Tplyr table module for DaVinci's module manager

This module integrates Tplyr-based table generation into the DaVinci module manager framework. It allows users to define and render summary tables using Tplyr, with support for custom table-building functions and dataset listings.

Usage

mod_Tplyr_table(
  module_id,
  output_list,
  subjid_var = "USUBJID",
  default_vars = NULL,
  pagination = NULL,
  intended_use_label =
    "Use only for internal review and monitoring during the conduct of clinical trials.",
  receiver_id = NULL,
  review = NULL
)

Arguments

module_id

[character(1)]

A character string that serves as unique identifier for the module.

output_list

[list(list())]

A named list defining the outputs to be generated. Each element of the list corresponds to a table or listing and must be a named list with one of the following structures:

For summary tables:

tplyr_tab_fun

A function that takes one or more datasets as input and returns a tplyr_table object.

build_fun

A function that takes the tplyr_table object and returns a built table (typically using Tplyr::build()). The metadata argument of Tplyr::build() needs to be set to TURE, so that the corresponding listing can be shown.

For listings:

dataset_names

A character vector of dataset names required to generate the listing.

The names of the top-level list elements are used as identifiers for the outputs.

subjid_var

[character(1) | NULL]

Column corresponding to subject ID. Default value is 'USUBJID'

default_vars

an argument of listings_server of dv.listings will be passed through.

pagination

an argument of listings_server of dv.listings will be passed through.

intended_use_label

an argument of listings_server of dv.listings will be passed through.

receiver_id

an argument of listings_server of dv.listings will be passed through

review

an argument of listings_server of dv.listings will be passed through.

Examples

if (FALSE) { # \dontrun{
dm <- pharmaversesdtm::dm
ae <- pharmaversesdtm::ae

my_tplyr_fun <- function(dm) {
  tab <- Tplyr::tplyr_table(dm, ARM) |>
    Tplyr::add_layer(Tplyr::group_desc(AGE, by = "Age (years)")) |>
    Tplyr::add_layer(Tplyr::group_count(SEX, by = "Sex")) |>
    Tplyr::add_layer(Tplyr::group_count(RACE, by = "Race"))
  return(tab)
}

build_func <- function(tab) {
  Tplyr::build(tab, metadata = TRUE) |>
    dplyr::mutate(row_label2 = ifelse(row_label2 == row_label1, "Total (%)", row_label2)) |>
    Tplyr::apply_row_masks(row_breaks = TRUE)
}

my_tplyr_fun2 <- function(dm, ae) {
  dm_arm <- dm |> dplyr::select(USUBJID, ARM)
  ae_arm <- ae |> dplyr::inner_join(dm_arm, by = "USUBJID")

  tab <- Tplyr::tplyr_table(ae_arm, ARM) |>
    Tplyr::set_pop_data(dm) |>
    Tplyr::set_pop_treat_var(ARM) |>
    Tplyr::add_layer(
      Tplyr::group_count("All subjects") |>
        Tplyr::set_distinct_by(USUBJID) |>
        Tplyr::set_format_strings(Tplyr::f_str("xx", distinct_total))
    ) |>
    Tplyr::add_layer(
      Tplyr::group_count("Subjects with adverse events") |>
        Tplyr::set_distinct_by(USUBJID) |>
        Tplyr::set_format_strings(Tplyr::f_str("xx (xx %)", distinct_n, distinct_pct))
    ) |>
    Tplyr::add_layer(
      Tplyr::group_count(AESEV, by = "Adverse event severity") |>
        Tplyr::set_distinct_by(USUBJID) |>
        Tplyr::set_format_strings(Tplyr::f_str("xx (xx %)", distinct_n, distinct_pct))
    ) |>
    Tplyr::add_layer(
      Tplyr::group_count("Subjects with serious AE", where = AESER == "Y") |>
        Tplyr::set_distinct_by(USUBJID) |>
        Tplyr::set_format_strings(Tplyr::f_str("xx (xx %)", distinct_n, distinct_pct))
    )
  return(tab)
}

build_func2 <- function(tab) {
  Tplyr::build(tab, metadata = TRUE) |>
    Tplyr::apply_row_masks(row_breaks = TRUE)
}

output_list <- list(
  "Demographic" = list(
    tplyr_tab_fun = my_tplyr_fun,
    build_fun = build_func
  ),
  "Adverse Events" = list(
    tplyr_tab_fun = my_tplyr_fun2,
    build_fun = build_func
  ),
  "Listing" = list(
    dataset_names = c("dm", "ae")
  )
)

module_list <- list(
  "Table" = dv.tables::mod_Tplyr_table(
    module_id = "tplyr_table",
    output_list = output_list
  )
)

dv.manager::run_app(
  data = list("My data" = list(ae = ae, dm = dm)),
  module_list = module_list,
  filter_data = "dm",
  filter_type = "datasets"
)
} # }