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

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

# S3 method for step_log
tidy(x, ...)

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 which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used.

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(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.

x

A step_log object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the columns that will be affected) and base.

See also

Examples

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_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)
#> # A tibble: 1 x 3 #> terms base id #> <chr> <dbl> <chr> #> 1 all_predictors() 2.72 log_IhS7o
tidy(log_obj, number = 1)
#> # A tibble: 2 x 3 #> terms base id #> <chr> <dbl> <chr> #> 1 V1 2.72 log_IhS7o #> 2 V2 2.72 log_IhS7o
# 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_predictors()) %>% prep(training = examples2) %>% bake(examples2)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> # A tibble: 20 x 2 #> V1 V2 #> <dbl> <dbl> #> 1 -0.209 NaN #> 2 1.71 NaN #> 3 1.12 1.06 #> 4 1.65 1.19 #> 5 NaN 2.18 #> 6 1.15 1.08 #> 7 NaN 0.555 #> 8 0.102 NaN #> 9 0.670 1.37 #> 10 NaN 1.02 #> 11 NaN NaN #> 12 NaN NaN #> 13 NaN NaN #> 14 1.25 -0.0880 #> 15 2.21 0.774 #> 16 NaN NaN #> 17 NaN 2.49 #> 18 NaN 1.47 #> 19 NaN NaN #> 20 NaN NaN
recipe(~ V1 + V2, data = examples2) %>% step_log(all_predictors(), signed = TRUE) %>% prep(training = examples2) %>% bake(examples2)
#> # A tibble: 20 x 2 #> V1 V2 #> <dbl> <dbl> #> 1 0 -1.24 #> 2 1.71 -1.81 #> 3 1.12 1.06 #> 4 1.65 1.19 #> 5 -1.63 2.18 #> 6 1.15 1.08 #> 7 -0.604 0.555 #> 8 0.102 -0.565 #> 9 0.670 1.37 #> 10 -2.65 1.02 #> 11 -1.34 -1.04 #> 12 -2.06 -1.51 #> 13 -0.613 -1.75 #> 14 1.25 0 #> 15 2.21 0.774 #> 16 -1.90 -0.0814 #> 17 -0.762 2.49 #> 18 -1.40 1.47 #> 19 -1.22 -0.825 #> 20 -1.20 -2.27