tidyroll makes it easy to work with irregular time slices for modeling
and prediciton with tidymodels.
The main function rolling_origin_nested() is a wrapper around
rsample::rolling_origin and facilitates rolling over different time
units instead of a fixed window. The motivation for this function comes
from this
vignette.
# install.packages("devtools")
devtools::install_github("gacolitti/tidyroll")suppressPackageStartupMessages(library(tidyroll))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(rsample))
#> Warning: package 'rsample' was built under R version 4.0.2
data("airquality2")
roll <- rolling_origin_nested(
data = airquality2,
time_var = "date",
unit = "month",
start = "2017-08-01",
end = "2017-11-01",
assess = 1
)
roll
#> # Rolling origin forecast resampling
#> # A tibble: 3 x 2
#> splits id
#> <list> <chr>
#> 1 <split [4/1]> Slice1
#> 2 <split [5/1]> Slice2
#> 3 <split [6/1]> Slice3
analysis(roll$splits[[1]])
#> # A tibble: 4 x 2
#> .date data
#> <dttm> <list>
#> 1 2017-05-01 00:00:00 <tibble [31 × 7]>
#> 2 2017-06-01 00:00:00 <tibble [30 × 7]>
#> 3 2017-07-01 00:00:00 <tibble [31 × 7]>
#> 4 2017-08-01 00:00:00 <tibble [31 × 7]>
assessment(roll$splits[[1]])
#> # A tibble: 1 x 2
#> .date data
#> <dttm> <list>
#> 1 2017-09-01 00:00:00 <tibble [30 × 7]>
analysis(roll$splits[[1]])$data %>% last() %>% tail()
#> # A tibble: 6 x 7
#> date ozone solar_rad wind temp ozone_sample ozone_sample_date
#> <dttm> <int> <int> <dbl> <int> <dbl> <date>
#> 1 2017-08-26 00:00:00 73 215 8 86 70.1 2017-08-19
#> 2 2017-08-27 00:00:00 NA 153 5.7 88 NA 2017-08-19
#> 3 2017-08-28 00:00:00 76 203 9.7 97 74.6 2017-08-22
#> 4 2017-08-29 00:00:00 118 225 2.3 94 91.2 2017-08-16
#> 5 2017-08-30 00:00:00 84 237 6.3 96 120. 2017-08-22
#> 6 2017-08-31 00:00:00 85 188 6.3 94 79.0 2017-08-21
assessment(roll$splits[[1]])$data %>% first() %>% head()
#> # A tibble: 6 x 7
#> date ozone solar_rad wind temp ozone_sample ozone_sample_date
#> <dttm> <int> <int> <dbl> <int> <dbl> <date>
#> 1 2017-09-01 00:00:00 96 167 6.9 91 86.6 2017-08-29
#> 2 2017-09-02 00:00:00 78 197 5.1 92 77.9 2017-08-24
#> 3 2017-09-03 00:00:00 73 183 2.8 93 96.3 2017-08-29
#> 4 2017-09-04 00:00:00 91 189 4.6 93 86.4 2017-08-26
#> 5 2017-09-05 00:00:00 47 95 7.4 87 60.9 2017-08-28
#> 6 2017-09-06 00:00:00 32 92 15.5 84 34.9 2017-08-30