Add new variables using dplyr
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 class 'step_mutate'
tidy(x, ...)
## S3 method for class '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 |
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.
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).
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)
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)
# 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)
# The difference:
tidy(qq_rec, number = 1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.