @return none
#' @export
#'
modify_qmd <- function(file, format) {
readLines(file) |>
specify_qmd_format(fileformat = "all") |>
writeLines(paste0(tools::file_path_sans_ext(file), "_format.", tools::file_ext(file)))
}
########
#### Current file: R//shiny_freesearcheR.R
########
#' Launch the freesearcheR tool locally
#'
#' @description
#' All data.frames in the global environment will be accessible through the app.
#'
#'
#' @param ... arguments passed on to `shiny::runApp()`
#'
#' @return shiny app
#' @export
#'
#' @examples
#' \dontrun{
#' data(mtcars)
#' shiny_freesearcheR(launch.browser = TRUE)
#' }
shiny_freesearcheR <- function(...) {
appDir <- system.file("apps", "freesearcheR", package = "freesearcheR")
if (appDir == "") {
stop("Could not find the app directory. Try re-installing `freesearcheR`.", call. = FALSE)
}
a <- shiny::runApp(appDir = paste0(appDir,"/app.R"), ...)
return(invisible(a))
}
#' Easily launch the freesearcheR app
#'
#' @param ... passed on to `shiny::runApp()`
#'
#' @returns shiny app
#' @export
#'
launch_freesearcheR <- function(...){
shiny_freesearcheR(...)
}
########
#### Current file: R//theme.R
########
#' Custom theme based on unity
#'
#' @param ... everything passed on to bslib::bs_theme()
#'
#' @returns theme list
#' @export
custom_theme <- function(...,
version = 5,
primary = "#1E4A8F",
secondary = "#FF6F61",
bootswatch = "united",
base_font = bslib::font_google("Montserrat"),
heading_font = bslib::font_google("Public Sans",wght = "700"),
code_font = bslib::font_google("Open Sans")
# success = "#1E4A8F",
# info = ,
# warning = ,
# danger = ,
# fg = "#000",
# bg="#fff",
# base_font = bslib::font_google("Alice"),
# heading_font = bslib::font_google("Jost", wght = "800"),
# heading_font = bslib::font_google("Noto Serif"),
# heading_font = bslib::font_google("Alice"),
){
bslib::bs_theme(
...,
"navbar-bg" = primary,
version = version,
primary = primary,
secondary = secondary,
bootswatch = bootswatch,
base_font = base_font,
heading_font = heading_font,
code_font = code_font
)
}
#' GGplot default theme for plotting in Shiny
#'
#' @param data ggplot object
#'
#' @returns ggplot object
#' @export
#'
gg_theme_shiny <- function(){
ggplot2::theme(
axis.title = ggplot2::element_text(size = 18),
axis.text = ggplot2::element_text(size = 14),
strip.text = ggplot2::element_text(size = 14),
legend.title = ggplot2::element_text(size = 18),
legend.text = ggplot2::element_text(size = 14),
plot.title = ggplot2::element_text(size = 24),
plot.subtitle = ggplot2::element_text(size = 18),
legend.position = "none"
)
}
#' GGplot default theme for plotting export objects
#'
#' @param data ggplot object
#'
#' @returns ggplot object
#' @export
#'
gg_theme_export <- function(){
ggplot2::theme(
axis.title = ggplot2::element_text(size = 18),
axis.text.x = ggplot2::element_text(size = 14),
legend.title = ggplot2::element_text(size = 18),
legend.text = ggplot2::element_text(size = 14),
plot.title = ggplot2::element_text(size = 24)
)
}
########
#### Current file: R//update-factor-ext.R
########
## Works, but not implemented
##
## These edits mainly allows for
#' @title Module to Reorder the Levels of a Factor Variable
#'
#' @description
#' This module contain an interface to reorder the levels of a factor variable.
#'
#'
#' @param id Module ID.
#'
#' @return A [shiny::reactive()] function returning the data.
#' @export
#'
#' @importFrom shiny NS fluidRow tagList column actionButton
#' @importFrom shinyWidgets virtualSelectInput prettyCheckbox
#' @importFrom toastui datagridOutput
#' @importFrom htmltools tags
#'
#' @name update-factor
#'
update_factor_ui <- function(id) {
ns <- NS(id)
tagList(
tags$style(
".tui-grid-row-header-draggable span {width: 3px !important; height: 3px !important;}"
),
fluidRow(
column(
width = 6,
virtualSelectInput(
inputId = ns("variable"),
label = i18n("Factor variable to reorder:"),
choices = NULL,
width = "100%",
zIndex = 50
)
),
column(
width = 3,
class = "d-flex align-items-end",
actionButton(
inputId = ns("sort_levels"),
label = tagList(
ph("sort-ascending"),
i18n("Sort by levels")
),
class = "btn-outline-primary mb-3",
width = "100%"
)
),
column(
width = 3,
class = "d-flex align-items-end",
actionButton(
inputId = ns("sort_occurrences"),
label = tagList(
ph("sort-ascending"),
i18n("Sort by count")
),
class = "btn-outline-primary mb-3",
width = "100%"
)
)
),
datagridOutput(ns("grid")),
tags$div(
class = "float-end",
prettyCheckbox(
inputId = ns("new_var"),
label = i18n("Create a new variable (otherwise replaces the one selected)"),
value = FALSE,
status = "primary",
outline = TRUE,
inline = TRUE
),
actionButton(
inputId = ns("create"),
label = tagList(ph("arrow-clockwise"), i18n("Update factor variable")),
class = "btn-outline-primary"
)
),
tags$div(class = "clearfix")
)
}
#' @param data_r A [shiny::reactive()] function returning a `data.frame`.
#'
#' @export
#'
#' @importFrom shiny moduleServer observeEvent reactive reactiveValues req bindEvent isTruthy updateActionButton
#' @importFrom shinyWidgets updateVirtualSelect
#' @importFrom toastui renderDatagrid datagrid grid_columns grid_colorbar
#'
#' @rdname update-factor
update_factor_server <- function(id, data_r = reactive(NULL)) {
moduleServer(
id,
function(input, output, session) {
rv <- reactiveValues(data = NULL, data_grid = NULL)
bindEvent(observe({
data <- data_r()
rv$data <- data
vars_factor <- vapply(data, is.factor, logical(1))
vars_factor <- names(vars_factor)[vars_factor]
updateVirtualSelect(
inputId = "variable",
choices = vars_factor,
selected = if (isTruthy(input$variable)) input$variable else vars_factor[1]
)
}), data_r(), input$hidden)
observeEvent(input$variable, {
data <- req(data_r())
variable <- req(input$variable)
grid <- as.data.frame(table(data[[variable]]))
rv$data_grid <- grid
})
observeEvent(input$sort_levels, {
if (input$sort_levels %% 2 == 1) {
decreasing <- FALSE
label <- tagList(
ph("sort-descending"),
"Sort Levels"
)
} else {
decreasing <- TRUE
label <- tagList(
ph("sort-ascending"),
"Sort Levels"
)
}
updateActionButton(inputId = "sort_levels", label = as.character(label))
rv$data_grid <- rv$data_grid[order(rv$data_grid[[1]], decreasing = decreasing), ]
})
observeEvent(input$sort_occurrences, {
if (input$sort_occurrences %% 2 == 1) {
decreasing <- FALSE
label <- tagList(
ph("sort-descending"),
i18n("Sort count")
)
} else {
decreasing <- TRUE
label <- tagList(
ph("sort-ascending"),
i18n("Sort count")
)
}
updateActionButton(inputId = "sort_occurrences", label = as.character(label))
rv$data_grid <- rv$data_grid[order(rv$data_grid[[2]], decreasing = decreasing), ]
})
output$grid <- renderDatagrid({
req(rv$data_grid)
gridTheme <- getOption("datagrid.theme")
if (length(gridTheme) < 1) {
datamods:::apply_grid_theme()
}
on.exit(toastui::reset_grid_theme())
data <- rv$data_grid
data <- add_var_toset(data, "Var1", "New label")
grid <- datagrid(
data = data,
draggable = TRUE,
sortable = FALSE,
data_as_input = TRUE
)
grid <- grid_columns(
grid,
columns = c("Var1", "Var1_toset", "Freq"),
header = c(i18n("Levels"), "New label", i18n("Count"))
)
grid <- grid_colorbar(
grid,
column = "Freq",
label_outside = TRUE,
label_width = "30px",
background = "#D8DEE9",
bar_bg = datamods:::get_primary_color(),
from = c(0, max(rv$data_grid$Freq) + 1)
)
grid <- toastui::grid_style_column(
grid = grid,
column = "Var1_toset",
fontStyle = "italic"
)
grid <- toastui::grid_editor(
grid = grid,
column = "Var1_toset",
type = "text"
)
grid
})
data_updated_r <- reactive({
data <- req(data_r())
variable <- req(input$variable)
grid <- req(input$grid_data)
name_var <- if (isTRUE(input$new_var)) {
paste0(variable, "_updated")
} else {
variable
}
data[[name_var]] <- factor(
as.character(data[[variable]]),
levels = grid[["Var1"]]
)
data[[name_var]] <- factor(
data[[variable]],
labels = ifelse(grid[["Var1_toset"]]=="New label",grid[["Var1"]],grid[["Var1_toset"]])
)
data
})
data_returned_r <- observeEvent(input$create, {
rv$data <- data_updated_r()
})
return(reactive(rv$data))
}
)
}
#' @inheritParams shiny::modalDialog
#' @export
#'
#' @importFrom shiny showModal modalDialog textInput
#' @importFrom htmltools tagList
#'
#' @rdname update-factor
modal_update_factor <- function(id,
title = i18n("Update levels of a factor"),
easyClose = TRUE,
size = "l",
footer = NULL) {
ns <- NS(id)
showModal(modalDialog(
title = tagList(title, datamods:::button_close_modal()),
update_factor_ui(id),
tags$div(
style = "display: none;",
textInput(inputId = ns("hidden"), label = NULL, value = datamods:::genId())
),
easyClose = easyClose,
size = size,
footer = footer
))
}
#' @inheritParams shinyWidgets::WinBox
#' @export
#'
#' @importFrom shinyWidgets WinBox wbOptions wbControls
#' @importFrom htmltools tagList
#' @rdname create-column
winbox_update_factor <- function(id,
title = i18n("Update levels of a factor"),
options = shinyWidgets::wbOptions(),
controls = shinyWidgets::wbControls()) {
ns <- NS(id)
WinBox(
title = title,
ui = tagList(
update_factor_ui(id),
tags$div(
style = "display: none;",
textInput(inputId = ns("hidden"), label = NULL, value = genId())
)
),
options = modifyList(
shinyWidgets::wbOptions(height = "615px", modal = TRUE),
options
),
controls = controls,
auto_height = FALSE
)
}
########
#### Current file: R//update-variables-ext.R
########
library(data.table)
library(rlang)
#' Select, rename and convert variables
#'
#' @param id Module id. See [shiny::moduleServer()].
