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#' Shared parameters documentation
#'
#' Defines common arguments shared across multiple functions in the package
#' to avoid repetition by using `inheritParams`.
#'
#' @param plot_height (`numeric`) optional, specifies the plot height as a three-element vector of
#' `value`, `min`, and `max` intended for use with a slider UI element.
#' @param plot_width (`numeric`) optional, specifies the plot width as a three-element vector of
#' `value`, `min`, and `max` for a slider encoding the plot width.
#' @param rotate_xaxis_labels (`logical`) optional, whether to rotate plot X axis labels. Does not
#' rotate by default (`FALSE`).
#' @param ggtheme (`character`) optional, `ggplot2` theme to be used by default. Defaults to `"gray"`.
#' @param ggplot2_args (`ggplot2_args`) object created by [teal.widgets::ggplot2_args()]
#' with settings for the module plot.
#' The argument is merged with options variable `teal.ggplot2_args` and default module setup.
#'
#' For more details see the vignette: `vignette("custom-ggplot2-arguments", package = "teal.widgets")`
#' @param basic_table_args (`basic_table_args`) object created by [teal.widgets::basic_table_args()]
#' with settings for the module table.
#' The argument is merged with options variable `teal.basic_table_args` and default module setup.
#'
#' For more details see the vignette: `vignette("custom-basic-table-arguments", package = "teal.widgets")`
#' @param pre_output (`shiny.tag`) optional, text or UI element to be displayed before the module's output,
#' providing context or a title.
#'  with text placed before the output to put the output into context. For example a title.
#' @param post_output (`shiny.tag`) optional, text or UI element to be displayed after the module's output,
#' adding context or further instructions. Elements like `shiny::helpText()` are useful.
#' @param alpha (`integer(1)` or `integer(3)`) optional, specifies point opacity.
#' - When the length of `alpha` is one: the plot points will have a fixed opacity.
#' - When the length of `alpha` is three: the plot points opacity are dynamically adjusted based on
#' vector of `value`, `min`, and `max`.
#' @param size (`integer(1)` or `integer(3)`) optional, specifies point size.
#' - When the length of `size` is one: the plot point sizes will have a fixed size.
#' - When the length of `size` is three: the plot points size are dynamically adjusted based on
#' vector of `value`, `min`, and `max`.
#' @param decorators `r lifecycle::badge("experimental")`
#' (named `list` of lists of `teal_transform_module`) optional,
#' decorator for tables or plots included in the module output reported.
#' The decorators are applied to the respective output objects.
#'
#' See section "Decorating Module" below for more details.
#'
#' @return Object of class `teal_module` to be used in `teal` applications.
#'
#' @name shared_params
#' @keywords internal
NULL

#' Add labels for facets to a `ggplot2` object
#'
#' Enhances a `ggplot2` plot by adding labels that describe
#' the faceting variables along the x and y axes.
#'
#' @param p (`ggplot2`) object to which facet labels will be added.
#' @param xfacet_label (`character`) Label for the facet along the x-axis.
#' If `NULL`, no label is added. If a vector, labels are joined with " & ".
#' @param yfacet_label (`character`) Label for the facet along the y-axis.
#' Similar behavior to `xfacet_label`.
#'
#' @return Returns `grid` or `grob` object (to be drawn with `grid.draw`)
#'
#' @examples
#' library(ggplot2)
#' library(grid)
#'
#' p <- ggplot(mtcars) +
#'   aes(x = mpg, y = disp) +
#'   geom_point() +
#'   facet_grid(gear ~ cyl)
#'
#' xfacet_label <- "cylinders"
#' yfacet_label <- "gear"
#' res <- add_facet_labels(p, xfacet_label, yfacet_label)
#' grid.newpage()
#' grid.draw(res)
#'
#' grid.newpage()
#' grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label))
#' grid.newpage()
#' grid.draw(add_facet_labels(p, xfacet_label, yfacet_label = NULL))
#' grid.newpage()
#' grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL))
#'
#' @export
#'
add_facet_labels <- function(p, xfacet_label = NULL, yfacet_label = NULL) {
  checkmate::assert_class(p, classes = "ggplot")
  checkmate::assert_character(xfacet_label, null.ok = TRUE, min.len = 1)
  checkmate::assert_character(yfacet_label, null.ok = TRUE, min.len = 1)
  if (is.null(xfacet_label) && is.null(yfacet_label)) {
    return(ggplot2::ggplotGrob(p))
  }
  grid::grid.grabExpr({
    g <- ggplot2::ggplotGrob(p)

    # we are going to replace these, so we make sure they have nothing in them
    checkmate::assert_class(g$grobs[[grep("xlab-t", g$layout$name, fixed = TRUE)]], "zeroGrob")
    checkmate::assert_class(g$grobs[[grep("ylab-r", g$layout$name, fixed = TRUE)]], "zeroGrob")

    xaxis_label_grob <- g$grobs[[grep("xlab-b", g$layout$name, fixed = TRUE)]]
    xaxis_label_grob$children[[1]]$label <- paste(xfacet_label, collapse = " & ")
    yaxis_label_grob <- g$grobs[[grep("ylab-l", g$layout$name, fixed = TRUE)]]
    yaxis_label_grob$children[[1]]$label <- paste(yfacet_label, collapse = " & ")
    yaxis_label_grob$children[[1]]$rot <- 270

    top_height <- if (is.null(xfacet_label)) 0 else grid::unit(2, "line")
    right_width <- if (is.null(yfacet_label)) 0 else grid::unit(2, "line")

    grid::grid.newpage()
    grid::pushViewport(grid::plotViewport(margins = c(0, 0, top_height, right_width), name = "ggplot"))
    grid::grid.draw(g)
    grid::upViewport(1)

    # draw x facet
    if (!is.null(xfacet_label)) {
      grid::pushViewport(grid::viewport(
        x = 0, y = grid::unit(1, "npc") - top_height, width = grid::unit(1, "npc"),
        height = top_height, just = c("left", "bottom"), name = "topxaxis"
      ))
      grid::grid.draw(xaxis_label_grob)
      grid::upViewport(1)
    }

    # draw y facet
    if (!is.null(yfacet_label)) {
      grid::pushViewport(grid::viewport(
        x = grid::unit(1, "npc") - grid::unit(as.numeric(right_width) / 2, "line"), y = 0, width = right_width,
        height = grid::unit(1, "npc"), just = c("left", "bottom"), name = "rightyaxis"
      ))
      grid::grid.draw(yaxis_label_grob)
      grid::upViewport(1)
    }
  })
}

#' Call a function with a character vector for the `...` argument
#'
#' @param fun (`character`) Name of a function where the `...` argument shall be replaced by values from `str_args`.
#' @param str_args (`character`) A character vector that the function shall be executed with
#'
#' @return
#' Value of call to `fun` with arguments specified in `str_args`.
#'
#' @keywords internal
call_fun_dots <- function(fun, str_args) {
  do.call("call", c(list(fun), lapply(str_args, as.name)), quote = TRUE)
}

#' Generate a string for a variable including its label
#'
#' @param var_names (`character`) Name of variable to extract labels from.
#' @param dataset (`dataset`) Name of analysis dataset.
#' @param prefix,suffix (`character`) String to paste to the beginning/end of the variable name with label.
#' @param wrap_width (`numeric`) Number of characters to wrap original label to. Defaults to 80.
#'
#' @return (`character`) String with variable name and label.
#'
#' @keywords internal
#'
varname_w_label <- function(var_names,
                            dataset,
                            wrap_width = 80,
                            prefix = NULL,
                            suffix = NULL) {
  add_label <- function(var_names) {
    label <- vapply(
      dataset[var_names], function(x) {
        attr_label <- attr(x, "label")
        `if`(is.null(attr_label), "", attr_label)
      },
      character(1)
    )

    if (length(label) == 1 && !is.na(label) && !identical(label, "")) {
      paste0(prefix, label, " [", var_names, "]", suffix)
    } else {
      var_names
    }
  }

  if (length(var_names) < 1) {
    NULL
  } else if (length(var_names) == 1) {
    stringr::str_wrap(add_label(var_names), width = wrap_width)
  } else if (length(var_names) > 1) {
    stringr::str_wrap(vapply(var_names, add_label, character(1)), width = wrap_width)
  }
}

# see vignette("ggplot2-specs", package="ggplot2")
shape_names <- c(
  "circle", paste("circle", c("open", "filled", "cross", "plus", "small")), "bullet",
  "square", paste("square", c("open", "filled", "cross", "plus", "triangle")),
  "diamond", paste("diamond", c("open", "filled", "plus")),
  "triangle", paste("triangle", c("open", "filled", "square")),
  paste("triangle down", c("open", "filled")),
  "plus", "cross", "asterisk"
)

#' Get icons to represent variable types in dataset
#'
#' @param var_type (`character`) of R internal types (classes).
#' @return (`character`) vector of HTML icons corresponding to data type in each column.
#' @keywords internal
variable_type_icons <- function(var_type) {
  checkmate::assert_character(var_type, any.missing = FALSE)

  class_to_icon <- list(
    numeric = "arrow-up-1-9",
    integer = "arrow-up-1-9",
    logical = "pause",
    Date = "calendar",
    POSIXct = "calendar",
    POSIXlt = "calendar",
    factor = "chart-bar",
    character = "keyboard",
    primary_key = "key",
    unknown = "circle-question"
  )
  class_to_icon <- lapply(class_to_icon, function(icon_name) toString(icon(icon_name, lib = "font-awesome")))

  unname(vapply(
    var_type,
    FUN.VALUE = character(1),
    FUN = function(class) {
      if (class == "") {
        class
      } else if (is.null(class_to_icon[[class]])) {
        class_to_icon[["unknown"]]
      } else {
        class_to_icon[[class]]
      }
    }
  ))
}

#'
#' @param id (`character(1)`) the id of the tab panel with tabs.
#' @param name (`character(1)`) the name of the tab.
#' @return JavaScript expression to be used in `shiny::conditionalPanel()` to determine
#' if the specified tab is active.
#' @keywords internal
#'
is_tab_active_js <- function(id, name) {
  # supporting the bs3 and higher version at the same time
  sprintf(
    "$(\"#%1$s > li.active\").text().trim() == '%2$s' || $(\"#%1$s > li a.active\").text().trim() == '%2$s'",
    id, name
  )
}

#' Assert single selection on `data_extract_spec` object
#' Helper to reduce code in assertions
#' @noRd
#'
assert_single_selection <- function(x,
                                    .var.name = checkmate::vname(x)) { # nolint: object_name.
  if (any(vapply(x, function(.x) .x$select$multiple, logical(1)))) {
    stop("'", .var.name, "' should not allow multiple selection")
  }
  invisible(TRUE)
}

#' Wrappers around `srv_transform_teal_data` that allows to decorate the data
#' @inheritParams teal::srv_transform_teal_data
#' @param expr (`expression` or `reactive`) to evaluate on the output of the decoration.
#' When an expression it must be inline code. See [within()]
#' Default is `NULL` which won't evaluate any appending code.
#' @param expr_is_reactive (`logical(1)`) whether `expr` is a reactive expression
#' that skips defusing the argument.
#' @details
#' `srv_decorate_teal_data` is a wrapper around `srv_transform_teal_data` that
#' allows to decorate the data with additional expressions.
#' When original `teal_data` object is in error state, it will show that error
#' first.
#'
#' @keywords internal
srv_decorate_teal_data <- function(id, data, decorators, expr, expr_is_reactive = FALSE) {
  checkmate::assert_class(data, classes = "reactive")
  checkmate::assert_list(decorators, "teal_transform_module")
  checkmate::assert_flag(expr_is_reactive)

  missing_expr <- missing(expr)
  if (!missing_expr && !expr_is_reactive) {
    expr <- dplyr::enexpr(expr) # Using dplyr re-export to avoid adding rlang to Imports
  }

  moduleServer(id, function(input, output, session) {
    decorated_output <- srv_transform_teal_data("inner", data = data, transformators = decorators)

    reactive({
      data_out <- try(data(), silent = TRUE)
      if (inherits(data_out, "qenv.error")) {
        data()
      } else {
        # ensure original errors are displayed and `eval_code` is never executed with NULL
        req(data(), decorated_output())
        if (missing_expr) {
          decorated_output()
        } else if (expr_is_reactive) {
          teal.code::eval_code(decorated_output(), expr())
        } else {
          teal.code::eval_code(decorated_output(), expr)
        }
      }
    })
  })
}

#' @rdname srv_decorate_teal_data
#' @details
#' `ui_decorate_teal_data` is a wrapper around `ui_transform_teal_data`.
#' @keywords internal
ui_decorate_teal_data <- function(id, decorators, ...) {
  teal::ui_transform_teal_data(NS(id, "inner"), transformators = decorators, ...)
}

#' Internal function to check if decorators is a valid object
#' @noRd
check_decorators <- function(x, names = NULL) { # nolint: object_name.

  check_message <- checkmate::check_list(x, names = "named")

  if (!is.null(names)) {
    if (isTRUE(check_message)) {
      if (length(names(x)) != length(unique(names(x)))) {
        check_message <- sprintf(
          "The `decorators` must contain unique names from these names: %s.",
          paste(names, collapse = ", ")
        )
      }
    } else {
      check_message <- sprintf(
        "The `decorators` must be a named list from these names: %s.",
        paste(names, collapse = ", ")
      )
    }
  }

  if (!isTRUE(check_message)) {
    return(check_message)
  }

  valid_elements <- vapply(
    x,
    checkmate::test_class,
    classes = "teal_transform_module",
    FUN.VALUE = logical(1L)
  )

  if (all(valid_elements)) {
    return(TRUE)
  }

  "Make sure that the named list contains 'teal_transform_module' objects created using `teal_transform_module()`."
}
#' Internal assertion on decorators
#' @noRd
assert_decorators <- checkmate::makeAssertionFunction(check_decorators)

#' Subset decorators based on the scope
#'
#' @param scope (`character`) a character vector of decorator names to include.
#' @param decorators (named `list`) of list decorators to subset.
#'
#' @return Subsetted list with all decorators to include.
#' It can be an empty list if none of the scope exists in `decorators` argument.
#' @keywords internal
select_decorators <- function(decorators, scope) {
  checkmate::assert_character(scope, null.ok = TRUE)
  if (scope %in% names(decorators)) {
    decorators[scope]
  } else {
    list()
  }
}

1		.onLoad <- function(libname, pkgname) {
2	!	teal.logger::register_logger(namespace = "teal.modules.general")
3	!	teal.logger::register_handlers("teal.modules.general")
4		}
5
6		### global variables
7		ggplot_themes <- c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void")
8
9		#' @importFrom lifecycle deprecated
10		interactive <- NULL

