Integrating R with Geographic Information Systems (GIS) extends R’s statistical capabilities with numerous geoprocessing and data handling tools available in a GIS. QGIS is one of the most popular open-source GIS, and it furthermore integrates other GIS programs such as the System for Automated Geoscientific Analyses (SAGA) GIS and the Geographic Resources Analysis Support System (GRASS) GIS within a single software environment. This and its QGIS Python API makes it a perfect candidate for console-based geoprocessing. By establishing an interface, the R package RQGIS makes it possible to use QGIS as a geoprocessing workhorse from within R. Compared to other packages building a bridge to GIS (e.g., rgrass7, RSAGA, RPyGeo), RQGIS offers a wider range of geoalgorithms, and is often easier to use due to various convenience functions. Finally, RQGIS supports the seamless integration of Python code using reticulate from within R for improved extendability.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-067.zip
maptools, raster, sp, sf, mapview, mapmisc, osmar, dodgr, RArcInfo, rgrass7, mapedit, rgdal, rgeos, RSAGA, RPyGeo, RQGIS, reticulate, rPython, sperrorest, nlme, mgcv, spgrass6, leaflet
Spatial, SpatioTemporal, Econometrics, Environmetrics, NumericalMathematics, SocialSciences, Bayesian, ChemPhys, Finance, HighPerformanceComputing, OfficialStatistics, Psychometrics, WebTechnologies
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For attribution, please cite this work as
Muenchow, et al., "The R Journal: RQGIS: Integrating R with QGIS for Statistical Geocomputing", The R Journal, 2017
BibTeX citation
@article{RJ-2017-067,
author = {Muenchow, Jannes and Schratz, Patrick and Brenning, Alexander},
title = {The R Journal: RQGIS: Integrating R with QGIS for Statistical Geocomputing},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-067},
doi = {10.32614/RJ-2017-067},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {409-428}
}