step_custom_transformation: Custom Transformation
In customsteps: Customizable Higher-Order Recipe Step Functions

Description Usage Arguments Value See Also Examples

'step_custom_transformation' creates a *specification* of a higher order recipe step that will make a transformation of the input data from (custom) 'prep' and 'bake' helper functions.

step_custom_transformation(recipe, ..., role = "predictor",
  trained = FALSE, prep_function = NULL, prep_options = NULL,
  prep_output = NULL, bake_function = NULL, bake_options = NULL,
  bake_how = "bind_cols", selected_vars = NULL, skip = FALSE,
  id = rand_id("custom_transformation"))

## S3 method for class 'step_custom_transformation'
tidy(x, ...)

`recipe`	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 are affected by the step. See [recipes::selections()] for more details. The names of the selected variables will be stored in the 'selected_vars' argument.
`role`	For model terms created by this step, what analysis role should they be assigned? By default, the function assumes that the new columns will be used as predictors in a model.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`prep_function`	A function. This is a helper function for the [recipes::prep.recipe()] method. It will be invoked, when the recipe is 'prepped' by [recipes::prep.recipe()]. The function MUST satisfy the following conditions: (1) the function must take an argument 'x': the the subset of selected variables ('selected_vars') from the initial data set, (2) the function MUST return the (required) estimated parameters that can be later applied to other data sets. This output can be of any appropriate type and shape. Leave 'prep_function' as NULL, if the preparation of new data sets does not depend on parameters learned on the initial data set.
`prep_options`	A list with (any) additional arguments for the prep helper function call EXCEPT for the 'x' argument. Leave as NULL, if no 'prep_function' is given.
`prep_output`	Output from prep helper ('prep_function') function call consisting of the estimated parameters from the initial data set set, that will be applied to other data sets. Results are not computed until [recipes::prep.recipe()] is called.
`bake_function`	A function. This is a helper function for the 'bake' method. It will be invoked, when the recipe is 'baked' by 'bake.recipe()'. The function MUST satisfy the following conditions: (1) the function must take an argument 'x': the new data set, that the transformation will be applied to, (2) IF the preparation of new data sets depends on parameters learned on the initial data set, the function must take the argument 'prep_output': the output from the prep helper fct ('prep_function'), (3) the output from from the function should be the transformed variables. The output must be of a type and shape, that allows it to be binded column wise to the new data set after converting it to a 'tibble'.
`bake_options`	A list with (any) arguments for the 'bake_function' function call EXCEPT for the 'x' and 'prep_output' arguments.
`bake_how`	A character. How should the transformed variables be appended to the new data set? Choose from options (1) 'bind_cols': simply bind the transformed variables to the new data set or (2) 'replace': replace the selected variables ('selected vars') from the new data set with the transformed variables.
`selected_vars`	A character string that contains the names of the selected variables. These values are not determined until [recipes::prep.recipe()] is called.
`skip`	A logical. Should the step be skipped when the recipe is baked by [recipes::bake.recipe()]? While all operations are baked when [recipes::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.
`id`	A character string that is unique to this step to identify it.
`x`	A 'step_custom_transformation' 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 columns 'terms' (the selectors or variables selected) as well as the step 'id'.

[recipes::recipe()] [recipes::prep.recipe()] [recipes::bake.recipe()]

library(dplyr)
library(purrr)
library(tibble)
library(recipes)
library(generics)

# divide 'mtcars' into two data sets.
cars_initial <- mtcars[1:16, ]
cars_new <- mtcars[17:nrow(mtcars), ]

# define prep helper function, that computes means and standard deviations
# for (an arbitrary number of) numeric variables.
compute_means_sd <- function(x) {
 
 map(.x = x, ~ list(mean = mean(.x), sd = sd(.x)))
 
}

# define bake helper function, that centers numeric variables to have
# a mean of 'alpha' and scale them to have a standard deviation of
# 'beta'.
center_scale <- function(x, prep_output, alpha, beta) {
  
  # extract only the relevant variables from the new data set.
  new_data <- select(x, names(prep_output))
  
  # apply transformation to each of these variables.
  # variables are centered around 'alpha' and scaled to have a standard 
  # deviation of 'beta'.
  map2(.x = new_data,
       .y = prep_output,
       ~ alpha + (.x - .y$mean) * beta / .y$sd)
  
}

# create recipe.
rec <- recipe(cars_initial) %>%
  step_custom_transformation(mpg, disp,
                             prep_function = compute_means_sd,
                             bake_function = center_scale,
                             bake_options = list(alpha = 0, beta = 1),
                             bake_how = "replace")

# prep recipe.
rec_prep <- prep(rec)

# bake recipe.
rec_baked <- bake(rec_prep, cars_new)
rec_baked

# inspect output.
rec
rec_baked
tidy(rec)
tidy(rec, 1)
tidy(rec_prep)
tidy(rec_prep, 1)