Convert Factors to Strings

step_factor2string will convert one or more factor vectors to strings.

step_factor2string(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = FALSE,
  skip = FALSE,
  id = rand_id("factor2string")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose variables for this step. See 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.
columns	A character string of variables that will be converted. This is NULL until computed by prep.recipe().
skip	A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when 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 = TRUE as it may affect the computations for subsequent operations.
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 any existing operations.

Details

prep has an option strings_as_factors that defaults to TRUE. If this step is used with the default option, the string(s() produced by this step will be converted to factors after all of the steps have been prepped.

When you tidy() this step, a tibble with columns terms (the columns that will be affected) is returned.

See also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy_multi_choice(), step_dummy(), step_holiday(), step_indicate_na(), step_integer(), step_novel(), step_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_relevel(), step_string2factor(), step_unknown(), step_unorder()

Examples

library(modeldata)
data(okc)

rec <- recipe(~ diet + location, data = okc)

rec <- rec %>%
  step_string2factor(diet)

factor_test <- rec %>%
  prep(training = okc,
       strings_as_factors = FALSE) %>%
  juice
# diet is a
class(factor_test$diet)
#> [1] "factor"

rec <- rec %>%
  step_factor2string(diet)

string_test <- rec %>%
  prep(training = okc,
       strings_as_factors = FALSE) %>%
  juice
# diet is a
class(string_test$diet)
#> [1] "character"

tidy(rec, number = 1)
#> # A tibble: 1 × 3
#>   terms ordered id                 
#>   <chr> <lgl>   <chr>              
#> 1 diet  FALSE   string2factor_oEGyP