step_dummy_extract: Extract patterns from nominal data
In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

step_dummy_extract

R Documentation

Extract patterns from nominal data

Description

step_dummy_extract() creates a specification of a recipe step that will convert nominal data (e.g. characters or factors) into one or more integer model terms for the extracted levels.

Usage

step_dummy_extract(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  sep = NULL,
  pattern = NULL,
  threshold = 0,
  other = "other",
  naming = dummy_extract_names,
  levels = NULL,
  sparse = "auto",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("dummy_extract")
)

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 this step. See `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.
`sep`	Character string containing a regular expression to use for splitting. `strsplit()` is used to perform the split. `sep` takes priority if `pattern` is also specified.
`pattern`	Character string containing a regular expression used for extraction. `gregexpr()` and `regmatches()` are used to perform pattern extraction using `perl = TRUE`.
`threshold`	A numeric value between 0 and 1, or an integer greater or equal to one. If less than one, then factor levels with a rate of occurrence in the training set below `threshold` will be pooled to `other`. If greater or equal to one, then this value is treated as a frequency and factor levels that occur less than `threshold` times will be pooled to `other`.
`other`	A single character value for the other category, default to `"other"`.
`naming`	A function that defines the naming convention for new dummy columns. See Details below.
`levels`	A list that contains the information needed to create dummy variables for each variable contained in `terms`. This is `NULL` until the step is trained 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 `FALSE`.
`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.

Details

step_dummy_extract() will create a set of integer dummy variables from a character variable by extracting individual strings by either splitting or extracting then counting those to create count variables.

Note that threshold works in a very specific way for this step. While it is possible for one label to be present multiple times in the same row, it will only be counted once when calculating the occurrences and frequencies.

This recipe step allows for flexible naming of the resulting variables. For an unordered factor named x, with levels "a" and "b", the default naming convention would be to create a new variable called x_b. The naming format can be changed using the naming argument; the function dummy_names() is the default.

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, columns , and id:

terms: character, the selectors or variables selected
columns: character, names of resulting columns
id: character, id of this step

The return value is ordered according to the frequency of columns entries in the training data set.

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

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Examples


data(tate_text, package = "modeldata")

dummies <- recipe(~ artist + medium, data = tate_text) %>%
  step_dummy_extract(artist, medium, sep = ", ") %>%
  prep()

dummy_data <- bake(dummies, new_data = NULL)

dummy_data %>%
  select(starts_with("medium")) %>%
  names() %>%
  head()

# More detailed splitting
dummies_specific <- recipe(~medium, data = tate_text) %>%
  step_dummy_extract(medium, sep = "(, )|( and )|( on )") %>%
  prep()

dummy_data_specific <- bake(dummies_specific, new_data = NULL)

dummy_data_specific %>%
  select(starts_with("medium")) %>%
  names() %>%
  head()

tidy(dummies, number = 1)
tidy(dummies_specific, number = 1)

# pattern argument can be useful to extract harder patterns
color_examples <- tibble(
  colors = c(
    "['red', 'blue']",
    "['red', 'blue', 'white']",
    "['blue', 'blue', 'blue']"
  )
)

dummies_color <- recipe(~colors, data = color_examples) %>%
  step_dummy_extract(colors, pattern = "(?<=')[^',]+(?=')") %>%
  prep()

dummies_data_color <- dummies_color %>%
  bake(new_data = NULL)

dummies_data_color

tidymodels/recipes documentation built on April 17, 2025, 6:17 p.m.