When I came across Ryan Ames’ LinkedIn profile, I was struck by how similar his work is to my own. Based in Portland (like me), he works at the intersection of data science and program evaluation (the field I worked in prior to starting R for the Rest of Us). My curiosity piqued at seeing a professional doppelgänger, I reached out to ask if he’d answer some questions about his path to R. He kindly agreed and you can see the results below.
Ryan also shared a bit about his professional and personal background. He comes from a blue-collar automotive belt in Michigan that served as an inspiration to discover why there were such stark socio-economic divides in our communities. His work has included measuring the impact of increasing food security, providing access to healthy food in schools, food banks, farmer’s markets, and conducting community evaluations of organizations improving child health. When he is not diving into data, you can likely find Ryan playing music or exploring the forests of the Pacific Northwest.
Why did you decide to learn R?
I first decided to learn R because I wanted better data visualizations than I had been creating in SAS, Stata, and SPSS. I was especially drawn to ggplot and interactive mapping. Over time I realized R was a way for me to create more powerful workflows by creating repeatable processes and code that scaled from one project to the next.
How easy or difficult was learning R? What resources were most helpful?
Learning R was a gradual process for me since initially I was using it for visualization and specific statistical analysis. I sought out specific tutorials, package documentation, and a few online courses. I also learned R from several of our Graduate and Research Assistants (I was working at the Community Research Institute at Grand Valley State University) who were interested in applying what they were learning in their Biostatistics courses. So we incorporated R into a few of our research projects by altering the existing data workflows and deliverables.
R was fairly difficult to learn at first. It was just a whole new framework for me (since the only scripts I had written were in SAS, Stata, and SPSS). That said, whenever I successfully got a new data wrangling format, visualization, or statistical model to work, it seemed easy, and like magic. I became hooked on that feeling of success and on how scalable the code was for me within and across different projects. And I really believe the R community (Twitter, RStudio Community, local Meetups in Portland) is great. It’s my favorite tech-based community I’ve been part of.
In what ways has learning R changed your work?
R has provided a way for me to better organize my workflows and scale the work I’m doing for one repeated or annual process to another. I’ve felt more control over the process, the data, and the deliverables. For example, I sometimes struggle to understand our Salesforce instance. It’s grown quite organically (and features many weeds) over the years and R has helped me better understand what’s there and manipulate the data so that it can be put to better use for various analyses (we use the Rforce.com package all the time for querying data, manipulating it, and pushing it back into Salesforce).
I feel like R gave me a framework and mechanism to dig deeper into my data, connect disparate sources, and develop fully customizable outputs that people rely on. We overhauled our reporting by using Rmarkdown for a wide variety of needs. We use it for quick exploratory analysis or to better track various sources of our data. We use it for short one and two pager data reports with visualizations that are produced in bulk for every state and school. And we use it to create fairly polished summary reports to enhance our learning.
What do you think people considering learning R might not appreciate about it?
The base syntax. It’s a little brutal. I’ve seen a good number of folx really balk at R from seeing base R code and not wanting to go much further with R because of that. And honestly, I don’t blame them. However, when I’m able to show nice, clean Tidyverse code and custom functions I’ve created to simplify the work we do, there’s better reception to it. To me, R is kind of like an acquired taste. Where, once you really give it a real try, you end up loving it more deeply and coming back to it much more often than many of the other easy-to-swallow tastes.