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ard_continuous.R
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ard_continuous.R
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#' Continuous ARD Statistics
#'
#' Compute Analysis Results Data (ARD) for simple continuous summary statistics.
#'
#' @param data (`data.frame`)\cr
#' a data frame
#' @param variables ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
#' columns to include in summaries. Default is `everything()`.
#' @param by,strata ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
#' columns to tabulate by/stratify by for summary statistic
#' calculation. Arguments are similar, but with an important distinction:
#'
#' `by`: results are calculated for **all combinations** of the columns specified,
#' including unobserved combinations and unobserved factor levels.
#'
#' `strata`: results are calculated for **all _observed_ combinations** of the
#' columns specified.
#'
#' Arguments may be used in conjunction with one another.
#' @param statistic ([`formula-list-selector`][syntax])\cr
#' a named list, a list of formulas,
#' or a single formula where the list element is a named list of functions
#' (or the RHS of a formula),
#' e.g. `list(mpg = list(mean = \(x) mean(x)))`.
#'
#' The value assigned to each variable must also be a named list, where the names
#' are used to reference a function and the element is the function object.
#' Typically, this function will return a scalar statistic, but a function that
#' returns a named list of results is also acceptable, e.g.
#' `list(conf.low = -1, conf.high = 1)`. However, when errors occur, the messaging
#' will be less clear in this setting.
#' @param fmt_fn ([`formula-list-selector`][syntax])\cr
#' a named list, a list of formulas,
#' or a single formula where the list element is a named list of functions
#' (or the RHS of a formula),
#' e.g. `list(mpg = list(mean = \(x) round(x, digits = 2) |> as.character()))`.
#' @param stat_label ([`formula-list-selector`][syntax])\cr
#' a named list, a list of formulas, or a single formula where
#' the list element is either a named list or a list of formulas defining the
#' statistic labels, e.g. `everything() ~ list(mean = "Mean", sd = "SD")` or
#' `everything() ~ list(mean ~ "Mean", sd ~ "SD")`.
#' @inheritParams rlang::args_dots_used
#'
#' @return an ARD data frame of class 'card'
#' @name ard_continuous
#'
#' @examples
#' ard_continuous(ADSL, by = "ARM", variables = "AGE")
#'
#' # if a single function returns a named list, the named
#' # results will be placed in the resulting ARD
#' ADSL |>
#' dplyr::group_by(ARM) |>
#' ard_continuous(
#' variables = "AGE",
#' statistic =
#' ~ list(conf.int = \(x) t.test(x)[["conf.int"]] |>
#' as.list() |>
#' setNames(c("conf.low", "conf.high")))
#' )
NULL
#' @rdname ard_continuous
#' @export
ard_continuous <- function(data, ...) {
check_not_missing(data)
UseMethod("ard_continuous")
}
#' @rdname ard_continuous
#' @export
ard_continuous.data.frame <- function(data,
variables,
by = dplyr::group_vars(data),
strata = NULL,
statistic = everything() ~ continuous_summary_fns(),
fmt_fn = NULL,
stat_label = everything() ~ default_stat_labels(),
...) {
set_cli_abort_call()
check_dots_used()
# check inputs ---------------------------------------------------------------
check_not_missing(variables)
.check_no_ard_columns(data)
# process arguments ----------------------------------------------------------
process_selectors(data,
variables = {{ variables }},
by = {{ by }},
strata = {{ strata }}
)
data <- dplyr::ungroup(data)
process_formula_selectors(
data[variables],
statistic = statistic,
fmt_fn = fmt_fn,
stat_label = stat_label
)
fill_formula_selectors(
data[variables],
statistic = formals(asNamespace("cards")[["ard_continuous.data.frame"]])[["stat_label"]] |> eval(),
stat_label = formals(asNamespace("cards")[["ard_continuous.data.frame"]])[["stat_label"]] |> eval()
)
check_list_elements(
x = statistic,
predicate = function(x) is.list(x) && is_named(x) && every(x, is.function),
error_msg =
c("Error in the argument {.arg {arg_name}} for variable {.val {variable}}.",
"i" = "Value must be a named list of functions."
