Select variables using dplyrSource:
step_select() creates a specification of a recipe step that will select
step_select( recipe, ..., role = NA, trained = FALSE, skip = FALSE, id = rand_id("select") )
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.
For model terms selected by this step, what analysis role should they be assigned?
A logical to indicate if the quantities for preprocessing have been estimated.
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 = TRUEas 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 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.
tidy() this step, a tibble with column
terms which contains the
select expressions as character strings
(and are not reparsable) is returned.
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) #>  TRUE rec_test <- bake(rec, iris_test) all.equal(dplyr_test, rec_test) #>  TRUE # 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 #> #> ── Recipe ──────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> predictor: 5 #> #> ── Operations #> • Variables selected: Species, all_of(sepal_vars) #> • Variables selected: Species, ...