A few months ago, I was beyond thrilled to be chosen to give an e-poster at RStudio::conf(2019) on the topic of Upgrading to R. I had such a blast talking to people who are trying to get their team to use more R, sharing some of the lessons I’ve learned over the last few years. My slides are available online, but I’d also like to share some of the things I spoke with people about, and a little on how I made the slides.
Upgrading to R
So many data scientists have had the same experience - walking onto a team where something other than R or Python was the language of choice. Unless that other language was Julia or Scala, this was almost certainly a disappointing moment. But it doesn’t have a be a reason to run screaming for the hills.
I’ve now worked on several teams that have successfully transitioned from Excel or Stata to R. These transitions, while sometimes painful, were definitely worth it. Along the way, I learned a few tips and made a lot of mistakes. I decided I’d try to share as much as possible to help anyone else in the same boat. These lessons will be particularly applicable to someone leading a data science team that’s transitioning. Some will also be relevant if you’re a junior staffer trying to push change, but not all.
In thinking about the transitions I’ve been part of, here are tips that might’ve helped me if I’d known them when I started. Links will be added as the posts go live.
- Excitement >> Skills
- Failure’s Coming, Get Ready
- You are the Data Engineer your Team Needs
- It Takes a Team to Write a Package
- Git: Sometimes the Right Thing isn’t the Easiest
- Have you met my Friend RMarkdown?
- Don’t Get Too Excited Just Yet
I plan to write a blog post on each of these, plus an extra about how I put together these tips and the accompanying plots.