step_stopwords creates a specification of a recipe step that will filter a tokenlist for stopwords(keep or remove).

  role = NA,
  trained = FALSE,
  columns = NULL,
  language = "en",
  keep = FALSE,
  stopword_source = "snowball",
  custom_stopword_source = NULL,
  skip = FALSE,
  id = rand_id("stopwords")

# S3 method for step_stopwords
tidy(x, ...)



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 step_stopwords, this indicates the variables to be encoded into a tokenlist. See recipes::selections() for more details. For the tidy method, these are not currently used.


Not used by this step since no new variables are created.


A logical to indicate if the recipe has been baked.


A list of tibble results that define the encoding. This is NULL until the step is trained by recipes::prep.recipe().


A character to indicate the language of stopwords by ISO 639-1 coding scheme.


A logical. Specifies whether to keep the stopwords or discard them.


A character to indicate the stopwords source as listed in stopwords::stopwords_getsources.


A character vector to indicate a custom list of words that cater to the users specific problem.


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 = TRUE as it may affect the computations for subsequent operations.


A character string that is unique to this step to identify it.


A step_stopwords object.


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


Stop words are words which sometimes are remove before natural language processing tasks. While stop words usually refers to the most common words in the language there is no universal stop word list.

The argument custom_stopword_source allows you to pass a character vector to filter against. With the keep argument one can specify to keep the words instead of removing thus allowing you to select words with a combination of these two arguments.

See also

step_tokenize() to turn character into tokenlist.

Other tokenlist to tokenlist steps: step_lemma(), step_ngram(), step_pos_filter(), step_stem(), step_tokenfilter(), step_tokenmerge()


library(recipes) library(modeldata) data(okc_text) if (requireNamespace("stopwords", quietly = TRUE)) { okc_rec <- recipe(~ ., data = okc_text) %>% step_tokenize(essay0) %>% step_stopwords(essay0) okc_obj <- okc_rec %>% prep() juice(okc_obj, essay0) %>% slice(1:2) juice(okc_obj) %>% slice(2) %>% pull(essay0) tidy(okc_rec, number = 2) tidy(okc_obj, number = 2) }
#> # A tibble: 1 x 4 #> terms value keep id #> <quos> <chr> <lgl> <chr> #> 1 essay0 snowball FALSE stopwords_KUzZj
# With a custom stopwords list okc_rec <- recipe(~ ., data = okc_text) %>% step_tokenize(essay0) %>% step_stopwords(essay0, custom_stopword_source = c("twice", "upon")) okc_obj <- okc_rec %>% prep(traimomg = okc_text) juice(okc_obj) %>% slice(2) %>% pull(essay0)
#> <textrecipes_tokenlist[1]> #> [1] [24 tokens] #> # Unique Tokens: 18