)
)
# return empty ARD if no variables selected ----------------------------------
if (is_empty(variables)) {
return(dplyr::tibble() |> as_card())
}
# check factor levels --------------------------------------------------------
check_no_na_factor_levels(data[c(by, strata)])
check_factor_has_levels(data[c(by, strata)])
# calculate statistics -------------------------------------------------------
df_nested <-
data |>
nest_for_ard(
by = by,
strata = strata,
key = "...ard_nested_data..."
)
# calculate statistics indicated by user in statistics argument
df_nested <-
.calculate_stats_as_ard(
df_nested = df_nested,
variables = variables,
statistic = statistic,
new_col_name = "...ard_all_stats...",
by = by,
strata = strata,
data = data
)
# unnest results
df_results <-
df_nested |>
dplyr::select(all_ard_groups(), "...ard_all_stats...") |>
tidyr::unnest(cols = "...ard_all_stats...")
# final processing of fmt_fn -------------------------------------------------
df_results <-
.process_nested_list_as_df(
x = df_results,
arg = fmt_fn,
new_column = "fmt_fn"
) |>
.default_fmt_fn()
# final processing of stat labels --------------------------------------------
df_results <-
.process_nested_list_as_df(
x = df_results,
arg = stat_label,
new_column = "stat_label",
unlist = TRUE
) |>
dplyr::mutate(stat_label = dplyr::coalesce(.data$stat_label, .data$stat_name))
# add meta data and class ----------------------------------------------------
df_results |>
dplyr::mutate(context = "continuous") |>
tidy_ard_column_order() |>
as_card()
}
#' Check Protected Column Names
#'
#' Checks that column names in a passed data frame are not protected, that is,
#' they do not begin with `"...ard_"` and end with `"..."`.
#'
#' @param x (`data.frame`)\cr
#' a data frame
#' @param exceptions (`string`)\cr
#' character string of column names to exclude from checks
#'
#' @return returns invisible if check is successful, throws an error message if not.
#' @keywords internal
#'
#' @examples
#' data <- data.frame("ard_x" = 1)
#'
#' cards:::.check_no_ard_columns(data)
.check_no_ard_columns <- function(x, exceptions = "...ard_dummy_for_counting...") {
colnames <- names(x)
ard_colnames <-
colnames[startsWith(colnames, "...ard_") & endsWith(colnames, "...")] |>
setdiff(exceptions)
if (!is_empty(ard_colnames)) {
"Columns beginning with {.val '...ard_'} and ending with {.val '...'} are not allowed." |>
cli::cli_abort(call = get_cli_abort_call())
}
}
#' Calculate Continuous Statistics
#'
#' Calculate statistics and return in an ARD format
#'
#' @param df_nested (`data.frame`)\cr
#' a nested data frame
#' @param variables (`character`)\cr
#' character vector of variables
#' @param statistic (named `list`)\cr
#' named list of statistical functions
#'
#' @return an ARD data frame of class 'card'
#' @keywords internal
#'
#' @examples
#' data_nested <- ADSL |>
#' nest_for_ard(
#' by = "ARM",
#' strata = NULL,
#' key = "...ard_nested_data..."
#' )
#'
#' cards:::.calculate_stats_as_ard(
#' df_nested = data_nested,
#' variables = "AGE",
#' statistic = list(mean = "mean"),
#' by = "ARM",
#' strata = NULL,
#' data = ADSL
#' )
.calculate_stats_as_ard <- function(df_nested, variables, statistic,
by, strata, data,
new_col_name = "...ard_all_stats...") {
df_nested[[new_col_name]] <-
map(
df_nested[["...ard_nested_data..."]],
function(nested_data) {
map(
variables,
function(variable) {
map2(
statistic[[variable]], names(statistic[[variable]]),
function(fun, fun_name) {
.lst_results_as_df(
x = # calculate results, and place in tibble
eval_capture_conditions(
getOption(
"cards.calculate_stats_as_ard.eval_fun",
default = expr(do.call(fun, args = list(stats::na.omit(nested_data[[variable]]))))
)
),
variable = variable,
fun_name = fun_name
)
}
) |>
unname()
}
) |>
dplyr::bind_rows()
}
)
df_nested
}
#' Prepare Results as Data Frame
#'
#' Function takes the results from [eval_capture_conditions()], which is a
#' named list, e.g. `list(result=, warning=, error=)`, and converts it to a data
#' frame.
