Split name and level columns into the columns

Pivots the input dataframe so the values of the name columns are transformed into columns, which values come from the specified level column.

Usage

splitNameLevel(
  result,
  name = "group_name",
  level = "group_level",
  keep = FALSE,
  fill = "overall"
)

Arguments

result

A summarised_result object.

name

Column with the names.

level

Column with the levels.

keep

Whether to keep the original group_name and group_level columns.

fill

Optionally, a character that specifies what value should be filled in with when missing.

Value

A dataframe with the specified name column values as columns.

Examples

mockSummarisedResult() |>
  splitNameLevel(name = "group_name",
                 level = "group_level",
                 keep = FALSE)
#> # A tibble: 126 × 12
#>    result_id cdm_name cohort_name strata_name       strata_level   variable_name
#>        <int> <chr>    <chr>       <chr>             <chr>          <chr>        
#>  1         1 mock     cohort1     overall           overall        number subje…
#>  2         1 mock     cohort1     age_group &&& sex <40 &&& Male   number subje…
#>  3         1 mock     cohort1     age_group &&& sex >=40 &&& Male  number subje…
#>  4         1 mock     cohort1     age_group &&& sex <40 &&& Female number subje…
#>  5         1 mock     cohort1     age_group &&& sex >=40 &&& Fema… number subje…
#>  6         1 mock     cohort1     sex               Male           number subje…
#>  7         1 mock     cohort1     sex               Female         number subje…
#>  8         1 mock     cohort1     age_group         <40            number subje…
#>  9         1 mock     cohort1     age_group         >=40           number subje…
#> 10         1 mock     cohort2     overall           overall        number subje…
#> # ℹ 116 more rows
#> # ℹ 6 more variables: variable_level <chr>, estimate_name <chr>,
#> #   estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> #   additional_level <chr>