step_range: Scaling numeric data to a specific range
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_range

R Documentation

Scaling numeric data to a specific range

Description

step_range() creates a specification of a recipe step that will normalize numeric data to be within a pre-defined range of values.

Usage

step_range(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  min = 0,
  max = 1,
  clipping = TRUE,
  ranges = NULL,
  skip = FALSE,
  id = rand_id("range")
)

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.
`min`, `max`	Single numeric values for the smallest (or largest) value in the transformed data.
`clipping`	A single logical value for determining whether application of transformation onto new data should be forced to be inside `min` and `max`. Defaults to TRUE.
`ranges`	A character vector of variables that will be normalized. Note that this is ignored until the values are determined by `prep()`. Setting this value will be ineffective.
`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

When a new data point is outside of the ranges seen in the training set, the new values are truncated at min or max.

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, min, max , and id:

terms: character, the selectors or variables selected
min: numeric, lower range
max: numeric, upper range
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

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
)

ranged_trans <- rec |>
  step_range(carbon, hydrogen)

ranged_obj <- prep(ranged_trans, training = biomass_tr)

transformed_te <- bake(ranged_obj, biomass_te)

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

tidy(ranged_trans, number = 1)
tidy(ranged_obj, number = 1)

recipes documentation built on June 8, 2025, 10:21 a.m.