step_normalize: Center and scale numeric data

View source: R/normalize.R

step_normalizeR Documentation

Center and scale numeric data


step_normalize creates a specification of a recipe step that will normalize numeric data to have a standard deviation of one and a mean of zero.


  role = NA,
  trained = FALSE,
  means = NULL,
  sds = NULL,
  na_rm = TRUE,
  skip = FALSE,
  id = rand_id("normalize")



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.


Not used by this step since no new variables are created.


A logical to indicate if the quantities for preprocessing have been estimated.


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


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


A logical value indicating whether NA values should be removed when computing the standard deviation and mean.


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.


A character string that is unique to this step to identify it.


Centering data means that the average of a variable is subtracted from the data. Scaling data means that the standard deviation of a variable is divided out of the data. step_normalize estimates the variable standard deviations and means from the data used in the training argument of prep.recipe. bake.recipe then applies the scaling to new data sets using these estimates.


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


When you tidy() this step, a tibble with columns terms (the selectors or variables selected), value (the standard deviations and means), and statistic for the type of value is returned.

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

See Also

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


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

norm_trans <- rec %>%
  step_normalize(carbon, hydrogen)

norm_obj <- prep(norm_trans, training = biomass_tr)

transformed_te <- bake(norm_obj, biomass_te)

biomass_te[1:10, names(transformed_te)]
tidy(norm_trans, number = 1)
tidy(norm_obj, number = 1)

# To keep the original variables in the output, use `step_mutate_at`:
norm_keep_orig <- rec %>%
  step_mutate_at(all_numeric_predictors(), fn = list(orig = ~.)) %>%
  step_normalize(-contains("orig"), -all_outcomes())

keep_orig_obj <- prep(norm_keep_orig, training = biomass_tr)
keep_orig_te <- bake(keep_orig_obj, biomass_te)

recipes documentation built on April 25, 2023, 9:12 a.m.