My experience with R was somewhat different. R was my first computational language in 2006 (version 2.3, IIRC), and parsing real life data (biological, in my ca

My experience with R was somewhat different. R was my first computational language in 2006 (version 2.3, IIRC), and parsing real life data (biological, in my case) into a format acceptable to R was a non-trivial exercise. I had somebody write me a perl script to parse the raw data into a clean CSV, but that has its own problems. The tools that were the kernel of the tidyverse (created 2014) were just beginning to show up, and even magrittr pipes were many years away. The only tidyverse tool even close to mature at the time was ggplot. For me data munging was the limiting factor, and at some point I discovered many people prefer Python for these initial steps. In 2013 I learnt Python with the explicit aim of data munging, while continuing analyses in R. With Pandas I could cover 80% of my use case for R, and eventually dropped it completely. Again, this predates the creation of the tidyverse, which I noted with some irony. For what its worth, Hadley Wickham was asked in a Reddit AMA several years ago about which platform he'd choose if he was just starting out. He pointed to Julia as his pick.