step_stem creates a specification of a recipe step that will convert a tokenlist to have its tokens stemmed.

step_stem(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  options = list(),
  custom_stemmer = NULL,
  skip = FALSE,
  id = rand_id("stem")
)

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

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 step_stem, 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.

role

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

trained

A logical to indicate if the recipe has been baked.

columns

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

options

A list of options passed to the stemmer function.

custom_stemmer

A custom stemming function. If none is provided it will default to "SnowballC".

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

id

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

x

A step_stem object.

Value

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

Details

Words tend to have different forms depending on context, such as organize, organizes, and organizing. In many situations it is beneficial to have these words condensed into one to allow for a smaller pool of words. Stemming is the act of choping off the end of words using a set of heuristics.

Note that the steming will only be done at the end of the word and will therefore not work reliably on ngrams or sentences.

See also

step_tokenize() to turn character into tokenlist.

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

Examples

library(recipes) library(modeldata) data(okc_text) okc_rec <- recipe(~ ., data = okc_text) %>% step_tokenize(essay0) %>% step_stem(essay0) okc_obj <- okc_rec %>% prep() juice(okc_obj, essay0) %>% slice(1:2)
#> # A tibble: 2 x 1 #> essay0 #> <tknlist> #> 1 [184 tokens] #> 2 [24 tokens]
juice(okc_obj) %>% slice(2) %>% pull(essay0)
#> <textrecipes_tokenlist[1]> #> [1] [24 tokens] #> # Unique Tokens: 18
tidy(okc_rec, number = 2)
#> # A tibble: 1 x 3 #> terms value id #> <chr> <chr> <chr> #> 1 essay0 <NA> stem_GD9Ux
tidy(okc_obj, number = 2)
#> # A tibble: 1 x 3 #> terms is_custom_stemmer id #> <quos> <lgl> <chr> #> 1 essay0 TRUE stem_GD9Ux
# Using custom stemmer. Here a custom stemmer that removes the last letter # if it is a "s". remove_s <- function(x) gsub("s$", "", x) okc_rec <- recipe(~ ., data = okc_text) %>% step_tokenize(essay0) %>% step_stem(essay0, custom_stemmer = remove_s) okc_obj <- okc_rec %>% prep() juice(okc_obj, essay0) %>% slice(1:2)
#> # A tibble: 2 x 1 #> essay0 #> <tknlist> #> 1 [184 tokens] #> 2 [24 tokens]
juice(okc_obj) %>% slice(2) %>% pull(essay0)
#> <textrecipes_tokenlist[1]> #> [1] [24 tokens] #> # Unique Tokens: 18