# process_data()

`process_data()` converts a count-based data frame to one row per icon. `group_var` and `sum_var` are unquoted (tidy eval); `high_group_var` takes a character string.

    df_raw <- data.frame(
      sex = c("Female", "Male"),
      n   = c(55, 45)
    )

    df_plot <- process_data(
      data        = df_raw,
      group_var   = sex,
      sum_var     = n,
      sample_size = 20
    )

    head(df_plot, 4)
    #>     type  n prop
    #> 1   Male 45 0.45
    #> 2   Male 45 0.45
    #> 3   Male 45 0.45
    #> 4 Female 55 0.55

The result has one row per icon. The `type` column carries the original group label and is what you map to `color` and `icon` in `geom_pop()`.

## With `high_group_var`

Use `high_group_var` when your data has a faceting variable. It samples independently within each panel so proportions are correct per group.

    df_region_raw <- data.frame(
      region = c("North", "North", "South", "South"),
      sex    = c("Female", "Male", "Female", "Male"),
      n      = c(30, 20, 25, 25)
    )

    df_region <- process_data(
      data           = df_region_raw,
      group_var      = sex,
      sum_var        = n,
      sample_size    = 20,
      high_group_var = "region"
    )

    head(df_region, 4)
    #>   group   type  n prop
    #> 1 North Female 30  0.6
    #> 2 North Female 30  0.6
    #> 3 North   Male 20  0.4
    #> 4 North Female 30  0.6

The `group` column in the output contains the panel label — pass it to `facet_wrap(~group)` and `geom_pop(facet = group)`.