Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).

Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users
    define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and 
    resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, 
    applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, 
    exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features.  
    All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. 
    See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.

R's 'sf' package ships with self-contained 'GDAL'
    executables, including a bare bones interface to several
    'GDAL'-related utility programs collectively known as the 'GDAL
    utilities'. For each of those utilities, this package provides an
    R wrapper whose formal arguments closely mirror those of the
    'GDAL' command line interface. The utilities operate on data
    stored in files and typically write their output to other
    files. Therefore, to process data stored in any of R's more common
    spatial formats (i.e. those supported by the 'sf' and 'terra'
    packages), first write them to disk, then process them with the
    package's wrapper functions before reading the outputted results
    back into R. GDAL function arguments introduced in GDAL version
    3.5.2 or earlier are supported.

Various R programming tools for data manipulation, including
  medical unit conversions, combining objects, character vector operations,
  factor manipulation, obtaining information about R objects, generating
  fixed-width format files, extracting components of date & time objects,
  operations on columns of data frames, matrix operations, operations on
  vectors, operations on data frames, value of last evaluated expression, and a
  resample() wrapper for sample() that ensures consistent behavior for both
  scalar and vector arguments.