Split group_name and group_level columns

Pivots the input dataframe so the values of the column group_name are transformed into columns that contain values from the group_level column.

Usage

splitGroup(result, keep = FALSE, fill = "overall")

Arguments

result

A dataframe with at least the columns group_name and group_level.

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.

Examples

{
  library(dplyr)
  library(omopgenerics)

  x <- tibble(
    "result_id" = as.integer(c(1, 2)),
    "cdm_name" = c("cprd", "eunomia"),
    "group_name" = "cohort_name",
    "group_level" = "my_cohort",
    "strata_name" = "sex",
    "strata_level" = "male",
    "variable_name" = "Age group",
    "variable_level" = "10 to 50",
    "estimate_name" = "count",
    "estimate_type" = "numeric",
    "estimate_value" = "5",
    "additional_name" = "overall",
    "additional_level" = "overall"
  ) |>
    newSummarisedResult(settings = tibble(
      "result_id" = c(1, 2), "custom" = c("A", "B")
    ))

  x

  x |> splitGroup()
}
#> `result_type`, `package_name`, and `package_version` added to settings.
#> # A tibble: 2 × 12
#>   result_id cdm_name cohort_name strata_name strata_level variable_name
#>       <int> <chr>    <chr>       <chr>       <chr>        <chr>        
#> 1         1 cprd     my_cohort   sex         male         Age group    
#> 2         2 eunomia  my_cohort   sex         male         Age group    
#> # ℹ 6 more variables: variable_level <chr>, estimate_name <chr>,
#> #   estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> #   additional_level <chr>