#' @param title Module's title, if `TRUE` use the default title,
#' use \code{NULL} for no title or a `shiny.tag` for a custom one.
#'
#' @return A [shiny::reactive()] function returning the updated data.
#' @export
#'
#' @name update-variables
#'
update_variables_ui <- function(id, title = TRUE) {
ns <- NS(id)
if (isTRUE(title)) {
title <- htmltools::tags$h4(
i18n("Update & select variables"),
class = "datamods-title"
)
}
htmltools::tags$div(
class = "datamods-update",
shinyWidgets::html_dependency_pretty(),
title,
htmltools::tags$div(
style = "min-height: 25px;",
htmltools::tags$div(
shiny::uiOutput(outputId = ns("data_info"), inline = TRUE),
shiny::tagAppendAttributes(
shinyWidgets::dropMenu(
placement = "bottom-end",
shiny::actionButton(
inputId = ns("settings"),
label = phosphoricons::ph("gear"),
class = "pull-right float-right"
),
shinyWidgets::textInputIcon(
inputId = ns("format"),
label = i18n("Date format:"),
value = "%Y-%m-%d",
icon = list(phosphoricons::ph("clock"))
),
shinyWidgets::textInputIcon(
inputId = ns("origin"),
label = i18n("Date to use as origin to convert date/datetime:"),
value = "1970-01-01",
icon = list(phosphoricons::ph("calendar"))
),
shinyWidgets::textInputIcon(
inputId = ns("dec"),
label = i18n("Decimal separator:"),
value = ".",
icon = list("0.00")
)
),
style = "display: inline;"
)
),
htmltools::tags$br(),
toastui::datagridOutput(outputId = ns("table"))
),
htmltools::tags$br(),
htmltools::tags$div(
id = ns("update-placeholder"),
shinyWidgets::alert(
id = ns("update-result"),
status = "info",
phosphoricons::ph("info"),
paste(
"Select variables to keep (if none selected, all are kept), rename",
"variables and labels, and convert variable type/class in the table",
"above. Apply changes by clicking the button below."
)
)
),
shiny::actionButton(
inputId = ns("validate"),
label = htmltools::tagList(
phosphoricons::ph("arrow-circle-right", title = i18n("Apply changes")),
datamods::i18n("Apply changes")
),
width = "100%"
)
)
}
#' @export
#'
#' @param id Module's ID
#' @param data a \code{data.frame} or a \code{reactive} function returning a \code{data.frame}.
#' @param height Height for the table.
#' @param return_data_on_init Return initial data when module is called.
#' @param try_silent logical: should the report of error messages be suppressed?
#'
#' @rdname update-variables
#'
update_variables_server <- function(id,
data,
height = NULL,
return_data_on_init = FALSE,
try_silent = FALSE) {
shiny::moduleServer(
id = id,
module = function(input, output, session) {
ns <- session$ns
updated_data <- shiny::reactiveValues(x = NULL)
data_r <- shiny::reactive({
if (shiny::is.reactive(data)) {
data()
} else {
data
}
})
output$data_info <- shiny::renderUI({
shiny::req(data_r())
data <- data_r()
sprintf(i18n("Data has %s observations and %s variables."), nrow(data), ncol(data))
})
variables_r <- shiny::reactive({
shiny::validate(
shiny::need(data(), i18n("No data to display."))
)
data <- data_r()
if (isTRUE(return_data_on_init)) {
updated_data$x <- data
} else {
updated_data$x <- NULL
}
summary_vars(data)
})
output$table <- toastui::renderDatagrid({
shiny::req(variables_r())
# browser()
variables <- variables_r()
# variables <- variables |>
# dplyr::mutate(vals=as.list(dplyr::as_tibble(data_r())))
# variables <- variables |>
# dplyr::mutate(n_id=seq_len(nrow(variables)))
update_variables_datagrid(
variables,
height = height,
selectionId = ns("row_selected"),
buttonId = "validate"
)
})
shiny::observeEvent(input$validate,
{
updated_data$list_rename <- NULL
updated_data$list_select <- NULL
updated_data$list_mutate <- NULL
updated_data$list_relabel <- NULL
data <- data_r()
new_selections <- input$row_selected
if (length(new_selections) < 1) {
new_selections <- seq_along(data)
}
# browser()
data_inputs <- data.table::as.data.table(input$table_data)
data.table::setorderv(data_inputs, "rowKey")
old_names <- data_inputs$name
new_names <- data_inputs$name_toset
new_names[new_names == "New name"] <- NA
new_names[is.na(new_names)] <- old_names[is.na(new_names)]
new_names[new_names == ""] <- old_names[new_names == ""]
old_label <- data_inputs$label
new_label <- data_inputs$label_toset
new_label[new_label == "New label"] <- ""
new_label[is.na(new_label)] <- old_label[is.na(new_label)]
new_label[new_label == ""] <- old_label[new_label == ""]
new_classes <- data_inputs$class_toset
new_classes[new_classes == "Select"] <- NA
# browser()
data_sv <- variables_r()
vars_to_change <- get_vars_to_convert(data_sv, setNames(as.list(new_classes), old_names))
res_update <- try(
{
# convert
if (nrow(vars_to_change) > 0) {
data <- convert_to(
data = data,
variable = vars_to_change$name,
new_class = vars_to_change$class_to_set,
origin = input$origin,
format = input$format,
dec = input$dec
)
}
list_mutate <- attr(data, "code_03_convert")
# rename
list_rename <- setNames(
as.list(old_names),
unlist(new_names, use.names = FALSE)
)
list_rename <- list_rename[names(list_rename) != unlist(list_rename, use.names = FALSE)]
names(data) <- unlist(new_names, use.names = FALSE)
# relabel
list_relabel <- as.list(new_label)
data <- purrr::map2(
data, list_relabel,
\(.data, .label){
if (!(is.na(.label) | .label == "")) {
REDCapCAST::set_attr(.data, .label, attr = "label")
} else {
attr(x = .data, which = "label") <- NULL
.data
}
}
) |> dplyr::bind_cols(.name_repair = "unique_quiet")
# select
list_select <- setdiff(names(data), names(data)[new_selections])
data <- data[, new_selections, drop = FALSE]
},
silent = try_silent
)
if (inherits(res_update, "try-error")) {
datamods:::insert_error(selector = "update")
} else {
datamods:::insert_alert(
selector = ns("update"),
status = "success",
tags$b(phosphoricons::ph("check"), datamods::i18n("Data successfully updated!"))