1		#' `teal` module: Stack plots of variables and show association with reference variable
2		#'
3		#' Module provides functionality for visualizing the distribution of variables and
4		#' their association with a reference variable.
5		#' It supports configuring the appearance of the plots, including themes and whether to show associations.
6		#'
7		#'
8		#' @note For more examples, please see the vignette "Using association plot" via
9		#' `vignette("using-association-plot", package = "teal.modules.general")`.
10		#'
11		#' @inheritParams teal::module
12		#' @inheritParams shared_params
13		#' @param ref (`data_extract_spec` or `list` of multiple `data_extract_spec`)
14		#' Reference variable, must accepts a `data_extract_spec` with `select_spec(multiple = FALSE)`
15		#' to ensure single selection option.
16		#' @param vars (`data_extract_spec` or `list` of multiple `data_extract_spec`)
17		#' Variables to be associated with the reference variable.
18		#' @param show_association (`logical`) optional, whether show association of `vars`
19		#' with reference variable. Defaults to `TRUE`.
20		#' @param distribution_theme,association_theme (`character`) optional, `ggplot2` themes to be used by default.
21		#' Default to `"gray"`.
22		#'
23		#' @param ggplot2_args `r roxygen_ggplot2_args_param("Bivariate1", "Bivariate2")`
24		#'
25		#' @inherit shared_params return
26		#'
27		#' @section Decorating Module:
28		#'
29		#' This module generates the following objects, which can be modified in place using decorators:
30		#' - `plot` (`grob` created with [ggplot2::ggplotGrob()])
31		#'
32		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
33		#' The name of this list corresponds to the name of the output to which the decorator is applied.
34		#' See code snippet below:
35		#'
36		#' ```
37		#' tm_g_association(
38		#' ..., # arguments for module
39		#' decorators = list(
40		#' plot = teal_transform_module(...) # applied to the `plot` output
41		#' )
42		#' )
43		#' ```
44		#'
45		#' For additional details and examples of decorators, refer to the vignette
46		#' `vignette("decorate-module-output", package = "teal.modules.general")`.
47		#'
48		#' To learn more please refer to the vignette
49		#' `vignette("transform-module-output", package = "teal")` or the [`teal::teal_transform_module()`] documentation.
50		#'
51		#' @examplesShinylive
52		#' library(teal.modules.general)
53		#' interactive <- function() TRUE
54		#' {{ next_example }}
55		#' @examples
56		#' # general data example
57		#' data <- teal_data()
58		#' data <- within(data, {
59		#' require(nestcolor)
60		#' CO2 <- CO2
61		#' factors <- names(Filter(isTRUE, vapply(CO2, is.factor, logical(1L))))
62		#' CO2[factors] <- lapply(CO2[factors], as.character)
63		#' })
64		#'
65		#' app <- init(
66		#' data = data,
67		#' modules = modules(
68		#' tm_g_association(
69		#' ref = data_extract_spec(
70		#' dataname = "CO2",
71		#' select = select_spec(
72		#' label = "Select variable:",
73		#' choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
74		#' selected = "Plant",
75		#' fixed = FALSE
76		#' )
77		#' ),
78		#' vars = data_extract_spec(
79		#' dataname = "CO2",
80		#' select = select_spec(
81		#' label = "Select variables:",
82		#' choices = variable_choices(data[["CO2"]], c("Plant", "Type", "Treatment")),
83		#' selected = "Treatment",
84		#' multiple = TRUE,
85		#' fixed = FALSE
86		#' )
87		#' )
88		#' )
89		#' )
90		#' )
91		#' if (interactive()) {
92		#' shinyApp(app$ui, app$server)
93		#' }
94		#'
95		#' @examplesShinylive
96		#' library(teal.modules.general)
97		#' interactive <- function() TRUE
98		#' {{ next_example }}
99		#' @examples
100		#' # CDISC data example
101		#' data <- teal_data()
102		#' data <- within(data, {
103		#' require(nestcolor)
104		#' ADSL <- teal.data::rADSL
105		#' })
106		#' join_keys(data) <- default_cdisc_join_keys[names(data)]
107		#'
108		#' app <- init(
109		#' data = data,
110		#' modules = modules(
111		#' tm_g_association(
112		#' ref = data_extract_spec(
113		#' dataname = "ADSL",
114		#' select = select_spec(
115		#' label = "Select variable:",
116		#' choices = variable_choices(
117		#' data[["ADSL"]],
118		#' c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2")
119		#' ),
120		#' selected = "RACE",
121		#' fixed = FALSE
122		#' )
123		#' ),
124		#' vars = data_extract_spec(
125		#' dataname = "ADSL",
126		#' select = select_spec(
127		#' label = "Select variables:",
128		#' choices = variable_choices(
129		#' data[["ADSL"]],
130		#' c("SEX", "RACE", "COUNTRY", "ARM", "STRATA1", "STRATA2", "ITTFL", "BMRKR2")
131		#' ),
132		#' selected = "BMRKR2",
133		#' multiple = TRUE,
134		#' fixed = FALSE
135		#' )
136		#' )
137		#' )
138		#' )
139		#' )
140		#' if (interactive()) {
141		#' shinyApp(app$ui, app$server)
142		#' }
143		#'
144		#' @export
145		#'
146		tm_g_association <- function(label = "Association",
147		ref,
148		vars,
149		show_association = TRUE,
150		plot_height = c(600, 400, 5000),
151		plot_width = NULL,
152		distribution_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"), # nolint: line_length.
153		association_theme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"), # nolint: line_length.
154		pre_output = NULL,
155		post_output = NULL,
156		ggplot2_args = teal.widgets::ggplot2_args(),
157		transformators = list(),
158		decorators = list()) {
159	!	message("Initializing tm_g_association")
160
161		# Normalize the parameters
162	!	if (inherits(ref, "data_extract_spec")) ref <- list(ref)
163	!	if (inherits(vars, "data_extract_spec")) vars <- list(vars)
164	!	if (inherits(ggplot2_args, "ggplot2_args")) ggplot2_args <- list(default = ggplot2_args)
165
166		# Start of assertions
167	!	checkmate::assert_string(label)
168
169	!	checkmate::assert_list(ref, types = "data_extract_spec")
170	!	if (!all(vapply(ref, function(x) !x$select$multiple, logical(1)))) {
171	!	stop("'ref' should not allow multiple selection")
172		}
173
174	!	checkmate::assert_list(vars, types = "data_extract_spec")
175	!	checkmate::assert_flag(show_association)
176
177	!	checkmate::assert_numeric(plot_height, len = 3, any.missing = FALSE, finite = TRUE)
178	!	checkmate::assert_numeric(plot_height[1], lower = plot_height[2], upper = plot_height[3], .var.name = "plot_height")
179	!	checkmate::assert_numeric(plot_width, len = 3, any.missing = FALSE, null.ok = TRUE, finite = TRUE)
180	!	checkmate::assert_numeric(
181	!	plot_width[1],
182	!	lower = plot_width[2], upper = plot_width[3], null.ok = TRUE, .var.name = "plot_width"
183		)
184
185	!	distribution_theme <- match.arg(distribution_theme)
186	!	association_theme <- match.arg(association_theme)
187
188	!	checkmate::assert_multi_class(pre_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
189	!	checkmate::assert_multi_class(post_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
190
191	!	plot_choices <- c("Bivariate1", "Bivariate2")
192	!	checkmate::assert_list(ggplot2_args, types = "ggplot2_args")
193	!	checkmate::assert_subset(names(ggplot2_args), c("default", plot_choices))
194
195	!	assert_decorators(decorators, "plot")
196		# End of assertions
197
198		# Make UI args
199	!	args <- as.list(environment())
200
201	!	data_extract_list <- list(
202	!	ref = ref,
203	!	vars = vars
204		)
205
206	!	ans <- module(
207	!	label = label,
208	!	server = srv_tm_g_association,
209	!	ui = ui_tm_g_association,
210	!	ui_args = args,
211	!	server_args = c(
212	!	data_extract_list,
213	!	list(plot_height = plot_height, plot_width = plot_width, ggplot2_args = ggplot2_args, decorators = decorators)
214		),
215	!	transformators = transformators,
216	!	datanames = teal.transform::get_extract_datanames(data_extract_list)
217		)
218	!	attr(ans, "teal_bookmarkable") <- TRUE
219	!	ans
220		}
221
222		# UI function for the association module
223		ui_tm_g_association <- function(id, ...) {
224	!	ns <- NS(id)
225	!	args <- list(...)
226	!	is_single_dataset_value <- teal.transform::is_single_dataset(args$ref, args$vars)
227
228	!	teal.widgets::standard_layout(
229	!	output = teal.widgets::white_small_well(
230	!	textOutput(ns("title")),
231	!	tags$br(),
232	!	teal.widgets::plot_with_settings_ui(id = ns("myplot"))
233		),
234	!	encoding = tags$div(
235		### Reporter
236	!	teal.reporter::add_card_button_ui(ns("add_reporter"), label = "Add Report Card"),
237	!	tags$br(), tags$br(),
238		###
239	!	tags$label("Encodings", class = "text-primary"),
240	!	teal.transform::datanames_input(args[c("ref", "vars")]),
241	!	teal.transform::data_extract_ui(
242	!	id = ns("ref"),
243	!	label = "Reference variable",
244	!	data_extract_spec = args$ref,
245	!	is_single_dataset = is_single_dataset_value
246		),
247	!	teal.transform::data_extract_ui(
248	!	id = ns("vars"),
249	!	label = "Associated variables",
250	!	data_extract_spec = args$vars,
251	!	is_single_dataset = is_single_dataset_value
252		),
253	!	checkboxInput(
254	!	ns("association"),
255	!	"Association with reference variable",
256	!	value = args$show_association
257		),
258	!	checkboxInput(
259	!	ns("show_dist"),
260	!	"Scaled frequencies",
261	!	value = FALSE
262		),
263	!	checkboxInput(
264	!	ns("log_transformation"),
265	!	"Log transformed",
266	!	value = FALSE
267		),
268	!	ui_decorate_teal_data(ns("decorator"), decorators = select_decorators(args$decorators, "plot")),
269	!	bslib::accordion(
270	!	open = TRUE,
271	!	bslib::accordion_panel(
272	!	title = "Plot settings",
273	!	teal.widgets::optionalSliderInputValMinMax(ns("alpha"), "Scatterplot opacity:", c(0.5, 0, 1), ticks = FALSE),
274	!	teal.widgets::optionalSliderInputValMinMax(ns("size"), "Scatterplot points size:", c(2, 1, 8), ticks = FALSE),
275	!	checkboxInput(ns("swap_axes"), "Swap axes", value = FALSE),
276	!	checkboxInput(ns("rotate_xaxis_labels"), "Rotate X axis labels", value = FALSE),
277	!	selectInput(
278	!	inputId = ns("distribution_theme"),
279	!	label = "Distribution theme (by ggplot):",
280	!	choices = ggplot_themes,
281	!	selected = args$distribution_theme,
282	!	multiple = FALSE
283		),
284	!	selectInput(
285	!	inputId = ns("association_theme"),
286	!	label = "Association theme (by ggplot):",
287	!	choices = ggplot_themes,
288	!	selected = args$association_theme,
289	!	multiple = FALSE
290		)
291		)
292		)
293		),
294	!	forms = tagList(
295	!	teal.widgets::verbatim_popup_ui(ns("rcode"), "Show R code")
296		),
297	!	pre_output = args$pre_output,
298	!	post_output = args$post_output
299		)
300		}
301
302		# Server function for the association module
303		srv_tm_g_association <- function(id,
304		data,
305		reporter,
306		filter_panel_api,
307		ref,
308		vars,
309		plot_height,
310		plot_width,
311		ggplot2_args,
312		decorators) {
313	!	with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
314	!	with_filter <- !missing(filter_panel_api) && inherits(filter_panel_api, "FilterPanelAPI")
315	!	checkmate::assert_class(data, "reactive")
316	!	checkmate::assert_class(isolate(data()), "teal_data")
317
318	!	moduleServer(id, function(input, output, session) {
319	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
320
321	!	selector_list <- teal.transform::data_extract_multiple_srv(
322	!	data_extract = list(ref = ref, vars = vars),
323	!	datasets = data,
324	!	select_validation_rule = list(
325	!	ref = shinyvalidate::compose_rules(
326	!	shinyvalidate::sv_required("A reference variable needs to be selected."),
327	!	~ if ((.) %in% selector_list()$vars()$select) {
328	!	"Associated variables and reference variable cannot overlap"
329		}
330		),
331	!	vars = shinyvalidate::compose_rules(
332	!	shinyvalidate::sv_required("An associated variable needs to be selected."),
333	!	~ if (length(selector_list()$ref()$select) != 0 && selector_list()$ref()$select %in% (.)) {
334	!	"Associated variables and reference variable cannot overlap"
335		}
336		)
337		)
338		)
339
340	!	iv_r <- reactive({
341	!	iv <- shinyvalidate::InputValidator$new()
342	!	teal.transform::compose_and_enable_validators(iv, selector_list)
343		})
344
345	!	anl_merged_input <- teal.transform::merge_expression_srv(
346	!	datasets = data,
347	!	selector_list = selector_list
348		)
349
350	!	qenv <- reactive(
351	!	teal.code::eval_code(data(), 'library("ggplot2");library("dplyr");library("tern")') # nolint quotes
352		)
353	!	anl_merged_q <- reactive({
354	!	req(anl_merged_input())
355	!	qenv() %>% teal.code::eval_code(as.expression(anl_merged_input()$expr))
356		})
357
358	!	merged <- list(
359	!	anl_input_r = anl_merged_input,
360	!	anl_q_r = anl_merged_q
361		)
362
363	!	output_q <- reactive({
364	!	teal::validate_inputs(iv_r())
365
366	!	ANL <- merged$anl_q_r()[["ANL"]]
367	!	teal::validate_has_data(ANL, 3)
368
369	!	vars_names <- merged$anl_input_r()$columns_source$vars
370
371	!	ref_name <- as.vector(merged$anl_input_r()$columns_source$ref)
372	!	association <- input$association
373	!	show_dist <- input$show_dist
374	!	log_transformation <- input$log_transformation
375	!	rotate_xaxis_labels <- input$rotate_xaxis_labels
376	!	swap_axes <- input$swap_axes
377	!	distribution_theme <- input$distribution_theme
378	!	association_theme <- input$association_theme
379
380	!	is_scatterplot <- is.numeric(ANL[[ref_name]]) && any(vapply(ANL[vars_names], is.numeric, logical(1)))
381	!	if (is_scatterplot) {
382	!	shinyjs::show("alpha")
383	!	shinyjs::show("size")
384	!	alpha <- input$alpha
385	!	size <- input$size
386		} else {
387	!	shinyjs::hide("alpha")
388	!	shinyjs::hide("size")
389	!	alpha <- 0.5
390	!	size <- 2
391		}
392
393	!	teal::validate_has_data(ANL[, c(ref_name, vars_names)], 3, complete = TRUE, allow_inf = FALSE)
394
395		# reference
396	!	ref_class <- class(ANL[[ref_name]])[1]
397	!	if (is.numeric(ANL[[ref_name]]) && log_transformation) {
398		# works for both integers and doubles
399	!	ref_cl_name <- call("log", as.name(ref_name))
400	!	ref_cl_lbl <- varname_w_label(ref_name, ANL, prefix = "Log of ")
401		} else {
402		# silently ignore when non-numeric even if `log` is selected because some
403		# variables may be numeric and others not
404	!	ref_cl_name <- as.name(ref_name)
405	!	ref_cl_lbl <- varname_w_label(ref_name, ANL)
406		}
407
408	!	user_ggplot2_args <- teal.widgets::resolve_ggplot2_args(
409	!	user_plot = ggplot2_args[["Bivariate1"]],
410	!	user_default = ggplot2_args$default
411		)
412
413	!	ref_call <- bivariate_plot_call(
414	!	data_name = "ANL",
415	!	x = ref_cl_name,
416	!	x_class = ref_class,
417	!	x_label = ref_cl_lbl,
418	!	freq = !show_dist,
419	!	theme = distribution_theme,
420	!	rotate_xaxis_labels = rotate_xaxis_labels,
421	!	swap_axes = FALSE,
422	!	size = size,
423	!	alpha = alpha,
424	!	ggplot2_args = user_ggplot2_args
425		)
426
427		# association
428	!	ref_class_cov <- ifelse(association, ref_class, "NULL")
429
430	!	var_calls <- lapply(vars_names, function(var_i) {
431	!	var_class <- class(ANL[[var_i]])[1]
432	!	if (is.numeric(ANL[[var_i]]) && log_transformation) {
433		# works for both integers and doubles
434	!	var_cl_name <- call("log", as.name(var_i))
435	!	var_cl_lbl <- varname_w_label(var_i, ANL, prefix = "Log of ")
436		} else {
437		# silently ignore when non-numeric even if `log` is selected because some
438		# variables may be numeric and others not
439	!	var_cl_name <- as.name(var_i)
440	!	var_cl_lbl <- varname_w_label(var_i, ANL)
441		}
442
443	!	user_ggplot2_args <- teal.widgets::resolve_ggplot2_args(
444	!	user_plot = ggplot2_args[["Bivariate2"]],
445	!	user_default = ggplot2_args$default
446		)
447
448	!	bivariate_plot_call(
449	!	data_name = "ANL",
450	!	x = ref_cl_name,
451	!	y = var_cl_name,
452	!	x_class = ref_class_cov,
453	!	y_class = var_class,
454	!	x_label = ref_cl_lbl,
455	!	y_label = var_cl_lbl,
456	!	theme = association_theme,
457	!	freq = !show_dist,
458	!	rotate_xaxis_labels = rotate_xaxis_labels,
459	!	swap_axes = swap_axes,
460	!	alpha = alpha,
461	!	size = size,
462	!	ggplot2_args = user_ggplot2_args
463		)
464		})
465
466		# helper function to format variable name
467	!	format_varnames <- function(x) {
468	!	if (is.numeric(ANL[[x]]) && log_transformation) {
469	!	varname_w_label(x, ANL, prefix = "Log of ")
470		} else {
471	!	varname_w_label(x, ANL)
472		}
473		}
474	!	new_title <-
475	!	if (association) {
476	!	switch(as.character(length(vars_names)),
477	!	"0" = sprintf("Value distribution for %s", ref_cl_lbl),
478	!	"1" = sprintf(
479	!	"Association between %s and %s",
480	!	ref_cl_lbl,
481	!	format_varnames(vars_names)
482		),
483	!	sprintf(
484	!	"Associations between %s and: %s",
485	!	ref_cl_lbl,
486	!	paste(lapply(vars_names, format_varnames), collapse = ", ")
487		)
488		)
489		} else {
490	!	switch(as.character(length(vars_names)),
491	!	"0" = sprintf("Value distribution for %s", ref_cl_lbl),
492	!	sprintf(
493	!	"Value distributions for %s and %s",
494	!	ref_cl_lbl,
495	!	paste(lapply(vars_names, format_varnames), collapse = ", ")
496		)
497		)
498		}
499	!	teal.code::eval_code(
500	!	merged$anl_q_r(),
501	!	substitute(
502	!	expr = title <- new_title,
503	!	env = list(new_title = new_title)
504		)
505		) %>%
506	!	teal.code::eval_code(
507	!	substitute(
508	!	expr = {
509	!	plots <- plot_calls
510	!	plot_top <- plots[[1]]
511	!	plot_bottom <- plots[[2]]
512	!	plot <- gridExtra::grid.arrange(plot_top, plot_bottom, ncol = 1)
513		},
514	!	env = list(
515	!	plot_calls = do.call(
516	!	"call",
517	!	c(list("list", ref_call), var_calls),
518	!	quote = TRUE
519		)
520		)
521		)
522		)
523		})
524
525	!	decorated_output_grob_q <- srv_decorate_teal_data(
526	!	id = "decorator",
527	!	data = output_q,
528	!	decorators = select_decorators(decorators, "plot"),
529	!	expr = {
530	!	grid::grid.newpage()
531	!	grid::grid.draw(plot)
532		}
533		)
534
535	!	plot_r <- reactive({
536	!	req(iv_r()$is_valid())
537	!	req(decorated_output_grob_q())[["plot"]]
538		})
539
540	!	pws <- teal.widgets::plot_with_settings_srv(
541	!	id = "myplot",
542	!	plot_r = plot_r,
543	!	height = plot_height,
544	!	width = plot_width
545		)
546
547	!	output$title <- renderText(output_q()[["title"]])
548
549		# Render R code.
550	!	source_code_r <- reactive(teal.code::get_code(req(decorated_output_grob_q())))
551
552	!	teal.widgets::verbatim_popup_srv(
553	!	id = "rcode",
554	!	verbatim_content = source_code_r,
555	!	title = "Association Plot"
556		)
557
558		### REPORTER
559	!	if (with_reporter) {
560	!	card_fun <- function(comment, label) {
561	!	card <- teal::report_card_template(
562	!	title = "Association Plot",
563	!	label = label,
564	!	with_filter = with_filter,
565	!	filter_panel_api = filter_panel_api
566		)
567	!	card$append_text("Plot", "header3")
568	!	card$append_plot(plot_r(), dim = pws$dim())
569	!	if (!comment == "") {
570	!	card$append_text("Comment", "header3")
571	!	card$append_text(comment)
572		}
573	!	card$append_src(source_code_r())
574	!	card
575		}
576	!	teal.reporter::add_card_button_srv("add_reporter", reporter = reporter, card_fun = card_fun)
577		}
578		###
579		})
580		}

