Skip to content

spatialsample 0.5.1

CRAN release: 2023-11-07

spatialsample 0.5.0

CRAN release: 2023-11-03

  • spatial_block_cv() gains an argument, expand_bbox, which represents the proportion a bounding box should be expanded by (each corner of the bounding box is expanded by bbox_corner_value * expand_bbox).

    • This is a breaking change for data in planar coordinate reference systems. Set to 0 to obtain previous behaviors.
    • Data in geographic coordinates was already having its bounding box expanded by the default 0.00001.
    • This makes it so that regularly spaced data is less likely to fall precisely along grid lines (and therefore fall into two assessment sets) and so that geographic data falls is more likely to fall within the constructed grid.
    • Thanks to Nikos on StackOverflow for reporting this behavior:
  • spatial_block_cv() will now throw an error if observations are in multiple assessment folds (caused by observations, or observation centroids, falling precisely along grid polygon boundaries).

  • In spatial_nndm_cv(), passing a single polygon (or multipolygon) to the prediction_sites argument will result in prediction sites being sampled from that polygon, rather than from its bounding box.

  • get_rsplit() is now re-exported from the rsample package. This provides a more natural, pipe-able interface for accessing individual splits; get_rsplit(rset, 1) is identical to rset$splits[[1]].

spatialsample 0.4.0

CRAN release: 2023-05-17

spatialsample 0.3.0

CRAN release: 2023-01-17

Breaking changes

New features

  • All functions now have a repeats argument, defaulting to 1, allowing for repeated cross-validation (#122, #125, #126).

  • spatial_clustering_cv() now has a distance_function argument, set by default to as.dist(sf::st_distance(x)) (#126).

Minor improvements and fixes

spatialsample 0.2.1

CRAN release: 2022-08-05

  • Mike Mahoney is taking over as package maintainer, as Julia Silge (who remains a package author) moves to focus on ModelOps work.

  • Functions will now return rsplits without out_id, like most rsample functions, whenever buffer is NULL.

  • spatial_block_cv(), spatial_buffer_vfold_cv(), and buffering now support using sf or sfc objects with a missing CRS. The assumption is that data in an NA CRS is projected, with all distance values in the same unit as the projection. Trying to use alternative units will fail. Set a CRS if these assumptions aren’t correct.

  • spatial_buffer_vfold_cv() and buffering no longer support tibble or data.frame inputs (they now require sf or sfc objects). It was not easy to use these to begin with, but should have always caused an error: use rsample::vfold_cv() instead or transform your data into an sf object.

  • spatial_buffer_vfold_cv() has had some attribute changes to match rsample:

    • strata attribute is now the name of the column used for stratification, or not set if there was no stratification.
    • pool and breaks have been added as attributes
    • radius and buffer are now set to 0 if they were passed as NULL.

spatialsample 0.2.0

CRAN release: 2022-06-17

New features

  • spatial_buffer_vfold_cv() is a new function which wraps rsample::vfold_cv(), allowing users to add inclusion radii and exclusion buffers to their vfold resamples. This is the supported way to perform spatially buffered leave-one-out cross validation (set v to nrow(data)).

  • spatial_leave_location_out_cv() is a new function with wraps rsample::group_vfold_cv(), allowing users to add inclusion radii and exclusion buffers to their vfold resamples.

  • spatial_block_cv() is a new function for performing spatial block cross-validation. It currently supports randomly assigning blocks to folds.

  • spatial_clustering_cv() gains an argument, cluster_function, which specifies what type of clustering to perform. cluster_function = "kmeans", the default, uses stats::kmeans() for k-means clustering, while cluster_function = "hclust" uses stats::hclust() for hierarchical clustering. Users can also provide their own clustering function.

  • spatial_clustering_cv() now supports sf objects! Coordinates are inferred automatically when using sf objects, and anything passed to coords will be ignored with a warning. Clusters made using sf objects will take coordinate reference systems into account (using sf::st_distance()), unlike those made using data frames.

  • All resampling functions now support spatial buffering using two arguments. radius lets you specify an inclusion radius for your test set, where any data within radius of the original assessment set will be added to the assessment set. buffer specifies an exclusion buffer around the test set, where any data within buffer of the assessment set (after radius is applied) will be excluded from both sets.

  • autoplot() now has a method for spatial resamples built from sf objects. It works both on rset objects and on rsplit objects, and has a special method for outputs from spatial_block_cv().

  • boston_canopy is a new dataset with data on tree canopy change over time in Boston, Massachusetts, USA. It uses a projected coordinate reference system and US customary units; see ?boston_canopy for instructions on how to install these into your PROJ installation if needed.


  • The “Getting Started” vignette has been revised to demonstrate the new features and clustering methods.

  • A new vignette has been added walking through the spatial buffering process.

Dependency changes

  • R versions before 3.4 are no longer supported.

  • glue, sf, and units have been added to Imports.

  • ggplot2 has been moved to Imports. It had been in Suggests.

  • covr, gifski, lwgeom, and vdiffr are now in Suggests.

  • rlang now has a minimum version of 1.0.0 (was previously unversioned).

spatialsample 0.1.0

CRAN release: 2021-03-04

  • Added a file to track changes to the package.