Hi, I’m David Keyes. I started R for the Rest of Us this year to help evaluators (and others) embrace the incredibly powerful piece of software that is R. This awkwardly named tool has the power to completely transform the way we work. There are many reasons to learn R. Here are a few of the most relevant reasons for evaluators.
You can do data analysis incredibly quickly and efficiently. Starting out, most people will get a ton of value using the so-called Tidyverse, a set of packages (i.e. add-ons designed for specific purposes). With code that is easy to understand, it makes learning R a ton easier. And once you learn to do data analysis in R, you can reuse any code you write in future work. No more struggling to recreate the series of clicks you made six months ago to run the same analysis!
Working in R often means using RMarkdown, which allows you to integrate all of your work, from data importing to final reporting, in a single tool. Instead of doing data and analysis in SPSS, exporting data to Excel to produce charts, then copying these charts into Word, you can write everything in RMarkdown. I wrote an article laying out the benefits of RMarkdown if you’re interested in learning more. Dana Wanzer has also written about how using RMarkdown enables her to provide immediate feedback to clients.
The data visualization capabilities of R are incredible. Many of the visualizations you see in places like the New York Times, the Economist, the BBC, and the Financial Times are made in R. Some of these organizations even release code that allows you to easily copy their style. For example, the BBC released a package called bbplot earlier this year, which allows you to make BBC-style plots with a single line of code.
R also enables you to make novel types of data visualizations. As part of my own data visualization practice, I’ve made maps, waffle charts, and ridgeline plots in R. There are packages that make it incredibly easy to make each of these types of visualizations.
Data visualization I’ve produced in R
Learning a new tool like R can be intimidating. A final reason to consider taking on the challenge is that the community of R users is incredibly welcoming and supportive. While many parts of the online coding universe can be less-than-welcoming to newcomers, the R corner of it is the exact opposite. If you’re on Twitter, check out the #rstats hashtag. Also, check out R for Data Science, a Slack community that is extremely welcoming to R newbies.
Oh, and did I mention that R is free?
Interested in trying out R? I’ve put together a free online course called Getting Started with R that walks you through the basics. I’ve also got a page on the R for the Rest of Us website with resources to help people learn R. Good luck on your R journey!
Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like to submit an aea365 Tip? Please send a note of interest to email@example.com. aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.