spatialsample 0.3.0 is now on CRAN

Photo by Nik Shuliahin 💛💙 on Unsplash

A new release of spatialsample – an R package extending the rsample framework to handle spatial resampling and cross-validation – just landed on CRAN. This post will describe the new breaking changes to the spatial_clustering_cv() function, as well as improvements made to how spatialsample handles repeated cross-validation, as well as improvements made to how spatialsample handles repeated cross-validation; for a full list of changes, check out this release’s NEWS entry

You can install the package from CRAN via:

install.packages("spatialsample")

Breaking changes to spatial_clustering_cv()

The only (intentional!) breaking changes in this version are to the function spatial_clustering_cv(). This function is the oldest part of the package, and as such didn’t quite work like any of the other parts of the package: distance calculations sometimes used dist() instead of sf::st_distance(), distances used centroids instead of polygon edges, and trying to adapt this function to work with both sf and non-spatial data had made the internals a bit hairy.

As of rsample 1.1.1, all of those non-spatial elements have been outsourced to rsample::clustering_cv(). If you’ve been using spatial_clustering_cv() for non-spatial data, this function is pretty close to a drop-in replacement, and you should migrate code over.

For spatial point data, spatial_clustering_cv() should work identically to past versions. For other geometry types however, note that distance calculations have now changed to use edge-to-edge distance between geometries, rather than centroids as in past versions. This means this function now uses distances in a way that’s more consistent with the rest of the package, and more consistent with what I personally view as best practice; two polygons sharing a lot of perimeter but with centroids separated by a decent margin are still likely to be highly similar.

library(spatialsample)
spatial_clustering_cv(boston_canopy, 2)

#  2-fold spatial cross-validation 
# A tibble: 2 × 2
  splits            id   
  <list>            <chr>
1 <split [277/405]> Fold1
2 <split [405/277]> Fold2

Repetition, repetition, repetition¹

I think the most exciting part of this release is that all spatialsample functions can now handle cross-validation with repeats. Simply pass your desired number of repeats to the repeats argument of any function.² The autoplot() function will now also automatically detect when you’ve used repeated cross-validation, and facet plots accordingly:

boston_canopy |> 
  spatial_block_cv(v = 5, repeats = 4) |> 
  autoplot()

…and more!

This is just scratching the surface of the new features and improvements in this release of spatialsample. You can see a full list in this release’s NEWS entry.

Repetition!

Footnotes

1. This joke I keep making is stolen from an old Kurt Braunohler video. Or potentially, this other Kurt Braunohler video. Or potentially, that first Kurt Braunohler video.

2. Though note that spatialsample should error if repeats would return the same exact folds, such as when using spatial_leave_location_out_cv() with v = NULL. This behavior might change in the future if anyone complains, but for the modeling projects I’ve worked on, you’d only repeat identical CV folds by accident.