textrecipes (version 1.1.0)

step_clean_levels: Clean Categorical Levels

Description

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")
)

Value

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

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.

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.

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.

See Also

step_clean_names(), recipes::step_factor2string(), recipes::step_string2factor(), recipes::step_regex(), recipes::step_unknown(), recipes::step_novel(), recipes::step_other()

Other Steps for Text Cleaning: step_clean_names()

Examples

if (FALSE) { # rlang::is_installed(c("modeldata", "janitor"))
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)

# novel levels are replaced with missing
bake(rec, smith_te)
}