The R language has for the past couple years been my go to language. As a brief introduction to the language it is important to note that it is a statistical language, meaning it was built so stats professors could code up regression equations with minimal work. There is not much extra work involved in forming loops and regression or distribution functions do not require calling any packages. In the past decade a new universe of packages has been created alongside R, called the tidyverse, which changes much of the notation to a self-described easier system. With all of this in mind the upside of R is clearly its ease in writing. With little experience you can quickly load your data and perform statistical tests. The primary downside is speed. If you do not think carefully about your code, such as using many for loops, the code will run very slowly. Secondarily, if you learn R and try to transition to another language you may not know proper code formatting or other good practices that are common in computer science.
I mostly learned R through trial and error, however since then I have found several great resources. The first is the built in swirl package, that walks you through the basics of R. Moving on from there one great book is R for Data Science by Hadley Wickham, the main man behind the Tidyverse. He has put out many great (and free!) books, all worth checking out. Something to focus in on in these books is data visualization, which is a particular strength of R.
Or! You can read my code. I have broke them down into the various versions mentioned on the main tic tac toe page. Descriptions of syntax and logic abound (hopefully!).