bake: Apply a trained preprocessing recipe

Description

For a recipe with at least one preprocessing operation that has been trained by prep(), apply the computations to new data.

Usage

bake(object, ...)
# S3 method for recipe
bake(object, new_data, ..., composition = "tibble")

Value

A tibble, matrix, or sparse matrix that may have different columns than the original columns in new_data.

Arguments

object: A trained object such as a recipe() with at least one preprocessing operation.
...: One or more selector functions to choose which variables will be returned by the function. See selections() for more details. If no selectors are given, the default is to use everything().
new_data: A data frame or tibble for whom the preprocessing will be applied. If NULL is given to new_data, the pre-processed training data will be returned (assuming that prep(retain = TRUE) was used).
composition: Either "tibble", "matrix", "data.frame", or "dgCMatrix" for the format of the processed data set. Note that all computations during the baking process are done in a non-sparse format. Also, note that this argument should be called after any selectors and the selectors should only resolve to numeric columns (otherwise an error is thrown).

Details

bake() takes a trained recipe and applies its operations to a data set to create a design matrix. If you are using a recipe as a preprocessor for modeling, we highly recommend that you use a workflow() instead of manually applying a recipe (see the example in recipe()).

If the data set is not too large, time can be saved by using the retain = TRUE option of prep(). This stores the processed version of the training set. With this option set, bake(object, new_data = NULL) will return it for free.

Also, any steps with skip = TRUE will not be applied to the data when bake() is invoked with a data set in new_data. bake(object, new_data = NULL) will always have all of the steps applied.

Examples

Run this code

data(ames, package = "modeldata")

ames <- mutate(ames, Sale_Price = log10(Sale_Price))

ames_rec <-
  recipe(Sale_Price ~ ., data = ames[-(1:6), ]) %>%
  step_other(Neighborhood, threshold = 0.05) %>%
  step_dummy(all_nominal()) %>%
  step_interact(~ starts_with("Central_Air"):Year_Built) %>%
  step_ns(Longitude, Latitude, deg_free = 2) %>%
  step_zv(all_predictors()) %>%
  prep()

# return the training set (already embedded in ames_rec)
bake(ames_rec, new_data = NULL)

# apply processing to other data:
bake(ames_rec, new_data = head(ames))

# only return selected variables:
bake(ames_rec, new_data = head(ames), all_numeric_predictors())
bake(ames_rec, new_data = head(ames), starts_with(c("Longitude", "Latitude")))