As steps are estimated by prep, these operations are applied to the training set. Rather than running bake to duplicate this processing, this function will return variables from the processed training set.

juice(object, ..., composition = "tibble")

## Arguments

object A recipe object that has been prepared with the option retain = TRUE. 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(). 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

When preparing a recipe, if the training data set is retained using retain = TRUE, there is no need to bake the recipe to get the preprocessed training set.

juice will return the results of a recipes where all steps have been applied to the data, irrespective of the value of the step's skip argument.

recipe() prep.recipe() bake.recipe()

## Examples

library(modeldata)
data(biomass)

biomass_tr <- biomass[biomass$dataset == "Training",] biomass_te <- biomass[biomass$dataset == "Testing",]

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr)

sp_signed <- rec %>%
step_normalize(all_predictors()) %>%
step_spatialsign(all_predictors())

sp_signed_trained <- prep(sp_signed, training = biomass_tr)

tr_values <- bake(sp_signed_trained, new_data = biomass_tr, all_predictors())
og_values <- juice(sp_signed_trained, all_predictors())

all.equal(tr_values, og_values)#> [1] TRUE