)
updated_data$x <- data
updated_data$list_rename <- list_rename
updated_data$list_select <- list_select
updated_data$list_mutate <- list_mutate
updated_data$list_relabel <- list_relabel
}
},
ignoreNULL = TRUE,
ignoreInit = TRUE
)
return(shiny::reactive({
data <- updated_data$x
code <- list()
if (!is.null(data) && shiny::isTruthy(updated_data$list_mutate) && length(updated_data$list_mutate) > 0) {
code <- c(code, list(rlang::call2("mutate", !!!updated_data$list_mutate)))
}
if (!is.null(data) && shiny::isTruthy(updated_data$list_rename) && length(updated_data$list_rename) > 0) {
code <- c(code, list(rlang::call2("rename", !!!updated_data$list_rename)))
}
if (!is.null(data) && shiny::isTruthy(updated_data$list_select) && length(updated_data$list_select) > 0) {
code <- c(code, list(rlang::expr(select(-any_of(c(!!!updated_data$list_select))))))
}
if (!is.null(data) && shiny::isTruthy(updated_data$list_relabel) && length(updated_data$list_relabel) > 0) {
code <- c(code, list(rlang::call2("purrr::map2(list_relabel,
function(.data,.label){
REDCapCAST::set_attr(.data,.label,attr = 'label')
}) |> dplyr::bind_cols(.name_repair = 'unique_quiet')")))
}
if (length(code) > 0) {
attr(data, "code") <- Reduce(
f = function(x, y) rlang::expr(!!x %>% !!y),
x = code
)
}
return(data)
}))
}
)
}
modal_update_variables <- function(id,
title = "Select, rename and reclass variables",
easyClose = TRUE,
size = "xl",
footer = NULL) {
ns <- NS(id)
showModal(modalDialog(
title = tagList(title, datamods:::button_close_modal()),
update_variables_ui(id),
tags$div(
style = "display: none;",
textInput(inputId = ns("hidden"), label = NULL, value = datamods:::genId())
),
easyClose = easyClose,
size = size,
footer = footer
))
}
# utils -------------------------------------------------------------------
#' Get variables classes from a \code{data.frame}
#'
#' @param data a \code{data.frame}
#'
#' @return a \code{character} vector as same length as number of variables
#' @noRd
#'
#' @examples
#'
#' get_classes(mtcars)
get_classes <- function(data) {
classes <- lapply(
X = data,
FUN = function(x) {
paste(class(x), collapse = ", ")
}
)
unlist(classes, use.names = FALSE)
}
#' Get count of unique values in variables of \code{data.frame}
#'
#' @param data a \code{data.frame}
#'
#' @return a \code{numeric} vector as same length as number of variables
#' @noRd
#'
#'
#' @examples
#' get_n_unique(mtcars)
get_n_unique <- function(data) {
u <- lapply(data, FUN = function(x) {
if (is.atomic(x)) {
data.table::uniqueN(x)
} else {
NA_integer_
}
})
unlist(u, use.names = FALSE)
}
#' Add padding 0 to a vector
#'
#' @param x a \code{vector}
#'
#' @return a \code{character} vector
#' @noRd
#'
#' @examples
#'
#' pad0(1:10)
#' pad0(c(1, 15, 150, NA))
pad0 <- function(x) {
NAs <- which(is.na(x))
x <- formatC(x, width = max(nchar(as.character(x)), na.rm = TRUE), flag = "0")
x[NAs] <- NA
x
}
#' Variables summary
#'
#' @param data a \code{data.frame}
#'
#' @return a \code{data.frame}
#' @noRd
#'
#' @examples
#'
#' summary_vars(iris)
#' summary_vars(mtcars)
summary_vars <- function(data) {
data <- as.data.frame(data)
datsum <- dplyr::tibble(
name = names(data),
label = lapply(data, \(.x) REDCapCAST::get_attr(.x, "label")) |> unlist(),
class = get_classes(data),
n_missing = unname(colSums(is.na(data))),
p_complete = 1 - n_missing / nrow(data),
n_unique = get_n_unique(data)
)
datsum
}
add_var_toset <- function(data, var_name, default = "") {
datanames <- names(data)
datanames <- append(
x = datanames,
values = paste0(var_name, "_toset"),
after = which(datanames == var_name)
)
data[[paste0(var_name, "_toset")]] <- default
data[, datanames]
}
#' Modified from the datamods pacakge
#'
#' @param data data
#'
#' @param height height
#' @param selectionId selectionId
#' @param buttonId buttonId
#'
#' @examples
#' mtcars |>
#' summary_vars() |>
#' update_variables_datagrid()
#'
update_variables_datagrid <- function(data, height = NULL, selectionId = NULL, buttonId = NULL) {
# browser()
data <- add_var_toset(data, "name", "New name")
data <- add_var_toset(data, "class", "Select")
data <- add_var_toset(data, "label", "New label")
gridTheme <- getOption("datagrid.theme")
if (length(gridTheme) < 1) {
datamods:::apply_grid_theme()
}
on.exit(toastui::reset_grid_theme())
col.names <- names(data)
std_names <- c(
"name", "name_toset", "label", "label_toset", "class", "class_toset", "n_missing", "p_complete", "n_unique"
) |>
setNames(c(
"Name", "New name", "Label", "New label", "Class", "New class", "Missing", "Complete", "Unique"
))
headers <- lapply(col.names, \(.x){
if (.x %in% std_names) {
names(std_names)[match(.x, std_names)]
} else {
.x
}
}) |> unlist()
grid <- toastui::datagrid(
data = data,
theme = "default",
colwidths = NULL
)
grid <- toastui::grid_columns(
grid = grid,
columns = col.names,
header = headers,
minWidth = 100
)
grid <- toastui::grid_format(
grid = grid,
"p_complete",
formatter = toastui::JS("function(obj) {return (obj.value*100).toFixed(0) + '%';}")
)
grid <- toastui::grid_style_column(
grid = grid,
column = "name_toset",
fontStyle = "italic"
)
grid <- toastui::grid_style_column(
grid = grid,
column = "label_toset",
fontStyle = "italic"
)
grid <- toastui::grid_style_column(
grid = grid,
column = "class_toset",
fontStyle = "italic"
)
grid <- toastui::grid_filters(
grid = grid,
column = "name",
# columns = unname(std_names[std_names!="vals"]),
showApplyBtn = FALSE,
showClearBtn = TRUE,
type = "text"
)
# grid <- toastui::grid_columns(
# grid = grid,
# columns = "name_toset",
# editor = list(type = "text"),
# validation = toastui::validateOpts()
# )
#
# grid <- toastui::grid_columns(
# grid = grid,
# columns = "label_toset",
# editor = list(type = "text"),
# validation = toastui::validateOpts()
# )
#
# grid <- toastui::grid_columns(
# grid = grid,
# columns = "class_toset",
# editor = list(
# type = "radio",
# options = list(
# instantApply = TRUE,
# listItems = lapply(
# X = c("Select", "character", "factor", "numeric", "integer", "date", "datetime", "hms"),
# FUN = function(x) {
# list(text = x, value = x)
# }
# )
# )
# ),
# validation = toastui::validateOpts()
# )
grid <- toastui::grid_editor(
grid = grid,
column = "name_toset",
type = "text"
)
grid <- toastui::grid_editor(
grid = grid,
column = "label_toset",
type = "text"
)
grid <- toastui::grid_editor(
grid = grid,
column = "class_toset",
type = "select",
choices = c("Select", "character", "factor", "numeric", "integer", "date", "datetime", "hms")
)
grid <- toastui::grid_editor_opts(
grid = grid,
editingEvent = "click",
actionButtonId = NULL,
session = NULL
)
grid <- toastui::grid_selection_row(
grid = grid,
inputId = selectionId,
type = "checkbox",
return = "index"
)
return(grid)
}
#' Convert a variable to specific new class
#'
#' @param data A \code{data.frame}
#' @param variable Name of the variable to convert
#' @param new_class Class to set
#' @param ... Other arguments passed on to methods.
