Esri’s 2018 UC was a spectacular event for the R-ArcGIS bridge. Not only did the bridge unveil its support for raster data this year, along with the release of multiple new resources, but also, session turnout was at an all-time high. 2018 marked the first year the R-ArcGIS bridge team offered a full-day, hands-on, preconference seminar on the bridge, Statistical Spatial Data Analysis with R and ArcGIS, which featured all the latest regarding using the bridge within R, ArcGIS, Jupyter notebooks, and alongside Conda and Python. As the thrill from UC transitions back into daily routines, there is no better time to catch-up with the latest developments on the bridge and to build it into your workflows. Let the R-ArcGIS bridge help you expand your analyses by bringing in the latest statistical and field-specific methods from R and by making sharing your results easy.
If you have already been working with the R-ArcGIS bridge and are eager to dive into the latest advancements, make sure you update your arcgisbinding package to the latest version to checkout several new functions. We have also updated our documentation included in R/RStudio to provide even more details and examples on how you can utilize the bridge in your workflows. To learn how you can do this and to access this documentation, see our latest resource on installing and setting-up the R-ArcGIS bridge, which includes details for every different installation option, along with information on how to update the bridge.
If you are new to the bridge, or looking for an easy way to apply what you saw at the UC, take advantage of all of our newest resources detailing everything from getting started with the bridge, to R code examples on the bridge’s reading, converting, and writing functionality, to script tool creation and sharing results, to the bridge’s support for mosaic datasets and time-series rasters, and finally, a bonus resource on getting started with Conda. For full-length workflows detailing the power of the bridge in action, consider checking out our learn lessons on determining a suitable habitat for African buffalo or on analyzing crime in San Francisco.
The bridge represents an exciting frontier into the full integration of ArcGIS’ spatial analysis power with novel, and field-specific statistical analyses from R. Stay tuned for more posts with details on new ways to make this integration even more versatile with support for big data and with the ability to work exclusively in R.