step_time {recipes} | R Documentation |
Time feature generator
Description
step_time()
creates a specification of a recipe step that will convert
date-time data into one or more factor or numeric variables.
Usage
step_time(
recipe,
...,
role = "predictor",
trained = FALSE,
features = c("hour", "minute", "second"),
columns = NULL,
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("time")
)
Arguments
recipe |
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
|
role |
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. |
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: |
columns |
A character string of the selected variable names. This field
is a placeholder and will be populated once |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
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. |
Details
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.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Tidying
When you tidy()
this step, a tibble is returned with
columns terms
, value
, and id
:
- terms
character, the selectors or variables selected
- value
character, the feature names
- id
character, id of this step
See Also
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()
Examples
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)
time_rec <- prep(time_rec, training = examples)
time_values <- bake(time_rec, new_data = examples)
time_values
tidy(time_rec, number = 1)