QGIS, the leading open‑source desktop GIS, and R, the dominant language for statistical computing, complement each other perfectly.
By bridging cartographic tools with advanced analytics, practitioners unlock workflows that neither environment can do alone.
Why Combine QGIS and R?
- Seamless Data Pipeline
Send layers from QGIS directly into R scripts for statistical modeling, then return results to the map canvas. - Reproducible Science
RMarkdown or Quarto notebooks keep every step—data prep, analysis, visualization—in one shareable document while QGIS handles interactive cartography. - Cost‑Effective
Both projects are open‑source, license‑free, and backed by active communities for plugins, tutorials, and rapid support.
Common Use Cases
1. Exploratory Spatial Data Analysis
- Workflow:
1. Inspect raw layers in QGIS.
2. Export attribute tables or geometries to R forsf
‑based EDA, clustering, or hotspot detection.
3. Push summary layers back to QGIS for interactive inspection. - Alternatives: ArcGIS Pro with ArcPy; GeoPandas in a pure‑Python notebook; PostGIS with pgAdmin for SQL‑centric EDA.
2. Spatial Statistics and Modeling
- Scenarios:
- Kriging soil nutrients using the
gstat
package. - Spatial econometrics with
spdep
for housing price gradients. - Bayesian disease mapping via
INLA
. - QGIS Role: Define study boundaries, create variogram clouds, and style prediction rasters.
- Alternatives: GeoDa for quick spatial‑stats GUI; SAGA‑GIS for geostatistics tools with limited scripting.
3. Machine Learning on Raster Stacks
- Workflow:
1. Assemble multispectral rasters in QGIS.
2. Use theterra
andcaret
ortidymodels
packages in R to train land‑cover classifiers.
3. Write prediction rasters back as GeoTIFF and symbolize with QGIS’s color ramps. - Alternatives: Google Earth Engine JavaScript API; Python with Rasterio + LightGBM; commercial ENVI or eCognition for classification.
4. Automated Map Production
- Scenario: Generate weekly incident‑density maps.
- R script fetches new data, performs kernel density estimation, and updates layers.
- QGIS atlas templates read updated layers and export PDFs automatically via the QGIS Processing R Provider.
- Alternatives: ArcGIS’s arcpy mapping module; Mapbox Studio for style‑driven vector tiles if interactivity is the priority.
5. Custom Plugins and Extensions
- Example: A QGIS plugin that calls an R script for species distribution modeling, then renders probability surfaces on the fly.
- Tools: Use the Processing Toolbox “R Script” algorithm or build standalone plugins with PyQGIS plus an R backend.
- Alternatives: FME for no‑code spatial ETL plus Python; Esri ModelBuilder plus R‑ArcGIS Bridge for hybrid scripting.
Integration Pathways
- Processing Toolbox R Provider – Write R scripts as processing algorithms accessible like any native QGIS tool.
- R‑QGIS Bridge (
qgisprocess
) – Call QGIS algorithms from R, ideal for batch‑processing pipelines. - Python + R via
reticulate
– Combine PyQGIS functions and R analytics in a single Quarto notebook. - File‑based exchange – Simple read/write of GeoJSON, GeoTIFF, or SpatiaLite for quick hand‑offs without live linkage.
Best Practices
- Keep coordinate reference systems consistent across both environments.
- Version‑control R scripts alongside QGIS project files for auditability.
- Use virtual environments or Docker to pin QGIS and R package versions, ensuring reproducible deployments.
- Document dependencies and data sources in README or metadata fields within QGIS.
Final Thoughts
Pairing QGIS’s intuitive spatial editing and cartography with R’s analytical depth enables end‑to‑end geospatial science without proprietary constraints.
Whether you’re running regressions on urban accessibility, building predictive land‑cover models, or automating map books, the QGIS‑R ecosystem delivers flexibility, transparency, and a thriving open‑source community ready to help you push spatial analysis further.