The R Journal: article published in 2017, volume 9:2

RQGIS: Integrating R with QGIS for Statistical Geocomputing PDF download
Jannes Muenchow, Patrick Schratz and Alexander Brenning , The R Journal (2017) 9:2, pages 409-428.

Abstract 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.

Received: 2017-07-07; online 2017-12-04, supplementary material, (1.9 Kb)
CRAN packages: maptools, raster, sp, sf, mapview, mapmisc, osmar, dodgr, RArcInfo, rgrass7, mapedit, rgdal, rgeos, RSAGA, RPyGeo, RQGIS, reticulate, rPython, sperrorest, nlme, mgcv, spgrass6, leaflet
CRAN Task Views implied by cited CRAN packages: Spatial, SpatioTemporal, Econometrics, Environmetrics, NumericalMathematics, SocialSciences, Bayesian, ChemPhys, Finance, HighPerformanceComputing, OfficialStatistics, Psychometrics, WebTechnologies

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  author = {Jannes Muenchow and Patrick Schratz and Alexander Brenning},
  title = {{RQGIS: Integrating R with QGIS for Statistical Geocomputing}},
  year = {2017},
  journal = {{The R Journal}},
  url = {},
  pages = {409--428},
  volume = {9},
  number = {2}