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The R Journal

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Date of this Version

12-2017

Document Type

Article

Citation

The R Journal (December 2017) 9(2); Editor: Roger Bivand

Comments

Copyright 2017, The R Foundation. Open access material. License: CC BY 4.0

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, RQGISsupports the seamless integration of Python code using reticulate from within R for improved extendability.

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