Logarithmic transformation based on skewness

step_log_skewness() creates a specification of a recipe step that will log transform numeric variables if the skewness exceeds a given threshold.

Usage

step_log_skewness(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  skewness = NULL,
  base = exp(1),
  offset = 0,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("log_skewness")
)

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 recipes::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.
skewness: Numeric threshold for the skewness. If the skewness of a variable exceeds this threshold, it will be log-transformed. Otherwise, it will remain as-is. If NULL, all selected numeric variables will be transformed.
base: A numeric value for the base.
offset: An optional value to add to the data prior to logging (to avoid log(0)).
columns: A character vector of the variable names that are log-transformed. This field is a placeholder and will be populated once recipes::prep() is used.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake()? While all operations are baked when recipes::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.

Tidying

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

terms: character, the selectors or variables selected
base: numeric, value for the base
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

Author

Modified from recipes::step_log().

Examples

set.seed(313)
examples <- matrix(exp(rnorm(40)), ncol = 2)
examples <- as.data.frame(examples)

rec <- recipes::recipe(~ V1 + V2, data = examples)

log_trans <- rec |>
  step_log_skewness(recipes::all_numeric_predictors(), skewness = 1)

log_obj <- recipes::prep(log_trans, training = examples)

transformed_te <- recipes::bake(log_obj, examples)
plot(examples$V1, transformed_te$V1)

plot(examples$V2, transformed_te$V2)