Wrapping up the stars project

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This is the fourth blog on the
stars project, an it completes the
R-Consortium funded project for spatiotemporal tidy arrays with R. It
reports on the current status of the project, and current development
directions. Although this project ends, with the release of stars 0.3 on
CRAN, the
adoption, update, enthusiasm and participation in the development of the
stars project have really only started, and will without doubt increase
and continue.


The stars package has now five
vignettes (called “Articles” on
the pkgdown site) that explain its main features. Besides writing these
vignettes, a lot of work over the past few months went into

  • writing support for stars_proxy objects, objects for which the
    metadata has been read but for which the payload is still on disk.
    This allows handling raster files or data cubes that do not fit into
    memory. Manipulating them uses lazy evaluation: only when pixel
    values are really needed they are read and processed: this is for
    instance when a plot is needed, or results are to be written with
    write_stars. In case of plotting, no more pixels are processed
    than can be seen on the device.
  • making rectilinear and curvilinear grids work, by better parsing
    NetCDF files directly (rather than through GDAL), reading their
    bounds, and by writing conversions to sf objects so that they can
    be plotted;
  • writing a tighter integration with GDAL, e.g. for warping grids,
    contouring grids, and rasterizing polygons;
  • supporting 360-day and 365-day (noleap) calendars, which are used
    often in climate model data;
  • providing an off-cran starsdata package, with around 1 Gb of real
    imagery, too large for submitting to CRAN or GitHub, but used for
    testing and demonstration;
  • resolving issues (we’re at 154 now) and managing pull requests;
  • adding stars support to
    a package for spatial and spatiotemporal geostatistical modelling,
    interpolation and simulation.

I have used stars and sf successfully last week in a two-day course
at Munich Re on Spatial Data Science with
(online material), focusing on
data handling and geostatistics. Both packages worked out beautifully
(with a minor amount of rough edges), in particular in conjunction with
each other and with the tidyverse.

Further resources on the status of the project are found in

  • the
    of my rstudio::conf presentation on “Spatial data science in the
  • chapter 4 of
    the Spatial Data Science book (under development)


Near future development will entail experiments with very large
datasets, such as the entire Sentinel-2
. We secured earlier
from the R Consortium for doing this, and first outcomes will be
presented shortly in a follow-up blog. A large challenge here is the
handling of multi-resolution imagery, imagery spread over different
coordinate reference systems (e.g., crossing multiple UTM zones) and the
temporal resampling needed to form space-time raster cubes. This is
being handled gracefully by the
gdalcubes C++ library and R
package developed by Marius Appel. The gdalcubes package has been
submitted to CRAN.

Earlier stars blogs

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