step_center_to: Centering numeric data to a value besides their mean

Description

`step_center_to` generalizes `step_center` to allow for a different function than the `mean` function to calculate centers. It creates a *specification* of a recipe step that will normalize numeric data to have a 'center' of zero.

Usage

step_center_to(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  centers = NULL,
  center_fn = mean,
  na_rm = TRUE,
  skip = FALSE,
  id = rand_id("center_to")
)
# S3 method for step_center_to
tidy(x, ...)

Value

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) and `value` (the centers).

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 which variables are affected by the step. See [selections()] for more details. For the `tidy` method, these are not currently used.
role: Not used by this step since no new variables are created.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
centers: A named numeric vector of centers. This is `NULL` until computed by [prep.recipe()] (or it can be specified as a named numeric vector as well?).
center_fn: a function to be used to calculate where the center should be
na_rm: A logical value indicating whether `NA` values should be removed during computations.
skip: 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
id: A character string that is unique to this step to identify it.
x: A `step_center_to` object.

Details

Centering data means that the average of a variable is subtracted from the data. `step_center_to` estimates the variable centers from the data used in the `training` argument of `prep.recipe`. `bake.recipe` then applies the centering to new data sets using these centers.

Examples

Run this code

data(biomass, package = "modeldata")

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

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

center_trans <- rec %>%
  step_center_to(carbon, contains("gen"), -hydrogen)

center_obj <- recipes::prep(center_trans, training = biomass_tr)

transformed_te <- recipes::bake(center_obj, biomass_te)

biomass_te[1:10, names(transformed_te)]
transformed_te

recipes::tidy(center_trans)
recipes::tidy(center_obj)