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,
clipping = TRUE,
ranges = NULL,
skip = FALSE,
id = rand_id("range")
)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 variables for this step. 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, max
Single numeric values for the smallest (or largest) value in the transformed data.
- clipping
A single logical value for determining whether application of transformation onto new data should be forced to be inside
minandmax. Defaults to TRUE.- ranges
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.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()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 usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
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.
Tidying
When you tidy() this step, a tibble is returned with
columns terms, min, max , and id:
- terms
character, the selectors or variables selected
- min
numeric, lower range
- max
numeric, upper range
- id
character, id of this step
See also
Other normalization steps:
step_center(),
step_normalize(),
step_scale()
Examples
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