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

  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = rand_id("select")

# S3 method for step_select
tidy(x, ...)



A recipe object. The step will be added to the sequence of operations for this recipe.


One or more selector functions to choose which variables will be selected when baking. See selections() for more details. For the tidy method, these are not currently used.


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.


A step_select object


An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with column terms which contains the select expressions as character strings (and are not reparsable).


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.


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
#> Data Recipe #> #> Inputs: #> #> role #variables #> predictor 5 #> #> Operations: #> #> Terms selected ~Species, ~all_of(sepal_vars) #> Terms selected ~Species, ~all_of(c("Sepal.Width", "Sepal.Length"))