
step_rename()
creates a specification of a recipe step that will add
variables using dplyr::rename()
.
step_rename(
recipe,
...,
role = "predictor",
trained = FALSE,
inputs = NULL,
skip = FALSE,
id = rand_id("rename")
)
An updated version of recipe
with the new step added to the
sequence of any existing operations.
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more unquoted expressions separated by commas. See
dplyr::rename()
where the convention is new_name = old_name
.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
Quosure(s) of ...
.
A logical. Should the step be skipped when the
recipe is baked by bake()
? While all operations are baked
when prep()
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.
A character string that is unique to this step to identify it.
When you tidy()
this step, a tibble is returned with
columns terms
, value
, and id
:
character, the selectors or variables selected
character, rename
expression
character, id of this step
This step can be applied to sparse_data such that it is preserved. Nothing needs to be done for this to happen as it is done automatically.
The underlying operation does not allow for case weights.
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).
Other dplyr steps:
step_arrange()
,
step_filter()
,
step_mutate()
,
step_mutate_at()
,
step_rename_at()
,
step_sample()
,
step_select()
,
step_slice()
recipe(~., data = iris) %>%
step_rename(Sepal_Width = Sepal.Width) %>%
prep() %>%
bake(new_data = NULL) %>%
slice(1:5)
vars <- c(var1 = "cyl", var2 = "am")
car_rec <-
recipe(~., data = mtcars) %>%
step_rename(!!!vars)
car_rec %>%
prep() %>%
bake(new_data = NULL)
car_rec %>%
tidy(number = 1)
Run the code above in your browser using DataLab