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") ) # S3 method for step_mutate tidy(x, ...) # S3 method for step_mutate_at tidy(x, ...)
recipe | A recipe object. The step will be added to the sequence of operations for this recipe. |
---|---|
... | Name-value pairs of expressions. See |
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 |
id | A character string that is unique to this step to identify it. |
x | A |
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 values
which
contains the mutate
expressions as character strings
(and are not reparsable).
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.
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] TRUEdplyr_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#> # A tibble: 2 x 3 #> terms value id #> <chr> <chr> <chr> #> 1 bad_approach Sepal.Width * const mutate_7WkM5 #> 2 best_approach Sepal.Width * 1.414 mutate_7WkM5