Combine Multiple Token Variables Into One

step_tokenmerge() creates a specification of a recipe step that will take multiple token variables and combine them into one token variable.

Usage

step_tokenmerge(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  prefix = "tokenmerge",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("tokenmerge")
)

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

For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created by the original variables will be used as predictors in a model.

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().

prefix

A prefix for generated column names, default to "tokenmerge".

keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.

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).

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected).

Case weights

The underlying operation does not allow for case weights.

See also

step_tokenize() to turn characters into tokens

Other Steps for Token Modification: step_lemma(), step_ngram(), step_pos_filter(), step_stem(), step_stopwords(), step_tokenfilter()

Examples

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

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

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL)
#> # A tibble: 4,284 × 4
#>        id title                                            year tokenmerge
#>     <dbl> <fct>                                           <dbl>  <tknlist>
#>  1  21926 Proposals for a Habitat                          1990 [9 tokens]
#>  2  20472 Michael                                          1990 [5 tokens]
#>  3  20474 Geoffrey                                         1990 [5 tokens]
#>  4  20473 Jake                                             1990 [5 tokens]
#>  5  20513 To the Studios                                   1990 [6 tokens]
#>  6  21389 Phaëthon                                         1990 [7 tokens]
#>  7 121187 Untitled                                         1990 [6 tokens]
#>  8  19455 Green VIII                                       1990 [5 tokens]
#>  9  20938 Present Bound                                    1990 [8 tokens]
#> 10 105941 Joseph Beuys: A Private Collection. A11 Artfor…  1990 [5 tokens]
#> # ℹ 4,274 more rows

tidy(tate_rec, number = 2)
#> # A tibble: 2 × 2
#>   terms  id              
#>   <chr>  <chr>           
#> 1 medium tokenmerge_tdWfB
#> 2 artist tokenmerge_tdWfB
tidy(tate_obj, number = 2)
#> # A tibble: 2 × 2
#>   terms  id              
#>   <chr>  <chr>           
#> 1 medium tokenmerge_tdWfB
#> 2 artist tokenmerge_tdWfB