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My R Journey: Alex Michel

David Keyes David Keyes
June 25th, 2019

Alex Michel recently graduated from the OHSU-PSU School of Public Health in Portland, Oregon with a Master of Public Health. She’s worked in science communication and science writing for about 10 years and hopes to continue to use R to improve public access and engagement with research.

Why did you decide to learn R?

I first really got interested in R when I saw a guy who was making these really great graphs that also used these hilarious color palettes inspired by the nineties X-Men cartoon and Wes Anderson movies. I was working at a small psychology association and I started seeing that lots of scientists were really excited about this thing called R.

My background is in science communication and science writing, so I had never messed around with coding before at all. It turned out that scientists and journalists were all super excited about R, so I actually ended up writing and editing a big story on how folks were using R and ended up getting really excited about learning it myself. As a writer who does not particularly enjoy crunching numbers, I got really excited about how R is being used to tell important stories.

As a writer who does not particularly enjoy crunching numbers, I got really excited about how R is being used to tell important stories.

How easy or difficult was learning R? What resources were most helpful?

I tried learning R a couple of times, because I think it does have a pretty steep learning curve for someone unfamiliar with coding. For me the hardest thing was adjusting to more of a "it's ok to get an error" mindset: I just wasn't used to making mistakes that crashed everything and having to troubleshoot to figure out what went wrong. At first, I felt like I was doing horribly and maybe this just wasn't for me. A professor explained that this is totally ok and it's actually part of the fun of working with code sometimes. That really helped me build some confidence and re-focus my learning on playing with code and not stressing about getting it "right."

At first, I felt like I was doing horribly and maybe this just wasn't for me. A professor explained that this is totally ok and it's actually part of the fun of working with code sometimes. That really helped me build some confidence and re-focus my learning on playing with code and not stressing about getting it "right."

Nathaniel Phillips published a fantastic free guide to R for beginners that doesn't take itself too seriously, YaRrr! The Pirate's Guide to R. I also learned about a ton of wonderful, very accessible resources from Sara Weston and Debbie Yee. Another person who has put together a ton of amazing free resources for learning R for my field of public health is Ted Laderas. There's just such a generally positive and supportive community around R, which is really helpful when you're just getting started.

In what ways has learning R changed your work?

In my field Stata and SAS are the standards most folks use, but for me R is particularly useful for making figures and graphs that make your analysis clear and understandable. I'm a science communication person, not a statistician or a scientist, and with R I can make these incredible visualizations that really help me tell the stories I want to tell.

I'm a science communication person, not a statistician or a scientist, and with R I can make these incredible visualizations that really help me tell the stories I want to tell.

What do you think people considering learning R might not appreciate about it?

I think for me a big hurdle to learning R was that it felt easier to just keep using the tools I already was familiar with. But investing the time in learning R actually ended up saving me a ton of time and frustration later on from having to futz around with Excel or just accepting that I wasn't able to do what I wanted to do.

With R I can definitely do pretty much anything I want to do — either with visualizations or with the analysis — because the community is just so creative and generous. People are always building and improving packages for R that do amazing things.

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