ARD Selectors

These selection helpers match variables according to a given pattern.

• all_ard_groups(): Function selects grouping columns, e.g. columns named "group##" or "group##_level".

• all_ard_variables(): Function selects variables columns, e.g. columns named "variable" or "variable_level".

• all_ard_group_n(): Function selects n grouping columns.

• all_missing_columns(): Function selects columns that are all NA or empty.

Usage

all_ard_groups(types = c("names", "levels"))

all_ard_variables(types = c("names", "levels"))

all_ard_group_n(n)

all_missing_columns()

Arguments

types

(character)
type(s) of columns to select. "names" selects the columns variable name columns, and "levels" selects the level columns. Default is c("names", "levels").

n

(integer)
integer(s) indicating which grouping columns to select.

Value

tidyselect output

Examples

ard <- ard_categorical(ADSL, by = "ARM", variables = "AGEGR1")

ard |> dplyr::select(all_ard_groups())
#> {cards} data frame: 27 x 2
#>    group1 group1_level
#> 1     ARM      Placebo
#> 2     ARM      Placebo
#> 3     ARM      Placebo
#> 4     ARM      Placebo
#> 5     ARM      Placebo
#> 6     ARM      Placebo
#> 7     ARM      Placebo
#> 8     ARM      Placebo
#> 9     ARM      Placebo
#> 10    ARM    Xanomeli…
#> ℹ 17 more rows
#> ℹ Use `print(n = ...)` to see more rows
ard |> dplyr::select(all_ard_variables())
#> {cards} data frame: 27 x 2
#>    variable variable_level
#> 1    AGEGR1          65-80
#> 2    AGEGR1          65-80
#> 3    AGEGR1          65-80
#> 4    AGEGR1            <65
#> 5    AGEGR1            <65
#> 6    AGEGR1            <65
#> 7    AGEGR1            >80
#> 8    AGEGR1            >80
#> 9    AGEGR1            >80
#> 10   AGEGR1          65-80
#> ℹ 17 more rows
#> ℹ Use `print(n = ...)` to see more rows