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,
clipping = TRUE,
ranges = NULL,
skip = FALSE,
id = rand_id("range")
)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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
Single numeric values for the smallest (or largest) value in the transformed data.
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.
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.
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.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
When a new data point is outside of the ranges seen in
the training set, the new values are truncated at min or
max.
When you tidy() this step, a tibble is returned with
columns terms, min, max , and id:
character, the selectors or variables selected
numeric, lower range
numeric, upper range
character, id of this step
The underlying operation does not allow for case weights.
Other normalization steps:
step_center(),
step_normalize(),
step_scale()
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)]
#> 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 × 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
#> # ℹ 70 more rows
tidy(ranged_trans, number = 1)
#> # A tibble: 2 × 4
#> terms min max id
#> <chr> <dbl> <dbl> <chr>
#> 1 carbon NA NA range_RcYYk
#> 2 hydrogen NA NA range_RcYYk
tidy(ranged_obj, number = 1)
#> # A tibble: 2 × 4
#> terms min max id
#> <chr> <dbl> <dbl> <chr>
#> 1 carbon 14.6 97.2 range_RcYYk
#> 2 hydrogen 0.03 11.6 range_RcYYk