#'
#' @return A \code{data.frame}
#' @noRd
#'
#' @importFrom utils type.convert
#' @importFrom rlang sym expr
#'
#' @examples
#' dat <- data.frame(
#' v1 = month.name,
#' v2 = month.abb,
#' v3 = 1:12,
#' v4 = as.numeric(Sys.Date() + 0:11),
#' v5 = as.character(Sys.Date() + 0:11),
#' v6 = as.factor(c("a", "a", "b", "a", "b", "a", "a", "b", "a", "b", "b", "a")),
#' v7 = as.character(11:22),
#' stringsAsFactors = FALSE
#' )
#'
#' str(dat)
#'
#' str(convert_to(dat, "v3", "character"))
#' str(convert_to(dat, "v6", "character"))
#' str(convert_to(dat, "v7", "numeric"))
#' str(convert_to(dat, "v4", "date", origin = "1970-01-01"))
#' str(convert_to(dat, "v5", "date"))
#'
#' str(convert_to(dat, c("v1", "v3"), c("factor", "character")))
#'
#' str(convert_to(dat, c("v1", "v3", "v4"), c("factor", "character", "date"), origin = "1970-01-01"))
#'
convert_to <- function(data,
variable,
new_class = c("character", "factor", "numeric", "integer", "date", "datetime", "hms"),
...) {
new_class <- match.arg(new_class, several.ok = TRUE)
stopifnot(length(new_class) == length(variable))
args <- list(...)
args$format <- clean_sep(args$format)
if (length(variable) > 1) {
for (i in seq_along(variable)) {
data <- convert_to(data, variable[i], new_class[i], ...)
}
return(data)
}
if (identical(new_class, "character")) {
data[[variable]] <- as.character(x = data[[variable]], ...)
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(as.character(!!sym(variable)))), variable)
)
} else if (identical(new_class, "factor")) {
data[[variable]] <- as.factor(x = data[[variable]])
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(as.factor(!!sym(variable)))), variable)
)
} else if (identical(new_class, "numeric")) {
data[[variable]] <- as.numeric(type.convert(data[[variable]], as.is = TRUE, ...))
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(as.numeric(!!sym(variable)))), variable)
)
} else if (identical(new_class, "integer")) {
data[[variable]] <- as.integer(x = data[[variable]], ...)
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(as.integer(!!sym(variable)))), variable)
)
} else if (identical(new_class, "date")) {
data[[variable]] <- as.Date(x = clean_date(data[[variable]]), ...)
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(as.Date(clean_date(!!sym(variable)), origin = !!args$origin, format=clean_sep(!!args$format)))), variable)
)
} else if (identical(new_class, "datetime")) {
data[[variable]] <- as.POSIXct(x = data[[variable]], ...)
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(as.POSIXct(!!sym(variable)))), variable)
)
} else if (identical(new_class, "hms")) {
data[[variable]] <- hms::as_hms(x = data[[variable]])
attr(data, "code_03_convert") <- c(
attr(data, "code_03_convert"),
setNames(list(expr(hms::as_hms(!!sym(variable)))), variable)
)
}
return(data)
}
#' Get variable(s) to convert
#'
#' @param vars Output of [summary_vars()]
#' @param classes_input List of inputs containing new classes
#'
#' @return a `data.table`.
#' @noRd
#'
#' @importFrom data.table data.table as.data.table
#'
#' @examples
#' # 2 variables to convert
#' new_classes <- list(
#' "Sepal.Length" = "numeric",
#' "Sepal.Width" = "numeric",
#' "Petal.Length" = "character",
#' "Petal.Width" = "numeric",
#' "Species" = "character"
#' )
#' get_vars_to_convert(summary_vars(iris), new_classes)
#'
#'
#' # No changes
#' new_classes <- list(
#' "Sepal.Length" = "numeric",
#' "Sepal.Width" = "numeric",
#' "Petal.Length" = "numeric",
#' "Petal.Width" = "numeric",
#' "Species" = "factor"
#' )
#' get_vars_to_convert(summary_vars(iris), new_classes)
#'
#' # Not set = NA or ""
#' new_classes <- list(
#' "Sepal.Length" = NA,
#' "Sepal.Width" = NA,
#' "Petal.Length" = NA,
#' "Petal.Width" = NA,
#' "Species" = NA
#' )
#' get_vars_to_convert(summary_vars(iris), new_classes)
#'
#' # Set for one var
#' new_classes <- list(
#' "Sepal.Length" = "",
#' "Sepal.Width" = "",
#' "Petal.Length" = "",
#' "Petal.Width" = "",
#' "Species" = "character"
#' )
#' get_vars_to_convert(summary_vars(iris), new_classes)
#'
#' new_classes <- list(
#' "mpg" = "character",
#' "cyl" = "numeric",
#' "disp" = "character",
#' "hp" = "numeric",
#' "drat" = "character",
#' "wt" = "character",
#' "qsec" = "numeric",
#' "vs" = "character",
#' "am" = "numeric",
#' "gear" = "character",
#' "carb" = "integer"
#' )
#' get_vars_to_convert(summary_vars(mtcars), new_classes)
get_vars_to_convert <- function(vars, classes_input) {
vars <- data.table::as.data.table(vars)
classes_input <- data.table::data.table(
name = names(classes_input),
class_to_set = unlist(classes_input, use.names = FALSE),
stringsAsFactors = FALSE
)
classes_input <- classes_input[!is.na(class_to_set) & class_to_set != ""]
classes_df <- merge(x = vars, y = classes_input, by = "name")
classes_df <- classes_df[!is.na(class_to_set)]
classes_df[class != class_to_set]
}
#' gsub wrapper for piping with default values for separator substituting
#'
#' @param data character vector
#' @param old.sep old separator
#' @param new.sep new separator
#'
#' @returns character vector
#' @export
#'
clean_sep <- function(data,old.sep="[-.,/]",new.sep="-"){
gsub(old.sep,new.sep,data)
}
#' Attempts at applying uniform date format
#'
#' @param data character string vector of possible dates
#'
#' @returns character string
#' @export
#'
clean_date <- function(data){
data |>
clean_sep() |>
sapply(\(.x){
if (is.na(.x)){
.x
} else {
strsplit(.x,"-") |>
unlist()|>
lapply(\(.y){
if (nchar(.y)==1) paste0("0",.y) else .y
}) |> paste(collapse="-")
}
}) |>
unname()
}
########
#### Current file: /Users/au301842/freesearcheR/inst/apps/freesearcheR/ui.R
########
# ns <- NS(id)
ui_elements <- list(
##############################################################################
#########
######### Home panel
#########
##############################################################################
"home" = bslib::nav_panel(
title = "freesearcheR",
shiny::fluidRow(
shiny::column(width = 2),
shiny::column(
width = 8,
shiny::markdown(readLines("www/intro.md")),
shiny::column(width = 2)
)
),
icon = shiny::icon("home")
),
##############################################################################
#########
######### Import panel
#########
##############################################################################
"import" = bslib::nav_panel(
title = "Import",
shiny::fluidRow(
shiny::column(width = 2),
shiny::column(
width = 8,
shiny::h4("Choose your data source"),
shiny::br(),
shinyWidgets::radioGroupButtons(
inputId = "source",
selected = "env",
choices = c(
"File upload" = "file",
"REDCap server export" = "redcap",
"Local or sample data" = "env"
),
width = "100%"
),
shiny::helpText("Upload a file from your device, get data directly from REDCap or select a sample data set for testing from the app."),
shiny::br(),
shiny::br(),
shiny::conditionalPanel(
condition = "input.source=='file'",
datamods::import_file_ui("file_import",
title = "Choose a datafile to upload",
file_extensions = c(".csv", ".txt", ".xls", ".xlsx", ".rds", ".fst", ".sas7bdat", ".sav", ".ods", ".dta")
)
),
shiny::conditionalPanel(
condition = "input.source=='redcap'",
m_redcap_readUI("redcap_import")
),
shiny::conditionalPanel(
condition = "input.source=='env'",
import_globalenv_ui(id = "env", title = NULL)
),
shiny::conditionalPanel(
condition = "input.source=='redcap'",
DT::DTOutput(outputId = "redcap_prev")
),
shiny::br(),
shiny::br(),
shiny::h5("Exclude in-complete variables"),
shiny::fluidRow(
shiny::column(width=6,
shiny::br(),
shiny::br(),
shiny::p("Filter incomplete variables, by setting a completeness threshold:"),
shiny::br()
),
shiny::column(width=6,
shinyWidgets::noUiSliderInput(
inputId = "complete_cutoff",
label = NULL,
min = 0,
max = 100,
step = 5,
value = 70,
format = shinyWidgets::wNumbFormat(decimals = 0),
color = datamods:::get_primary_color()
),
shiny::helpText("Include variables with completeness above the specified percentage.")
)
),
shiny::br(),
shiny::br(),
shiny::actionButton(
inputId = "act_start",
label = "Start",
width = "100%",
icon = shiny::icon("play"),
disabled = TRUE
),
shiny::helpText('After importing, hit "Start" or navigate to the desired tab.'),
shiny::br(),
shiny::br(),
shiny::column(width = 2)
)
)
),
##############################################################################
#########
######### Data overview panel
#########
##############################################################################
"overview" =
# bslib::nav_panel_hidden(
bslib::nav_panel(
# value = "overview",
title = "Data",
bslib::navset_bar(
fillable = TRUE,
bslib::nav_panel(
title = "Overview",
tags$h3("Overview and filtering"),
fluidRow(
shiny::column(
width = 9,
shiny::tags$p(
"Below is a short summary table of the provided data.
