Andrea Zellner is a Phd student in the Ed Psych/Ed Tech program at Michigan State University. She can be found on Twitter @AndreaZellner.
I have recently dedicated myself to learning R, a programming language and environment for focusing largely on statistical analysis and computing. The benefit of using R over other statistical computing packages is that it is free, open-source, and has a hugely active community around its use. R can be used cross-platform (PCs, Macs, and Linux) and is incredibly robust and versatile. For those familiar with writing syntax for other statistical packages such as the Statistical Package for the Social Sciences or SPSS (the one I was required to learn when I was getting started), the jump to R doesn’t seem so difficult. For those relying only on the menus, it can be a little intimidating. Learning R is seen by many in the field as an essential, and highly marketable skills. It has been seen as useful to those across disciplines, including in digital humanities and the hard sciences. If you use data in any way, R can be useful (even for designing t-shirts!) . If you are interested in learning to code in general, R is a good language to play with, though there are lots of other options, too!
This post will focus completely on the ways in which I have approached beginning the learning R journey: I am in no way an expert, and I am very much a novice. I am hoping that for those who’ve never encountered R before and would like some resources for dipping a toe in, this post will be a conversation starter as we build resources and community around our learning.
1. Find friends. I knew that it might be difficult to sustain my commitment when the going got tough, so I started asking around to find out who already knew R and who also might be interested in learning with me. After I found my crew of approximately 10 classmates (and growing), we set up a private facebook group to share tips, tricks, and resources, including workshops offered at our university. We also use this to coordinate face-to-face meetups where we can all sit in a room together and work out our issues. We are also making our own t-shirts because, well, t-shirts!
2. Organize your tools and resources. Our group has been really happy using R studio, an Integrated Development Environment (IDE) for R. It’s free and really helps in terms of having an editor targeted right to the R language, allows for multiple documents to be opened in tabs, and it also includes a debugger and a language reference, among other features. Once I got cooking with R studio, I realized there were tons of sites out there dedicated to helping people use R. Here’s the Pinterest board I’ve been keeping of my favorites which links to the sites themselves, as well as to bookmarks my friends have found useful.
- Consider a MOOC. Both Coursera and Udacity have been offering multiple courses that either focus directly on learning R or on learning statistics through using the R language. Coursera’s course, “R Programming,” is part of their Data Science specialization and has been running on the first of every month. The Coursera course uses this great library called SWIRL as an interactive tutorial right in R. However, the Coursera course is not self-paced, and there are deadlines to meet. This personally stressed me out, and for that reason I have had much more success in the Udacity course “Exploratory Data Analysis using R.” The Udacity course offers a number of tutorials and opportunities to play with R, as well as allowing me to binge on Udacity videos and then not touch them for weeks at a time without making me feel guilty. I also make sure that I take written notes for each session so that I am more likely to retain the information between binge coding.
4. Set aside time to play. As with any new skill, it takes time to learn how to make things happen in R. I’ve been setting aside at least 30 minutes each day just to mess around in R, and at least once a week, I set aside much longer blocks to either dip into my MOOCs or spend time figuring out how to do analyses that pertain to my own research. I also receive the R bloggers email which is a great source for inspiration and fun projects (you can also just visit the site periodically).
So far, I’ve been loving playing with and learning about R. I have found the community to be so helpful and enthusiastic, and my own little crew has kept me going and inspired as well. A few of us have been tweeting on the #RClub hashtag, and we always welcome people to connect that way, too.
What advice do you have to those new to R? Are you learning R yourself right now? We’d love to hear from you in the comments!
- How to Ask for R help: http://www.r-bloggers.com/how-to-ask-for-r-help/
- An oldie, but a goodie: “Data Analysts Captivated by R’s Power”
[Image by R Foundation, from http://www.r-project.org [GPL (http://www.gnu.org/licenses/gpl.html)], via Wikimedia Commons]
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