Scaling numeric data

step_scale() creates a specification of a recipe step that will normalize numeric data to have a standard deviation of one.

Usage

step_scale(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  sds = NULL,
  factor = 1,
  na_rm = TRUE,
  skip = FALSE,
  id = rand_id("scale")
)

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.
sds: A named numeric vector of standard deviations. This is NULL until computed by prep().
factor: A numeric value of either 1 or 2 that scales the numeric inputs by one or two standard deviations. By dividing by two standard deviations, the coefficients attached to continuous predictors can be interpreted the same way as with binary inputs. Defaults to 1. More in reference below.
na_rm: A logical value indicating whether NA values should be removed when computing the standard deviation.
skip: 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.
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

Scaling data means that the standard deviation of a variable is divided out of the data. step_scale() estimates the variable standard deviations from the data used in the training argument of prep(). bake() then applies the scaling to new data sets using these standard deviations.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value , and id:

terms: character, the selectors or variables selected
value: numeric, the standard deviations
id: character, id of this step

Sparse data

This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

References

Gelman, A. (2007) "Scaling regression inputs by dividing by two standard deviations." Unpublished. Source: https://sites.stat.columbia.edu/gelman/research/unpublished/standardizing.pdf.

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
)

scaled_trans <- rec |>
  step_scale(carbon, hydrogen)

scaled_obj <- prep(scaled_trans, training = biomass_tr)

transformed_te <- bake(scaled_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   4.45     4.74   47.2     0.3    0.22  18.3
#>  2   4.16     4.60   48.1     2.85   0.34  17.6
#>  3   4.10     4.60   49.1     2.4    0.3   17.2
#>  4   4.46     5.10   37.3     1.8    0.5   18.9
#>  5   4.68     5.28   42.8     0.2    0     20.5
#>  6   4.26     4.60   41.7     0.7    0.2   18.5
#>  7   3.74     4.37   54.1     1.19   0.51  15.1
#>  8   4.04     3.89   33.8     0.95   0.2   16.2
#>  9   2.81     3.68   31.1     0.14   4.9   11.1
#> 10   2.67     3.15   23.7     4.63   1.05  10.8
#> # ℹ 70 more rows
tidy(scaled_trans, number = 1)
#> # A tibble: 2 × 3
#>   terms    value id         
#>   <chr>    <dbl> <chr>      
#> 1 carbon      NA scale_nNppk
#> 2 hydrogen    NA scale_nNppk
tidy(scaled_obj, number = 1)
#> # A tibble: 2 × 3
#>   terms    value id         
#>   <chr>    <dbl> <chr>      
#> 1 carbon   10.4  scale_nNppk
#> 2 hydrogen  1.20 scale_nNppk