recipes (version 1.1.0)

step_range: 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")
)

Value

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

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

A single numeric value for the smallest value in the range.

max

A single numeric value for the largest value in the range.

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.

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.

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.

See Also

Other normalization steps: step_center(), step_normalize(), 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
)

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)