step_mutate()
creates a specification of a recipe step that will add
variables using dplyr::mutate()
.
Arguments
- recipe
A recipe object. The step will be added to the sequence of operations for this recipe.
- ...
Name-value pairs of expressions. See
dplyr::mutate()
.- .pkgs
Character vector, package names of functions used in expressions
...
. Should be specified if using non-base functions.- role
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.
- trained
A logical to indicate if the quantities for preprocessing have been estimated.
- inputs
Quosure(s) of
...
.- 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 = TRUE
as 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
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()
.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
, value
, and id
:
- terms
character, the selectors or variables selected
- value
character, expression passed to
mutate()
- id
character, id of this step
See also
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()
Examples
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