R recipes step_time 時間特征生成器

step_time(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  features = c("hour", "minute", "second"),
  columns = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("time")
)

其他虛擬變量和編碼步驟：step_bin2factor() , step_count() , step_date() , step_dummy_extract() , step_dummy_multi_choice() , step_dummy() , 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