1		#' Shared parameters documentation
2		#'
3		#' Defines common arguments shared across multiple functions in the package
4		#' to avoid repetition by using `inheritParams`.
5		#'
6		#' @param plot_height (`numeric`) optional, specifies the plot height as a three-element vector of
7		#' `value`, `min`, and `max` intended for use with a slider UI element.
8		#' @param plot_width (`numeric`) optional, specifies the plot width as a three-element vector of
9		#' `value`, `min`, and `max` for a slider encoding the plot width.
10		#' @param rotate_xaxis_labels (`logical`) optional, whether to rotate plot X axis labels. Does not
11		#' rotate by default (`FALSE`).
12		#' @param ggtheme (`character`) optional, `ggplot2` theme to be used by default. Defaults to `"gray"`.
13		#' @param ggplot2_args (`ggplot2_args`) object created by [teal.widgets::ggplot2_args()]
14		#' with settings for the module plot.
15		#' The argument is merged with options variable `teal.ggplot2_args` and default module setup.
16		#'
17		#' For more details see the vignette: `vignette("custom-ggplot2-arguments", package = "teal.widgets")`
18		#' @param basic_table_args (`basic_table_args`) object created by [teal.widgets::basic_table_args()]
19		#' with settings for the module table.
20		#' The argument is merged with options variable `teal.basic_table_args` and default module setup.
21		#'
22		#' For more details see the vignette: `vignette("custom-basic-table-arguments", package = "teal.widgets")`
23		#' @param pre_output (`shiny.tag`) optional, text or UI element to be displayed before the module's output,
24		#' providing context or a title.
25		#' with text placed before the output to put the output into context. For example a title.
26		#' @param post_output (`shiny.tag`) optional, text or UI element to be displayed after the module's output,
27		#' adding context or further instructions. Elements like `shiny::helpText()` are useful.
28		#' @param alpha (`integer(1)` or `integer(3)`) optional, specifies point opacity.
29		#' - When the length of `alpha` is one: the plot points will have a fixed opacity.
30		#' - When the length of `alpha` is three: the plot points opacity are dynamically adjusted based on
31		#' vector of `value`, `min`, and `max`.
32		#' @param size (`integer(1)` or `integer(3)`) optional, specifies point size.
33		#' - When the length of `size` is one: the plot point sizes will have a fixed size.
34		#' - When the length of `size` is three: the plot points size are dynamically adjusted based on
35		#' vector of `value`, `min`, and `max`.
36		#' @param decorators `r lifecycle::badge("experimental")`
37		#' (named `list` of lists of `teal_transform_module`) optional,
38		#' decorator for tables or plots included in the module output reported.
39		#' The decorators are applied to the respective output objects.
40		#'
41		#' See section "Decorating Module" below for more details.
42		#'
43		#' @return Object of class `teal_module` to be used in `teal` applications.
44		#'
45		#' @name shared_params
46		#' @keywords internal
47		NULL
48
49		#' Add labels for facets to a `ggplot2` object
50		#'
51		#' Enhances a `ggplot2` plot by adding labels that describe
52		#' the faceting variables along the x and y axes.
53		#'
54		#' @param p (`ggplot2`) object to which facet labels will be added.
55		#' @param xfacet_label (`character`) Label for the facet along the x-axis.
56		#' If `NULL`, no label is added. If a vector, labels are joined with " & ".
57		#' @param yfacet_label (`character`) Label for the facet along the y-axis.
58		#' Similar behavior to `xfacet_label`.
59		#'
60		#' @return Returns `grid` or `grob` object (to be drawn with `grid.draw`)
61		#'
62		#' @examples
63		#' library(ggplot2)
64		#' library(grid)
65		#'
66		#' p <- ggplot(mtcars) +
67		#' aes(x = mpg, y = disp) +
68		#' geom_point() +
69		#' facet_grid(gear ~ cyl)
70		#'
71		#' xfacet_label <- "cylinders"
72		#' yfacet_label <- "gear"
73		#' res <- add_facet_labels(p, xfacet_label, yfacet_label)
74		#' grid.newpage()
75		#' grid.draw(res)
76		#'
77		#' grid.newpage()
78		#' grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label))
79		#' grid.newpage()
80		#' grid.draw(add_facet_labels(p, xfacet_label, yfacet_label = NULL))
81		#' grid.newpage()
82		#' grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL))
83		#'
84		#' @export
85		#'
86		add_facet_labels <- function(p, xfacet_label = NULL, yfacet_label = NULL) {
87	!	checkmate::assert_class(p, classes = "ggplot")
88	!	checkmate::assert_character(xfacet_label, null.ok = TRUE, min.len = 1)
89	!	checkmate::assert_character(yfacet_label, null.ok = TRUE, min.len = 1)
90	!	if (is.null(xfacet_label) && is.null(yfacet_label)) {
91	!	return(ggplot2::ggplotGrob(p))
92		}
93	!	grid::grid.grabExpr({
94	!	g <- ggplot2::ggplotGrob(p)
95
96		# we are going to replace these, so we make sure they have nothing in them
97	!	checkmate::assert_class(g$grobs[[grep("xlab-t", g$layout$name, fixed = TRUE)]], "zeroGrob")
98	!	checkmate::assert_class(g$grobs[[grep("ylab-r", g$layout$name, fixed = TRUE)]], "zeroGrob")
99
100	!	xaxis_label_grob <- g$grobs[[grep("xlab-b", g$layout$name, fixed = TRUE)]]
101	!	xaxis_label_grob$children[[1]]$label <- paste(xfacet_label, collapse = " & ")
102	!	yaxis_label_grob <- g$grobs[[grep("ylab-l", g$layout$name, fixed = TRUE)]]
103	!	yaxis_label_grob$children[[1]]$label <- paste(yfacet_label, collapse = " & ")
104	!	yaxis_label_grob$children[[1]]$rot <- 270
105
106	!	top_height <- if (is.null(xfacet_label)) 0 else grid::unit(2, "line")
107	!	right_width <- if (is.null(yfacet_label)) 0 else grid::unit(2, "line")
108
109	!	grid::grid.newpage()
110	!	grid::pushViewport(grid::plotViewport(margins = c(0, 0, top_height, right_width), name = "ggplot"))
111	!	grid::grid.draw(g)
112	!	grid::upViewport(1)
113
114		# draw x facet
115	!	if (!is.null(xfacet_label)) {
116	!	grid::pushViewport(grid::viewport(
117	!	x = 0, y = grid::unit(1, "npc") - top_height, width = grid::unit(1, "npc"),
118	!	height = top_height, just = c("left", "bottom"), name = "topxaxis"
119		))
120	!	grid::grid.draw(xaxis_label_grob)
121	!	grid::upViewport(1)
122		}
123
124		# draw y facet
125	!	if (!is.null(yfacet_label)) {
126	!	grid::pushViewport(grid::viewport(
127	!	x = grid::unit(1, "npc") - grid::unit(as.numeric(right_width) / 2, "line"), y = 0, width = right_width,
128	!	height = grid::unit(1, "npc"), just = c("left", "bottom"), name = "rightyaxis"
129		))
130	!	grid::grid.draw(yaxis_label_grob)
131	!	grid::upViewport(1)
132		}
133		})
134		}
135
136		#' Call a function with a character vector for the `...` argument
137		#'
138		#' @param fun (`character`) Name of a function where the `...` argument shall be replaced by values from `str_args`.
139		#' @param str_args (`character`) A character vector that the function shall be executed with
140		#'
141		#' @return
142		#' Value of call to `fun` with arguments specified in `str_args`.
143		#'
144		#' @keywords internal
145		call_fun_dots <- function(fun, str_args) {
146	!	do.call("call", c(list(fun), lapply(str_args, as.name)), quote = TRUE)
147		}
148
149		#' Generate a string for a variable including its label
150		#'
151		#' @param var_names (`character`) Name of variable to extract labels from.
152		#' @param dataset (`dataset`) Name of analysis dataset.
153		#' @param prefix,suffix (`character`) String to paste to the beginning/end of the variable name with label.
154		#' @param wrap_width (`numeric`) Number of characters to wrap original label to. Defaults to 80.
155		#'
156		#' @return (`character`) String with variable name and label.
157		#'
158		#' @keywords internal
159		#'
160		varname_w_label <- function(var_names,
161		dataset,
162		wrap_width = 80,
163		prefix = NULL,
164		suffix = NULL) {
165	!	add_label <- function(var_names) {
166	!	label <- vapply(
167	!	dataset[var_names], function(x) {
168	!	attr_label <- attr(x, "label")
169	!	`if`(is.null(attr_label), "", attr_label)
170		},
171	!	character(1)
172		)
173
174	!	if (length(label) == 1 && !is.na(label) && !identical(label, "")) {
175	!	paste0(prefix, label, " [", var_names, "]", suffix)
176		} else {
177	!	var_names
178		}
179		}
180
181	!	if (length(var_names) < 1) {
182	!	NULL
183	!	} else if (length(var_names) == 1) {
184	!	stringr::str_wrap(add_label(var_names), width = wrap_width)
185	!	} else if (length(var_names) > 1) {
186	!	stringr::str_wrap(vapply(var_names, add_label, character(1)), width = wrap_width)
187		}
188		}
189
190		# see vignette("ggplot2-specs", package="ggplot2")
191		shape_names <- c(
192		"circle", paste("circle", c("open", "filled", "cross", "plus", "small")), "bullet",
193		"square", paste("square", c("open", "filled", "cross", "plus", "triangle")),
194		"diamond", paste("diamond", c("open", "filled", "plus")),
195		"triangle", paste("triangle", c("open", "filled", "square")),
196		paste("triangle down", c("open", "filled")),
197		"plus", "cross", "asterisk"
198		)
199
200		#' Get icons to represent variable types in dataset
201		#'
202		#' @param var_type (`character`) of R internal types (classes).
203		#' @return (`character`) vector of HTML icons corresponding to data type in each column.
204		#' @keywords internal
205		variable_type_icons <- function(var_type) {
206	!	checkmate::assert_character(var_type, any.missing = FALSE)
207
208	!	class_to_icon <- list(
209	!	numeric = "arrow-up-1-9",
210	!	integer = "arrow-up-1-9",
211	!	logical = "pause",
212	!	Date = "calendar",
213	!	POSIXct = "calendar",
214	!	POSIXlt = "calendar",
215	!	factor = "chart-bar",
216	!	character = "keyboard",
217	!	primary_key = "key",
218	!	unknown = "circle-question"
219		)
220	!	class_to_icon <- lapply(class_to_icon, function(icon_name) toString(icon(icon_name, lib = "font-awesome")))
221
222	!	unname(vapply(
223	!	var_type,
224	!	FUN.VALUE = character(1),
225	!	FUN = function(class) {
226	!	if (class == "") {
227	!	class
228	!	} else if (is.null(class_to_icon[[class]])) {
229	!	class_to_icon[["unknown"]]
230		} else {
231	!	class_to_icon[[class]]
232		}
233		}
234		))
235		}
236
237		#'
238		#' @param id (`character(1)`) the id of the tab panel with tabs.
239		#' @param name (`character(1)`) the name of the tab.
240		#' @return JavaScript expression to be used in `shiny::conditionalPanel()` to determine
241		#' if the specified tab is active.
242		#' @keywords internal
243		#'
244		is_tab_active_js <- function(id, name) {
245		# supporting the bs3 and higher version at the same time
246	!	sprintf(
247	!	"$(\"#%1$s > li.active\").text().trim() == '%2$s' \|\| $(\"#%1$s > li a.active\").text().trim() == '%2$s'",
248	!	id, name
249		)
250		}
251
252		#' Assert single selection on `data_extract_spec` object
253		#' Helper to reduce code in assertions
254		#' @noRd
255		#'
256		assert_single_selection <- function(x,
257		.var.name = checkmate::vname(x)) { # nolint: object_name.
258	104x	if (any(vapply(x, function(.x) .x$select$multiple, logical(1)))) {
259	4x	stop("'", .var.name, "' should not allow multiple selection")
260		}
261	100x	invisible(TRUE)
262		}
263
264		#' Wrappers around `srv_transform_teal_data` that allows to decorate the data
265		#' @inheritParams teal::srv_transform_teal_data
266		#' @param expr (`expression` or `reactive`) to evaluate on the output of the decoration.
267		#' When an expression it must be inline code. See [within()]
268		#' Default is `NULL` which won't evaluate any appending code.
269		#' @param expr_is_reactive (`logical(1)`) whether `expr` is a reactive expression
270		#' that skips defusing the argument.
271		#' @details
272		#' `srv_decorate_teal_data` is a wrapper around `srv_transform_teal_data` that
273		#' allows to decorate the data with additional expressions.
274		#' When original `teal_data` object is in error state, it will show that error
275		#' first.
276		#'
277		#' @keywords internal
278		srv_decorate_teal_data <- function(id, data, decorators, expr, expr_is_reactive = FALSE) {
279	!	checkmate::assert_class(data, classes = "reactive")
280	!	checkmate::assert_list(decorators, "teal_transform_module")
281	!	checkmate::assert_flag(expr_is_reactive)
282
283	!	missing_expr <- missing(expr)
284	!	if (!missing_expr && !expr_is_reactive) {
285	!	expr <- dplyr::enexpr(expr) # Using dplyr re-export to avoid adding rlang to Imports
286		}
287
288	!	moduleServer(id, function(input, output, session) {
289	!	decorated_output <- srv_transform_teal_data("inner", data = data, transformators = decorators)
290
291	!	reactive({
292	!	data_out <- try(data(), silent = TRUE)
293	!	if (inherits(data_out, "qenv.error")) {
294	!	data()
295		} else {
296		# ensure original errors are displayed and `eval_code` is never executed with NULL
297	!	req(data(), decorated_output())
298	!	if (missing_expr) {
299	!	decorated_output()
300	!	} else if (expr_is_reactive) {
301	!	teal.code::eval_code(decorated_output(), expr())
302		} else {
303	!	teal.code::eval_code(decorated_output(), expr)
304		}
305		}
306		})
307		})
308		}
309
310		#' @rdname srv_decorate_teal_data
311		#' @details
312		#' `ui_decorate_teal_data` is a wrapper around `ui_transform_teal_data`.
313		#' @keywords internal
314		ui_decorate_teal_data <- function(id, decorators, ...) {
315	!	teal::ui_transform_teal_data(NS(id, "inner"), transformators = decorators, ...)
316		}
317
318		#' Internal function to check if decorators is a valid object
319		#' @noRd
320		check_decorators <- function(x, names = NULL) { # nolint: object_name.
321
322	5x	check_message <- checkmate::check_list(x, names = "named")
323
324	5x	if (!is.null(names)) {
325	5x	if (isTRUE(check_message)) {
326	5x	if (length(names(x)) != length(unique(names(x)))) {
327	!	check_message <- sprintf(
328	!	"The `decorators` must contain unique names from these names: %s.",
329	!	paste(names, collapse = ", ")
330		)
331		}
332		} else {
333	!	check_message <- sprintf(
334	!	"The `decorators` must be a named list from these names: %s.",
335	!	paste(names, collapse = ", ")
336		)
337		}
338		}
339
340	5x	if (!isTRUE(check_message)) {
341	!	return(check_message)
342		}
343
344	5x	valid_elements <- vapply(
345	5x	x,
346	5x	checkmate::test_class,
347	5x	classes = "teal_transform_module",
348	5x	FUN.VALUE = logical(1L)
349		)
350
351	5x	if (all(valid_elements)) {
352	5x	return(TRUE)
353		}
354
355	!	"Make sure that the named list contains 'teal_transform_module' objects created using `teal_transform_module()`."
356		}
357		#' Internal assertion on decorators
358		#' @noRd
359		assert_decorators <- checkmate::makeAssertionFunction(check_decorators)
360
361		#' Subset decorators based on the scope
362		#'
363		#' @param scope (`character`) a character vector of decorator names to include.
364		#' @param decorators (named `list`) of list decorators to subset.
365		#'
366		#' @return Subsetted list with all decorators to include.
367		#' It can be an empty list if none of the scope exists in `decorators` argument.
368		#' @keywords internal
369		select_decorators <- function(decorators, scope) {
370	!	checkmate::assert_character(scope, null.ok = TRUE)
371	!	if (scope %in% names(decorators)) {
372	!	decorators[scope]
373		} else {
374	!	list()
375		}
376		}

1		#' `teal` module: Data table viewer
2		#'
3		#' Module provides a dynamic and interactive way to view `data.frame`s in a `teal` application.
4		#' It uses the `DT` package to display data tables in a paginated, searchable, and sortable format,
5		#' which helps to enhance data exploration and analysis.
6		#'
7		#' The `DT` package has an option `DT.TOJSON_ARGS` to show `Inf` and `NA` in data tables.
8		#' Configure the `DT.TOJSON_ARGS` option via
9		#' `options(DT.TOJSON_ARGS = list(na = "string"))` before running the module.
10		#' Note though that sorting of numeric columns with `NA`/`Inf` will be lexicographic not numerical.
11		#'
12		#' @inheritParams teal::module
13		#' @inheritParams shared_params
14		#' @param variables_selected (`named list`) Character vectors of the variables (i.e. columns)
15		#' which should be initially shown for each dataset.
16		#' Names of list elements should correspond to the names of the datasets available in the app.
17		#' If no entry is specified for a dataset, the first six variables from that
18		#' dataset will initially be shown.
19		#' @param datasets_selected (`character`) `r lifecycle::badge("deprecated")` A vector of datasets which should be
20		#' shown and in what order. Use `datanames` instead.
21		#' @param dt_args (`named list`) Additional arguments to be passed to [DT::datatable()]
22		#' (must not include `data` or `options`).
23		#' @param dt_options (`named list`) The `options` argument to `DT::datatable`. By default
24		#' `list(searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 30, 100), scrollX = TRUE)`
25		#' @param server_rendering (`logical`) should the data table be rendered server side
26		#' (see `server` argument of [DT::renderDataTable()])
27		#'
28		#' @inherit shared_params return
29		#'
30		#' @examplesShinylive
31		#' library(teal.modules.general)
32		#' interactive <- function() TRUE
33		#' {{ next_example }}
34		#' @examples
35		#' # general data example
36		#' data <- teal_data()
37		#' data <- within(data, {
38		#' require(nestcolor)
39		#' iris <- iris
40		#' })
41		#'
42		#' app <- init(
43		#' data = data,
44		#' modules = modules(
45		#' tm_data_table(
46		#' variables_selected = list(
47		#' iris = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species")
48		#' ),
49		#' dt_args = list(caption = "IRIS Table Caption")
50		#' )
51		#' )
52		#' )
53		#' if (interactive()) {
54		#' shinyApp(app$ui, app$server)
55		#' }
56		#'
57		#' @examplesShinylive
58		#' library(teal.modules.general)
59		#' interactive <- function() TRUE
60		#' {{ next_example }}
61		#' @examples
62		#' # CDISC data example
63		#' data <- teal_data()
64		#' data <- within(data, {
65		#' require(nestcolor)
66		#' ADSL <- teal.data::rADSL
67		#' })
68		#' join_keys(data) <- default_cdisc_join_keys[names(data)]
69		#'
70		#' app <- init(
71		#' data = data,
72		#' modules = modules(
73		#' tm_data_table(
74		#' variables_selected = list(ADSL = c("STUDYID", "USUBJID", "SUBJID", "SITEID", "AGE", "SEX")),
75		#' dt_args = list(caption = "ADSL Table Caption")
76		#' )
77		#' )
78		#' )
79		#' if (interactive()) {
80		#' shinyApp(app$ui, app$server)
81		#' }
82		#'
83		#' @export
84		#'
85		tm_data_table <- function(label = "Data Table",
86		variables_selected = list(),
87		datasets_selected = deprecated(),
88		datanames = if (missing(datasets_selected)) "all" else datasets_selected,
89		dt_args = list(),
90		dt_options = list(
91		searching = FALSE,
92		pageLength = 30,
93		lengthMenu = c(5, 15, 30, 100),
94		scrollX = TRUE
95		),
96		server_rendering = FALSE,
97		pre_output = NULL,
98		post_output = NULL,
99		transformators = list()) {
100	!	message("Initializing tm_data_table")
101
102		# Start of assertions
103	!	checkmate::assert_string(label)
104
105	!	checkmate::assert_list(variables_selected, min.len = 0, types = "character", names = "named")
106	!	if (length(variables_selected) > 0) {
107	!	lapply(seq_along(variables_selected), function(i) {
108	!	checkmate::assert_character(variables_selected[[i]], min.chars = 1, min.len = 1)
109	!	if (!is.null(names(variables_selected[[i]]))) {
110	!	checkmate::assert_names(names(variables_selected[[i]]))
111		}
112		})
113		}
114	!	if (!missing(datasets_selected)) {
115	!	lifecycle::deprecate_stop(
116	!	when = "0.4.0",
117	!	what = "tm_data_table(datasets_selected)",
118	!	with = "tm_data_table(datanames)",
119	!	details = 'Use tm_data_table(datanames = "all") to keep the previous behavior and avoid this warning.',
120		)
121		}
122	!	checkmate::assert_character(datanames, min.len = 0, min.chars = 1, null.ok = TRUE)
123	!	checkmate::assert(
124	!	checkmate::check_list(dt_args, len = 0),
125	!	checkmate::check_subset(names(dt_args), choices = names(formals(DT::datatable)))
126		)
127	!	checkmate::assert_list(dt_options, names = "named")
128	!	checkmate::assert_flag(server_rendering)
129	!	checkmate::assert_multi_class(pre_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
130	!	checkmate::assert_multi_class(post_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
131
132		# End of assertions
133
134	!	ans <- module(
135	!	label,
136	!	server = srv_page_data_table,
137	!	ui = ui_page_data_table,
138	!	datanames = datanames,
139	!	server_args = list(
140	!	datanames = if (is.null(datanames)) "all" else datanames,
141	!	variables_selected = variables_selected,
142	!	dt_args = dt_args,
143	!	dt_options = dt_options,
144	!	server_rendering = server_rendering
145		),
146	!	ui_args = list(
147	!	pre_output = pre_output,
148	!	post_output = post_output
149		),
150	!	transformators = transformators
151		)
152	!	attr(ans, "teal_bookmarkable") <- TRUE
153	!	ans
154		}
155
156		# UI page module
157		ui_page_data_table <- function(id, pre_output = NULL, post_output = NULL) {
158	!	ns <- NS(id)
159
160	!	tagList(
161	!	teal.widgets::standard_layout(
162	!	output = teal.widgets::white_small_well(
163	!	bslib::page_fluid(
164	!	checkboxInput(
165	!	ns("if_distinct"),
166	!	"Show only distinct rows:",
167	!	value = FALSE
168		)
169		),
170	!	bslib::page_fluid(
171	!	uiOutput(ns("dataset_table"))
172		)
173		),
174	!	pre_output = pre_output,
175	!	post_output = post_output
176		)
177		)
178		}
179
180		# Server page module
181		srv_page_data_table <- function(id,
182		data,
183		datanames,
184		variables_selected,
185		dt_args,
186		dt_options,
187		server_rendering) {
188	!	checkmate::assert_class(data, "reactive")
189	!	checkmate::assert_class(isolate(data()), "teal_data")
190	!	moduleServer(id, function(input, output, session) {
191	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
192
193	!	if_filtered <- reactive(as.logical(input$if_filtered))
194	!	if_distinct <- reactive(as.logical(input$if_distinct))
195
196	!	datanames <- Filter(function(name) {
197	!	is.data.frame(isolate(data())[[name]])
198	!	}, if (identical(datanames, "all")) names(isolate(data())) else datanames)
199
200
201	!	output$dataset_table <- renderUI({
202	!	do.call(
203	!	tabsetPanel,
204	!	c(
205	!	list(id = session$ns("dataname_tab")),
206	!	lapply(
207	!	datanames,
208	!	function(x) {
209	!	dataset <- isolate(data()[[x]])
210	!	choices <- names(dataset)
211	!	labels <- vapply(
212	!	dataset,
213	!	function(x) ifelse(is.null(attr(x, "label")), "", attr(x, "label")),
214	!	character(1)
215		)
216	!	names(choices) <- ifelse(
217	!	is.na(labels) \| labels == "",
218	!	choices,
219	!	paste(choices, labels, sep = ": ")
220		)
221	!	variables_selected <- if (!is.null(variables_selected[[x]])) {
222	!	variables_selected[[x]]
223		} else {
224	!	utils::head(choices)
225		}
226	!	tabPanel(
227	!	title = x,
228	!	bslib::layout_columns(
229	!	col_widths = 12,
230	!	ui_data_table(
231	!	id = session$ns(x),
232	!	choices = choices,
233	!	selected = variables_selected
234		)
235		)
236		)
237		}
238		)
239		)
240		)
241		})
242
243	!	lapply(
244	!	datanames,
245	!	function(x) {
246	!	srv_data_table(
247	!	id = x,
248	!	data = data,
249	!	dataname = x,
250	!	if_filtered = if_filtered,
251	!	if_distinct = if_distinct,
252	!	dt_args = dt_args,
253	!	dt_options = dt_options,
254	!	server_rendering = server_rendering
255		)
256		}
257		)
258		})
259		}
260
261		# UI function for the data_table module
262		ui_data_table <- function(id, choices, selected) {
263	!	ns <- NS(id)
264
265	!	if (!is.null(selected)) {
266	!	all_choices <- choices
267	!	choices <- c(selected, setdiff(choices, selected))
268	!	names(choices) <- names(all_choices)[match(choices, all_choices)]
269		}
270
271	!	tagList(
272	!	teal.widgets::get_dt_rows(ns("data_table"), ns("dt_rows")),
273	!	bslib::page_fluid(
274	!	teal.widgets::optionalSelectInput(
275	!	ns("variables"),
276	!	"Select variables:",
277	!	choices = choices,
278	!	selected = selected,
279	!	multiple = TRUE,
280	!	width = "100%"
281		)
282		),
283	!	bslib::page_fluid(
284	!	DT::dataTableOutput(ns("data_table"), width = "100%")
285		)
286		)
287		}
288
289		# Server function for the data_table module
290		srv_data_table <- function(id,
291		data,
292		dataname,
293		if_filtered,
294		if_distinct,
295		dt_args,
296		dt_options,
297		server_rendering) {
298	!	moduleServer(id, function(input, output, session) {
299	!	iv <- shinyvalidate::InputValidator$new()
300	!	iv$add_rule("variables", shinyvalidate::sv_required("Please select valid variable names"))
301	!	iv$add_rule("variables", shinyvalidate::sv_in_set(
302	!	set = names(isolate(data())[[dataname]]), message_fmt = "Not all selected variables exist in the data"
303		))
304	!	iv$enable()
305
306	!	data_table_data <- reactive({
307	!	df <- data()[[dataname]]
308
309	!	teal::validate_has_data(df, min_nrow = 1L, msg = paste("data", dataname, "is empty"))
310	!	qenv <- teal.code::eval_code(
311	!	data(),
312	!	'library("dplyr");library("DT")' # nolint quotes
313		)
314	!	teal.code::eval_code(
315	!	qenv,
316	!	substitute(
317	!	expr = {
318	!	variables <- vars
319	!	dataframe_selected <- if (if_distinct) {
320	!	dplyr::count(dataname, dplyr::across(dplyr::all_of(variables)))
321		} else {
322	!	dataname[variables]
323		}
324	!	dt_args <- args
325	!	dt_args$options <- dt_options
326	!	if (!is.null(dt_rows)) {
327	!	dt_args$options$pageLength <- dt_rows
328		}
329	!	dt_args$data <- dataframe_selected
330	!	table <- do.call(DT::datatable, dt_args)
331		},
332	!	env = list(
333	!	dataname = as.name(dataname),
334	!	if_distinct = if_distinct(),
335	!	vars = input$variables,
336	!	args = dt_args,
337	!	dt_options = dt_options,
338	!	dt_rows = input$dt_rows
339		)
340		)
341		)
342		})
343
344	!	output$data_table <- DT::renderDataTable(server = server_rendering, {
345	!	teal::validate_inputs(iv)
346	!	req(data_table_data())[["table"]]
347		})
348		})
349		}

