step_log() creates a specification of a recipe step that will log
transform data.
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 the selected variable names. This field is a placeholder and will be populated once
prep()is used.- 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.- signed
A logical indicating whether to take the signed log. This is
sign(x) * log(abs(x))whenabs(x) => 1or0 if abs(x) < 1. IfTRUEtheoffsetargument 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.
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.
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_percentile(),
step_poly(),
step_relu(),
step_sqrt()
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_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)
#> # A tibble: 1 × 3
#> terms base id
#> <chr> <dbl> <chr>
#> 1 all_numeric_predictors() 2.72 log_IhS7o
tidy(log_obj, number = 1)
#> # A tibble: 2 × 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_numeric_predictors()) |>
prep(training = examples2) |>
bake(examples2)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> # A tibble: 20 × 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_numeric_predictors(), signed = TRUE) |>
prep(training = examples2) |>
bake(examples2)
#> # A tibble: 20 × 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