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split_cols_by_groups.R
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split_cols_by_groups.R
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#' Convert list of groups to a data frame
#'
#' This converts a list of group levels into a data frame format which is expected by [rtables::add_combo_levels()].
#'
#' @param groups_list (named `list` of `character`)\cr specifies the new group levels via the names and the
#' levels that belong to it in the character vectors that are elements of the list.
#'
#' @return A `tibble` in the required format.
#'
#' @examples
#' grade_groups <- list(
#' "Any Grade (%)" = c("1", "2", "3", "4", "5"),
#' "Grade 3-4 (%)" = c("3", "4"),
#' "Grade 5 (%)" = "5"
#' )
#' groups_list_to_df(grade_groups)
#'
#' @export
groups_list_to_df <- function(groups_list) {
checkmate::assert_list(groups_list, names = "named")
lapply(groups_list, checkmate::assert_character)
tibble::tibble(
valname = make_names(names(groups_list)),
label = names(groups_list),
levelcombo = unname(groups_list),
exargs = replicate(length(groups_list), list())
)
}
#' Reference and treatment group combination
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Facilitate the re-combination of groups divided as reference and treatment groups; it helps in arranging groups of
#' columns in the `rtables` framework and teal modules.
#'
#' @param fct (`factor`)\cr the variable with levels which needs to be grouped.
#' @param ref (`character`)\cr the reference level(s).
#' @param collapse (`string`)\cr a character string to separate `fct` and `ref`.
#'
#' @return A `list` with first item `ref` (reference) and second item `trt` (treatment).
#'
#' @examples
#' groups <- combine_groups(
#' fct = DM$ARM,
#' ref = c("B: Placebo")
#' )
#'
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups) %>%
#' add_colcounts() %>%
#' analyze_vars("AGE") %>%
#' build_table(DM)
#'
#' @export
combine_groups <- function(fct,
ref = NULL,
collapse = "/") {
checkmate::assert_string(collapse)
checkmate::assert_character(ref, min.chars = 1, any.missing = FALSE, null.ok = TRUE)
checkmate::assert_multi_class(fct, classes = c("factor", "character"))
fct <- as_factor_keep_attributes(fct)
group_levels <- levels(fct)
if (is.null(ref)) {
ref <- group_levels[1]
} else {
checkmate::assert_subset(ref, group_levels)
}
groups <- list(
ref = group_levels[group_levels %in% ref],
trt = group_levels[!group_levels %in% ref]
)
stats::setNames(groups, nm = lapply(groups, paste, collapse = collapse))
}
#' Split columns by groups of levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @inheritParams groups_list_to_df
#' @param ... additional arguments to [rtables::split_cols_by()] in order. For instance, to
#' control formats (`format`), add a joint column for all groups (`incl_all`).
#'
#' @return A layout object suitable for passing to further layouting functions. Adding
#' this function to an `rtable` layout will add a column split including the given
#' groups to the table layout.
#'
#' @seealso [rtables::split_cols_by()]
#'
#' @examples
#' # 1 - Basic use
#'
#' # Without group combination `split_cols_by_groups` is
#' # equivalent to [rtables::split_cols_by()].
#' basic_table() %>%
#' split_cols_by_groups("ARM") %>%
#' add_colcounts() %>%
#' analyze("AGE") %>%
#' build_table(DM)
#'
#' # Add a reference column.
#' basic_table() %>%
#' split_cols_by_groups("ARM", ref_group = "B: Placebo") %>%
#' add_colcounts() %>%
#' analyze(
#' "AGE",
#' afun = function(x, .ref_group, .in_ref_col) {
#' if (.in_ref_col) {
#' in_rows("Diff Mean" = rcell(NULL))
#' } else {
#' in_rows("Diff Mean" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#' }
#' }
#' ) %>%
#' build_table(DM)
#'
#' # 2 - Adding group specification
#'
#' # Manual preparation of the groups.
