R: Scaling Numeric Data to a Specific Range

step_range {recipes} | 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,
      ranges = NULL, skip = FALSE)

    ## S3 method for class 'step_range'
    tidy(x, ...)

### 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. For the `tidy` method, these are not currently used.
`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.
`skip` | A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` 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
`x` | A `step_range` object.

### 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 existing steps (if any). For the `tidy` method, a tibble with columns `terms` (the selectors or variables selected), `min`, and `max`.

### Examples

    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

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

[Package recipes version 0.1.2 Index]