4 steps to unleash the valuable insights hidden in your (spatial) data

R for Spatial Data Science

These are exciting times for Data Scientists who want to incorporate location awareness in their workflow in R. R, an open source software environment for statistical computing and graphics, has been around since the early nineteen nineties. And although geospatial analysis in R has a long-standing tradition, there have been some interesting developments in recent years which have made incorporating geographic information in R more easy and intuitive than ever before. In addition, both ArcGIS and FME provide bridges to R, creating unique added value for both Data Scientists and GIS specialists.

And what’s new in R to be so thrilled about?

Firstly, already a few years ago, the package leaflet has brought interactive mapping to R. But more recently the mapview and tmap libraries, who build on top of leaflet, have even further extended the options for interactive exploration on the map. And secondly, the recent release of sfSimple Features for R, has really caused a revolution in the R ecosystem with it’s simple and consistent approach to the management of spatial data in R.

In this blog I share four steps to incorporate the full potential of geography into your data analysis with R.


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