#'
#' @param x (named `list`)\cr
#' the result from [eval_capture_conditions()]
#' @param variable (`string`)\cr
#' variable name of the results
#' @param fun_name (`string`)\cr
#' name of function called to get results in `x`
#'
#' @return a data frame
#' @keywords internal
#'
#' @examples
#' msgs <- eval_capture_conditions({
#' warning("Warning 1")
#' warning("Warning 2")
#' letters[1:2]
#' })
#'
#' cards:::.lst_results_as_df(msgs, "result", "mean")
.lst_results_as_df <- function(x, variable, fun_name) {
# unnesting results if needed
if (.is_named_list(x$result, allow_df = TRUE)) {
if (is.data.frame(x$result)) x$result <- unclass(x$result)
df_ard <-
dplyr::tibble(
stat_name = names(x$result),
result = unname(x$result),
warning = list(x$warning),
error = list(x$error)
)
}
# if result is not a nested list, return a single row tibble
else {
df_ard <-
map(x, list) |>
dplyr::as_tibble() |>
dplyr::mutate(stat_name = .env$fun_name)
}
df_ard |>
dplyr::mutate(variable = .env$variable) |>
dplyr::rename(stat = "result")
}
#' Convert Nested Lists to Column
#'
#' Some arguments, such as `stat_label`, are passed as nested lists. This
#' function properly unnests these lists and adds them to the results data frame.
#'
#' @param x (`data.frame`)\cr
#' result data frame
#' @param arg (`list`)\cr
#' the nested list
#' @param new_column (`string`)\cr
#' new column name
#' @param unlist (`logical`)\cr
#' whether to fully unlist final results
#'
#' @return a data frame
#' @keywords internal
#'
#' @examples
#' ard <- ard_categorical(ADSL, by = "ARM", variables = "AGEGR1")
#'
#' cards:::.process_nested_list_as_df(ard, NULL, "new_col")
.process_nested_list_as_df <- function(x, arg, new_column, unlist = FALSE) {
# add fmt_fn column if not already present
if (!new_column %in% names(x)) {
x[[new_column]] <- list(NULL)
}
# process argument if not NULL, and update new column
if (!is_empty(arg)) {
df_argument <-
imap(
arg,
function(enlst_arg, variable) {
lst_stat_names <-
x[c("variable", "stat_name")] |>
dplyr::filter(.data$variable %in% .env$variable) |>
unique() %>%
{stats::setNames(as.list(.[["stat_name"]]), .[["stat_name"]])} # styler: off
compute_formula_selector(
data = lst_stat_names,
x = enlst_arg
) %>%
# styler: off
{dplyr::tibble(
variable = variable,
stat_name = names(.),
"{new_column}" := unname(.)
)}
# styler: on
}
) |>
dplyr::bind_rows()
x <- x |> dplyr::rows_update(df_argument, by = c("variable", "stat_name"), unmatched = "ignore")
}
if (isTRUE(unlist)) {
x[[new_column]] <- lapply(x[[new_column]], function(x) x %||% NA) |> unlist()
}
x
}
#' Add Default Formatting Functions
#'
#' @param x (`data.frame`)\cr
#' an ARD data frame of class 'card'
#'
#' @return a data frame
#' @keywords internal
#'
#' @examples
#' ard <- ard_categorical(ADSL, by = "ARM", variables = "AGEGR1") |>
#' dplyr::mutate(fmt_fn = NA)
#'
#' cards:::.default_fmt_fn(ard)
.default_fmt_fn <- function(x) {
x |>
dplyr::mutate(
fmt_fn =
pmap(
list(.data$stat_name, .data$stat, .data$fmt_fn),
function(stat_name, stat, fmt_fn) {
if (!is_empty(fmt_fn)) {
return(fmt_fn)
}
if (stat_name %in% c("p", "p_miss", "p_nonmiss")) {
return(label_cards(digits = 1, scale = 100))
}
if (is.integer(stat)) {
return(0L)
}
if (is.numeric(stat)) {
return(1L)
}
return(as.character)
}
)
)
}