recipes (version 0.1.17)

step_log: Logarithmic Transformation

Description

step_log creates a specification of a recipe step that will log transform data.

Usage

step_log(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  base = exp(1),
  offset = 0,
  columns = NULL,
  skip = FALSE,
  signed = FALSE,
  id = rand_id("log")
)

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.

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 string of variable names that will be populated (eventually) by the terms argument.

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.

signed

A logical indicating whether to take the signed log. This is sign(x) * abs(log(x)) when abs(x) => 1 or 0 if abs(x) < 1. If TRUE the offset argument will be ignored.

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 you tidy() this step, a tibble with columns terms (the columns that will be affected) and base.

See Also

Other individual transformation steps: step_BoxCox(), step_YeoJohnson(), step_bs(), step_harmonic(), step_hyperbolic(), step_inverse(), step_invlogit(), step_logit(), step_mutate(), step_ns(), step_poly(), step_relu(), step_sqrt()

Examples

# NOT RUN {
set.seed(313)
examples <- matrix(exp(rnorm(40)), ncol = 2)
examples <- as.data.frame(examples)

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

log_trans <- rec  %>%
  step_log(all_numeric_predictors())

log_obj <- prep(log_trans, training = examples)

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

tidy(log_trans, number = 1)
tidy(log_obj, number = 1)

# using the signed argument with negative values

examples2 <- matrix(rnorm(40, sd = 5), ncol = 2)
examples2 <- as.data.frame(examples2)

recipe(~ V1 + V2, data = examples2) %>%
  step_log(all_numeric_predictors()) %>%
  prep(training = examples2) %>%
  bake(examples2)

recipe(~ V1 + V2, data = examples2) %>%
  step_log(all_numeric_predictors(), signed = TRUE) %>%
  prep(training = examples2) %>%
  bake(examples2)

# }