On the right hand side you have the option to create filters.
At the bottom you'll find a raw overview of the original vs the modified data."
)
)
),
fluidRow(
shiny::column(
width = 9,
data_summary_ui(id = "data_summary")
),
shiny::column(
width = 3,
IDEAFilter::IDEAFilter_ui("data_filter"),
shiny::tags$br()
)
)
),
bslib::nav_panel(
title = "Browse",
tags$h3("Browse the provided data"),
shiny::tags$p(
"Below is a table with all the modified data provided to browse and understand data."
),
shinyWidgets::html_dependency_winbox(),
fluidRow(
toastui::datagridOutput(outputId = "table_mod")
),
shiny::tags$br(),
shiny::tags$br(),
shiny::tags$br(),
shiny::tags$br(),
shiny::tags$br()
),
bslib::nav_panel(
title = "Modify",
tags$h3("Subset, rename and convert variables"),
fluidRow(
shiny::column(
width = 9,
shiny::tags$p(shiny::markdown("Below, you can subset the data (select variables to include on clicking 'Apply changes'), rename variables, set new labels (for nicer tables in the report) and change variable classes (numeric, factor/categorical etc.).
Italic text can be edited/changed.
On the right, you can create and modify factor/categorical variables as well as create new variables with *R* code."))
)
),
fluidRow(
shiny::column(
width = 2
),
shiny::column(
width = 8,
fluidRow(
shiny::column(
width = 6,
tags$h4("Update variables"),
shiny::tags$br(),
shiny::actionButton(
inputId = "modal_variables",
label = "Subset, rename and change class/type",
width = "100%"
),
shiny::tags$br(),
shiny::helpText("Subset variables, rename variables and labels, and apply new class to variables"),
shiny::tags$br(),
shiny::tags$br(),
shiny::actionButton(
inputId = "modal_update",
label = "Reorder factor levels",
width = "100%"
),
shiny::tags$br(),
shiny::helpText("Reorder the levels of factor/categorical variables."),
shiny::tags$br(),
shiny::tags$br()
),
shiny::column(
width = 6,
tags$h4("Create new variables"),
shiny::tags$br(),
shiny::actionButton(
inputId = "modal_cut",
label = "Create factor variable",
width = "100%"
),
shiny::tags$br(),
shiny::helpText("Create factor/categorical variable from an other value."),
shiny::tags$br(),
shiny::tags$br(),
shiny::actionButton(
inputId = "modal_column",
label = "New variable",
width = "100%"
),
shiny::tags$br(),
shiny::helpText(shiny::markdown("Create a new variable/column based on an *R*-expression.")),
shiny::tags$br(),
shiny::tags$br()
)
),
tags$h4("Restore"),
shiny::actionButton(
inputId = "data_reset",
label = "Restore original data",
width = "100%"
),
shiny::tags$br(),
shiny::helpText("Reset to original imported dataset. Careful! There is no un-doing.")
),
shiny::column(
width = 2
)
),
shiny::tags$br(),
shiny::tags$br(),
tags$h4("Restore"),
shiny::tags$br(),
shiny::tags$p(
"Below, you'll find a raw overview of the original vs the modified data."
),
shiny::tags$br(),
shiny::tags$br(),
fluidRow(
column(
width = 6,
tags$b("Original data:"),
# verbatimTextOutput("original"),
verbatimTextOutput("original_str")
),
column(
width = 6,
tags$b("Modified data:"),
# verbatimTextOutput("modified"),
verbatimTextOutput("modified_str")
)
)
)
)
),
##############################################################################
#########
######### Descriptive analyses panel
#########
##############################################################################
"describe" =
bslib::nav_panel(
title = "Evaluate",
id = "navdescribe",
bslib::navset_bar(
title = "",
sidebar = bslib::sidebar(
bslib::accordion(
open = "acc_chars",
multiple = FALSE,
bslib::accordion_panel(
value = "acc_chars",
title = "Characteristics",
icon = bsicons::bs_icon("table"),
shiny::uiOutput("strat_var"),
shiny::helpText("Only factor/categorical variables are available for stratification. Go back to the 'Data' tab to reclass a variable if it's not on the list."),
shiny::conditionalPanel(
condition = "input.strat_var!='none'",
shiny::radioButtons(
inputId = "add_p",
label = "Compare strata?",
selected = "no",
inline = TRUE,
choices = list(
"No" = "no",
"Yes" = "yes"
)
),
shiny::helpText("Option to perform statistical comparisons between strata in baseline table.")
)
),
bslib::accordion_panel(
vlaue = "acc_cor",
title = "Correlations",
icon = bsicons::bs_icon("table"),
shiny::uiOutput("outcome_var_cor"),
shiny::helpText("This variable will be excluded from the correlation plot."),
shiny::br(),
shinyWidgets::noUiSliderInput(
inputId = "cor_cutoff",
label = "Correlation cut-off",
min = 0,
max = 1,
step = .01,
value = .8,
format = shinyWidgets::wNumbFormat(decimals = 2),
color = datamods:::get_primary_color()
)
)
)
),
bslib::nav_panel(
title = "Baseline characteristics",
gt::gt_output(outputId = "table1")
),
bslib::nav_panel(
title = "Variable correlations",
data_correlations_ui(id = "correlations", height = 600)
)
)
),
##############################################################################
#########
######### Download panel
#########
##############################################################################
"visuals" = bslib::nav_panel(
title = "Visuals",
id = "navvisuals",
do.call(
bslib::navset_bar,
c(
data_visuals_ui("visuals"),
shiny::tagList(
bslib::nav_spacer(),
bslib::nav_panel(
title = "Notes",
shiny::fluidRow(
shiny::column(width = 2),
shiny::column(
width = 8,
shiny::markdown(readLines("www/notes_visuals.md")),
shiny::column(width = 2)
)
)
)
)
)
)
),
##############################################################################
#########
######### Regression analyses panel
#########
##############################################################################
"analyze" =
bslib::nav_panel(
title = "Regression",
id = "navanalyses",
bslib::navset_bar(
title = "",
# bslib::layout_sidebar(
# fillable = TRUE,
sidebar = bslib::sidebar(
bslib::accordion(
open = "acc_reg",
multiple = FALSE,
bslib::accordion_panel(
value = "acc_reg",
title = "Regression",
icon = bsicons::bs_icon("calculator"),
shiny::uiOutput("outcome_var"),
# shiny::selectInput(
# inputId = "design",
# label = "Study design",
# selected = "no",
# inline = TRUE,
# choices = list(
# "Cross-sectional" = "cross-sectional"
# )
# ),
shiny::uiOutput("regression_type"),
shiny::radioButtons(
inputId = "add_regression_p",
label = "Add p-value",
inline = TRUE,
selected = "yes",
choices = list(
"Yes" = "yes",
"No" = "no"
)
),
bslib::input_task_button(
id = "load",
label = "Analyse",
# icon = shiny::icon("pencil", lib = "glyphicon"),
icon = bsicons::bs_icon("pencil"),
label_busy = "Working...",
icon_busy = fontawesome::fa_i("arrows-rotate",
class = "fa-spin",
"aria-hidden" = "true"
),
type = "secondary",
auto_reset = TRUE
),
shiny::helpText("Press 'Analyse' again after changing parameters."),
shiny::tags$br(),
shiny::uiOutput("plot_model")
),
bslib::accordion_panel(
value = "acc_advanced",
title = "Advanced",
icon = bsicons::bs_icon("gear"),
shiny::radioButtons(
inputId = "all",
label = "Specify covariables",
inline = TRUE, selected = 2,
choiceNames = c(
"Yes",
"No"
),
choiceValues = c(1, 2)
),
shiny::conditionalPanel(
condition = "input.all==1",
shiny::uiOutput("include_vars")
)
)
),
# shiny::helpText(em("Please specify relevant settings for your data, and press 'Analyse'")),
# shiny::radioButtons(
# inputId = "specify_factors",
# label = "Specify categorical variables?",
# selected = "no",
# inline = TRUE,
# choices = list(
# "Yes" = "yes",
# "No" = "no"
# )
# ),
# shiny::conditionalPanel(
# condition = "input.specify_factors=='yes'",
# shiny::uiOutput("factor_vars")
# ),
# shiny::conditionalPanel(
# condition = "output.ready=='yes'",
# shiny::tags$hr(),
),
bslib::nav_panel(
title = "Regression table",
gt::gt_output(outputId = "table2")
),
bslib::nav_panel(
title = "Coefficient plot",
shiny::plotOutput(outputId = "regression_plot")
),
bslib::nav_panel(
title = "Model checks",
shiny::plotOutput(outputId = "check")
# shiny::uiOutput(outputId = "check_1")
)
)
),
##############################################################################
#########
######### Download panel
#########
##############################################################################
