README.md

In gacolitti/tidyroll: Time-Series Prediction with Irregular Time Slices using Tidymodels

tidyroll

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.

Installation

# install.packages("devtools")
devtools::install_github("gacolitti/tidyroll")

Example

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

gacolitti/tidyroll documentation built on Sept. 13, 2020, 8 p.m.