I was taught to leave a place better than I found it. In my science career, this has taken the form of teaching R. Whether you are a new student actively seeking help or a career scientist not looking to change your ways, I believe R can improve research efficiency, reproducibility, and accessibility. The catch is convincing everyone else. In this talk, I share my experiences spreading R as the first bioinformatician in my group at the U of Washington’s School of Medicine. When I arrived, the extent of code was a couple STATA scripts that few knew how to run. Now, about two years later, all analyses have reproducible scripts, publications have accompanying GitHub repositories, and (nearly) everyone uses R. I describe a range of methods from passive to active, structured to unstructured that I have found helpful in introducing non-coders to R and convincing the inconvincible to change their ways. I highlight things that worked, what I learned from things that didn’t, and how the pandemic helped and hurt my efforts.
Bio: Dr. Kim Dill-McFarland is a bioinformatician at the U. of Washington. She works at the intersection of microbiology and computer science, applying computational approaches to biological problems. Using sequencing and other high-throughput techniques, she works with several UW labs to research how the human immune system responds to disease.