"download" =
bslib::nav_panel(
title = "Download",
id = "navdownload",
shiny::fluidRow(
shiny::column(width = 2),
shiny::column(
width = 8,
shiny::fluidRow(
shiny::column(
width = 6,
shiny::h4("Report"),
shiny::helpText("Choose your favourite output file format for further work, and download, when the analyses are done."),
shiny::br(),
shiny::br(),
shiny::selectInput(
inputId = "output_type",
label = "Output format",
selected = NULL,
choices = list(
"MS Word" = "docx",
"LibreOffice" = "odt"
# ,
# "PDF" = "pdf",
# "All the above" = "all"
)
),
shiny::br(),
# Button
shiny::downloadButton(
outputId = "report",
label = "Download report",
icon = shiny::icon("download")
)
# shiny::helpText("If choosing to output to MS Word, please note, that when opening the document, two errors will pop-up. Choose to repair and choose not to update references. The issue is being worked on. You can always choose LibreOffice instead."),
),
shiny::column(
width = 6,
shiny::h4("Data"),
shiny::helpText("Choose your favourite output data format to download the modified data."),
shiny::br(),
shiny::br(),
shiny::selectInput(
inputId = "data_type",
label = "Data format",
selected = NULL,
choices = list(
"R" = "rds",
"stata" = "dta",
"CSV" = "csv"
)
),
shiny::helpText("No metadata is saved when exporting to csv."),
shiny::br(),
shiny::br(),
# Button
shiny::downloadButton(
outputId = "data_modified",
label = "Download data",
icon = shiny::icon("download")
)
)
),
shiny::br(),
shiny::br(),
shiny::tags$b("Code snippets:"),
shiny::verbatimTextOutput(outputId = "code_import"),
shiny::verbatimTextOutput(outputId = "code_data"),
shiny::verbatimTextOutput(outputId = "code_filter"),
shiny::tags$br(),
shiny::br(),
shiny::column(width = 2)
)
)
),
##############################################################################
#########
######### Documentation panel
#########
##############################################################################
"docs" = bslib::nav_item(
# shiny::img(shiny::icon("book")),
shiny::tags$a(
href = "https://agdamsbo.github.io/freesearcheR/",
"Docs (external)",
target = "_blank",
rel = "noopener noreferrer"
)
)
# bslib::nav_panel(
# title = "Documentation",
# # shiny::tags$iframe("www/docs.html", height=600, width=535),
# shiny::htmlOutput("docs_file"),
# shiny::br()
# )
)
# Initial attempt at creating light and dark versions
light <- custom_theme()
dark <- custom_theme(
bg = "#000",
fg = "#fff"
)
# Fonts to consider:
# https://webdesignerdepot.com/17-open-source-fonts-youll-actually-love/
ui <- bslib::page_fixed(
shiny::tags$head(includeHTML(("www/umami-app.html"))),
shiny::tags$style(
type = "text/css",
# add the name of the tab you want to use as title in data-value
shiny::HTML(
".container-fluid > .nav > li >
a[data-value='freesearcheR'] {font-size: 28px}"
)
),
title = "freesearcheR",
theme = light,
shiny::useBusyIndicators(),
bslib::page_navbar(
id = "main_panel",
ui_elements$home,
ui_elements$import,
ui_elements$overview,
ui_elements$describe,
ui_elements$visuals,
ui_elements$analyze,
ui_elements$download,
bslib::nav_spacer(),
ui_elements$docs,
fillable = FALSE,
footer = shiny::tags$footer(
style = "background-color: #14131326; padding: 4px; text-align: center; bottom: 0; width: 100%;",
shiny::p(
style = "margin: 1",
"Data is only stored for analyses and deleted immediately afterwards."
),
shiny::p(
style = "margin: 1; color: #888;",
"AG Damsbo | v", app_version(), " | AGPLv3 license | ", shiny::tags$a("Source on Github", href = "https://github.com/agdamsbo/freesearcheR/", target = "_blank", rel = "noopener noreferrer")
),
)
)
)
########
#### Current file: /Users/au301842/freesearcheR/inst/apps/freesearcheR/server.R
########
library(readr)
library(MASS)
library(stats)
library(gt)
library(openxlsx2)
library(haven)
library(readODS)
require(shiny)
library(bslib)
library(assertthat)
library(dplyr)
library(quarto)
library(here)
library(broom)
library(broom.helpers)
# library(REDCapCAST)
library(easystats)
library(esquisse)
library(patchwork)
library(DHARMa)
library(apexcharter)
library(toastui)
library(datamods)
library(data.table)
library(IDEAFilter)
library(shinyWidgets)
library(DT)
library(gtsummary)
# library(freesearcheR)
# source("functions.R")
data(mtcars)
trial <- gtsummary::trial |> default_parsing()
# light <- custom_theme()
#
# dark <- custom_theme(bg = "#000",fg="#fff")
server <- function(input, output, session) {
## Listing files in www in session start to keep when ending and removing
## everything else.
files.to.keep <- list.files("www/")
output$docs_file <- shiny::renderUI({
# shiny::includeHTML("www/docs.html")
shiny::HTML(readLines("www/docs.html"))
})
##############################################################################
#########
######### Night mode (just very popular, not really needed)
#########
##############################################################################
# observeEvent(input$dark_mode,{
# session$setCurrentTheme(
# if (isTRUE(input$dark_mode)) dark else light
# )})
# observe({
# if(input$dark_mode==TRUE)
# session$setCurrentTheme(bs_theme_update(theme = custom_theme(version = 5)))
# if(input$dark_mode==FALSE)
# session$setCurrentTheme(bs_theme_update(theme = custom_theme(version = 5, bg = "#000",fg="#fff")))
# })
##############################################################################
#########
######### Setting reactive values
#########
##############################################################################
rv <- shiny::reactiveValues(
list = list(),
ds = NULL,
local_temp = NULL,
ready = NULL,
test = "no",
data_original = NULL,
data = NULL,
data_filtered = NULL,
models = NULL,
code = list()
)
##############################################################################
#########
######### Data import section
#########
##############################################################################
consider.na <- c("NA", "\"\"", "", "\'\'", "na")
data_file <- datamods::import_file_server(
id = "file_import",
show_data_in = "popup",
trigger_return = "change",
return_class = "data.frame",
read_fns = list(
ods = function(file) {
readODS::read_ods(
path = file,
# Sheet and skip not implemented for .ods in the original implementation
# sheet = sheet,
# skip = skip,
na = consider.na
)
},
dta = function(file) {
haven::read_dta(
file = file,
.name_repair = "unique_quiet"
)
},
csv = function(file) {
readr::read_csv(
file = file,
na = consider.na,
name_repair = "unique_quiet"
)
},
xls = function(file) {
openxlsx2::read_xlsx(
file = file,
sheet = sheet,
skip_empty_rows = TRUE,
start_row = skip - 1,
na.strings = consider.na
)
},
xlsx = function(file) {
openxlsx2::read_xlsx(
file = file,
sheet = sheet,
skip_empty_rows = TRUE,
start_row = skip - 1,
na.strings = consider.na)
},
rds = function(file) {
readr::read_rds(
file = file,
name_repair = "unique_quiet")
}
)
)
shiny::observeEvent(data_file$data(), {
shiny::req(data_file$data())
rv$data_original <- data_file$data()
rv$code <- append_list(data = data_file$code(), list = rv$code, index = "import")
})
data_redcap <- m_redcap_readServer(
id = "redcap_import"#,
# output.format = "list"
)
shiny::observeEvent(data_redcap(), {
# rv$data_original <- purrr::pluck(data_redcap(), "data")()
rv$data_original <- data_redcap()
})
output$redcap_prev <- DT::renderDT(
{
DT::datatable(head(data_redcap(), 5),
# DT::datatable(head(purrr::pluck(data_redcap(), "data")(), 5),
caption = "First 5 observations"
)
},
server = TRUE
)
from_env <- datamods::import_globalenv_server(
id = "env",
trigger_return = "change",
btn_show_data = FALSE,
reset = reactive(input$hidden)
)
shiny::observeEvent(from_env$data(), {
shiny::req(from_env$data())
rv$data_original <- from_env$data()
# rv$code <- append_list(data = from_env$code(),list = rv$code,index = "import")
})
shiny::observeEvent(rv$data_original, {
if (is.null(rv$data_original) | NROW(rv$data_original) == 0) {
shiny::updateActionButton(inputId = "act_start", disabled = TRUE)
} else {
shiny::updateActionButton(inputId = "act_start", disabled = FALSE)
}
})
