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step_ngram creates a specification of a recipe step that will convert a token variable into a token variable of ngrams.

Usage

step_ngram(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  num_tokens = 3L,
  min_num_tokens = 3L,
  delim = "_",
  skip = FALSE,
  id = rand_id("ngram")
)

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().

num_tokens

The number of tokens in the n-gram. This must be an integer greater than or equal to 1. Defaults to 3.

min_num_tokens

The minimum number of tokens in the n-gram. This must be an integer greater than or equal to 1 and smaller than n. Defaults to 3.

delim

The separator between words in an n-gram. 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

The use of this step will leave the ordering of the tokens meaningless. If min_num_tokens < num_tokens then the tokens order in increasing fashion with respect to the number of tokens in the n-gram. If min_num_tokens = 1 and num_tokens = 3 then the output contains all the 1-grams followed by all the 2-grams followed by all the 3-grams.

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_pos_filter(), step_stem(), step_stopwords(), step_tokenfilter(), step_tokenmerge()

Examples

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

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

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [6 tokens]
#> 2 [1 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [1 tokens]
#> # Unique Tokens: 1

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms  value id         
#>   <chr>  <chr> <chr>      
#> 1 medium NA    ngram_O30rG
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 2
#>   terms  id         
#>   <chr>  <chr>      
#> 1 medium ngram_O30rG