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")
)

## 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 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.recipe()? While all operations are baked when prep.recipe() 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.

## Value

An updated version of recipe with the new step added to the sequence of any existing operations.

## 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.

When you tidy() this step, a tibble with column terms which contains the select expressions as character strings (and are not reparsable) is returned.

Other variable filter steps: step_corr(), 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()

## 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)
#> [1] TRUE

rec_test <- bake(rec, iris_test)
all.equal(dplyr_test, rec_test)
#> [1] 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:
#>
#>       role #variables
#>  predictor          5
#>
#> Operations:
#>
#> Terms selected  ~Species, ~all_of(sepal_vars)
#> Terms selected  ~Species, ~all_of(c("Sepal.Width", "Sepal.Length"))