##############################################################################
#########
######### Data modification section
#########
##############################################################################
shiny::observeEvent(
eventExpr = list(
rv$data_original,
input$complete_cutoff
),
handlerExpr = {
shiny::req(rv$data_original)
rv$data <- rv$data_original |>
# janitor::clean_names() |>
default_parsing() |>
remove_empty_cols(
cutoff = input$complete_cutoff / 100
)
}
)
## For now this solution work, but I would prefer to solve this with the above
shiny::observeEvent(input$reset_confirm, {
if (isTRUE(input$reset_confirm)) {
shiny::req(rv$data_original)
rv$data <- rv$data_original |>
default_parsing() |>
remove_empty_cols(
cutoff = input$complete_cutoff / 100
)
}
}, ignoreNULL = TRUE)
shiny::observeEvent(input$data_reset, {
shinyWidgets::ask_confirmation(
cancelOnDismiss = TRUE,
inputId = "reset_confirm",
title = "Please confirm data reset?",
type = "warning"
)
})
# shiny::observeEvent(input$reset_confirm, {
# rv$data <- rv$data_original |> default_parsing()
# })
######### Overview
data_summary_server(
id = "data_summary",
data = shiny::reactive({
rv$data_filtered
}),
color.main = "#2A004E",
color.sec = "#C62300",
pagination = 20
)
#########
######### Modifications
#########
## Using modified version of the datamods::cut_variable_server function
## Further modifications are needed to have cut/bin options based on class of variable
## Could be defined server-side
shiny::observeEvent(
input$modal_variables,
modal_update_variables("modal_variables",title = "Modify factor levels")
)
######### Create factor
shiny::observeEvent(
input$modal_cut,
modal_cut_variable("modal_cut",title = "Modify factor levels")
)
data_modal_cut <- cut_variable_server(
id = "modal_cut",
data_r = shiny::reactive(rv$data)
)
shiny::observeEvent(data_modal_cut(), rv$data <- data_modal_cut())
######### Modify factor
shiny::observeEvent(
input$modal_update,
datamods::modal_update_factor(id = "modal_update")
)
data_modal_update <- datamods::update_factor_server(
id = "modal_update",
data_r = reactive(rv$data)
)
shiny::observeEvent(data_modal_update(), {
shiny::removeModal()
rv$data <- data_modal_update()
})
######### Create column
shiny::observeEvent(
input$modal_column,
datamods::modal_create_column(id = "modal_column")
)
data_modal_r <- datamods::create_column_server(
id = "modal_column",
data_r = reactive(rv$data)
)
shiny::observeEvent(
data_modal_r(),
{
rv$data <- data_modal_r()
}
)
######### Show result
tryCatch(
{
output$table_mod <- toastui::renderDatagrid({
shiny::req(rv$data)
# data <- rv$data
toastui::datagrid(
# data = rv$data # ,
data = data_filter(),
pagination = 10
# bordered = TRUE,
# compact = TRUE,
# striped = TRUE
)
})
},
warning = function(warn) {
showNotification(paste0(warn), type = "warning")
},
error = function(err) {
showNotification(paste0(err), type = "err")
}
)
output$code <- renderPrint({
attr(rv$data, "code")
})
# updated_data <- datamods::update_variables_server(
updated_data <- update_variables_server(
id = "modal_variables",
data = reactive(rv$data),
return_data_on_init = FALSE
)
output$original_str <- renderPrint({
str(rv$data_original)
})
output$modified_str <- renderPrint({
str(as.data.frame(rv$data_filtered) |>
REDCapCAST::set_attr(
label = NULL,
attr = "code"
))
})
shiny::observeEvent(updated_data(), {
rv$data <- updated_data()
})
# IDEAFilter has the least cluttered UI, but might have a License issue
data_filter <- IDEAFilter::IDEAFilter("data_filter", data = reactive(rv$data), verbose = TRUE)
shiny::observeEvent(
list(
shiny::reactive(rv$data),
shiny::reactive(rv$data_original),
data_filter(),
regression_vars(),
input$complete_cutoff
),
{
rv$data_filtered <- data_filter()
rv$list$data <- data_filter() |>
REDCapCAST::fct_drop()
}
)
shiny::observeEvent(
list(
shiny::reactive(rv$data),
shiny::reactive(rv$data_original),
data_filter(),
shiny::reactive(rv$data_filtered)
),
{
out <- gsub(
"filter", "dplyr::filter",
gsub(
"\\s{2,}", " ",
paste0(
capture.output(attr(rv$data_filtered, "code")),
collapse = " "
)
)
)
out <- strsplit(out, "%>%") |>
unlist() |>
(\(.x){
paste(c("data", .x[-1]), collapse = "|> \n ")
})()
rv$code <- append_list(data = out, list = rv$code, index = "filter")
}
)
# output$filtered_code <- shiny::renderPrint({
# out <- gsub(
# "filter", "dplyr::filter",
# gsub(
# "\\s{2,}", " ",
# paste0(
# capture.output(attr(rv$data_filtered, "code")),
# collapse = " "
# )
# )
# )
#
# out <- strsplit(out, "%>%") |>
# unlist() |>
# (\(.x){
# paste(c("data", .x[-1]), collapse = "|> \n ")
# })()
#
# cat(out)
# })
output$code_import <- shiny::renderPrint({
cat(rv$code$import)
})
output$code_data <- shiny::renderPrint({
attr(rv$data, "code")
})
output$code_filter <- shiny::renderPrint({
cat(rv$code$filter)
})
##############################################################################
#########
######### Data analyses Inputs
#########
##############################################################################
## Keep these "old" selection options as a simple alternative to the modification pane
output$include_vars <- shiny::renderUI({
shiny::selectizeInput(
inputId = "include_vars",
selected = NULL,
label = "Covariables to include",
choices = colnames(rv$data_filtered),
multiple = TRUE
)
})
output$outcome_var <- shiny::renderUI({
shiny::selectInput(
inputId = "outcome_var",
selected = NULL,
label = "Select outcome variable",
choices = colnames(rv$data_filtered),
multiple = FALSE
)
})
output$regression_type <- shiny::renderUI({
shiny::req(input$outcome_var)
shiny::selectizeInput(
inputId = "regression_type",
label = "Choose regression analysis",
## The below ifelse statement handles the case of loading a new dataset
choices = possible_functions(
data = dplyr::select(
rv$data_filtered,
ifelse(input$outcome_var %in% names(rv$data_filtered),
input$outcome_var,
names(rv$data_filtered)[1]
)
), design = "cross-sectional"
),
multiple = FALSE
)
})
output$factor_vars <- shiny::renderUI({
shiny::selectizeInput(
inputId = "factor_vars",
selected = colnames(rv$data_filtered)[sapply(rv$data_filtered, is.factor)],
label = "Covariables to format as categorical",
choices = colnames(rv$data_filtered),
multiple = TRUE
)
})
## Collected regression variables
regression_vars <- shiny::reactive({
if (is.null(input$include_vars)) {
out <- colnames(rv$data_filtered)
} else {
out <- unique(c(input$include_vars, input$outcome_var))
}
return(out)
})
output$strat_var <- shiny::renderUI({
shiny::selectInput(
inputId = "strat_var",
selected = "none",
label = "Select variable to stratify baseline",
choices = c(
"none",
rv$data_filtered |>
(\(.x){
lapply(.x, \(.c){
if (identical("factor", class(.c))) {
.c
}
}) |>
dplyr::bind_cols()
})() |>
colnames()
),
multiple = FALSE
)
})
output$plot_model <- shiny::renderUI({
shiny::req(rv$list$regression$tables)
shiny::selectInput(
inputId = "plot_model",
selected = "none",
label = "Select models to plot",
choices = names(rv$list$regression$tables),
multiple = TRUE
)
})
##############################################################################
#########
######### Descriptive evaluations
#########
##############################################################################
shiny::observeEvent(
# ignoreInit = TRUE,
list(
shiny::reactive(rv$list$data),
shiny::reactive(rv$data),
shiny::reactive(rv$data_original),
data_filter(),
input$strat_var,
input$include_vars,
input$add_p,
input$complete_cutoff
),
{
shiny::req(input$strat_var)
shiny::req(rv$list$data)
if (input$strat_var == "none" | !input$strat_var %in% names(rv$list$data)) {
by.var <- NULL
} else {
by.var <- input$strat_var
}
rv$list$table1 <-
rv$list$data |>
baseline_table(
fun.args =
list(
by = by.var
)
) |>
(\(.x){
if (!is.null(by.var)) {
.x |> gtsummary::add_overall()
} else {
.x
}
})() |>
(\(.x){
if (input$add_p == "yes" & !is.null(by.var)) {
.x |>
gtsummary::add_p() |>
gtsummary::bold_p()
} else {
.x
}
})()
# gtsummary::as_kable(rv$list$table1) |>
# readr::write_lines(file="./www/_table1.md")
}
)