1		#' `teal` module: Response plot
2		#'
3		#' Generates a response plot for a given `response` and `x` variables.
4		#' This module allows users customize and add annotations to the plot depending
5		#' on the module's arguments.
6		#' It supports showing the counts grouped by other variable facets (by row / column),
7		#' swapping the coordinates, show count annotations and displaying the response plot
8		#' as frequency or density.
9		#'
10		#' @inheritParams teal::module
11		#' @inheritParams shared_params
12		#' @param response (`data_extract_spec` or `list` of multiple `data_extract_spec`)
13		#' Which variable to use as the response.
14		#' You can define one fixed column by setting `fixed = TRUE` inside the `select_spec`.
15		#'
16		#' The `data_extract_spec` must not allow multiple selection in this case.
17		#' @param x (`data_extract_spec` or `list` of multiple `data_extract_spec`)
18		#' Specifies which variable to use on the X-axis of the response plot.
19		#' Allow the user to select multiple columns from the `data` allowed in teal.
20		#'
21		#' The `data_extract_spec` must not allow multiple selection in this case.
22		#' @param row_facet (`data_extract_spec` or `list` of multiple `data_extract_spec`)
23		#' optional specification of the data variable(s) to use for faceting rows.
24		#' @param col_facet (`data_extract_spec` or `list` of multiple `data_extract_spec`)
25		#' optional specification of the data variable(s) to use for faceting columns.
26		#' @param coord_flip (`logical(1)`)
27		#' Indicates whether to flip coordinates between `x` and `response`.
28		#' The default value is `FALSE` and it will show the `x` variable on the x-axis
29		#' and the `response` variable on the y-axis.
30		#' @param count_labels (`logical(1)`)
31		#' Indicates whether to show count labels.
32		#' Defaults to `TRUE`.
33		#' @param freq (`logical(1)`)
34		#' Indicates whether to display frequency (`TRUE`) or density (`FALSE`).
35		#' Defaults to density (`FALSE`).
36		#'
37		#' @inherit shared_params return
38		#'
39		#' @note For more examples, please see the vignette "Using response plot" via
40		#' `vignette("using-response-plot", package = "teal.modules.general")`.
41		#'
42		#' @section Decorating Module:
43		#'
44		#' This module generates the following objects, which can be modified in place using decorators:
45		#' - `plot` (`ggplot`)
46		#'
47		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
48		#' The name of this list corresponds to the name of the output to which the decorator is applied.
49		#' See code snippet below:
50		#'
51		#' ```
52		#' tm_g_response(
53		#' ..., # arguments for module
54		#' decorators = list(
55		#' plot = teal_transform_module(...) # applied to the `plot` output
56		#' )
57		#' )
58		#' ```
59		#'
60		#' For additional details and examples of decorators, refer to the vignette
61		#' `vignette("decorate-module-output", package = "teal.modules.general")`.
62		#'
63		#' To learn more please refer to the vignette
64		#' `vignette("transform-module-output", package = "teal")` or the [`teal::teal_transform_module()`] documentation.
65		#'
66		#' @examplesShinylive
67		#' library(teal.modules.general)
68		#' interactive <- function() TRUE
69		#' {{ next_example }}
70		#' @examples
71		#' # general data example
72		#' data <- teal_data()
73		#' data <- within(data, {
74		#' require(nestcolor)
75		#' mtcars <- mtcars
76		#' for (v in c("cyl", "vs", "am", "gear")) {
77		#' mtcars[[v]] <- as.factor(mtcars[[v]])
78		#' }
79		#' })
80		#'
81		#' app <- init(
82		#' data = data,
83		#' modules = modules(
84		#' tm_g_response(
85		#' label = "Response Plots",
86		#' response = data_extract_spec(
87		#' dataname = "mtcars",
88		#' select = select_spec(
89		#' label = "Select variable:",
90		#' choices = variable_choices(data[["mtcars"]], c("cyl", "gear")),
91		#' selected = "cyl",
92		#' multiple = FALSE,
93		#' fixed = FALSE
94		#' )
95		#' ),
96		#' x = data_extract_spec(
97		#' dataname = "mtcars",
98		#' select = select_spec(
99		#' label = "Select variable:",
100		#' choices = variable_choices(data[["mtcars"]], c("vs", "am")),
101		#' selected = "vs",
102		#' multiple = FALSE,
103		#' fixed = FALSE
104		#' )
105		#' )
106		#' )
107		#' )
108		#' )
109		#' if (interactive()) {
110		#' shinyApp(app$ui, app$server)
111		#' }
112		#'
113		#' @examplesShinylive
114		#' library(teal.modules.general)
115		#' interactive <- function() TRUE
116		#' {{ next_example }}
117		#' @examples
118		#' # CDISC data example
119		#' data <- teal_data()
120		#' data <- within(data, {
121		#' require(nestcolor)
122		#' ADSL <- teal.data::rADSL
123		#' })
124		#' join_keys(data) <- default_cdisc_join_keys[names(data)]
125		#'
126		#' app <- init(
127		#' data = data,
128		#' modules = modules(
129		#' tm_g_response(
130		#' label = "Response Plots",
131		#' response = data_extract_spec(
132		#' dataname = "ADSL",
133		#' select = select_spec(
134		#' label = "Select variable:",
135		#' choices = variable_choices(data[["ADSL"]], c("BMRKR2", "COUNTRY")),
136		#' selected = "BMRKR2",
137		#' multiple = FALSE,
138		#' fixed = FALSE
139		#' )
140		#' ),
141		#' x = data_extract_spec(
142		#' dataname = "ADSL",
143		#' select = select_spec(
144		#' label = "Select variable:",
145		#' choices = variable_choices(data[["ADSL"]], c("SEX", "RACE")),
146		#' selected = "RACE",
147		#' multiple = FALSE,
148		#' fixed = FALSE
149		#' )
150		#' )
151		#' )
152		#' )
153		#' )
154		#' if (interactive()) {
155		#' shinyApp(app$ui, app$server)
156		#' }
157		#'
158		#' @export
159		#'
160		tm_g_response <- function(label = "Response Plot",
161		response,
162		x,
163		row_facet = NULL,
164		col_facet = NULL,
165		coord_flip = FALSE,
166		count_labels = TRUE,
167		rotate_xaxis_labels = FALSE,
168		freq = FALSE,
169		plot_height = c(600, 400, 5000),
170		plot_width = NULL,
171		ggtheme = c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void"),
172		ggplot2_args = teal.widgets::ggplot2_args(),
173		pre_output = NULL,
174		post_output = NULL,
175		transformators = list(),
176		decorators = list()) {
177	!	message("Initializing tm_g_response")
178
179		# Normalize the parameters
180	!	if (inherits(response, "data_extract_spec")) response <- list(response)
181	!	if (inherits(x, "data_extract_spec")) x <- list(x)
182	!	if (inherits(row_facet, "data_extract_spec")) row_facet <- list(row_facet)
183	!	if (inherits(col_facet, "data_extract_spec")) col_facet <- list(col_facet)
184
185		# Start of assertions
186	!	checkmate::assert_string(label)
187
188	!	checkmate::assert_list(response, types = "data_extract_spec")
189	!	if (!all(vapply(response, function(x) !("" %in% x$select$choices), logical(1)))) {
190	!	stop("'response' should not allow empty values")
191		}
192	!	assert_single_selection(response)
193
194	!	checkmate::assert_list(x, types = "data_extract_spec")
195	!	if (!all(vapply(x, function(x) !("" %in% x$select$choices), logical(1)))) {
196	!	stop("'x' should not allow empty values")
197		}
198	!	assert_single_selection(x)
199
200	!	checkmate::assert_list(row_facet, types = "data_extract_spec", null.ok = TRUE)
201	!	checkmate::assert_list(col_facet, types = "data_extract_spec", null.ok = TRUE)
202	!	checkmate::assert_flag(coord_flip)
203	!	checkmate::assert_flag(count_labels)
204	!	checkmate::assert_flag(rotate_xaxis_labels)
205	!	checkmate::assert_flag(freq)
206
207	!	checkmate::assert_numeric(plot_height, len = 3, any.missing = FALSE, finite = TRUE)
208	!	checkmate::assert_numeric(plot_height[1], lower = plot_height[2], upper = plot_height[3], .var.name = "plot_height")
209	!	checkmate::assert_numeric(plot_width, len = 3, any.missing = FALSE, null.ok = TRUE, finite = TRUE)
210	!	checkmate::assert_numeric(
211	!	plot_width[1],
212	!	lower = plot_width[2], upper = plot_width[3], null.ok = TRUE, .var.name = "plot_width"
213		)
214
215	!	ggtheme <- match.arg(ggtheme)
216	!	checkmate::assert_class(ggplot2_args, "ggplot2_args")
217
218	!	checkmate::assert_multi_class(pre_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
219	!	checkmate::assert_multi_class(post_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
220
221	!	assert_decorators(decorators, "plot")
222		# End of assertions
223
224		# Make UI args
225	!	args <- as.list(environment())
226
227	!	data_extract_list <- list(
228	!	response = response,
229	!	x = x,
230	!	row_facet = row_facet,
231	!	col_facet = col_facet
232		)
233
234	!	ans <- module(
235	!	label = label,
236	!	server = srv_g_response,
237	!	ui = ui_g_response,
238	!	ui_args = args,
239	!	server_args = c(
240	!	data_extract_list,
241	!	list(
242	!	plot_height = plot_height,
243	!	plot_width = plot_width,
244	!	ggplot2_args = ggplot2_args,
245	!	decorators = decorators
246		)
247		),
248	!	transformators = transformators,
249	!	datanames = teal.transform::get_extract_datanames(data_extract_list)
250		)
251	!	attr(ans, "teal_bookmarkable") <- TRUE
252	!	ans
253		}
254
255		# UI function for the response module
256		ui_g_response <- function(id, ...) {
257	!	ns <- NS(id)
258	!	args <- list(...)
259	!	is_single_dataset_value <- teal.transform::is_single_dataset(args$response, args$x, args$row_facet, args$col_facet)
260
261	!	teal.widgets::standard_layout(
262	!	output = teal.widgets::white_small_well(
263	!	teal.widgets::plot_with_settings_ui(id = ns("myplot"))
264		),
265	!	encoding = tags$div(
266		### Reporter
267	!	teal.reporter::add_card_button_ui(ns("add_reporter"), label = "Add Report Card"),
268	!	tags$br(), tags$br(),
269		###
270	!	tags$label("Encodings", class = "text-primary"),
271	!	teal.transform::datanames_input(args[c("response", "x", "row_facet", "col_facet")]),
272	!	teal.transform::data_extract_ui(
273	!	id = ns("response"),
274	!	label = "Response variable",
275	!	data_extract_spec = args$response,
276	!	is_single_dataset = is_single_dataset_value
277		),
278	!	teal.transform::data_extract_ui(
279	!	id = ns("x"),
280	!	label = "X variable",
281	!	data_extract_spec = args$x,
282	!	is_single_dataset = is_single_dataset_value
283		),
284	!	if (!is.null(args$row_facet)) {
285	!	teal.transform::data_extract_ui(
286	!	id = ns("row_facet"),
287	!	label = "Row facetting",
288	!	data_extract_spec = args$row_facet,
289	!	is_single_dataset = is_single_dataset_value
290		)
291		},
292	!	if (!is.null(args$col_facet)) {
293	!	teal.transform::data_extract_ui(
294	!	id = ns("col_facet"),
295	!	label = "Column facetting",
296	!	data_extract_spec = args$col_facet,
297	!	is_single_dataset = is_single_dataset_value
298		)
299		},
300	!	shinyWidgets::radioGroupButtons(
301	!	inputId = ns("freq"),
302	!	label = NULL,
303	!	choices = c("frequency", "density"),
304	!	selected = ifelse(args$freq, "frequency", "density"),
305	!	justified = TRUE
306		),
307	!	ui_decorate_teal_data(ns("decorator"), decorators = select_decorators(args$decorators, "plot")),
308	!	bslib::accordion(
309	!	open = TRUE,
310	!	bslib::accordion_panel(
311	!	title = "Plot settings",
312	!	checkboxInput(ns("count_labels"), "Add count labels", value = args$count_labels),
313	!	checkboxInput(ns("coord_flip"), "Swap axes", value = args$coord_flip),
314	!	checkboxInput(ns("rotate_xaxis_labels"), "Rotate X axis labels", value = args$rotate_xaxis_labels),
315	!	selectInput(
316	!	inputId = ns("ggtheme"),
317	!	label = "Theme (by ggplot):",
318	!	choices = ggplot_themes,
319	!	selected = args$ggtheme,
320	!	multiple = FALSE
321		)
322		)
323		)
324		),
325	!	forms = tagList(
326	!	teal.widgets::verbatim_popup_ui(ns("rcode"), "Show R code")
327		),
328	!	pre_output = args$pre_output,
329	!	post_output = args$post_output
330		)
331		}
332
333		# Server function for the response module
334		srv_g_response <- function(id,
335		data,
336		reporter,
337		filter_panel_api,
338		response,
339		x,
340		row_facet,
341		col_facet,
342		plot_height,
343		plot_width,
344		ggplot2_args,
345		decorators) {
346	!	with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
347	!	with_filter <- !missing(filter_panel_api) && inherits(filter_panel_api, "FilterPanelAPI")
348	!	checkmate::assert_class(data, "reactive")
349	!	checkmate::assert_class(isolate(data()), "teal_data")
350	!	moduleServer(id, function(input, output, session) {
351	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
352
353	!	data_extract <- list(response = response, x = x, row_facet = row_facet, col_facet = col_facet)
354
355	!	rule_diff <- function(other) {
356	!	function(value) {
357	!	if (other %in% names(selector_list())) {
358	!	othervalue <- selector_list()[[other]]()[["select"]]
359	!	if (!is.null(othervalue)) {
360	!	if (identical(value, othervalue)) {
361	!	"Row and column facetting variables must be different."
362		}
363		}
364		}
365		}
366		}
367
368	!	selector_list <- teal.transform::data_extract_multiple_srv(
369	!	data_extract = data_extract,
370	!	datasets = data,
371	!	select_validation_rule = list(
372	!	response = shinyvalidate::sv_required("Please define a column for the response variable"),
373	!	x = shinyvalidate::sv_required("Please define a column for X variable"),
374	!	row_facet = shinyvalidate::compose_rules(
375	!	shinyvalidate::sv_optional(),
376	!	~ if (length(.) > 1) "There must be 1 or no row facetting variable.",
377	!	rule_diff("col_facet")
378		),
379	!	col_facet = shinyvalidate::compose_rules(
380	!	shinyvalidate::sv_optional(),
381	!	~ if (length(.) > 1) "There must be 1 or no column facetting variable.",
382	!	rule_diff("row_facet")
383		)
384		)
385		)
386
387	!	iv_r <- reactive({
388	!	iv <- shinyvalidate::InputValidator$new()
389	!	iv$add_rule("ggtheme", shinyvalidate::sv_required("Please select a theme"))
390	!	teal.transform::compose_and_enable_validators(iv, selector_list)
391		})
392
393	!	anl_merged_input <- teal.transform::merge_expression_srv(
394	!	selector_list = selector_list,
395	!	datasets = data
396		)
397
398	!	qenv <- reactive(
399	!	teal.code::eval_code(data(), 'library("ggplot2");library("dplyr")') # nolint quotes
400		)
401
402	!	anl_merged_q <- reactive({
403	!	req(anl_merged_input())
404	!	qenv() %>%
405	!	teal.code::eval_code(as.expression(anl_merged_input()$expr))
406		})
407
408	!	merged <- list(
409	!	anl_input_r = anl_merged_input,
410	!	anl_q_r = anl_merged_q
411		)
412
413	!	output_q <- reactive({
414	!	teal::validate_inputs(iv_r())
415
416	!	qenv <- merged$anl_q_r()
417	!	ANL <- qenv[["ANL"]]
418	!	resp_var <- as.vector(merged$anl_input_r()$columns_source$response)
419	!	x <- as.vector(merged$anl_input_r()$columns_source$x)
420
421	!	validate(need(is.factor(ANL[[resp_var]]), "Please select a factor variable as the response."))
422	!	validate(need(is.factor(ANL[[x]]), "Please select a factor variable as the X-Variable."))
423	!	teal::validate_has_data(ANL, 10)
424	!	teal::validate_has_data(ANL[, c(resp_var, x)], 10, complete = TRUE, allow_inf = FALSE)
425
426	!	row_facet_name <- if (length(merged$anl_input_r()$columns_source$row_facet) == 0) {
427	!	character(0)
428		} else {
429	!	as.vector(merged$anl_input_r()$columns_source$row_facet)
430		}
431	!	col_facet_name <- if (length(merged$anl_input_r()$columns_source$col_facet) == 0) {
432	!	character(0)
433		} else {
434	!	as.vector(merged$anl_input_r()$columns_source$col_facet)
435		}
436
437	!	freq <- input$freq == "frequency"
438	!	swap_axes <- input$coord_flip
439	!	counts <- input$count_labels
440	!	rotate_xaxis_labels <- input$rotate_xaxis_labels
441	!	ggtheme <- input$ggtheme
442
443	!	arg_position <- if (freq) "stack" else "fill"
444
445	!	rowf <- if (length(row_facet_name) != 0) as.name(row_facet_name)
446	!	colf <- if (length(col_facet_name) != 0) as.name(col_facet_name)
447	!	resp_cl <- as.name(resp_var)
448	!	x_cl <- as.name(x)
449
450	!	if (swap_axes) {
451	!	qenv <- teal.code::eval_code(
452	!	qenv,
453	!	substitute(
454	!	expr = ANL[[x]] <- with(ANL, forcats::fct_rev(x_cl)),
455	!	env = list(x = x, x_cl = x_cl)
456		)
457		)
458		}
459
460	!	qenv <- teal.code::eval_code(
461	!	qenv,
462	!	substitute(
463	!	expr = ANL[[resp_var]] <- factor(ANL[[resp_var]]),
464	!	env = list(resp_var = resp_var)
465		)
466		) %>%
467		# rowf and colf will be a NULL if not set by a user
468	!	teal.code::eval_code(
469	!	substitute(
470	!	expr = ANL2 <- ANL %>%
471	!	dplyr::group_by_at(dplyr::vars(x_cl, resp_cl, rowf, colf)) %>%
472	!	dplyr::summarise(ns = dplyr::n()) %>%
473	!	dplyr::group_by_at(dplyr::vars(x_cl, rowf, colf)) %>%
474	!	dplyr::mutate(sums = sum(ns), percent = round(ns / sums * 100, 1)),
475	!	env = list(x_cl = x_cl, resp_cl = resp_cl, rowf = rowf, colf = colf)
476		)
477		) %>%
478	!	teal.code::eval_code(
479	!	substitute(
480	!	expr = ANL3 <- ANL %>%
481	!	dplyr::group_by_at(dplyr::vars(x_cl, rowf, colf)) %>%
482	!	dplyr::summarise(ns = dplyr::n()),
483	!	env = list(x_cl = x_cl, rowf = rowf, colf = colf)
484		)
485		)
486
487	!	plot_call <- substitute(
488	!	expr = ggplot2::ggplot(ANL2, ggplot2::aes(x = x_cl, y = ns)) +
489	!	ggplot2::geom_bar(ggplot2::aes(fill = resp_cl), stat = "identity", position = arg_position),
490	!	env = list(
491	!	x_cl = x_cl,
492	!	resp_cl = resp_cl,
493	!	arg_position = arg_position
494		)
495		)
496
497	!	if (!freq) {
498	!	plot_call <- substitute(
499	!	plot_call + ggplot2::expand_limits(y = c(0, 1.1)),
500	!	env = list(plot_call = plot_call)
501		)
502		}
503
504	!	if (counts) {
505	!	plot_call <- substitute(
506	!	expr = plot_call +
507	!	ggplot2::geom_text(
508	!	data = ANL2,
509	!	ggplot2::aes(label = ns, x = x_cl, y = ns, group = resp_cl),
510	!	col = "white",
511	!	vjust = "middle",
512	!	hjust = "middle",
513	!	position = position_anl2_value
514		) +
515	!	ggplot2::geom_text(
516	!	data = ANL3, ggplot2::aes(label = ns, x = x_cl, y = anl3_y),
517	!	hjust = hjust_value,
518	!	vjust = vjust_value,
519	!	position = position_anl3_value
520		),
521	!	env = list(
522	!	plot_call = plot_call,
523	!	x_cl = x_cl,
524	!	resp_cl = resp_cl,
525	!	hjust_value = if (swap_axes) "left" else "middle",
526	!	vjust_value = if (swap_axes) "middle" else -1,
527	!	position_anl2_value = if (!freq) quote(position_fill(0.5)) else quote(position_stack(0.5)), # nolint: line_length.
528	!	anl3_y = if (!freq) 1.1 else as.name("ns"),
529	!	position_anl3_value = if (!freq) "fill" else "stack"
530		)
531		)
532		}
533
534	!	if (swap_axes) {
535	!	plot_call <- substitute(plot_call + coord_flip(), env = list(plot_call = plot_call))
536		}
537
538	!	facet_cl <- facet_ggplot_call(row_facet_name, col_facet_name)
539
540	!	if (!is.null(facet_cl)) {
541	!	plot_call <- substitute(expr = plot_call + facet_cl, env = list(plot_call = plot_call, facet_cl = facet_cl))
542		}
543
544	!	dev_ggplot2_args <- teal.widgets::ggplot2_args(
545	!	labs = list(
546	!	x = varname_w_label(x, ANL),
547	!	y = varname_w_label(resp_var, ANL, prefix = "Proportion of "),
548	!	fill = varname_w_label(resp_var, ANL)
549		),
550	!	theme = list(legend.position = "bottom")
551		)
552
553	!	if (rotate_xaxis_labels) {
554	!	dev_ggplot2_args$theme[["axis.text.x"]] <- quote(ggplot2::element_text(angle = 45, hjust = 1))
555		}
556
557	!	all_ggplot2_args <- teal.widgets::resolve_ggplot2_args(
558	!	user_plot = ggplot2_args,
559	!	module_plot = dev_ggplot2_args
560		)
561
562	!	parsed_ggplot2_args <- teal.widgets::parse_ggplot2_args(
563	!	all_ggplot2_args,
564	!	ggtheme = ggtheme
565		)
566
567	!	plot_call <- substitute(expr = {
568	!	plot <- plot_call + labs + ggthemes + themes
569	!	}, env = list(
570	!	plot_call = plot_call,
571	!	labs = parsed_ggplot2_args$labs,
572	!	themes = parsed_ggplot2_args$theme,
573	!	ggthemes = parsed_ggplot2_args$ggtheme
574		))
575
576	!	teal.code::eval_code(qenv, plot_call)
577		})
578
579	!	decorated_output_plot_q <- srv_decorate_teal_data(
580	!	id = "decorator",
581	!	data = output_q,
582	!	decorators = select_decorators(decorators, "plot"),
583	!	expr = plot
584		)
585
586	!	plot_r <- reactive(req(decorated_output_plot_q())[["plot"]])
587
588		# Insert the plot into a plot_with_settings module from teal.widgets
589	!	pws <- teal.widgets::plot_with_settings_srv(
590	!	id = "myplot",
591	!	plot_r = plot_r,
592	!	height = plot_height,
593	!	width = plot_width
594		)
595
596		# Render R code.
597	!	source_code_r <- reactive(teal.code::get_code(req(decorated_output_plot_q())))
598
599	!	teal.widgets::verbatim_popup_srv(
600	!	id = "rcode",
601	!	verbatim_content = source_code_r,
602	!	title = "Show R Code for Response"
603		)
604
605		### REPORTER
606	!	if (with_reporter) {
607	!	card_fun <- function(comment, label) {
608	!	card <- teal::report_card_template(
609	!	title = "Response Plot",
610	!	label = label,
611	!	with_filter = with_filter,
612	!	filter_panel_api = filter_panel_api
613		)
614	!	card$append_text("Plot", "header3")
615	!	card$append_plot(plot_r(), dim = pws$dim())
616	!	if (!comment == "") {
617	!	card$append_text("Comment", "header3")
618	!	card$append_text(comment)
619		}
620	!	card$append_src(source_code_r())
621	!	card
622		}
623	!	teal.reporter::add_card_button_srv("add_reporter", reporter = reporter, card_fun = card_fun)
624		}
625		###
626		})
627		}

