step_select {recipes} | R Documentation |
Select variables using dplyr
Description
step_select()
creates a specification of a recipe step that will select
variables using dplyr::select()
.
Due to how step_select()
works with workflows::workflow()
, we no longer
recommend the usage of this step.If you are using step_select()
to remove
variables with -
then you can flip it around and use step_rm()
instead.
All other uses of step_select()
could be replaced by a call to
dplyr::select()
on the data before it is passed to recipe()
.
Usage
step_select(
recipe,
...,
role = NA,
trained = FALSE,
skip = FALSE,
id = rand_id("select")
)
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 |
role |
For model terms selected by this step, what analysis role should they be assigned? |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
skip |
A logical. Should the step be skipped when the recipe is baked by
|
id |
A character string that is unique to this step to identify it. |
Details
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.
This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.
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
and id
:
- terms
character, the selectors or variables selected
- id
character, id of this step
Sparse data
This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.
Case weights
The underlying operation does not allow for case weights.
See Also
Other variable filter steps:
step_corr()
,
step_filter_missing()
,
step_lincomb()
,
step_nzv()
,
step_rm()
,
step_zv()
Other dplyr steps:
step_arrange()
,
step_filter()
,
step_mutate()
,
step_mutate_at()
,
step_rename()
,
step_rename_at()
,
step_sample()
,
step_slice()
Examples
library(dplyr)
iris_tbl <- as_tibble(iris)
iris_train <- slice(iris_tbl, 1:75)
iris_test <- slice(iris_tbl, 76:150)
dplyr_train <- select(iris_train, Species, starts_with("Sepal"))
dplyr_test <- select(iris_test, Species, starts_with("Sepal"))
rec <- recipe(~., data = iris_train) |>
step_select(Species, starts_with("Sepal")) |>
prep(training = iris_train)
rec_train <- bake(rec, new_data = NULL)
all.equal(dplyr_train, rec_train)
rec_test <- bake(rec, iris_test)
all.equal(dplyr_test, rec_test)
# Local variables
sepal_vars <- c("Sepal.Width", "Sepal.Length")
qq_rec <-
recipe(~., data = iris_train) |>
# fine for interactive usage
step_select(Species, all_of(sepal_vars)) |>
# best approach for saving a recipe to disk
step_select(Species, all_of(!!sepal_vars))
# Note that `sepal_vars` is inlined in the second approach
qq_rec