output$outcome_var_cor <- shiny::renderUI({
shiny::selectInput(
inputId = "outcome_var_cor",
selected = NULL,
label = "Select outcome variable",
choices = c(
colnames(rv$list$data)
# ,"none"
),
multiple = FALSE
)
})
output$table1 <- gt::render_gt({
shiny::req(rv$list$table1)
rv$list$table1 |>
gtsummary::as_gt() |>
gt::tab_header(gt::md("**Table 1: Baseline Characteristics**"))
})
data_correlations_server(
id = "correlations",
data = shiny::reactive({
shiny::req(rv$list$data)
out <- dplyr::select(rv$list$data, -!!input$outcome_var_cor)
# input$outcome_var_cor=="none"){
# out <- rv$list$data
# }
out
}),
cutoff = shiny::reactive(input$cor_cutoff)
)
##############################################################################
#########
######### Data visuals
#########
##############################################################################
pl <- data_visuals_server("visuals", data = shiny::reactive(rv$data))
##############################################################################
#########
######### Regression model analyses
#########
##############################################################################
shiny::observeEvent(
input$load,
{
shiny::req(input$outcome_var)
# browser()
# Assumes all character variables can be formatted as factors
# data <- data_filter$filtered() |>
tryCatch(
{
## Which models to create should be decided by input
## Could also include
## imputed or
## minimally adjusted
model_lists <- list(
"Univariable" = regression_model_uv_list,
"Multivariable" = regression_model_list
) |>
lapply(\(.fun){
ls <- do.call(
.fun,
c(
list(data = rv$list$data|>
(\(.x){
.x[regression_vars()]
})()),
list(outcome.str = input$outcome_var),
list(fun.descr = input$regression_type)
)
)
})
# browser()
rv$list$regression$params <- get_fun_options(input$regression_type) |>
(\(.x){
.x[[1]]
})()
rv$list$regression$models <- model_lists
# names(rv$list$regression)
# rv$models <- lapply(model_lists, \(.x){
# .x$model
# })
},
warning = function(warn) {
showNotification(paste0(warn), type = "warning")
},
error = function(err) {
showNotification(paste0("Creating regression models failed with the following error: ", err), type = "err")
}
)
}
)
shiny::observeEvent(
ignoreInit = TRUE,
list(
rv$list$regression$models
),
{
shiny::req(rv$list$regression$models)
tryCatch(
{
rv$check <- lapply(rv$list$regression$models, \(.x){
.x$model
}) |>
purrr::pluck("Multivariable") |>
performance::check_model()
},
warning = function(warn) {
showNotification(paste0(warn), type = "warning")
},
error = function(err) {
showNotification(paste0("Running model assumptions checks failed with the following error: ", err), type = "err")
}
)
}
)
output$check <- shiny::renderPlot(
{
shiny::req(rv$check)
# browser()
# p <- plot(rv$check) +
# patchwork::plot_annotation(title = "Multivariable regression model checks")
p <- plot(rv$check) +
patchwork::plot_annotation(title = "Multivariable regression model checks")
for (i in seq_len(length(p))) {
p[[i]] <- p[[i]] + gg_theme_shiny()
}
p
# p + patchwork::plot_layout(ncol = 1, design = ggplot2::waiver())
# Generate checks in one column
# layout <- sapply(seq_len(length(p)), \(.x){
# patchwork::area(.x, 1)
# })
#
# p + patchwork::plot_layout(design = Reduce(c, layout))
# patchwork::wrap_plots(ncol=1) +
# patchwork::plot_annotation(title = 'Multivariable regression model checks')
},
height = 600,
alt = "Assumptions testing of the multivariable regression model"
)
shiny::observeEvent(
input$load,
{
shiny::req(rv$list$regression$models)
tryCatch(
{
out <- lapply(rv$list$regression$models, \(.x){
.x$model
}) |>
purrr::map(regression_table)
if (input$add_regression_p == "no") {
out <- out |>
lapply(\(.x){
.x |>
gtsummary::modify_column_hide(
column = "p.value"
)
})
}
rv$list$regression$tables <- out
# rv$list$regression$table <- out |>
# tbl_merge()
# gtsummary::as_kable(rv$list$regression$table) |>
# readr::write_lines(file="./www/_regression_table.md")
rv$list$input <- input
},
warning = function(warn) {
showNotification(paste0(warn), type = "warning")
},
error = function(err) {
showNotification(paste0("Creating a regression table failed with the following error: ", err), type = "err")
}
)
rv$ready <- "ready"
}
)
output$table2 <- gt::render_gt({
shiny::req(rv$list$regression$tables)
rv$list$regression$tables |>
tbl_merge() |>
gtsummary::as_gt() |>
gt::tab_header(gt::md(glue::glue("**Table 2: {rv$list$regression$params$descr}**")))
})
output$regression_plot <- shiny::renderPlot(
{
# shiny::req(rv$list$regression$plot)
shiny::req(input$plot_model)
out <- merge_long(rv$list$regression, input$plot_model) |>
plot.tbl_regression(
colour = "variable",
facet_col = "model"
)
out +
ggplot2::scale_y_discrete(labels = scales::label_wrap(15)) +
gg_theme_shiny()
# rv$list$regression$tables$Multivariable |>
# plot(colour = "variable") +
# ggplot2::scale_y_discrete(labels = scales::label_wrap(15)) +
# gg_theme_shiny()
},
height = 500,
alt = "Regression coefficient plot"
)
shiny::conditionalPanel(
condition = "output.uploaded == 'yes'",
)
##############################################################################
#########
######### Page navigation
#########
##############################################################################
shiny::observeEvent(input$act_start, {
bslib::nav_select(id = "main_panel", selected = "Data")
})
##############################################################################
#########
######### Reactivity
#########
##############################################################################
output$uploaded <- shiny::reactive({
if (is.null(rv$ds)) {
"no"
} else {
"yes"
}
})
shiny::outputOptions(output, "uploaded", suspendWhenHidden = FALSE)
output$ready <- shiny::reactive({
if (is.null(rv$ready)) {
"no"
} else {
"yes"
}
})
shiny::outputOptions(output, "ready", suspendWhenHidden = FALSE)
# Reimplement from environment at later time
# output$has_input <- shiny::reactive({
# if (rv$input) {
# "yes"
# } else {
# "no"
# }
# })
# shiny::outputOptions(output, "has_input", suspendWhenHidden = FALSE)
##############################################################################
#########
######### Downloads
#########
##############################################################################
# Could be rendered with other tables or should show progress
# Investigate quarto render problems
# On temp file handling: https://github.com/quarto-dev/quarto-cli/issues/3992
output$report <- downloadHandler(
filename = shiny::reactive({
paste0("report.", input$output_type)
}),
content = function(file, type = input$output_type) {
# shiny::req(rv$list$regression)
## Notification is not progressing
## Presumably due to missing
# Simplified for .rmd output attempt
format <- ifelse(type == "docx", "word_document", "odt_document")
shiny::withProgress(message = "Generating the report. Hold on for a moment..", {
rv$list |>
write_rmd(
output_format = format,
input = file.path(getwd(), "www/report.rmd")
)
# write_quarto(
# output_format = type,
# input = file.path(getwd(), "www/report.qmd")
# )
})
file.rename(paste0("www/report.", type), file)
}
)
output$data_modified <- downloadHandler(
filename = shiny::reactive({
paste0("modified_data.", input$data_type)
}),
content = function(file, type = input$data_type) {
if (type == "rds") {
readr::write_rds(rv$list$data, file = file)
} else if (type == "dta") {
haven::write_dta(as.data.frame(rv$list$data), path = file)
} else if (type == "csv") {
readr::write_csv(rv$list$data, file = file)
}
}
)
##############################################################################
#########
######### Clearing the session on end
#########
##############################################################################
session$onSessionEnded(function() {
cat("Session Ended\n")
files <- list.files("www/")
lapply(files[!files %in% files.to.keep], \(.x){
unlink(paste0("www/", .x), recursive = FALSE)
print(paste(.x, "deleted"))
})
})
}
########
#### Current file: /Users/au301842/freesearcheR/inst/apps/freesearcheR/launch.R
########
shinyApp(ui, server)