Untokenization of Token Variables

step_untokenize() creates a specification of a recipe step that will convert a token variable into a character predictor.

Usage

step_untokenize(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  sep = " ",
  skip = FALSE,
  id = rand_id("untokenize")
)

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 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.
columns: A character string of variable names that will be populated (eventually) by the terms argument. This is NULL until the step is trained by recipes::prep.recipe().
sep: a character to determine how the tokens should be separated when pasted together. Defaults to " ".
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

This steps will turn a token vector back into a character vector. This step is calling paste internally to put the tokens back together to a character.

Tidying

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

terms: character, the selectors or variables selected
value: character, seperator used for collapsing
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

Examples

library(recipes)
library(modeldata)
data(tate_text)

tate_rec <- recipe(~., data = tate_text) %>%
  step_tokenize(medium) %>%
  step_untokenize(medium)

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>   medium                                             
#>   <fct>                                              
#> 1 video monitor or projection colour and sound stereo
#> 2 etching on paper                                   

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> [1] etching on paper
#> 1029 Levels: 100 digital prints on paper ink on paper and wall text ...

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms  value id              
#>   <chr>  <chr> <chr>           
#> 1 medium NA    untokenize_JIOwC
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 3
#>   terms  value id              
#>   <chr>  <chr> <chr>           
#> 1 medium " "   untokenize_JIOwC