recipes (version 0.1.0)

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 have a standard deviation of one.

Usage

step_range(recipe, ..., role = NA, trained = FALSE, min = 0, max = 1,
  ranges = NULL)

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 will be scaled. 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

ranges

A character vector of variables that will be normalized. Note that this is ignored until the values are determined by prep.recipe. Setting this value will be ineffective.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

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

Examples

# NOT RUN {
data(biomass)

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
# }