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
attributesradius and buffer are now set to 0 if they
were passed as NULL.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.
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).
NEWS.md file to track changes to the
package.