Mapping In R

Analyzing and mapping data is a critical step in telling a story for any project. We want to know how to provide spatial information in a clear and concise way so any reader can extract the important bits. Communicating the details visually is sometimes a difficult task, made more complicated by the influx of tools and methods available. But… open-source programs like R (and RStudio) have made huge strides towards replacing other geospatial software. For me, using R/RStudio provides a powerful and flexible approach to reproducible mapping and visualization. Even better, it’s easier and more efficient because you can re-use your code, and see all the steps that took place.

I’ve included a few pages that demonstrate how to read spatial data in R, perform spatial operations commonly used in GIS programs (intersect, clip, buffer), and finally how to plot that data in both static and dynamic form.

Knowing Where to Go

This isn’t meant to be the in-depth deep-dive in geospatial analysis in R. It is hopefully, a gentle introduction to some of the more commonly used tools that will help folks go from the “middle of nowhere” to the outskirts of town, or if I continue to take this analogy too far, a gas station where you can learn more and advance farther.