Hello! there’s a longer post on this here, but here’s my shorter answer.
For hockey analytics, check out Meta Hockey. In particular, going to “Publications” --> “Articles” will give you a vast repository of articles written over the past decade. It sounds like you’ve already gotten the basics, so the “Introduction” posts may not be necessary, but check out whatever there catches your eye. You can also check out the archives of the sites that it links to in order to get a full sense of what that site was talking about at the time
For R, there’s a lot of different textbooks and courses available. It can also depend on precisely what it is you want to do with R - exploratory data analysis is different from machine learning is different from building a website. That said, assuming you want a general intro to using R to work with data, I highly recommend R for Data Science, a free online textbook. All of it is good, but the really essential chapters are 4 (basic workflow), 3 (making graphs), 5 (data manipulation) and 12 (cleaning and organizing data). Those cover 80% of what I use day to day. The other chapters are all extremely useful as well, and you should bounce between them to pick up what you need. Chapters 19 and later get a bit more advanced, so skim those so you know what exists but don’t worry about becoming fluent in it all. The most important thing is to take this and use it to actively write your own code - writing stuff and making mistakes is far far more effective for learning than passively reading any textbook.
If you are also interested in doing machine learning in R, check out this free online course and accompanying textbook. Not all of the models it teaches are state-of-the-art, but it’s the best intro I’ve seen to the key considerations of model building, the most common techniques, and it does everything in R.