Make NA values in factor columns explicit — explicit

Converts NA values in any factors in the dataframe into a new level - This is a thin wrapper for forcats::fct_explicit_na() but with missing value level added regardless of whether any values missing. This forces an empty row in count tables.

Usage

explicit_na(df, na_level = "<missing>", hide_if_empty = FALSE)

Arguments

df: the data frame
na_level: a label for NA valued factors
hide_if_empty: dont add a missing data category if no data is missing

Value

the dataframe with all factor columns containing explicit na values

Examples

# before
missing_diamonds %>% dplyr::group_by(cut) %>% dplyr::count()
#> # A tibble: 6 × 2
#> # Groups:   cut [6]
#>   cut           n
#>   <ord>     <int>
#> 1 Fair       1454
#> 2 Good       4462
#> 3 Very Good 10816
#> 4 Premium   12460
#> 5 Ideal     19361
#> 6 NA         5387
# after
missing_diamonds %>% explicit_na() %>% dplyr::group_by(cut) %>% dplyr::count()
#> # A tibble: 6 × 2
#> # Groups:   cut [6]
#>   cut           n
#>   <fct>     <int>
#> 1 Fair       1454
#> 2 Good       4462
#> 3 Very Good 10816
#> 4 Premium   12460
#> 5 Ideal     19361
#> 6 <missing>  5387