step_mutate() creates a specification of a recipe step that will add
variables using dplyr::mutate().
A recipe object. The step will be added to the sequence of operations for this recipe.
Name-value pairs of expressions. See dplyr::mutate().
Character vector, package names of functions used in expressions
.... Should be specified if using non-base functions.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
Quosure(s) of ....
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.
When using this flexible step, use extra care to avoid data leakage in your
preprocessing. Consider, for example, the transformation x = w > mean(w).
When applied to new data or testing data, this transformation would use the
mean of w from the new data, not the mean of w from the training data.
When an object in the user's global environment is referenced in the
expression defining the new variable(s), it is a good idea to use
quasiquotation (e.g. !!) to embed the value of the object in the expression
(to be portable between sessions). See the examples.
If a preceding step removes a column that is selected by name in
step_mutate(), the recipe will error when being estimated with prep().
When you tidy() this step, a tibble is returned with
columns terms, value , and id:
character, the selectors or variables selected
character, expression passed to mutate()
character, id of this step
The underlying operation does not allow for case weights.
Other individual transformation steps:
step_BoxCox(),
step_YeoJohnson(),
step_bs(),
step_harmonic(),
step_hyperbolic(),
step_inverse(),
step_invlogit(),
step_log(),
step_logit(),
step_ns(),
step_percentile(),
step_poly(),
step_relu(),
step_sqrt()
Other dplyr steps:
step_arrange(),
step_filter(),
step_mutate_at(),
step_rename(),
step_rename_at(),
step_sample(),
step_select(),
step_slice()
rec <-
recipe(~., data = iris) |>
step_mutate(
dbl_width = Sepal.Width * 2,
half_length = Sepal.Length / 2
)
prepped <- prep(rec, training = iris |> slice(1:75))
library(dplyr)
dplyr_train <-
iris |>
as_tibble() |>
slice(1:75) |>
mutate(
dbl_width = Sepal.Width * 2,
half_length = Sepal.Length / 2
)
rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)
#> [1] TRUE
dplyr_test <-
iris |>
as_tibble() |>
slice(76:150) |>
mutate(
dbl_width = Sepal.Width * 2,
half_length = Sepal.Length / 2
)
rec_test <- bake(prepped, iris |> slice(76:150))
all.equal(dplyr_test, rec_test)
#> [1] TRUE
# Embedding objects:
const <- 1.414
qq_rec <-
recipe(~., data = iris) |>
step_mutate(
bad_approach = Sepal.Width * const,
best_approach = Sepal.Width * !!const
) |>
prep(training = iris)
bake(qq_rec, new_data = NULL, contains("appro")) |> slice(1:4)
#> # A tibble: 4 × 2
#> bad_approach best_approach
#> <dbl> <dbl>
#> 1 4.95 4.95
#> 2 4.24 4.24
#> 3 4.52 4.52
#> 4 4.38 4.38
# The difference:
tidy(qq_rec, number = 1)
#> # A tibble: 2 × 3
#> terms value id
#> <chr> <chr> <chr>
#> 1 bad_approach Sepal.Width * const mutate_p75TX
#> 2 best_approach Sepal.Width * 1.414 mutate_p75TX
# Using across()
recipe(~., data = iris) |>
step_mutate(across(contains("Length"), .fns = ~ 1 / .)) |>
prep() |>
bake(new_data = NULL) |>
slice(1:10)
#> # A tibble: 10 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 0.196 3.5 0.714 0.2 setosa
#> 2 0.204 3 0.714 0.2 setosa
#> 3 0.213 3.2 0.769 0.2 setosa
#> 4 0.217 3.1 0.667 0.2 setosa
#> 5 0.2 3.6 0.714 0.2 setosa
#> 6 0.185 3.9 0.588 0.4 setosa
#> 7 0.217 3.4 0.714 0.3 setosa
#> 8 0.2 3.4 0.667 0.2 setosa
#> 9 0.227 2.9 0.714 0.2 setosa
#> 10 0.204 3.1 0.667 0.1 setosa
recipe(~., data = iris) |>
# leads to more columns being created.
step_mutate(
across(contains("Length"), .fns = list(log = log, sqrt = sqrt))
) |>
prep() |>
bake(new_data = NULL) |>
slice(1:10)
#> # A tibble: 10 × 9
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species Sepal.Length_log
#> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 5.1 3.5 1.4 0.2 setosa 1.63
#> 2 4.9 3 1.4 0.2 setosa 1.59
#> 3 4.7 3.2 1.3 0.2 setosa 1.55
#> 4 4.6 3.1 1.5 0.2 setosa 1.53
#> 5 5 3.6 1.4 0.2 setosa 1.61
#> 6 5.4 3.9 1.7 0.4 setosa 1.69
#> 7 4.6 3.4 1.4 0.3 setosa 1.53
#> 8 5 3.4 1.5 0.2 setosa 1.61
#> 9 4.4 2.9 1.4 0.2 setosa 1.48
#> 10 4.9 3.1 1.5 0.1 setosa 1.59
#> # ℹ 3 more variables: Sepal.Length_sqrt <dbl>, Petal.Length_log <dbl>,
#> # Petal.Length_sqrt <dbl>