1		#' `teal` module: File viewer
2		#'
3		#' The file viewer module provides a tool to view static files.
4		#' Supported formats include text formats, `PDF`, `PNG` `APNG`,
5		#' `JPEG` `SVG`, `WEBP`, `GIF` and `BMP`.
6		#'
7		#' @inheritParams teal::module
8		#' @inheritParams shared_params
9		#' @param input_path (`list`) of the input paths, optional. Each element can be:
10		#'
11		#' Paths can be specified as absolute paths or relative to the running directory of the application.
12		#' Default to the current working directory if not supplied.
13		#'
14		#' @inherit shared_params return
15		#'
16		#' @examplesShinylive
17		#' library(teal.modules.general)
18		#' interactive <- function() TRUE
19		#' {{ next_example }}
20		#' @examples
21		#' data <- teal_data()
22		#' data <- within(data, {
23		#' data <- data.frame(1)
24		#' })
25		#'
26		#' app <- init(
27		#' data = data,
28		#' modules = modules(
29		#' tm_file_viewer(
30		#' input_path = list(
31		#' folder = system.file("sample_files", package = "teal.modules.general"),
32		#' png = system.file("sample_files/sample_file.png", package = "teal.modules.general"),
33		#' txt = system.file("sample_files/sample_file.txt", package = "teal.modules.general"),
34		#' url = "https://fda.gov/files/drugs/published/Portable-Document-Format-Specifications.pdf"
35		#' )
36		#' )
37		#' )
38		#' )
39		#' if (interactive()) {
40		#' shinyApp(app$ui, app$server)
41		#' }
42		#'
43		#' @export
44		#'
45		tm_file_viewer <- function(label = "File Viewer Module",
46		input_path = list("Current Working Directory" = ".")) {
47	!	message("Initializing tm_file_viewer")
48
49		# Normalize the parameters
50	!	if (length(label) == 0 \|\| identical(label, "")) label <- " "
51	!	if (length(input_path) == 0 \|\| identical(input_path, "")) input_path <- list()
52
53		# Start of assertions
54	!	checkmate::assert_string(label)
55
56	!	checkmate::assert(
57	!	checkmate::check_list(input_path, types = "character", min.len = 0),
58	!	checkmate::check_character(input_path, min.len = 1)
59		)
60	!	if (length(input_path) > 0) {
61	!	valid_url <- function(url_input, timeout = 2) {
62	!	con <- try(url(url_input), silent = TRUE)
63	!	check <- suppressWarnings(try(open.connection(con, open = "rt", timeout = timeout), silent = TRUE)[1])
64	!	try(close.connection(con), silent = TRUE)
65	!	is.null(check)
66		}
67	!	idx <- vapply(input_path, function(x) file.exists(x) \|\| valid_url(x), logical(1))
68
69	!	if (!all(idx)) {
70	!	warning(
71	!	paste0(
72	!	"Non-existent file or url path. Please provide valid paths for:\n",
73	!	paste0(input_path[!idx], collapse = "\n")
74		)
75		)
76		}
77	!	input_path <- input_path[idx]
78		} else {
79	!	warning(
80	!	"No file or url paths were provided."
81		)
82		}
83		# End of assertions
84
85		# Make UI args
86	!	args <- as.list(environment())
87
88	!	ans <- module(
89	!	label = label,
90	!	server = srv_viewer,
91	!	server_args = list(input_path = input_path),
92	!	ui = ui_viewer,
93	!	ui_args = args,
94	!	datanames = NULL
95		)
96	!	attr(ans, "teal_bookmarkable") <- FALSE
97	!	ans
98		}
99
100		# UI function for the file viewer module
101		ui_viewer <- function(id, ...) {
102	!	args <- list(...)
103	!	ns <- NS(id)
104
105	!	tagList(
106	!	teal.widgets::standard_layout(
107	!	output = tags$div(
108	!	uiOutput(ns("output"))
109		),
110	!	encoding = tags$div(
111	!	style = "overflow-y: hidden; overflow-x: auto;",
112	!	tags$label("Encodings", class = "text-primary"),
113	!	shinyTree::shinyTree(
114	!	ns("tree"),
115	!	dragAndDrop = FALSE,
116	!	sort = FALSE,
117	!	theme = "proton",
118	!	multiple = FALSE,
119	!	search = TRUE
120		)
121		)
122		)
123		)
124		}
125
126		# Server function for the file viewer module
127		srv_viewer <- function(id, input_path) {
128	!	moduleServer(id, function(input, output, session) {
129	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
130
131	!	temp_dir <- tempfile()
132	!	if (!dir.exists(temp_dir)) {
133	!	dir.create(temp_dir, recursive = TRUE)
134		}
135	!	addResourcePath(basename(temp_dir), temp_dir)
136
137	!	test_path_text <- function(selected_path, type) {
138	!	out <- tryCatch(
139	!	expr = {
140	!	if (type != "url") {
141	!	selected_path <- normalizePath(selected_path, winslash = "/")
142		}
143	!	readLines(con = selected_path)
144		},
145	!	error = function(cond) FALSE,
146	!	warning = function(cond) {
147	!	`if`(grepl("^incomplete final line found on", cond[[1]]), suppressWarnings(eval(cond[[2]])), FALSE)
148		}
149		)
150		}
151
152	!	handle_connection_type <- function(selected_path) {
153	!	file_extension <- tools::file_ext(selected_path)
154	!	file_class <- suppressWarnings(file(selected_path))
155	!	close(file_class)
156
157	!	output_text <- test_path_text(selected_path, type = class(file_class)[1])
158
159	!	if (class(file_class)[1] == "url") {
160	!	list(selected_path = selected_path, output_text = output_text)
161		} else {
162	!	file.copy(normalizePath(selected_path, winslash = "/"), temp_dir)
163	!	selected_path <- file.path(basename(temp_dir), basename(selected_path))
164	!	list(selected_path = selected_path, output_text = output_text)
165		}
166		}
167
168	!	display_file <- function(selected_path) {
169	!	con_type <- handle_connection_type(selected_path)
170	!	file_extension <- tools::file_ext(selected_path)
171	!	if (file_extension %in% c("png", "apng", "jpg", "jpeg", "svg", "gif", "webp", "bmp")) {
172	!	tags$img(src = con_type$selected_path, alt = "file does not exist")
173	!	} else if (file_extension == "pdf") {
174	!	tags$embed(
175	!	style = "height: 600px; width: 100%;",
176	!	src = con_type$selected_path
177		)
178	!	} else if (!isFALSE(con_type$output_text[1])) {
179	!	tags$pre(paste0(con_type$output_text, collapse = "\n"))
180		} else {
181	!	tags$p("Please select a supported format.")
182		}
183		}
184
185	!	tree_list <- function(file_or_dir) {
186	!	nested_list <- lapply(file_or_dir, function(path) {
187	!	file_class <- suppressWarnings(file(path))
188	!	close(file_class)
189	!	if (class(file_class)[[1]] != "url") {
190	!	isdir <- file.info(path)$isdir
191	!	if (!isdir) {
192	!	structure(path, ancestry = path, sticon = "file")
193		} else {
194	!	files <- list.files(path, full.names = TRUE, include.dirs = TRUE)
195	!	out <- lapply(files, function(x) tree_list(x))
196	!	out <- unlist(out, recursive = FALSE)
197	!	if (length(files) > 0) names(out) <- basename(files)
198	!	out
199		}
200		} else {
201	!	structure(path, ancestry = path, sticon = "file")
202		}
203		})
204
205	!	missing_labels <- if (is.null(names(nested_list))) seq_along(nested_list) else which(names(nested_list) == "")
206	!	names(nested_list)[missing_labels] <- file_or_dir[missing_labels]
207	!	nested_list
208		}
209
210	!	output$tree <- shinyTree::renderTree({
211	!	if (length(input_path) > 0) {
212	!	tree_list(input_path)
213		} else {
214	!	list("Empty Path" = NULL)
215		}
216		})
217
218	!	output$output <- renderUI({
219	!	validate(
220	!	need(
221	!	length(shinyTree::get_selected(input$tree)) > 0,
222	!	"Please select a file."
223		)
224		)
225
226	!	obj <- shinyTree::get_selected(input$tree, format = "names")[[1]]
227	!	repo <- attr(obj, "ancestry")
228	!	repo_collapsed <- if (length(repo) > 1) paste0(repo, collapse = "/") else repo
229	!	is_not_named <- file.exists(file.path(c(repo_collapsed, obj[1])))[1]
230
231	!	if (is_not_named) {
232	!	selected_path <- do.call("file.path", as.list(c(repo, obj[1])))
233		} else {
234	!	if (length(repo) == 0) {
235	!	selected_path <- do.call("file.path", as.list(attr(input$tree[[obj[1]]], "ancestry")))
236		} else {
237	!	selected_path <- do.call("file.path", as.list(attr(input$tree[[repo]][[obj[1]]], "ancestry")))
238		}
239		}
240
241	!	validate(
242	!	need(
243	!	!isTRUE(file.info(selected_path)$isdir) && length(selected_path) > 0,
244	!	"Please select a single file."
245		)
246		)
247	!	display_file(selected_path)
248		})
249
250	!	onStop(function() {
251	!	removeResourcePath(basename(temp_dir))
252	!	unlink(temp_dir)
253		})
254		})
255		}

