step_cut() creates a specification of a recipe step that cuts a numeric
variable into a factor based on provided boundary values.
step_cut(
recipe,
...,
role = NA,
trained = FALSE,
breaks,
include_outside_range = FALSE,
skip = FALSE,
id = rand_id("cut")
)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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A numeric vector with at least one cut point.
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.
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.
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 any existing operations.
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.
When you tidy() this step, a tibble is returned with
columns terms, value , and id:
character, the selectors or variables selected
numeric, the location of the cuts
character, id of this step
The underlying operation does not allow for case weights.
Other discretization steps:
step_discretize()
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)
#> # A tibble: 10 × 2
#> x y
#> <fct> <int>
#> 1 [1,5] 5
#> 2 [1,5] 6
#> 3 [1,5] 7
#> 4 [1,5] 8
#> 5 [1,5] 9
#> 6 (5,10] 10
#> 7 (5,10] 11
#> 8 (5,10] 12
#> 9 (5,10] 13
#> 10 (5,10] 14
# 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)
#> # A tibble: 10 × 2
#> x y
#> <fct> <fct>
#> 1 [1,6] [5,6]
#> 2 [1,6] [5,6]
#> 3 [1,6] (6,9]
#> 4 [1,6] (6,9]
#> 5 [1,6] (6,9]
#> 6 [1,6] (9,14]
#> 7 (6,9] (9,14]
#> 8 (6,9] (9,14]
#> 9 (6,9] (9,14]
#> 10 (9,10] (9,14]
# 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)
#> # A tibble: 10 × 3
#> x y x_orig
#> <fct> <int> <int>
#> 1 [1,5] 5 1
#> 2 [1,5] 6 2
#> 3 [1,5] 7 3
#> 4 [1,5] 8 4
#> 5 [1,5] 9 5
#> 6 (5,10] 10 6
#> 7 (5,10] 11 7
#> 8 (5,10] 12 8
#> 9 (5,10] 13 9
#> 10 (5,10] 14 10
# 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)
#> # A tibble: 11 × 2
#> x y
#> <fct> <int>
#> 1 [min,5] 5
#> 2 [min,5] 6
#> 3 [min,5] 7
#> 4 [min,5] 8
#> 5 [min,5] 9
#> 6 (5,max] 10
#> 7 (5,max] 11
#> 8 (5,max] 12
#> 9 (5,max] 13
#> 10 (5,max] 14
#> 11 (5,max] 15
rec |>
step_cut(x, breaks = 5, include_outside_range = FALSE) |>
prep() |>
bake(new_df)
#> # A tibble: 11 × 2
#> x y
#> <fct> <int>
#> 1 [1,5] 5
#> 2 [1,5] 6
#> 3 [1,5] 7
#> 4 [1,5] 8
#> 5 [1,5] 9
#> 6 (5,10] 10
#> 7 (5,10] 11
#> 8 (5,10] 12
#> 9 (5,10] 13
#> 10 (5,10] 14
#> 11 NA 15