Summary table bridge

Bridge function for converting tbl_summary() (and similar) cards to basic gtsummary objects. All bridge functions begin with prefix brdg_*().

This file also contains helper functions for constructing the bridge, referred to as the piers (supports for a bridge) and begin with pier_*().

• brdg_summary(): The bridge function ingests an ARD data frame and returns a gtsummary table that includes .$table_body and a basic .$table_styling. The .$table_styling$header data frame includes the header statistics. Based on context, this function adds a column to the ARD data frame named "gts_column". This column is used during the reshaping in the pier_*() functions defining column names.

• pier_*(): these functions accept a cards tibble and returns a tibble that is a piece of the .$table_body. Typically these will be stacked to construct the final table body data frame. The ARD object passed here will have two primary parts: the calculated summary statistics and the attributes ARD. The attributes ARD is used for labeling. The ARD data frame passed to this function must include a "gts_column" column, which is added in brdg_summary().

brdg_summary(
  cards,
  variables,
  type,
  statistic,
  by = NULL,
  missing = "no",
  missing_stat = "{N_miss}",
  missing_text = "Unknown"
)

pier_summary_dichotomous(cards, variables, statistic)

pier_summary_categorical(cards, variables, statistic)

pier_summary_continuous2(cards, variables, statistic)

pier_summary_continuous(cards, variables, statistic)

pier_summary_missing_row(
  cards,
  variables,
  missing = "no",
  missing_stat = "{N_miss}",
  missing_text = "Unknown"
)

Arguments

cards

(card)
An ARD object of class "card" typically created with cards::ard_*() functions.

variables

(character)
character list of variables

type

(named list)
named list of summary types

statistic

(named list)
named list of summary statistic names

by

(string)
string indicating the stratifying column

missing, missing_text, missing_stat

Arguments dictating how and if missing values are presented:

• missing: must be one of c("ifany", "no", "always").

• missing_text: string indicating text shown on missing row. Default is "Unknown".

• missing_stat: statistic to show on missing row. Default is "{N_miss}". Possible values are N_miss, N_obs, N_nonmiss, p_miss, p_nonmiss.

Value

a gtsummary object

Examples

library(cards)

# first build ARD data frame
cards <-
  ard_stack(
    mtcars,
    ard_summary(variables = c("mpg", "hp")),
    ard_tabulate(variables = "cyl"),
    ard_tabulate_value(variables = "am"),
    .missing = TRUE,
    .attributes = TRUE
  ) |>
  # this column is used by the `pier_*()` functions
  dplyr::mutate(gts_column = ifelse(context == "attributes", NA, "stat_0"))

brdg_summary(
  cards = cards,
  variables = c("cyl", "am", "mpg", "hp"),
  type =
    list(
      cyl = "categorical",
      am = "dichotomous",
      mpg = "continuous",
      hp = "continuous2"
    ),
  statistic =
    list(
      cyl = "{n} / {N}",
      am = "{n} / {N}",
      mpg = "{mean} ({sd})",
      hp = c("{median} ({p25}, {p75})", "{mean} ({sd})")
    )
) |>
  as_tibble()
#> # A tibble: 9 × 2
#>   `**Characteristic**` stat_0             
#>   <chr>                <chr>              
#> 1 cyl                  NA                 
#> 2 4                    11 / 32            
#> 3 6                    7 / 32             
#> 4 8                    14 / 32            
#> 5 am                   13 / 32            
#> 6 mpg                  20.1 (6.0)         
#> 7 hp                   NA                 
#> 8 Median (Q1, Q3)      123.0 (96.0, 180.0)
#> 9 Mean (SD)            146.7 (68.6)       

pier_summary_dichotomous(
  cards = cards,
  variables = "am",
  statistic = list(am = "{n} ({p})")
)
#> # A tibble: 1 × 5
#>   row_type var_label variable label stat_0   
#>   <chr>    <chr>     <chr>    <chr> <chr>    
#> 1 label    am        am       am    13 (40.6)

pier_summary_categorical(
  cards = cards,
  variables = "cyl",
  statistic = list(cyl = "{n} ({p})")
)
#> # A tibble: 4 × 5
#>   variable var_label row_type label stat_0   
#>   <chr>    <chr>     <chr>    <chr> <chr>    
#> 1 cyl      cyl       label    cyl   NA       
#> 2 cyl      cyl       level    4     11 (34.4)
#> 3 cyl      cyl       level    6     7 (21.9) 
#> 4 cyl      cyl       level    8     14 (43.8)

pier_summary_continuous2(
  cards = cards,
  variables = "hp",
  statistic = list(hp = c("{median}", "{mean}"))
)
#> # A tibble: 3 × 5
#>   variable var_label row_type label  stat_0
#>   <chr>    <chr>     <chr>    <chr>  <chr> 
#> 1 hp       hp        label    hp     NA    
#> 2 hp       hp        level    Median 123.0 
#> 3 hp       hp        level    Mean   146.7 

pier_summary_continuous(
  cards = cards,
  variables = "mpg",
  statistic = list(mpg = "{median}")
)
#> # A tibble: 1 × 5
#>   row_type var_label variable label stat_0
#>   <chr>    <chr>     <chr>    <chr> <chr> 
#> 1 label    mpg       mpg      mpg   19.2