1		#' `teal` module: Cross-table
2		#'
3		#' Generates a simple cross-table of two variables from a dataset with custom
4		#' options for showing percentages and sub-totals.
5		#'
6		#' @inheritParams teal::module
7		#' @inheritParams shared_params
8		#' @param x (`data_extract_spec` or `list` of multiple `data_extract_spec`)
9		#' Object with all available choices with pre-selected option for variable X - row values.
10		#' In case of `data_extract_spec` use `select_spec(..., ordered = TRUE)` if table elements should be
11		#' rendered according to selection order.
12		#' @param y (`data_extract_spec` or `list` of multiple `data_extract_spec`)
13		#' Object with all available choices with pre-selected option for variable Y - column values.
14		#'
15		#' `data_extract_spec` must not allow multiple selection in this case.
16		#' @param show_percentage (`logical(1)`)
17		#' Indicates whether to show percentages (relevant only when `x` is a `factor`).
18		#' Defaults to `TRUE`.
19		#' @param show_total (`logical(1)`)
20		#' Indicates whether to show total column.
21		#' Defaults to `TRUE`.
22		#' @param remove_zero_columns (`logical(1)`)
23		#' Indicates whether to remove columns that contain only zeros from the output table.
24		#' Defaults to `FALSE`.
25		#'
26		#' @note For more examples, please see the vignette "Using cross table" via
27		#' `vignette("using-cross-table", package = "teal.modules.general")`.
28		#'
29		#' @inherit shared_params return
30		#'
31		#' @section Table Settings:
32		#' The module provides several table settings that can be adjusted:
33		#' \itemize{
34		#' \item \code{Show column percentage}: Shows column percentages when enabled
35		#' \item \code{Show total column}: Shows a total column when enabled
36		#' \item \code{Remove zero-only columns}: Removes columns that contain only zeros from the output table
37		#' }
38		#'
39		#' @section Decorating Module:
40		#'
41		#' This module generates the following objects, which can be modified in place using decorators:
42		#' - `table` (`ElementaryTable` - output of `rtables::build_table`)
43		#'
44		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
45		#' The name of this list corresponds to the name of the output to which the decorator is applied.
46		#' See code snippet below:
47		#'
48		#' ```
49		#' tm_t_crosstable(
50		#' ..., # arguments for module
51		#' decorators = list(
52		#' table = teal_transform_module(...) # applied to the `table` output
53		#' )
54		#' )
55		#' ```
56		#' For additional details and examples of decorators, refer to the vignette
57		#' `vignette("decorate-module-output", package = "teal.modules.general")`.
58		#'
59		#' To learn more please refer to the vignette
60		#' `vignette("transform-module-output", package = "teal")` or the [`teal::teal_transform_module()`] documentation.
61		#'
62		#' @examplesShinylive
63		#' library(teal.modules.general)
64		#' interactive <- function() TRUE
65		#' {{ next_example }}
66		#' @examples
67		#' # general data example
68		#' data <- teal_data()
69		#' data <- within(data, {
70		#' mtcars <- mtcars
71		#' for (v in c("cyl", "vs", "am", "gear")) {
72		#' mtcars[[v]] <- as.factor(mtcars[[v]])
73		#' }
74		#' mtcars[["primary_key"]] <- seq_len(nrow(mtcars))
75		#' })
76		#' join_keys(data) <- join_keys(join_key("mtcars", "mtcars", "primary_key"))
77		#'
78		#' app <- init(
79		#' data = data,
80		#' modules = modules(
81		#' tm_t_crosstable(
82		#' label = "Cross Table",
83		#' x = data_extract_spec(
84		#' dataname = "mtcars",
85		#' select = select_spec(
86		#' label = "Select variable:",
87		#' choices = variable_choices(data[["mtcars"]], c("cyl", "vs", "am", "gear")),
88		#' selected = c("cyl", "gear"),
89		#' multiple = TRUE,
90		#' ordered = TRUE,
91		#' fixed = FALSE
92		#' )
93		#' ),
94		#' y = data_extract_spec(
95		#' dataname = "mtcars",
96		#' select = select_spec(
97		#' label = "Select variable:",
98		#' choices = variable_choices(data[["mtcars"]], c("cyl", "vs", "am", "gear")),
99		#' selected = "vs",
100		#' multiple = FALSE,
101		#' fixed = FALSE
102		#' )
103		#' )
104		#' )
105		#' )
106		#' )
107		#' if (interactive()) {
108		#' shinyApp(app$ui, app$server)
109		#' }
110		#'
111		#' @examplesShinylive
112		#' library(teal.modules.general)
113		#' interactive <- function() TRUE
114		#' {{ next_example }}
115		#' @examples
116		#' # CDISC data example
117		#' data <- teal_data()
118		#' data <- within(data, {
119		#' ADSL <- teal.data::rADSL
120		#' })
121		#' join_keys(data) <- default_cdisc_join_keys[names(data)]
122		#'
123		#' app <- init(
124		#' data = data,
125		#' modules = modules(
126		#' tm_t_crosstable(
127		#' label = "Cross Table",
128		#' x = data_extract_spec(
129		#' dataname = "ADSL",
130		#' select = select_spec(
131		#' label = "Select variable:",
132		#' choices = variable_choices(data[["ADSL"]], subset = function(data) {
133		#' idx <- !vapply(data, inherits, logical(1), c("Date", "POSIXct", "POSIXlt"))
134		#' return(names(data)[idx])
135		#' }),
136		#' selected = "COUNTRY",
137		#' multiple = TRUE,
138		#' ordered = TRUE,
139		#' fixed = FALSE
140		#' )
141		#' ),
142		#' y = data_extract_spec(
143		#' dataname = "ADSL",
144		#' select = select_spec(
145		#' label = "Select variable:",
146		#' choices = variable_choices(data[["ADSL"]], subset = function(data) {
147		#' idx <- vapply(data, is.factor, logical(1))
148		#' return(names(data)[idx])
149		#' }),
150		#' selected = "SEX",
151		#' multiple = FALSE,
152		#' fixed = FALSE
153		#' )
154		#' )
155		#' )
156		#' )
157		#' )
158		#' if (interactive()) {
159		#' shinyApp(app$ui, app$server)
160		#' }
161		#'
162		#' @export
163		#'
164		tm_t_crosstable <- function(label = "Cross Table",
165		x,
166		y,
167		show_percentage = TRUE,
168		show_total = TRUE,
169		remove_zero_columns = FALSE,
170		pre_output = NULL,
171		post_output = NULL,
172		basic_table_args = teal.widgets::basic_table_args(),
173		transformators = list(),
174		decorators = list()) {
175	!	message("Initializing tm_t_crosstable")
176
177		# Normalize the parameters
178	!	if (inherits(x, "data_extract_spec")) x <- list(x)
179	!	if (inherits(y, "data_extract_spec")) y <- list(y)
180
181		# Start of assertions
182	!	checkmate::assert_string(label)
183	!	checkmate::assert_list(x, types = "data_extract_spec")
184
185	!	checkmate::assert_list(y, types = "data_extract_spec")
186	!	assert_single_selection(y)
187
188	!	checkmate::assert_flag(show_percentage)
189	!	checkmate::assert_flag(show_total)
190	!	checkmate::assert_flag(remove_zero_columns)
191	!	checkmate::assert_multi_class(pre_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
192	!	checkmate::assert_multi_class(post_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
193	!	checkmate::assert_class(basic_table_args, classes = "basic_table_args")
194
195	!	assert_decorators(decorators, "table")
196		# End of assertions
197
198		# Make UI args
199	!	ui_args <- as.list(environment())
200
201	!	server_args <- list(
202	!	label = label,
203	!	x = x,
204	!	y = y,
205	!	remove_zero_columns = remove_zero_columns,
206	!	basic_table_args = basic_table_args,
207	!	decorators = decorators
208		)
209
210	!	ans <- module(
211	!	label = label,
212	!	server = srv_t_crosstable,
213	!	ui = ui_t_crosstable,
214	!	ui_args = ui_args,
215	!	server_args = server_args,
216	!	transformators = transformators,
217	!	datanames = teal.transform::get_extract_datanames(list(x = x, y = y))
218		)
219	!	attr(ans, "teal_bookmarkable") <- TRUE
220	!	ans
221		}
222
223		# UI function for the cross-table module
224		ui_t_crosstable <- function(id, x, y, show_percentage, show_total, remove_zero_columns, pre_output, post_output, ...) {
225	!	args <- list(...)
226	!	ns <- NS(id)
227	!	is_single_dataset <- teal.transform::is_single_dataset(x, y)
228
229	!	join_default_options <- c(
230	!	"Full Join" = "dplyr::full_join",
231	!	"Inner Join" = "dplyr::inner_join",
232	!	"Left Join" = "dplyr::left_join",
233	!	"Right Join" = "dplyr::right_join"
234		)
235
236	!	teal.widgets::standard_layout(
237	!	output = teal.widgets::white_small_well(
238	!	textOutput(ns("title")),
239	!	teal.widgets::table_with_settings_ui(ns("table"))
240		),
241	!	encoding = tags$div(
242		### Reporter
243	!	teal.reporter::add_card_button_ui(ns("add_reporter"), label = "Add Report Card"),
244	!	tags$br(), tags$br(),
245		###
246	!	tags$label("Encodings", class = "text-primary"),
247	!	teal.transform::datanames_input(list(x, y)),
248	!	teal.transform::data_extract_ui(ns("x"), label = "Row values", x, is_single_dataset = is_single_dataset),
249	!	teal.transform::data_extract_ui(ns("y"), label = "Column values", y, is_single_dataset = is_single_dataset),
250	!	teal.widgets::optionalSelectInput(
251	!	ns("join_fun"),
252	!	label = "Row to Column type of join",
253	!	choices = join_default_options,
254	!	selected = join_default_options[1],
255	!	multiple = FALSE
256		),
257	!	tags$hr(),
258	!	bslib::accordion(
259	!	open = TRUE,
260	!	bslib::accordion_panel(
261	!	title = "Table settings",
262	!	checkboxInput(ns("show_percentage"), "Show column percentage", value = show_percentage),
263	!	checkboxInput(ns("show_total"), "Show total column", value = show_total),
264	!	checkboxInput(ns("remove_zero_columns"), "Remove zero-only columns", value = remove_zero_columns)
265		)
266		),
267	!	ui_decorate_teal_data(ns("decorator"), decorators = select_decorators(args$decorators, "table"))
268		),
269	!	forms = tagList(
270	!	teal.widgets::verbatim_popup_ui(ns("rcode"), "Show R code")
271		),
272	!	pre_output = pre_output,
273	!	post_output = post_output
274		)
275		}
276
277		# Server function for the cross-table module
278		srv_t_crosstable <- function(id,
279		data,
280		reporter,
281		filter_panel_api,
282		label,
283		x,
284		y,
285		remove_zero_columns,
286		basic_table_args,
287		decorators) {
288	!	with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
289	!	with_filter <- !missing(filter_panel_api) && inherits(filter_panel_api, "FilterPanelAPI")
290	!	checkmate::assert_class(data, "reactive")
291	!	checkmate::assert_class(isolate(data()), "teal_data")
292	!	moduleServer(id, function(input, output, session) {
293	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
294
295	!	selector_list <- teal.transform::data_extract_multiple_srv(
296	!	data_extract = list(x = x, y = y),
297	!	datasets = data,
298	!	select_validation_rule = list(
299	!	x = shinyvalidate::sv_required("Please define column for row variable."),
300	!	y = shinyvalidate::sv_required("Please define column for column variable.")
301		)
302		)
303
304	!	iv_r <- reactive({
305	!	iv <- shinyvalidate::InputValidator$new()
306	!	iv$add_rule("join_fun", function(value) {
307	!	if (!identical(selector_list()$x()$dataname, selector_list()$y()$dataname)) {
308	!	if (!shinyvalidate::input_provided(value)) {
309	!	"Please select a joining function."
310		}
311		}
312		})
313	!	teal.transform::compose_and_enable_validators(iv, selector_list)
314		})
315
316	!	observeEvent(
317	!	eventExpr = {
318	!	req(!is.null(selector_list()$x()) && !is.null(selector_list()$y()))
319	!	list(selector_list()$x(), selector_list()$y())
320		},
321	!	handlerExpr = {
322	!	if (identical(selector_list()$x()$dataname, selector_list()$y()$dataname)) {
323	!	shinyjs::hide("join_fun")
324		} else {
325	!	shinyjs::show("join_fun")
326		}
327		}
328		)
329
330	!	merge_function <- reactive({
331	!	if (is.null(input$join_fun)) {
332	!	"dplyr::full_join"
333		} else {
334	!	input$join_fun
335		}
336		})
337
338	!	anl_merged_input <- teal.transform::merge_expression_srv(
339	!	datasets = data,
340	!	selector_list = selector_list,
341	!	merge_function = merge_function
342		)
343	!	qenv <- reactive(
344	!	teal.code::eval_code(data(), 'library("rtables");library("tern");library("dplyr")') # nolint quotes
345		)
346	!	anl_merged_q <- reactive({
347	!	req(anl_merged_input())
348	!	qenv() %>%
349	!	teal.code::eval_code(as.expression(anl_merged_input()$expr))
350		})
351
352	!	merged <- list(
353	!	anl_input_r = anl_merged_input,
354	!	anl_q_r = anl_merged_q
355		)
356
357	!	output_q <- reactive({
358	!	teal::validate_inputs(iv_r())
359	!	ANL <- merged$anl_q_r()[["ANL"]]
360
361		# As this is a summary
362	!	x_name <- as.vector(merged$anl_input_r()$columns_source$x)
363	!	y_name <- as.vector(merged$anl_input_r()$columns_source$y)
364
365	!	teal::validate_has_data(ANL, 3)
366	!	teal::validate_has_data(ANL[, c(x_name, y_name)], 3, complete = TRUE, allow_inf = FALSE)
367
368	!	is_allowed_class <- function(x) is.numeric(x) \|\| is.factor(x) \|\| is.character(x) \|\| is.logical(x)
369	!	validate(need(
370	!	all(vapply(ANL[x_name], is_allowed_class, logical(1))),
371	!	"Selected row variable has an unsupported data type."
372		))
373	!	validate(need(
374	!	is_allowed_class(ANL[[y_name]]),
375	!	"Selected column variable has an unsupported data type."
376		))
377
378	!	show_percentage <- input$show_percentage
379	!	show_total <- input$show_total
380	!	remove_zero_columns <- input$remove_zero_columns
381
382	!	plot_title <- paste(
383	!	"Cross-Table of",
384	!	paste0(varname_w_label(x_name, ANL), collapse = ", "),
385	!	"(rows)", "vs.",
386	!	varname_w_label(y_name, ANL),
387	!	"(columns)"
388		)
389
390	!	labels_vec <- vapply(
391	!	x_name,
392	!	varname_w_label,
393	!	character(1),
394	!	ANL
395		)
396
397	!	obj <- teal.code::eval_code(
398	!	merged$anl_q_r(),
399	!	substitute(
400	!	expr = {
401	!	title <- plot_title
402		},
403	!	env = list(plot_title = plot_title)
404		)
405		) %>%
406	!	teal.code::eval_code(
407	!	substitute(
408	!	expr = {
409	!	table <- basic_tables %>%
410	!	split_call %>% # styler: off
411	!	rtables::add_colcounts() %>%
412	!	tern::analyze_vars(
413	!	vars = x_name,
414	!	var_labels = labels_vec,
415	!	na.rm = FALSE,
416	!	denom = "N_col",
417	!	.stats = c("mean_sd", "median", "range", count_value)
418		)
419		},
420	!	env = list(
421	!	basic_tables = teal.widgets::parse_basic_table_args(
422	!	basic_table_args = teal.widgets::resolve_basic_table_args(basic_table_args)
423		),
424	!	split_call = if (show_total) {
425	!	substitute(
426	!	expr = rtables::split_cols_by(
427	!	y_name,
428	!	split_fun = rtables::add_overall_level(label = "Total", first = FALSE)
429		),
430	!	env = list(y_name = y_name)
431		)
432		} else {
433	!	substitute(rtables::split_cols_by(y_name), env = list(y_name = y_name))
434		},
435	!	x_name = x_name,
436	!	labels_vec = labels_vec,
437	!	count_value = ifelse(show_percentage, "count_fraction", "count")
438		)
439		)
440		) %>%
441	!	teal.code::eval_code(
442	!	expression(ANL <- tern::df_explicit_na(ANL))
443		)
444
445	!	if (remove_zero_columns) {
446	!	obj <- obj %>%
447	!	teal.code::eval_code(
448	!	substitute(
449	!	expr = {
450	!	ANL[[y_name]] <- droplevels(ANL[[y_name]])
451	!	table <- rtables::build_table(lyt = table, df = ANL[order(ANL[[y_name]]), ])
452		},
453	!	env = list(y_name = y_name)
454		)
455		)
456		} else {
457	!	obj <- obj %>%
458	!	teal.code::eval_code(
459	!	substitute(
460	!	expr = {
461	!	table <- rtables::build_table(lyt = table, df = ANL[order(ANL[[y_name]]), ])
462		},
463	!	env = list(y_name = y_name)
464		)
465		)
466		}
467	!	obj
468		})
469
470	!	decorated_output_q <- srv_decorate_teal_data(
471	!	id = "decorator",
472	!	data = output_q,
473	!	decorators = select_decorators(decorators, "table"),
474	!	expr = table
475		)
476
477	!	output$title <- renderText(req(decorated_output_q())[["title"]])
478
479	!	table_r <- reactive({
480	!	req(iv_r()$is_valid())
481	!	req(decorated_output_q())[["table"]]
482		})
483
484	!	teal.widgets::table_with_settings_srv(
485	!	id = "table",
486	!	table_r = table_r
487		)
488
489		# Render R code.
490	!	source_code_r <- reactive(teal.code::get_code(req(decorated_output_q())))
491
492	!	teal.widgets::verbatim_popup_srv(
493	!	id = "rcode",
494	!	verbatim_content = source_code_r,
495	!	title = "Show R Code for Cross-Table"
496		)
497
498		### REPORTER
499	!	if (with_reporter) {
500	!	card_fun <- function(comment, label) {
501	!	card <- teal::report_card_template(
502	!	title = "Cross Table",
503	!	label = label,
504	!	with_filter = with_filter,
505	!	filter_panel_api = filter_panel_api
506		)
507	!	card$append_text("Table", "header3")
508	!	card$append_table(table_r())
509	!	if (!comment == "") {
510	!	card$append_text("Comment", "header3")
511	!	card$append_text(comment)
512		}
513	!	card$append_src(source_code_r())
514	!	card
515		}
516	!	teal.reporter::add_card_button_srv("add_reporter", reporter = reporter, card_fun = card_fun)
517		}
518		###
519		})
520		}

