Summarize variables in columns

The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars().

The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function.

summarize_colvars(
  lyt,
  na_str = default_na_str(),
  ...,
  .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

lyt: (PreDataTableLayouts)
layout that analyses will be added to.
na_str: (string)
string used to replace all NA or empty values in the output.
...: arguments passed to s_summary().
.stats: (character)
statistics to select for the table.
.stat_names: (character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.
.formats: (named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.
.labels: (named character)
labels for the statistics (without indent).
.indent_mods: (named vector of integer)
indent modifiers for the labels. Each element of the vector should be a name-value pair with name corresponding to a statistic specified in .stats and value the indentation for that statistic's row label.

Value

A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout.

Examples

dta_test <- data.frame(
  USUBJID = rep(1:6, each = 3),
  PARAMCD = rep("lab", 6 * 3),
  AVISIT = rep(paste0("V", 1:3), 6),
  ARM = rep(LETTERS[1:3], rep(6, 3)),
  AVAL = c(9:1, rep(NA, 9)),
  CHG = c(1:9, rep(NA, 9))
)

## Default output within a `rtables` pipeline.
basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT") %>%
  split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
  summarize_colvars() %>%
  build_table(dta_test)
#>                         A                             B                             C          
#>                 AVAL         CHG           AVAL               CHG           AVAL         CHG   
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> V1                                                                                             
#>   n               2           2              1                 1              0           0    
#>   Mean (SD)   7.5 (2.1)   2.5 (2.1)   3.0 (<Missing>)   7.0 (<Missing>)   <Missing>   <Missing>
#>   Median         7.5         2.5            3.0               7.0         <Missing>   <Missing>
#>   Min - Max   6.0 - 9.0   1.0 - 4.0      3.0 - 3.0         7.0 - 7.0      <Missing>   <Missing>
#> V2                                                                                             
#>   n               2           2              1                 1              0           0    
#>   Mean (SD)   6.5 (2.1)   3.5 (2.1)   2.0 (<Missing>)   8.0 (<Missing>)   <Missing>   <Missing>
#>   Median         6.5         3.5            2.0               8.0         <Missing>   <Missing>
#>   Min - Max   5.0 - 8.0   2.0 - 5.0      2.0 - 2.0         8.0 - 8.0      <Missing>   <Missing>
#> V3                                                                                             
#>   n               2           2              1                 1              0           0    
#>   Mean (SD)   5.5 (2.1)   4.5 (2.1)   1.0 (<Missing>)   9.0 (<Missing>)   <Missing>   <Missing>
#>   Median         5.5         4.5            1.0               9.0         <Missing>   <Missing>
#>   Min - Max   4.0 - 7.0   3.0 - 6.0      1.0 - 1.0         9.0 - 9.0      <Missing>   <Missing>

## Selection of statistics, formats and labels also work.
basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT") %>%
  split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
  summarize_colvars(
    .stats = c("n", "mean_sd"),
    .formats = c("mean_sd" = "xx.x, xx.x"),
    .labels = c(n = "n", mean_sd = "Mean, SD")
  ) %>%
  build_table(dta_test)
#>                       A                           B                            C          
#>                AVAL       CHG           AVAL             CHG           AVAL         CHG   
#> ——————————————————————————————————————————————————————————————————————————————————————————
#> V1                                                                                        
#>   n             2          2             1                1              0           0    
#>   Mean, SD   7.5, 2.1   2.5, 2.1   3.0, <Missing>   7.0, <Missing>   <Missing>   <Missing>
#> V2                                                                                        
#>   n             2          2             1                1              0           0    
#>   Mean, SD   6.5, 2.1   3.5, 2.1   2.0, <Missing>   8.0, <Missing>   <Missing>   <Missing>
#> V3                                                                                        
#>   n             2          2             1                1              0           0    
#>   Mean, SD   5.5, 2.1   4.5, 2.1   1.0, <Missing>   9.0, <Missing>   <Missing>   <Missing>

## Use arguments interpreted by `s_summary`.
basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT") %>%
  split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
  summarize_colvars(na.rm = FALSE) %>%
  build_table(dta_test)
#>                         A                             B                             C          
#>                 AVAL         CHG           AVAL               CHG           AVAL         CHG   
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> V1                                                                                             
#>   n               2           2              1                 1              0           0    
#>   Mean (SD)   7.5 (2.1)   2.5 (2.1)   3.0 (<Missing>)   7.0 (<Missing>)   <Missing>   <Missing>
#>   Median         7.5         2.5            3.0               7.0         <Missing>   <Missing>
#>   Min - Max   6.0 - 9.0   1.0 - 4.0      3.0 - 3.0         7.0 - 7.0      <Missing>   <Missing>
#> V2                                                                                             
#>   n               2           2              1                 1              0           0    
#>   Mean (SD)   6.5 (2.1)   3.5 (2.1)   2.0 (<Missing>)   8.0 (<Missing>)   <Missing>   <Missing>
#>   Median         6.5         3.5            2.0               8.0         <Missing>   <Missing>
#>   Min - Max   5.0 - 8.0   2.0 - 5.0      2.0 - 2.0         8.0 - 8.0      <Missing>   <Missing>
#> V3                                                                                             
#>   n               2           2              1                 1              0           0    
#>   Mean (SD)   5.5 (2.1)   4.5 (2.1)   1.0 (<Missing>)   9.0 (<Missing>)   <Missing>   <Missing>
#>   Median         5.5         4.5            1.0               9.0         <Missing>   <Missing>
#>   Min - Max   4.0 - 7.0   3.0 - 6.0      1.0 - 1.0         9.0 - 9.0      <Missing>   <Missing>

Arguments

Value

See also

Examples