Split columns by groups of levels

split_cols_by_groups(lyt, var, groups_list = NULL, ref_group = NULL, ...)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

var

(string)
single variable name that is passed by rtables when requested by a statistics function.

groups_list

(named list of character)
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.

ref_group

(data.frame or vector)
the data corresponding to the reference group.

...

additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all).

Value

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.

See also

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)
#>        A: Drug X   B: Placebo   C: Combination
#>         (N=121)     (N=106)        (N=129)    
#> ——————————————————————————————————————————————
#> Mean     34.91       33.02          34.57     

# 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)
#>             A: Drug X   B: Placebo   C: Combination
#>              (N=121)     (N=106)        (N=129)    
#> ———————————————————————————————————————————————————
#> Diff Mean     1.89                        1.55     

# 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)
#>        Arms A+B   Arms A+C
#>        (N=227)    (N=250) 
#> ——————————————————————————
#> Mean    34.03      34.73  

# 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)
#>                     Arms A+B   Arms A+C
#> ———————————————————————————————————————
#> Diff. of Averages                0.71  

# 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
#> $`A: Drug X/B: Placebo`
#> [1] "A: Drug X"  "B: Placebo"
#> 
#> $`C: Combination`
#> [1] "C: Combination"
#> 

# Use group definition without reference column.
basic_table() %>%
  split_cols_by_groups("ARM", groups_list = groups) %>%
  add_colcounts() %>%
  analyze("AGE") %>%
  build_table(DM)
#>        A: Drug X/B: Placebo   C: Combination
#>              (N=227)             (N=129)    
#> ————————————————————————————————————————————
#> Mean          34.03               34.57     

# 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)
#>             A: Drug X/B: Placebo   C: Combination
#>                   (N=227)             (N=129)    
#> —————————————————————————————————————————————————
#> Diff Mean                               0.54