Sharing is Caring
At RStudio (Posit) Conference this year, I realized I’d been dealing with a bit of impostor syndrome while sitting at a Birds of a Feather table for folks interested in spatial data. As I sat there, I started asking people about their spatial work and had largely assumed people knew more than I did. In part, this is because I’ve learned so many things from folks sharing through the #rstats hashtag on twitter and assumed people at the conference were just all farther down the line in the development of their skills than I was.
The reality was that most folks at my table were there because they were interested in learning how to work with Spatial Data in R and I was the only one at my table who had experience and background making maps, so folks asked for some resources and direction on how to get going with Spatial Data in R. For those at the table, I gave a very impromptu demo and walk through of core tools in my spatial workflow that a lot of people liked. Afterwards, some folks asked if I could share resources. So this post (or possibly series of posts?) is a first response to that.
At the core of my imposter syndrome was assuming folks already knew most of the stuff I did and, therefore, there wasn’t much of a need for me to share. So, at least with regards to spatial data, I’ll try to be better about contributing to folks in the #rstats community online by sharing about different things. Anyway, here we go.
First Question: Can you do any Spatial/GIS Work in R?
Yes.
Second Question: Are R tools any kind of good for Sptial/GIS workflows?
Absolutely. Positively. Definitively, yes. R is fan-freaking-tastic for a ton of random stuff, but it is especially amazing when it comes to spatial work. In fact, one of the reasons I’ve come to love R so much is how simplified workflows are, regardless of how complicated the data may be.
I often think back on a grad school GIS Course in 2017, where I first learned how to make maps. The workflows involved downloading separate spatial/numeric data files and required multiple pieces of software and hours of time to join those data, design the map, and export that map to a format you could use in a report or public-facing document. And that was really only static maps that end up in printed or PDF reports. Interactive workflows was an entirely different beast that required even more expensive software and other skills.
In contrast, consider the map below, which is built in this document. The code chunk at the very bottom of the document is what produces this map. It pulls the spatial and numeric data from its source (NCES Open Data) and visualizes it in about 10 lines of code. 🤯
More impressive is that, even though it contains over 100,000 school locations across the country, it’s very fast. Use your mouse/track-pad to hover the dots and zoom in-and-out.