1		#' `teal` module: Scatterplot matrix
2		#'
3		#' Generates a scatterplot matrix from selected `variables` from datasets.
4		#' Each plot within the matrix represents the relationship between two variables,
5		#' providing the overview of correlations and distributions across selected data.
6		#'
7		#' @note For more examples, please see the vignette "Using scatterplot matrix" via
8		#' `vignette("using-scatterplot-matrix", package = "teal.modules.general")`.
9		#'
10		#' @inheritParams teal::module
11		#' @inheritParams tm_g_scatterplot
12		#' @inheritParams shared_params
13		#'
14		#' @param variables (`data_extract_spec` or `list` of multiple `data_extract_spec`)
15		#' Specifies plotting variables from an incoming dataset with filtering and selecting. In case of
16		#' `data_extract_spec` use `select_spec(..., ordered = TRUE)` if plot elements should be
17		#' rendered according to selection order.
18		#'
19		#' @inherit shared_params return
20		#'
21		#' @section Decorating Module:
22		#'
23		#' This module generates the following objects, which can be modified in place using decorators:
24		#' - `plot` (`trellis` - output of `lattice::splom`)
25		#'
26		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
27		#' The name of this list corresponds to the name of the output to which the decorator is applied.
28		#' See code snippet below:
29		#'
30		#' ```
31		#' tm_g_scatterplotmatrix(
32		#' ..., # arguments for module
33		#' decorators = list(
34		#' plot = teal_transform_module(...) # applied to the `plot` output
35		#' )
36		#' )
37		#' ```
38		#'
39		#' For additional details and examples of decorators, refer to the vignette
40		#' `vignette("decorate-module-output", package = "teal.modules.general")`.
41		#'
42		#' To learn more please refer to the vignette
43		#' `vignette("transform-module-output", package = "teal")` or the [`teal::teal_transform_module()`] documentation.
44		#'
45		#' @examplesShinylive
46		#' library(teal.modules.general)
47		#' interactive <- function() TRUE
48		#' {{ next_example }}
49		#' @examples
50		#' # general data example
51		#' data <- teal_data()
52		#' data <- within(data, {
53		#' countries <- data.frame(
54		#' id = c("DE", "FR", "IT", "ES", "PT", "GR", "NL", "BE", "LU", "AT"),
55		#' government = factor(
56		#' c(2, 2, 2, 1, 2, 2, 1, 1, 1, 2),
57		#' labels = c("Monarchy", "Republic")
58		#' ),
59		#' language_family = factor(
60		#' c(1, 3, 3, 3, 3, 2, 1, 1, 3, 1),
61		#' labels = c("Germanic", "Hellenic", "Romance")
62		#' ),
63		#' population = c(83, 67, 60, 47, 10, 11, 17, 11, 0.6, 9),
64		#' area = c(357, 551, 301, 505, 92, 132, 41, 30, 2.6, 83),
65		#' gdp = c(3.4, 2.7, 2.1, 1.4, 0.3, 0.2, 0.7, 0.5, 0.1, 0.4),
66		#' debt = c(2.1, 2.3, 2.4, 2.6, 2.3, 2.4, 2.3, 2.4, 2.3, 2.4)
67		#' )
68		#' sales <- data.frame(
69		#' id = 1:50,
70		#' country_id = sample(
71		#' c("DE", "FR", "IT", "ES", "PT", "GR", "NL", "BE", "LU", "AT"),
72		#' size = 50,
73		#' replace = TRUE
74		#' ),
75		#' year = sort(sample(2010:2020, 50, replace = TRUE)),
76		#' venue = sample(c("small", "medium", "large", "online"), 50, replace = TRUE),
77		#' cancelled = sample(c(TRUE, FALSE), 50, replace = TRUE),
78		#' quantity = rnorm(50, 100, 20),
79		#' costs = rnorm(50, 80, 20),
80		#' profit = rnorm(50, 20, 10)
81		#' )
82		#' })
83		#' join_keys(data) <- join_keys(
84		#' join_key("countries", "countries", "id"),
85		#' join_key("sales", "sales", "id"),
86		#' join_key("countries", "sales", c("id" = "country_id"))
87		#' )
88		#'
89		#' app <- init(
90		#' data = data,
91		#' modules = modules(
92		#' tm_g_scatterplotmatrix(
93		#' label = "Scatterplot matrix",
94		#' variables = list(
95		#' data_extract_spec(
96		#' dataname = "countries",
97		#' select = select_spec(
98		#' label = "Select variables:",
99		#' choices = variable_choices(data[["countries"]]),
100		#' selected = c("area", "gdp", "debt"),
101		#' multiple = TRUE,
102		#' ordered = TRUE,
103		#' fixed = FALSE
104		#' )
105		#' ),
106		#' data_extract_spec(
107		#' dataname = "sales",
108		#' filter = filter_spec(
109		#' label = "Select variable:",
110		#' vars = "country_id",
111		#' choices = value_choices(data[["sales"]], "country_id"),
112		#' selected = c("DE", "FR", "IT", "PT", "GR", "NL", "BE", "LU", "AT"),
113		#' multiple = TRUE
114		#' ),
115		#' select = select_spec(
116		#' label = "Select variables:",
117		#' choices = variable_choices(data[["sales"]], c("quantity", "costs", "profit")),
118		#' selected = c("quantity", "costs", "profit"),
119		#' multiple = TRUE,
120		#' ordered = TRUE,
121		#' fixed = FALSE
122		#' )
123		#' )
124		#' )
125		#' )
126		#' )
127		#' )
128		#' if (interactive()) {
129		#' shinyApp(app$ui, app$server)
130		#' }
131		#'
132		#' @examplesShinylive
133		#' library(teal.modules.general)
134		#' interactive <- function() TRUE
135		#' {{ next_example }}
136		#' @examples
137		#' # CDISC data example
138		#' data <- teal_data()
139		#' data <- within(data, {
140		#' ADSL <- teal.data::rADSL
141		#' ADRS <- teal.data::rADRS
142		#' })
143		#' join_keys(data) <- default_cdisc_join_keys[names(data)]
144		#'
145		#' app <- init(
146		#' data = data,
147		#' modules = modules(
148		#' tm_g_scatterplotmatrix(
149		#' label = "Scatterplot matrix",
150		#' variables = list(
151		#' data_extract_spec(
152		#' dataname = "ADSL",
153		#' select = select_spec(
154		#' label = "Select variables:",
155		#' choices = variable_choices(data[["ADSL"]]),
156		#' selected = c("AGE", "RACE", "SEX"),
157		#' multiple = TRUE,
158		#' ordered = TRUE,
159		#' fixed = FALSE
160		#' )
161		#' ),
162		#' data_extract_spec(
163		#' dataname = "ADRS",
164		#' filter = filter_spec(
165		#' label = "Select endpoints:",
166		#' vars = c("PARAMCD", "AVISIT"),
167		#' choices = value_choices(data[["ADRS"]], c("PARAMCD", "AVISIT"), c("PARAM", "AVISIT")),
168		#' selected = "INVET - END OF INDUCTION",
169		#' multiple = TRUE
170		#' ),
171		#' select = select_spec(
172		#' label = "Select variables:",
173		#' choices = variable_choices(data[["ADRS"]]),
174		#' selected = c("AGE", "AVAL", "ADY"),
175		#' multiple = TRUE,
176		#' ordered = TRUE,
177		#' fixed = FALSE
178		#' )
179		#' )
180		#' )
181		#' )
182		#' )
183		#' )
184		#' if (interactive()) {
185		#' shinyApp(app$ui, app$server)
186		#' }
187		#'
188		#' @export
189		#'
190		tm_g_scatterplotmatrix <- function(label = "Scatterplot Matrix",
191		variables,
192		plot_height = c(600, 200, 2000),
193		plot_width = NULL,
194		pre_output = NULL,
195		post_output = NULL,
196		transformators = list(),
197		decorators = list()) {
198	!	message("Initializing tm_g_scatterplotmatrix")
199
200		# Normalize the parameters
201	!	if (inherits(variables, "data_extract_spec")) variables <- list(variables)
202
203		# Start of assertions
204	!	checkmate::assert_string(label)
205	!	checkmate::assert_list(variables, types = "data_extract_spec")
206
207	!	checkmate::assert_numeric(plot_height, len = 3, any.missing = FALSE, finite = TRUE)
208	!	checkmate::assert_numeric(plot_height[1], lower = plot_height[2], upper = plot_height[3], .var.name = "plot_height")
209	!	checkmate::assert_numeric(plot_width, len = 3, any.missing = FALSE, null.ok = TRUE, finite = TRUE)
210	!	checkmate::assert_numeric(
211	!	plot_width[1],
212	!	lower = plot_width[2], upper = plot_width[3], null.ok = TRUE, .var.name = "plot_width"
213		)
214
215	!	checkmate::assert_multi_class(pre_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
216	!	checkmate::assert_multi_class(post_output, c("shiny.tag", "shiny.tag.list", "html"), null.ok = TRUE)
217
218	!	assert_decorators(decorators, "plot")
219		# End of assertions
220
221		# Make UI args
222	!	args <- as.list(environment())
223
224	!	ans <- module(
225	!	label = label,
226	!	server = srv_g_scatterplotmatrix,
227	!	ui = ui_g_scatterplotmatrix,
228	!	ui_args = args,
229	!	server_args = list(
230	!	variables = variables,
231	!	plot_height = plot_height,
232	!	plot_width = plot_width,
233	!	decorators = decorators
234		),
235	!	transformators = transformators,
236	!	datanames = teal.transform::get_extract_datanames(variables)
237		)
238	!	attr(ans, "teal_bookmarkable") <- TRUE
239	!	ans
240		}
241
242		# UI function for the scatterplot matrix module
243		ui_g_scatterplotmatrix <- function(id, ...) {
244	!	args <- list(...)
245	!	is_single_dataset_value <- teal.transform::is_single_dataset(args$variables)
246	!	ns <- NS(id)
247	!	teal.widgets::standard_layout(
248	!	output = teal.widgets::white_small_well(
249	!	textOutput(ns("message")),
250	!	tags$br(),
251	!	teal.widgets::plot_with_settings_ui(id = ns("myplot"))
252		),
253	!	encoding = tags$div(
254		### Reporter
255	!	teal.reporter::add_card_button_ui(ns("add_reporter"), label = "Add Report Card"),
256	!	tags$br(), tags$br(),
257		###
258	!	tags$label("Encodings", class = "text-primary"),
259	!	teal.transform::datanames_input(args$variables),
260	!	teal.transform::data_extract_ui(
261	!	id = ns("variables"),
262	!	label = "Variables",
263	!	data_extract_spec = args$variables,
264	!	is_single_dataset = is_single_dataset_value
265		),
266	!	tags$hr(),
267	!	ui_decorate_teal_data(ns("decorator"), decorators = select_decorators(args$decorators, "plot")),
268	!	bslib::accordion(
269	!	open = TRUE,
270	!	bslib::accordion_panel(
271	!	title = "Plot settings",
272	!	sliderInput(
273	!	ns("alpha"), "Opacity:",
274	!	min = 0, max = 1,
275	!	step = .05, value = .5, ticks = FALSE
276		),
277	!	sliderInput(
278	!	ns("cex"), "Points size:",
279	!	min = 0.2, max = 3,
280	!	step = .05, value = .65, ticks = FALSE
281		),
282	!	checkboxInput(ns("cor"), "Add Correlation", value = FALSE),
283	!	radioButtons(
284	!	ns("cor_method"), "Select Correlation Method",
285	!	choiceNames = c("Pearson", "Kendall", "Spearman"),
286	!	choiceValues = c("pearson", "kendall", "spearman"),
287	!	inline = TRUE
288		),
289	!	checkboxInput(ns("cor_na_omit"), "Omit Missing Values", value = TRUE)
290		)
291		)
292		),
293	!	forms = tagList(
294	!	teal.widgets::verbatim_popup_ui(ns("rcode"), "Show R code")
295		),
296	!	pre_output = args$pre_output,
297	!	post_output = args$post_output
298		)
299		}
300
301		# Server function for the scatterplot matrix module
302		srv_g_scatterplotmatrix <- function(id,
303		data,
304		reporter,
305		filter_panel_api,
306		variables,
307		plot_height,
308		plot_width,
309		decorators) {
310	!	with_reporter <- !missing(reporter) && inherits(reporter, "Reporter")
311	!	with_filter <- !missing(filter_panel_api) && inherits(filter_panel_api, "FilterPanelAPI")
312	!	checkmate::assert_class(data, "reactive")
313	!	checkmate::assert_class(isolate(data()), "teal_data")
314	!	moduleServer(id, function(input, output, session) {
315	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
316
317	!	selector_list <- teal.transform::data_extract_multiple_srv(
318	!	data_extract = list(variables = variables),
319	!	datasets = data,
320	!	select_validation_rule = list(
321	!	variables = ~ if (length(.) <= 1) "Please select at least 2 columns."
322		)
323		)
324
325	!	iv_r <- reactive({
326	!	iv <- shinyvalidate::InputValidator$new()
327	!	teal.transform::compose_and_enable_validators(iv, selector_list)
328		})
329
330	!	anl_merged_input <- teal.transform::merge_expression_srv(
331	!	datasets = data,
332	!	selector_list = selector_list
333		)
334
335	!	anl_merged_q <- reactive({
336	!	req(anl_merged_input())
337	!	qenv <- teal.code::eval_code(data(), 'library("dplyr");library("lattice")') # nolint quotes
338	!	teal.code::eval_code(qenv, as.expression(anl_merged_input()$expr))
339		})
340
341	!	merged <- list(
342	!	anl_input_r = anl_merged_input,
343	!	anl_q_r = anl_merged_q
344		)
345
346		# plot
347	!	output_q <- reactive({
348	!	teal::validate_inputs(iv_r())
349
350	!	qenv <- merged$anl_q_r()
351	!	ANL <- qenv[["ANL"]]
352
353	!	cols_names <- merged$anl_input_r()$columns_source$variables
354	!	alpha <- input$alpha
355	!	cex <- input$cex
356	!	add_cor <- input$cor
357	!	cor_method <- input$cor_method
358	!	cor_na_omit <- input$cor_na_omit
359
360	!	cor_na_action <- if (isTruthy(cor_na_omit)) {
361	!	"na.omit"
362		} else {
363	!	"na.fail"
364		}
365
366	!	teal::validate_has_data(ANL, 10)
367	!	teal::validate_has_data(ANL[, cols_names, drop = FALSE], 10, complete = TRUE, allow_inf = FALSE)
368
369		# get labels and proper variable names
370	!	varnames <- varname_w_label(cols_names, ANL, wrap_width = 20)
371
372		# check character columns. If any, then those are converted to factors
373	!	check_char <- vapply(ANL[, cols_names], is.character, logical(1))
374	!	qenv <- teal.code::eval_code(qenv, 'library("dplyr")') # nolint quotes
375	!	if (any(check_char)) {
376	!	qenv <- teal.code::eval_code(
377	!	qenv,
378	!	substitute(
379	!	expr = ANL <- ANL[, cols_names] %>%
380	!	dplyr::mutate_if(is.character, as.factor) %>%
381	!	droplevels(),
382	!	env = list(cols_names = cols_names)
383		)
384		)
385		} else {
386	!	qenv <- teal.code::eval_code(
387	!	qenv,
388	!	substitute(
389	!	expr = ANL <- ANL[, cols_names] %>%
390	!	droplevels(),
391	!	env = list(cols_names = cols_names)
392		)
393		)
394		}
395
396
397		# create plot
398	!	if (add_cor) {
399	!	shinyjs::show("cor_method")
400	!	shinyjs::show("cor_use")
401	!	shinyjs::show("cor_na_omit")
402
403	!	qenv <- teal.code::eval_code(
404	!	qenv,
405	!	substitute(
406	!	expr = {
407	!	plot <- lattice::splom(
408	!	ANL,
409	!	varnames = varnames_value,
410	!	panel = function(x, y, ...) {
411	!	lattice::panel.splom(x = x, y = y, ...)
412	!	cpl <- lattice::current.panel.limits()
413	!	lattice::panel.text(
414	!	mean(cpl$xlim),
415	!	mean(cpl$ylim),
416	!	get_scatterplotmatrix_stats(
417	!	x,
418	!	y,
419	!	.f = stats::cor.test,
420	!	.f_args = list(method = cor_method, na.action = cor_na_action)
421		),
422	!	alpha = 0.6,
423	!	fontsize = 18,
424	!	fontface = "bold"
425		)
426		},
427	!	pch = 16,
428	!	alpha = alpha_value,
429	!	cex = cex_value
430		)
431		},
432	!	env = list(
433	!	varnames_value = varnames,
434	!	cor_method = cor_method,
435	!	cor_na_action = cor_na_action,
436	!	alpha_value = alpha,
437	!	cex_value = cex
438		)
439		)
440		)
441		} else {
442	!	shinyjs::hide("cor_method")
443	!	shinyjs::hide("cor_use")
444	!	shinyjs::hide("cor_na_omit")
445	!	qenv <- teal.code::eval_code(
446	!	qenv,
447	!	substitute(
448	!	expr = {
449	!	plot <- lattice::splom(
450	!	ANL,
451	!	varnames = varnames_value,
452	!	pch = 16,
453	!	alpha = alpha_value,
454	!	cex = cex_value
455		)
456		},
457	!	env = list(varnames_value = varnames, alpha_value = alpha, cex_value = cex)
458		)
459		)
460		}
461	!	qenv
462		})
463
464	!	decorated_output_q <- srv_decorate_teal_data(
465	!	id = "decorator",
466	!	data = output_q,
467	!	decorators = select_decorators(decorators, "plot"),
468	!	expr = plot
469		)
470
471	!	plot_r <- reactive(req(decorated_output_q())[["plot"]])
472
473		# Insert the plot into a plot_with_settings module
474	!	pws <- teal.widgets::plot_with_settings_srv(
475	!	id = "myplot",
476	!	plot_r = plot_r,
477	!	height = plot_height,
478	!	width = plot_width
479		)
480
481		# show a message if conversion to factors took place
482	!	output$message <- renderText({
483	!	req(iv_r()$is_valid())
484	!	req(selector_list()$variables())
485	!	ANL <- merged$anl_q_r()[["ANL"]]
486	!	cols_names <- unique(unname(do.call(c, merged$anl_input_r()$columns_source)))
487	!	check_char <- vapply(ANL[, cols_names], is.character, logical(1))
488	!	if (any(check_char)) {
489	!	is_single <- sum(check_char) == 1
490	!	paste(
491	!	"Character",
492	!	ifelse(is_single, "variable", "variables"),
493	!	paste0("(", paste(cols_names[check_char], collapse = ", "), ")"),
494	!	ifelse(is_single, "was", "were"),
495	!	"converted to",
496	!	ifelse(is_single, "factor.", "factors.")
497		)
498		} else {
499		""
500		}
501		})
502
503		# Render R code.
504	!	source_code_r <- reactive(teal.code::get_code(req(decorated_output_q())))
505
506	!	teal.widgets::verbatim_popup_srv(
507	!	id = "rcode",
508	!	verbatim_content = source_code_r,
509	!	title = "Show R Code for Scatterplotmatrix"
510		)
511
512		### REPORTER
513	!	if (with_reporter) {
514	!	card_fun <- function(comment, label) {
515	!	card <- teal::report_card_template(
516	!	title = "Scatter Plot Matrix",
517	!	label = label,
518	!	with_filter = with_filter,
519	!	filter_panel_api = filter_panel_api
520		)
521	!	card$append_text("Plot", "header3")
522	!	card$append_plot(plot_r(), dim = pws$dim())
523	!	if (!comment == "") {
524	!	card$append_text("Comment", "header3")
525	!	card$append_text(comment)
526		}
527	!	card$append_src(source_code_r())
528	!	card
529		}
530	!	teal.reporter::add_card_button_srv("add_reporter", reporter = reporter, card_fun = card_fun)
531		}
532		###
533		})
534		}
535
536		#' Get stats for x-y pairs in scatterplot matrix
537		#'
538		#' Uses [stats::cor.test()] per default for all numerical input variables and converts results
539		#' to character vector.
540		#' Could be extended if different stats for different variable types are needed.
541		#' Meant to be called from [lattice::panel.text()].
542		#'
543		#' Presently we need to use a formula input for `stats::cor.test` because
544		#' `na.fail` only gets evaluated when a formula is passed (see below).
545		#' ```
546		#' x = c(1,3,5,7,NA)
547		#' y = c(3,6,7,8,1)
548		#' stats::cor.test(x, y, na.action = "na.fail")
549		#' stats::cor.test(~ x + y, na.action = "na.fail")
550		#' ```
551		#'
552		#' @param x,y (`numeric`) vectors of data values. `x` and `y` must have the same length.
553		#' @param .f (`function`) function that accepts x and y as formula input `~ x + y`.
554		#' Default `stats::cor.test`.
555		#' @param .f_args (`list`) of arguments to be passed to `.f`.
556		#' @param round_stat (`integer(1)`) optional, number of decimal places to use when rounding the estimate.
557		#' @param round_pval (`integer(1)`) optional, number of decimal places to use when rounding the p-value.
558		#'
559		#' @return Character with stats. For [stats::cor.test()] correlation coefficient and p-value.
560		#'
561		#' @examples
562		#' set.seed(1)
563		#' x <- runif(25, 0, 1)
564		#' y <- runif(25, 0, 1)
565		#' x[c(3, 10, 18)] <- NA
566		#'
567		#' get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list(method = "pearson"))
568		#' get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list(
569		#' method = "pearson",
570		#' na.action = na.fail
571		#' ))
572		#'
573		#' @export
574		#'
575		get_scatterplotmatrix_stats <- function(x, y,
576		.f = stats::cor.test,
577		.f_args = list(),
578		round_stat = 2,
579		round_pval = 4) {
580	6x	if (is.numeric(x) && is.numeric(y)) {
581	3x	stat <- tryCatch(do.call(.f, c(list(~ x + y), .f_args)), error = function(e) NA)
582
583	3x	if (anyNA(stat)) {
584	1x	return("NA")
585	2x	} else if (all(c("estimate", "p.value") %in% names(stat))) {
586	2x	return(paste(
587	2x	c(
588	2x	paste0(names(stat$estimate), ":", round(stat$estimate, round_stat)),
589	2x	paste0("P:", round(stat$p.value, round_pval))
590		),
591	2x	collapse = "\n"
592		))
593		} else {
594	!	stop("function not supported")
595		}
596		} else {
597	3x	if ("method" %in% names(.f_args)) {
598	3x	if (.f_args$method == "pearson") {
599	1x	return("cor:-")
600		}
601	2x	if (.f_args$method == "kendall") {
602	1x	return("tau:-")
603		}
604	1x	if (.f_args$method == "spearman") {
605	1x	return("rho:-")
606		}
607		}
608	!	return("-")
609		}
610		}

