step_date
creates 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, skip = FALSE, id = rand_id("date") ) # S3 method for 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 |
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 |
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).
Unlike other steps, step_date
does not
remove the original date variables. step_rm()
can be
used for this purpose.
#> #>#>#> #>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)#> # A tibble: 3 x 4 #> terms value ordinal id #> <chr> <chr> <lgl> <chr> #> 1 all_predictors() dow FALSE date_4kbnH #> 2 all_predictors() month FALSE date_4kbnH #> 3 all_predictors() year FALSE date_4kbnHdate_rec <- prep(date_rec, training = examples) date_values <- bake(date_rec, new_data = examples) date_values#> # A tibble: 10 x 8 #> Dan Stefan Dan_dow Dan_month Dan_year Stefan_dow Stefan_month #> <date> <date> <fct> <fct> <dbl> <fct> <fct> #> 1 2002-03-05 2006-01-14 Tue Mar 2002 Sat Jan #> 2 2002-03-06 2006-01-15 Wed Mar 2002 Sun Jan #> 3 2002-03-07 2006-01-16 Thu Mar 2002 Mon Jan #> 4 2002-03-08 2006-01-17 Fri Mar 2002 Tue Jan #> 5 2002-03-09 2006-01-18 Sat Mar 2002 Wed Jan #> 6 2002-03-10 2006-01-19 Sun Mar 2002 Thu Jan #> 7 2002-03-11 2006-01-20 Mon Mar 2002 Fri Jan #> 8 2002-03-12 2006-01-21 Tue Mar 2002 Sat Jan #> 9 2002-03-13 2006-01-22 Wed Mar 2002 Sun Jan #> 10 2002-03-14 2006-01-23 Thu Mar 2002 Mon Jan #> # … with 1 more variable: Stefan_year <dbl>#> # A tibble: 6 x 4 #> terms value ordinal id #> <chr> <chr> <lgl> <chr> #> 1 Dan dow FALSE date_4kbnH #> 2 Dan month FALSE date_4kbnH #> 3 Dan year FALSE date_4kbnH #> 4 Stefan dow FALSE date_4kbnH #> 5 Stefan month FALSE date_4kbnH #> 6 Stefan year FALSE date_4kbnH