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 toNA.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas 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.
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.
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
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)
#> # 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