1		#' `teal` module: Front page
2		#'
3		#' Creates a simple front page for `teal` applications, displaying
4		#' introductory text, tables, additional `html` or `shiny` tags, and footnotes.
5		#'
6		#' @inheritParams teal::module
7		#' @param header_text (`character` vector) text to be shown at the top of the module, for each
8		#' element, if named the name is shown first in bold as a header followed by the value. The first
9		#' element's header is displayed larger than the others.
10		#' @param tables (`named list` of `data.frame`s) tables to be shown in the module.
11		#' @param additional_tags (`shiny.tag.list` or `html`) additional `shiny` tags or `html` to be included after the table,
12		#' for example to include an image, `tagList(tags$img(src = "image.png"))` or to include further `html`,
13		#' `HTML("html text here")`.
14		#' @param footnotes (`character` vector) of text to be shown at the bottom of the module, for each
15		#' element, if named the name is shown first in bold, followed by the value.
16		#' @param show_metadata (`logical`) `r lifecycle::badge("deprecated")` indicating
17		#' whether the metadata of the datasets be available on the module.
18		#' Metadata shown automatically when `datanames` set.
19		#' @inheritParams tm_variable_browser
20		#'
21		#' @inherit shared_params return
22		#'
23		#' @examplesShinylive
24		#' library(teal.modules.general)
25		#' interactive <- function() TRUE
26		#' {{ next_example }}
27		#' @examples
28		#' data <- teal_data()
29		#' data <- within(data, {
30		#' require(nestcolor)
31		#' ADSL <- teal.data::rADSL
32		#' attr(ADSL, "metadata") <- list("Author" = "NEST team", "data_source" = "synthetic data")
33		#' })
34		#' join_keys(data) <- default_cdisc_join_keys[names(data)]
35		#'
36		#' table_1 <- data.frame(Info = c("A", "B"), Text = c("A", "B"))
37		#' table_2 <- data.frame(`Column 1` = c("C", "D"), `Column 2` = c(5.5, 6.6), `Column 3` = c("A", "B"))
38		#' table_3 <- data.frame(Info = c("E", "F"), Text = c("G", "H"))
39		#'
40		#' table_input <- list(
41		#' "Table 1" = table_1,
42		#' "Table 2" = table_2,
43		#' "Table 3" = table_3
44		#' )
45		#'
46		#' app <- init(
47		#' data = data,
48		#' modules = modules(
49		#' tm_front_page(
50		#' header_text = c(
51		#' "Important information" = "It can go here.",
52		#' "Other information" = "Can go here."
53		#' ),
54		#' tables = table_input,
55		#' additional_tags = HTML("Additional HTML or shiny tags go here <br>"),
56		#' footnotes = c("X" = "is the first footnote", "Y is the second footnote")
57		#' )
58		#' )
59		#' ) \|>
60		#' modify_header(tags$h1("Sample Application")) \|>
61		#' modify_footer(tags$p("Application footer"))
62		#'
63		#' if (interactive()) {
64		#' shinyApp(app$ui, app$server)
65		#' }
66		#'
67		#' @export
68		#'
69		tm_front_page <- function(label = "Front page",
70		header_text = character(0),
71		tables = list(),
72		additional_tags = tagList(),
73		footnotes = character(0),
74		show_metadata = deprecated(),
75		datanames = if (missing(show_metadata)) NULL else "all",
76		transformators = list()) {
77	!	message("Initializing tm_front_page")
78
79		# Start of assertions
80	!	checkmate::assert_string(label)
81	!	checkmate::assert_character(header_text, min.len = 0, any.missing = FALSE)
82	!	checkmate::assert_list(tables, types = "data.frame", names = "named", any.missing = FALSE)
83	!	checkmate::assert_multi_class(additional_tags, classes = c("shiny.tag.list", "html"))
84	!	checkmate::assert_character(footnotes, min.len = 0, any.missing = FALSE)
85	!	if (!missing(show_metadata)) {
86	!	lifecycle::deprecate_stop(
87	!	when = "0.4.0",
88	!	what = "tm_front_page(show_metadata)",
89	!	with = "tm_front_page(datanames)",
90	!	details = c(
91	!	"With `datanames` you can select which datasets are displayed.",
92	!	i = "Use `tm_front_page(datanames = 'all')` to keep the previous behavior and avoid this warning."
93		)
94		)
95		}
96	!	checkmate::assert_character(datanames, min.len = 0, min.chars = 1, null.ok = TRUE)
97
98		# End of assertions
99
100		# Make UI args
101	!	args <- as.list(environment())
102
103	!	ans <- module(
104	!	label = label,
105	!	server = srv_front_page,
106	!	ui = ui_front_page,
107	!	ui_args = args,
108	!	server_args = list(tables = tables),
109	!	datanames = datanames, ,
110	!	transformators = transformators
111		)
112	!	attr(ans, "teal_bookmarkable") <- TRUE
113	!	ans
114		}
115
116		# UI function for the front page module
117		ui_front_page <- function(id, ...) {
118	!	args <- list(...)
119	!	ns <- NS(id)
120
121	!	tagList(
122	!	tags$div(
123	!	id = "front_page_content",
124	!	style = "margin-left: 2rem;",
125	!	tags$div(
126	!	id = "front_page_headers",
127	!	get_header_tags(args$header_text)
128		),
129	!	tags$div(
130	!	id = "front_page_tables",
131	!	style = "margin-left: 2rem;",
132	!	get_table_tags(args$tables, ns)
133		),
134	!	tags$div(
135	!	id = "front_page_custom_html",
136	!	style = "margin-left: 2rem;",
137	!	args$additional_tags
138		),
139	!	if (length(args$datanames) > 0L) {
140	!	tags$div(
141	!	id = "front_page_metabutton",
142	!	style = "margin: 1rem;",
143	!	actionButton(ns("metadata_button"), "Show metadata")
144		)
145		},
146	!	tags$footer(
147	!	class = "small",
148	!	get_footer_tags(args$footnotes)
149		)
150		)
151		)
152		}
153
154		# Server function for the front page module
155		srv_front_page <- function(id, data, tables) {
156	!	checkmate::assert_class(data, "reactive")
157	!	checkmate::assert_class(isolate(data()), "teal_data")
158	!	moduleServer(id, function(input, output, session) {
159	!	teal.logger::log_shiny_input_changes(input, namespace = "teal.modules.general")
160
161	!	ns <- session$ns
162
163	!	setBookmarkExclude("metadata_button")
164
165	!	lapply(seq_along(tables), function(idx) {
166	!	output[[paste0("table_", idx)]] <- renderTable(
167	!	tables[[idx]],
168	!	bordered = TRUE,
169	!	caption = names(tables)[idx],
170	!	caption.placement = "top"
171		)
172		})
173	!	if (length(isolate(names(data()))) > 0L) {
174	!	observeEvent(
175	!	input$metadata_button, showModal(
176	!	modalDialog(
177	!	title = "Metadata",
178	!	dataTableOutput(ns("metadata_table")),
179	!	size = "l",
180	!	easyClose = TRUE
181		)
182		)
183		)
184
185	!	metadata_data_frame <- reactive({
186	!	datanames <- names(data())
187	!	convert_metadata_to_dataframe(
188	!	lapply(datanames, function(dataname) attr(data()[[dataname]], "metadata")),
189	!	datanames
190		)
191		})
192
193	!	output$metadata_table <- renderDataTable({
194	!	validate(need(nrow(metadata_data_frame()) > 0, "The data has no associated metadata"))
195	!	metadata_data_frame()
196		})
197		}
198		})
199		}
200
201		## utils functions
202
203		get_header_tags <- function(header_text) {
204	!	if (length(header_text) == 0) {
205	!	return(list())
206		}
207
208	!	get_single_header_tags <- function(header_text, p_text, header_tag = tags$h4) {
209	!	tagList(
210	!	tags$div(
211	!	if (!is.null(header_text) && nchar(header_text) > 0) header_tag(header_text),
212	!	tags$p(p_text)
213		)
214		)
215		}
216
217	!	header_tags <- get_single_header_tags(names(header_text[1]), header_text[1], header_tag = tags$h3)
218	!	c(header_tags, mapply(get_single_header_tags, utils::tail(names(header_text), -1), utils::tail(header_text, -1)))
219		}
220
221		get_table_tags <- function(tables, ns) {
222	!	if (length(tables) == 0) {
223	!	return(list())
224		}
225	!	table_tags <- c(lapply(seq_along(tables), function(idx) {
226	!	list(
227	!	tableOutput(ns(paste0("table_", idx)))
228		)
229		}))
230	!	table_tags
231		}
232
233		get_footer_tags <- function(footnotes) {
234	!	if (length(footnotes) == 0) {
235	!	return(list())
236		}
237	!	bold_texts <- if (is.null(names(footnotes))) rep("", length(footnotes)) else names(footnotes)
238	!	footnote_tags <- mapply(function(bold_text, value) {
239	!	list(
240	!	tags$div(
241	!	tags$b(bold_text),
242	!	value,
243	!	tags$br()
244		)
245		)
246	!	}, bold_text = bold_texts, value = footnotes)
247		}
248
249		# take a list of metadata, one item per dataset (raw_metadata each element from datasets$get_metadata())
250		# and the corresponding datanames and output a data.frame with columns {Dataset, Name, Value}.
251		# which are, the Dataset the metadata came from, the metadata's name and value
252		convert_metadata_to_dataframe <- function(raw_metadata, datanames) {
253	4x	output <- mapply(function(metadata, dataname) {
254	6x	if (is.null(metadata)) {
255	2x	return(data.frame(Dataset = character(0), Name = character(0), Value = character(0)))
256		}
257	4x	data.frame(
258	4x	Dataset = dataname,
259	4x	Name = names(metadata),
260	4x	Value = unname(unlist(lapply(metadata, as.character)))
261		)
262	4x	}, raw_metadata, datanames, SIMPLIFY = FALSE)
263	4x	do.call(rbind, output)
264		}

1		#' `teal` module: Variable browser
2		#'
3		#' Module provides provides a detailed summary and visualization of variable distributions
4		#' for `data.frame` objects, with interactive features to customize analysis.
5		#'
6		#' Numeric columns with fewer than 30 distinct values can be treated as either discrete
7		#' or continuous with a checkbox allowing users to switch how they are treated(if < 6 unique values
8		#' then the default is discrete, otherwise it is continuous).
9		#'
10		#' @inheritParams teal::module
11		#' @inheritParams shared_params
12		#' @param parent_dataname (`character(1)`) string specifying a parent dataset.
13		#' If it exists in `datanames` then an extra checkbox will be shown to
14		#' allow users to not show variables in other datasets which exist in this `dataname`.
15		#' This is typically used to remove `ADSL` columns in `CDISC` data.
16		#' In non `CDISC` data this can be ignored. Defaults to `"ADSL"`.
17		#' @param datasets_selected (`character`) `r lifecycle::badge("deprecated")` vector of datasets to show, please
18		#' use the `datanames` argument.
19		#'
20		#' @inherit shared_params return
21		#'
22		#' @examplesShinylive
23		#' library(teal.modules.general)
24		#' interactive <- function() TRUE
25		#' {{ next_example }}
26		# nolint start: line_length_linter.
27		#' @examples
28		# nolint end: line_length_linter.
29		#' # general data example
30		#' data <- teal_data()
31		#' data <- within(data, {
32		#' iris <- iris
33		#' mtcars <- mtcars
34		#' women <- women
35		#' faithful <- faithful
36		#' CO2 <- CO2
37		#' })
38		#'
39		#' app <- init(
40		#' data = data,

1		#' `teal` module: Outliers analysis
2		#'
3		#' Module to analyze and identify outliers using different methods
4		#' such as IQR, Z-score, and Percentiles, and offers visualizations including
5		#' box plots, density plots, and cumulative distribution plots to help interpret the outliers.
6		#'
7		#' @inheritParams teal::module
8		#' @inheritParams shared_params
9		#'
10		#' @param outlier_var (`data_extract_spec` or `list` of multiple `data_extract_spec`)
11		#' Specifies variable(s) to be analyzed for outliers.
12		#' @param categorical_var (`data_extract_spec` or `list` of multiple `data_extract_spec`) optional,
13		#' specifies the categorical variable(s) to split the selected outlier variables on.
14		#' @param ggplot2_args `r roxygen_ggplot2_args_param("Boxplot", "Density Plot", "Cumulative Distribution Plot")`
15		#'
16		#' @inherit shared_params return
17		#'
18		#' @section Decorating Module:
19		#'
20		#' This module generates the following objects, which can be modified in place using decorators:
21		#' - `box_plot` (`ggplot`)
22		#' - `density_plot` (`ggplot`)
23		#' - `cumulative_plot` (`ggplot`)
24		#'
25		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
26		#' The name of this list corresponds to the name of the output to which the decorator is applied.
27		#' See code snippet below:
28		#'
29		#' ```
30		#' tm_outliers(
31		#' ..., # arguments for module
32		#' decorators = list(
33		#' box_plot = teal_transform_module(...), # applied only to `box_plot` output
34		#' density_plot = teal_transform_module(...), # applied only to `density_plot` output
35		#' cumulative_plot = teal_transform_module(...) # applied only to `cumulative_plot` output
36		#' )
37		#' )
38		#' ```
39		#'
40		#' For additional details and examples of decorators, refer to the vignette

1		#' `teal` module: Missing data analysis
2		#'
3		#' This module analyzes missing data in `data.frame`s to help users explore missing observations and
4		#' gain insights into the completeness of their data.
5		#' It is useful for clinical data analysis within the context of `CDISC` standards and
6		#' adaptable for general data analysis purposes.
7		#'
8		#' @inheritParams teal::module
9		#' @inheritParams shared_params
10		#' @param parent_dataname (`character(1)`) Specifies the parent dataset name. Default is `ADSL` for `CDISC` data.
11		#' If provided and exists, enables additional analysis "by subject". For non-`CDISC` data, this parameter can be
12		#' ignored.
13		# nolint start: line_length.
14		#' @param ggtheme (`character`) optional, specifies the default `ggplot2` theme for plots. Defaults to `classic`.
15		#' @param ggplot2_args `r roxygen_ggplot2_args_param("Summary Obs", "Summary Patients", "Combinations Main", "Combinations Hist", "By Subject")`
16		# nolint end: line_length.
17		#'
18		#' @inherit shared_params return
19		#'
20		#' @section Decorating Module:
21		#'
22		#' This module generates the following objects, which can be modified in place using decorators:
23		#' - `summary_plot` (`grob` created with [ggplot2::ggplotGrob()])
24		#' - `combination_plot` (`grob` created with [ggplot2::ggplotGrob()])
25		#' - `by_subject_plot` (`ggplot`)
26		#'
27		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
28		#' The name of this list corresponds to the name of the output to which the decorator is applied.
29		#' See code snippet below:
30		#'
31		#' ```
32		#' tm_missing_data(
33		#' ..., # arguments for module
34		#' decorators = list(
35		#' summary_plot = teal_transform_module(...), # applied only to `summary_plot` output
36		#' combination_plot = teal_transform_module(...), # applied only to `combination_plot` output
37		#' by_subject_plot = teal_transform_module(...) # applied only to `by_subject_plot` output
38		#' )
39		#' )
40		#' ```

1		#' `teal` module: Distribution analysis
2		#'
3		#' Module is designed to explore the distribution of a single variable within a given dataset.
4		#' It offers several tools, such as histograms, Q-Q plots, and various statistical tests to
5		#' visually and statistically analyze the variable's distribution.
6		#'
7		#' @inheritParams teal::module
8		#' @inheritParams teal.widgets::standard_layout
9		#' @inheritParams shared_params
10		#'
11		#' @param dist_var (`data_extract_spec` or `list` of multiple `data_extract_spec`)
12		#' Variable(s) for which the distribution will be analyzed.
13		#' @param strata_var (`data_extract_spec` or `list` of multiple `data_extract_spec`)
14		#' Categorical variable used to split the distribution analysis.
15		#' @param group_var (`data_extract_spec` or `list` of multiple `data_extract_spec`)
16		#' Variable used for faceting plot into multiple panels.
17		#' @param freq (`logical`) optional, whether to display frequency (`TRUE`) or density (`FALSE`).
18		#' Defaults to density (`FALSE`).
19		#' @param bins (`integer(1)` or `integer(3)`) optional, specifies the number of bins for the histogram.
20		#' - When the length of `bins` is one: The histogram bins will have a fixed size based on the `bins` provided.
21		#' - When the length of `bins` is three: The histogram bins are dynamically adjusted based on vector of `value`, `min`,
22		#' and `max`.
23		#' Defaults to `c(30L, 1L, 100L)`.
24		#'
25		#' @param ggplot2_args `r roxygen_ggplot2_args_param("Histogram", "QQplot")`
26		#'
27		#' @inherit shared_params return
28		#'
29		#' @section Decorating Module:
30		#'
31		#' This module generates the following objects, which can be modified in place using decorators::
32		#' - `histogram_plot` (`ggplot`)
33		#' - `qq_plot` (`ggplot`)
34		#'
35		#' A Decorator is applied to the specific output using a named list of `teal_transform_module` objects.
36		#' The name of this list corresponds to the name of the output to which the decorator is applied.
37		#' See code snippet below:
38		#'
39		#' ```
40		#' tm_g_distribution(