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_mkCFm #> 2 hydrogen NA NA range_mkCFm
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_mkCFm #> 2 hydrogen 0.03 11.6 range_mkCFm