step_time() creates a specification of a recipe step that will convert
date-time data into one or more factor or numeric variables.
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this step.
The selected variables should have class POSIXct or POSIXlt. See
selections() for more details.
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.
A character string that includes at least one of the
following values: am (is is AM), hour, hour12, minute, second,
decimal_day.
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
A logical to keep the original variables in the
output. Defaults to TRUE.
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.
An updated version of recipe with the new step added to the
sequence of any existing operations.
Unlike some other steps, step_time() does not remove the original time
variables by default. Set keep_original_cols to FALSE to remove them.
decimal_day return time of day as a decimal number between 0 and 24. for
example "07:15:00" would be transformed to 7.25 and "03:59:59" would
be transformed to 3.999722. The formula for these calculations are `hour(x)
(second(x) + minute(x) * 60) / 3600`.
See step_date() if you want to calculate features that are larger than
hours.
When you tidy() this step, a tibble is returned with
columns terms, value , and id:
character, the selectors or variables selected
character, the feature names
character, id of this step
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy(),
step_dummy_extract(),
step_dummy_multi_choice(),
step_factor2string(),
step_holiday(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_ordinalscore(),
step_other(),
step_regex(),
step_relevel(),
step_string2factor(),
step_unknown(),
step_unorder()
library(lubridate)
examples <- data.frame(
times = ymd_hms("2022-05-06 23:51:07") +
hours(1:5) + minutes(1:5) + seconds(1:5)
)
time_rec <- recipe(~ times, examples) |>
step_time(all_predictors())
tidy(time_rec, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <chr> <chr>
#> 1 all_predictors() hour time_wvoo0
#> 2 all_predictors() minute time_wvoo0
#> 3 all_predictors() second time_wvoo0
time_rec <- prep(time_rec, training = examples)
time_values <- bake(time_rec, new_data = examples)
time_values
#> # A tibble: 5 × 4
#> times times_hour times_minute times_second
#> <dttm> <int> <int> <dbl>
#> 1 2022-05-07 00:52:08 0 52 8
#> 2 2022-05-07 01:53:09 1 53 9
#> 3 2022-05-07 02:54:10 2 54 10
#> 4 2022-05-07 03:55:11 3 55 11
#> 5 2022-05-07 04:56:12 4 56 12
tidy(time_rec, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <chr> <chr>
#> 1 times hour time_wvoo0
#> 2 times minute time_wvoo0
#> 3 times second time_wvoo0