step_mutate creates a specification of a recipe step that will add variables using dplyr::mutate().

step_mutate(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  inputs = NULL,
  skip = FALSE,
  id = rand_id("mutate")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

...

Name-value pairs of expressions. See dplyr::mutate(). If the argument is not named, the expression is converted to a column name.

role

For model terms created by this step, what analysis role should they be assigned? By default, the function assumes that the new dimension columns created by the original variables will be used as predictors in a model.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

inputs

Quosure(s) of ....

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

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 existing steps (if any).

Details

When an object in the user's global environment is referenced in the expression defining the new variable(s), it is a good idea to use quasiquotation (e.g. !!) to embed the value of the object in the expression (to be portable between sessions). See the examples.

When you tidy() this step, a tibble with column values, which contains the mutate expressions as character strings (and are not reparsable), is returned.

Examples

rec <-
  recipe( ~ ., data = iris) %>%
  step_mutate(
    dbl_width = Sepal.Width * 2,
    half_length = Sepal.Length / 2
  )

prepped <- prep(rec, training = iris %>% slice(1:75))

library(dplyr)

dplyr_train <-
  iris %>%
  as_tibble() %>%
  slice(1:75) %>%
  mutate(
    dbl_width = Sepal.Width * 2,
    half_length = Sepal.Length / 2
  )

rec_train <- bake(prepped, new_data = NULL)
all.equal(dplyr_train, rec_train)
#> [1] TRUE

dplyr_test <-
  iris %>%
  as_tibble() %>%
  slice(76:150) %>%
  mutate(
    dbl_width = Sepal.Width * 2,
    half_length = Sepal.Length / 2
  )
rec_test <- bake(prepped, iris %>% slice(76:150))
all.equal(dplyr_test, rec_test)
#> [1] TRUE

# Embedding objects:
const <- 1.414

qq_rec <-
  recipe( ~ ., data = iris) %>%
  step_mutate(
    bad_approach = Sepal.Width * const,
    best_approach = Sepal.Width * !!const
  ) %>%
  prep(training = iris)

bake(qq_rec, new_data = NULL, contains("appro")) %>% slice(1:4)
#> # A tibble: 4 x 2
#>   bad_approach best_approach
#>          <dbl>         <dbl>
#> 1         4.95          4.95
#> 2         4.24          4.24
#> 3         4.52          4.52
#> 4         4.38          4.38

# The difference:
tidy(qq_rec, number = 1)
#> # A tibble: 2 x 3
#>   terms         value               id          
#>   <chr>         <chr>               <chr>       
#> 1 bad_approach  Sepal.Width * const mutate_p75TX
#> 2 best_approach Sepal.Width * 1.414 mutate_p75TX