step_count: Create counts of patterns using regular expressions
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_count

R Documentation

Create counts of patterns using regular expressions

Description

step_count() creates a specification of a recipe step that will create a variable that counts instances of a regular expression pattern in text.

Usage

step_count(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  pattern = ".",
  normalize = FALSE,
  options = list(),
  result = make.names(pattern),
  input = NULL,
  sparse = "auto",
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("count")
)

Arguments

`recipe`	A recipe object. The step will be added to the sequence of operations for this recipe.
`...`	A single selector function to choose which variable will be searched for the regex pattern. The selector should resolve to a single variable. See `selections()` for more details.
`role`	For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`pattern`	A character string containing a regular expression (or character string for `fixed = TRUE`) to be matched in the given character vector. Coerced by `as.character` to a character string if possible.
`normalize`	A logical; should the integer counts be divided by the total number of characters in the string?.
`options`	A list of options to `gregexpr()` that should not include `x` or `pattern`.
`result`	A single character value for the name of the new variable. It should be a valid column name.
`input`	A single character value for the name of the variable being searched. This is `NULL` until computed by `prep()`.
`sparse`	A single string. Should the columns produced be sparse vectors. Can take the values `"yes"`, `"no"`, and `"auto"`. If `sparse = "auto"` then workflows can determine the best option. Defaults to `"auto"`.
`keep_original_cols`	A logical to keep the original variables in the output. Defaults to `TRUE`.
`skip`	A logical. Should the step be skipped when the recipe is baked by `bake()`? While all operations are baked when `prep()` 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.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

When you tidy() this step, a tibble is returned with columns terms, result , and id:

terms: character, the selectors or variables selected
result: character, the new column names
id: character, id of this step

Sparse data

This step produces sparse columns if sparse = "yes" is being set. The default value "auto" won't trigger production fo sparse columns if a recipe is prep()ed, but allows for a workflow to toggle to "yes" or "no" depending on whether the model supports sparse_data and if the model is is expected to run faster with the data.

The mechanism for determining how much sparsity is produced isn't perfect, and there will be times when you want to manually overwrite by setting sparse = "yes" or sparse = "no".

Case weights

The underlying operation does not allow for case weights.

Examples


data(covers, package = "modeldata")

rec <- recipe(~description, covers) %>%
  step_count(description, pattern = "(rock|stony)", result = "rocks") %>%
  step_count(description, pattern = "famil", normalize = TRUE)

rec2 <- prep(rec, training = covers)
rec2

count_values <- bake(rec2, new_data = covers)
count_values

tidy(rec, number = 1)
tidy(rec2, number = 1)

recipes documentation built on April 13, 2025, 5:10 p.m.