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step_textfeature creates a specification of a recipe step that will extract a number of numeric features of a text column.


  role = "predictor",
  trained = FALSE,
  columns = NULL,
  extract_functions = textfeatures::count_functions,
  prefix = "textfeature",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("textfeature")



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.


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.


A logical to indicate if the quantities for preprocessing have been estimated.


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().


A named list of feature extracting functions. default to count_functions from the textfeatures package. See details for more information.


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


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


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.


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


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


This step will take a character column and returns a number of numeric columns equal to the number of functions in the list passed to the extract_functions argument. The default is a list of functions from the textfeatures package.

All the functions passed to extract_functions must take a character vector as input and return a numeric vector of the same length, otherwise an error will be thrown.


When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and functions (name of feature functions).

Case weights

The underlying operation does not allow for case weights.

See also

Other Steps for Numeric Variables From Characters: step_dummy_hash(), step_sequence_onehot()


if (requireNamespace("textfeatures", quietly = TRUE)) {

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

  tate_obj <- tate_rec %>%

  bake(tate_obj, new_data = NULL) %>%

  bake(tate_obj, new_data = NULL) %>%

  tidy(tate_rec, number = 1)
  tidy(tate_obj, number = 1)

  # Using custom extraction functions
  nchar_round_10 <- function(x) round(nchar(x) / 10) * 10

  recipe(~., data = tate_text) %>%
      extract_functions = list(nchar10 = nchar_round_10)
    ) %>%
    prep() %>%
    bake(new_data = NULL)
#> # A tibble: 4,284 × 5
#>        id artist             title                   year textfeature_med…
#>     <dbl> <fct>              <fct>                  <dbl>            <dbl>
#>  1  21926 Absalon            Proposals for a Habit…  1990               60
#>  2  20472 Auerbach, Frank    Michael                 1990               20
#>  3  20474 Auerbach, Frank    Geoffrey                1990               20
#>  4  20473 Auerbach, Frank    Jake                    1990               20
#>  5  20513 Auerbach, Frank    To the Studios          1990               20
#>  6  21389 Ayres, OBE Gillian Phaëthon                1990               20
#>  7 121187 Barlow, Phyllida   Untitled                1990               20
#>  8  19455 Baselitz, Georg    Green VIII              1990               20
#>  9  20938 Beattie, Basil     Present Bound           1990               30
#> 10 105941 Beuys, Joseph      Joseph Beuys: A Priva…  1990               10
#> # … with 4,274 more rows