Pivot a table wider combining counts and percentages

This function takes a data frame produced by functions like build_table, build_mtable, or build_qtable, which contains columns N and Percent, and pivots it into a wider format. It combines the N and Percent columns into a single xlr_n_percent vector for each pivoted column. If top_variable is not specified, it infers the variable to use for column names from the structure of the data frame.

Usage

make_wider(x, top_variable = NULL, names_prefix = "")

Arguments

x

A data frame or tibble containing at least the columns N and Percent. Typically the output of build_table, build_mtable, or build_qtable,.

top_variable

Optional. A bare column name to use for the names_from argument in pivot_wider. If NULL (default), the function infers the column based the default position.

names_prefix

String added to the start of every variable name. This is particularly useful if top_variable is a numeric vector and you want to create syntactic variable names.

Value

A xlr_table (if x is a xlr_table) or tibble::tibble (if tibble::tibble or data.frame) in a wider format with columns containing xlr_n_percent vectors.

See also

xlr_n_percent, pivot_wider

Examples

library(xlr)
# Assuming example data from build_table or similar
table <- clothes_opinions |>
            build_table(c(gender,age_group))
make_wider(table)
#> # A xlr_table: 4 x 4
#>   age_group    female      male `non-binary`
#>   <x_vctr>  <x_n_pct> <x_n_pct>    <x_n_pct>
#> 1 18-30     118 (27%) 116 (26%)     18 (25%)
#> 2 31-40      87 (20%)  96 (22%)     19 (27%)
#> 3 41-50      95 (22%)  82 (19%)     18 (25%)
#> 4 51-65     139 (32%) 146 (33%)     16 (23%)

# use top_variable to specify that we have gender as out selection column
make_wider(table, top_variable = age_group)
#> # A xlr_table: 3 x 5
#>   gender       `18-30`   `31-40`   `41-50`   `51-65`
#>   <x_vctr>   <x_n_pct> <x_n_pct> <x_n_pct> <x_n_pct>
#> 1 female     118 (27%)  87 (20%)  95 (22%) 139 (32%)
#> 2 male       116 (26%)  96 (22%)  82 (19%) 146 (33%)
#> 3 non-binary  18 (25%)  19 (27%)  18 (25%)  16 (23%)