step_select {recipes} | R Documentation |
step_select()
creates a specification of a recipe step
that will select variables using dplyr::select()
.
step_select( recipe, ..., role = NA, trained = FALSE, skip = FALSE, id = rand_id("select") )
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. |
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.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with column
terms
which contains the select
expressions as character strings
(and are not reparsable) is returned.
The underlying operation does not allow for case weights.
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_at()
,
step_mutate()
,
step_rename_at()
,
step_rename()
,
step_sample()
,
step_slice()
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