step_center {recipes}

Centering numeric data

Description

step_center() creates a specification of a recipe step that will normalize numeric data to have a mean of zero.

Usage

step_center(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  means = NULL,
  na_rm = TRUE,
  skip = FALSE,
  id = rand_id("center")
)

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 variables for this step. See selections() for more details.

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.

means

A named numeric vector of means. This is NULL until computed by prep().

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()? While all operations are baked when prep() 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.

Details

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

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, and id:

terms

character, the selectors or variables selected

value

numeric, the means

id

character, id of this step

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

See Also

Other normalization steps: step_normalize(), step_range(), step_scale()

Examples

data(biomass, package = "modeldata")

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

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

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

center_obj <- prep(center_trans, training = biomass_tr)

transformed_te <- bake(center_obj, biomass_te)

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

tidy(center_trans, number = 1)
tidy(center_obj, number = 1)