step_date {recipes} | R Documentation |
step_date
creates a a specification of a recipe
step that will convert date data into one or more factor or
numeric variables.
step_date(recipe, ..., role = "predictor", trained = FALSE, features = c("dow", "month", "year"), abbr = TRUE, label = TRUE, ordinal = FALSE, columns = NULL) ## S3 method for class 'step_date' tidy(x, ...)
recipe |
A recipe object. The step will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose which
variables that will be used to create the new variables. The
selected variables should have class |
role |
For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new variable 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. |
features |
A character string that includes at least one
of the following values: |
abbr |
A logical. Only available for features |
label |
A logical. Only available for features
|
ordinal |
A logical: should factors be ordered? Only
available for features |
columns |
A character string of variables that will be
used as inputs. This field is a placeholder and will be
populated once |
x |
A |
Unlike other steps, step_date
does not
remove the original date variables. step_rm()
can be
used for this purpose.
For step_date
, 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 terms
(the selectors or variables selected), value
(the feature
names), and ordinal
(a logical).
step_holiday()
step_rm()
recipe()
prep.recipe()
bake.recipe()
library(lubridate) examples <- data.frame(Dan = ymd("2002-03-04") + days(1:10), Stefan = ymd("2006-01-13") + days(1:10)) date_rec <- recipe(~ Dan + Stefan, examples) %>% step_date(all_predictors()) tidy(date_rec, number = 1) date_rec <- prep(date_rec, training = examples) date_values <- bake(date_rec, newdata = examples) date_values tidy(date_rec, number = 1)