Scaling Numeric Data to a Specific Range

Source: R/range.R

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

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

# S3 method for 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
id

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

A step_range object.

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.

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.

Examples

library(modeldata)
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)]

#>    carbon hydrogen oxygen nitrogen sulfur    HHV
#> 15  46.35     5.67  47.20     0.30   0.22 18.275
#> 20  43.25     5.50  48.06     2.85   0.34 17.560
#> 26  42.70     5.50  49.10     2.40   0.30 17.173
#> 31  46.40     6.10  37.30     1.80   0.50 18.851
#> 36  48.76     6.32  42.77     0.20   0.00 20.547
#> 41  44.30     5.50  41.70     0.70   0.20 18.467
#> 46  38.94     5.23  54.13     1.19   0.51 15.095
#> 51  42.10     4.66  33.80     0.95   0.20 16.240
#> 55  29.20     4.40  31.10     0.14   4.90 11.147
#> 65  27.80     3.77  23.69     4.63   1.05 10.750
transformed_te

#> # A tibble: 80 x 6
#>    carbon hydrogen oxygen nitrogen sulfur   HHV
#>     <dbl>    <dbl>  <dbl>    <dbl>  <dbl> <dbl>
#>  1  0.384    0.490   47.2     0.3    0.22  18.3
#>  2  0.347    0.475   48.1     2.85   0.34  17.6
#>  3  0.340    0.475   49.1     2.4    0.3   17.2
#>  4  0.385    0.527   37.3     1.8    0.5   18.9
#>  5  0.414    0.546   42.8     0.2    0     20.5
#>  6  0.360    0.475   41.7     0.7    0.2   18.5
#>  7  0.295    0.451   54.1     1.19   0.51  15.1
#>  8  0.333    0.402   33.8     0.95   0.2   16.2
#>  9  0.177    0.379   31.1     0.14   4.9   11.1
#> 10  0.160    0.325   23.7     4.63   1.05  10.8
#> # … with 70 more rows

tidy(ranged_trans, number = 1)

#> # A tibble: 2 x 4
#>   terms      min   max id         
#>   <chr>    <dbl> <dbl> <chr>      
#> 1 carbon      NA    NA range_gI6r3
#> 2 hydrogen    NA    NA range_gI6r3
tidy(ranged_obj, number = 1)

#> # A tibble: 2 x 4
#>   terms      min   max id         
#>   <chr>    <dbl> <dbl> <chr>      
#> 1 carbon   14.6   97.2 range_gI6r3
#> 2 hydrogen  0.03  11.6 range_gI6r3