Clean Categorical Levels

step_clean_levels() creates a specification of a recipe step that will clean nominal data (character or factor) so the levels consist only of letters, numbers, and the underscore.

Usage

step_clean_levels(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  clean = NULL,
  skip = FALSE,
  id = rand_id("clean_levels")
)

Arguments

recipe: A recipes::recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose which variables are affected by the step. See recipes::selections() for more details.
role: Not used by this step since no new variables are created.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
clean: A named character vector to clean and recode categorical levels. This is NULL until computed by recipes::prep.recipe(). Note that if the original variable is a character vector, it will be converted to a factor.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = FALSE.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

The new levels are cleaned and then reset with dplyr::recode_factor(). When data to be processed contains novel levels (i.e., not contained in the training set), they are converted to missing.

Tidying

When you tidy() this step, a tibble is returned with columns terms, orginal, value, and id:

terms: character, the selectors or variables selected
original: character, the original levels
value: character, the cleaned levels
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

Examples

library(recipes)
library(modeldata)
data(Smithsonian)

smith_tr <- Smithsonian[1:15, ]
smith_te <- Smithsonian[16:20, ]

rec <- recipe(~., data = smith_tr)

rec <- rec %>%
  step_clean_levels(name)
rec <- prep(rec, training = smith_tr)

cleaned <- bake(rec, smith_tr)

tidy(rec, number = 1)
#> # A tibble: 15 × 4
#>    terms original                                           value id   
#>    <chr> <chr>                                              <chr> <chr>
#>  1 name  Anacostia Community Museum                         anac… clea…
#>  2 name  Arthur M. Sackler Gallery                          arth… clea…
#>  3 name  Arts and Industries Building                       arts… clea…
#>  4 name  Cooper Hewitt, Smithsonian Design Museum           coop… clea…
#>  5 name  Freer Gallery of Art                               free… clea…
#>  6 name  George Gustav Heye Center                          geor… clea…
#>  7 name  Hirshhorn Museum and Sculpture Garden              hirs… clea…
#>  8 name  National Air and Space Museum                      nati… clea…
#>  9 name  National Museum of African American History and C… nati… clea…
#> 10 name  National Museum of African Art                     nati… clea…
#> 11 name  National Museum of American History                nati… clea…
#> 12 name  National Museum of Natural History                 nati… clea…
#> 13 name  National Museum of the American Indian             nati… clea…
#> 14 name  National Portrait Gallery                          nati… clea…
#> 15 name  Steven F. Udvar-Hazy Center                        stev… clea…

# novel levels are replaced with missing
bake(rec, smith_te)
#> # A tibble: 5 × 3
#>   name  latitude longitude
#>   <fct>    <dbl>     <dbl>
#> 1 NA        38.9     -77.0
#> 2 NA        38.9     -77.0
#> 3 NA        38.9     -77.0
#> 4 NA        38.9     -77.0
#> 5 NA        38.9     -77.1