step_cut {recipes}

Cut a numeric variable into a factor

Description

step_cut() creates a specification of a recipe step that cuts a numeric variable into a factor based on provided boundary values.

Usage

step_cut(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  breaks,
  include_outside_range = FALSE,
  skip = FALSE,
  id = rand_id("cut")
)

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.

breaks

A numeric vector with at least one cut point.

include_outside_range

Logical, indicating if values outside the range in the train set should be included in the lowest or highest bucket. Defaults to FALSE, values outside the original range will be set to NA.

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

Unlike the base::cut() function there is no need to specify the min and the max values in the breaks. All values before the lowest break point will end up in the first bucket, all values after the last break points will end up in the last.

step_cut() will call base::cut() in the baking step with include.lowest set to TRUE.

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, value , and id:

terms

character, the selectors or variables selected

value

numeric, the location of the cuts

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See Also

Other discretization steps: step_discretize()

Examples

df <- data.frame(x = 1:10, y = 5:14)
rec <- recipe(df)

# The min and max of the variable are used as boundaries
# if they exceed the breaks
rec %>%
  step_cut(x, breaks = 5) %>%
  prep() %>%
  bake(df)

# You can use the same breaks on multiple variables
# then for each variable the boundaries are set separately
rec %>%
  step_cut(x, y, breaks = c(6, 9)) %>%
  prep() %>%
  bake(df)

# You can keep the original variables using `step_mutate` or
# `step_mutate_at`, for transforming multiple variables at once
rec %>%
  step_mutate(x_orig = x) %>%
  step_cut(x, breaks = 5) %>%
  prep() %>%
  bake(df)

# It is up to you if you want values outside the
# range learned at prep to be included
new_df <- data.frame(x = 1:11, y = 5:15)
rec %>%
  step_cut(x, breaks = 5, include_outside_range = TRUE) %>%
  prep() %>%
  bake(new_df)

rec %>%
  step_cut(x, breaks = 5, include_outside_range = FALSE) %>%
  prep() %>%
  bake(new_df)