This is a simple function that will get the juiced data from a recipe.
See also
Other Data Wrangling:
pca_your_recipe()
Examples
suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
#> Warning: package 'dplyr' was built under R version 4.2.3
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(healthyR.data))
suppressPackageStartupMessages(library(rsample))
#> Warning: package 'rsample' was built under R version 4.2.2
suppressPackageStartupMessages(library(recipes))
#> Warning: package 'recipes' was built under R version 4.2.3
data_tbl <- healthyR_data %>%
select(visit_end_date_time) %>%
summarise_by_time(
.date_var = visit_end_date_time,
.by = "month",
value = n()
) %>%
set_names("date_col", "value") %>%
filter_by_time(
.date_var = date_col,
.start_date = "2013",
.end_date = "2020"
)
splits <- initial_split(data = data_tbl, prop = 0.8)
rec_obj <- recipe(value ~ ., training(splits))
get_juiced_data(rec_obj)
#> # A tibble: 76 × 2
#> date_col value
#> <dttm> <int>
#> 1 2020-02-01 00:00:00 1363
#> 2 2016-02-01 00:00:00 1474
#> 3 2018-03-01 00:00:00 1618
#> 4 2015-08-01 00:00:00 1609
#> 5 2015-07-01 00:00:00 1751
#> 6 2016-11-01 00:00:00 1513
#> 7 2013-03-01 00:00:00 1796
#> 8 2017-10-01 00:00:00 1614
#> 9 2013-05-01 00:00:00 2028
#> 10 2017-06-01 00:00:00 1661
#> # ℹ 66 more rows