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Select variables using dplyr

Source: R/select.R
step_select.Rd

step_select() creates a specification of a recipe step that will select variables using dplyr::select().

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 selections() for more details.

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 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 = 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 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.

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)
#> [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 
#> Number of variables by role
#> predictor: 5
#> 
#> ── Operations 
#> • Variables selected: Species all_of(sepal_vars)
#> • Variables selected: Species, ...