Things (IMO) that R has going for it: * RStudio IDE - none of python equivalents are anywhere near as mature * Functional programming - I can't fully articula

Things (IMO) that R has going for it: * RStudio IDE - none of python equivalents are anywhere near as mature * Functional programming - I can't fully articulate why yet, but to me, FP seems like it "fits" most DS programming challenges better than OO. * Tidyverse - I'm biased (being the author) but having a broad ecosystem of tools with shared underlying programming philosophies makes solving data science challenges particularly easy/painless/fun. * RMarkdown - makes it so easy to intermingle prose + code and then (via pandoc) render to a very wide variety of outputs (html, word, pdf, html slides, dashboards, ...) * Shiny - for cases where you don't want a single report but an app, an R user can quickly create an interactive app without knowing the details of html/css/js * Very broad ecosystem of statistical packages. Most statistics researchers use R, so you're more like to see cutting edge stat research in R first.