#' groups <- list(
#' "Arms A+B" = c("A: Drug X", "B: Placebo"),
#' "Arms A+C" = c("A: Drug X", "C: Combination")
#' )
#'
#' # Use of split_cols_by_groups without reference column.
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups) %>%
#' add_colcounts() %>%
#' analyze("AGE") %>%
#' build_table(DM)
#'
#' # Including differentiated output in the reference column.
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups_list = groups, ref_group = "Arms A+B") %>%
#' analyze(
#' "AGE",
#' afun = function(x, .ref_group, .in_ref_col) {
#' if (.in_ref_col) {
#' in_rows("Diff. of Averages" = rcell(NULL))
#' } else {
#' in_rows("Diff. of Averages" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#' }
#' }
#' ) %>%
#' build_table(DM)
#'
#' # 3 - Binary list dividing factor levels into reference and treatment
#'
#' # `combine_groups` defines reference and treatment.
#' groups <- combine_groups(
#' fct = DM$ARM,
#' ref = c("A: Drug X", "B: Placebo")
#' )
#' groups
#'
#' # Use group definition without reference column.
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups_list = groups) %>%
#' add_colcounts() %>%
#' analyze("AGE") %>%
#' build_table(DM)
#'
#' # Use group definition with reference column (first item of groups).
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups, ref_group = names(groups)[1]) %>%
#' add_colcounts() %>%
#' analyze(
#' "AGE",
#' afun = function(x, .ref_group, .in_ref_col) {
#' if (.in_ref_col) {
#' in_rows("Diff Mean" = rcell(NULL))
#' } else {
#' in_rows("Diff Mean" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#' }
#' }
#' ) %>%
#' build_table(DM)
#'
#' @export
split_cols_by_groups <- function(lyt,
var,
groups_list = NULL,
ref_group = NULL,
...) {
if (is.null(groups_list)) {
split_cols_by(
lyt = lyt,
var = var,
ref_group = ref_group,
...
)
} else {
groups_df <- groups_list_to_df(groups_list)
if (!is.null(ref_group)) {
ref_group <- groups_df$valname[groups_df$label == ref_group]
}
split_cols_by(
lyt = lyt,
var = var,
split_fun = add_combo_levels(groups_df, keep_levels = groups_df$valname),
ref_group = ref_group,
...
)
}
}
#' Combine counts
#'
#' Simplifies the estimation of column counts, especially when group combination is required.
#'
#' @inheritParams combine_groups
#' @inheritParams groups_list_to_df
#'
#' @return A `vector` of column counts.
#'
#' @seealso [combine_groups()]
#'
#' @examples
#' ref <- c("A: Drug X", "B: Placebo")
#' groups <- combine_groups(fct = DM$ARM, ref = ref)
#'
#' col_counts <- combine_counts(
#' fct = DM$ARM,
#' groups_list = groups
#' )
#'
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups) %>%
#' add_colcounts() %>%
#' analyze_vars("AGE") %>%
#' build_table(DM, col_counts = col_counts)
#'
#' ref <- "A: Drug X"
#' groups <- combine_groups(fct = DM$ARM, ref = ref)
#' col_counts <- combine_counts(
#' fct = DM$ARM,
#' groups_list = groups
#' )
#'
#' basic_table() %>%
#' split_cols_by_groups("ARM", groups) %>%
#' add_colcounts() %>%
#' analyze_vars("AGE") %>%
#' build_table(DM, col_counts = col_counts)
#'
#' @export
combine_counts <- function(fct, groups_list = NULL) {
checkmate::assert_multi_class(fct, classes = c("factor", "character"))
fct <- as_factor_keep_attributes(fct)
if (is.null(groups_list)) {
y <- table(fct)
y <- stats::setNames(as.numeric(y), nm = dimnames(y)[[1]])
} else {
y <- vapply(
X = groups_list,
FUN = function(x) sum(table(fct)[x]),
FUN.VALUE = 1
)
}
y
}