R has a thriving user base and therefore you can find lots of info in the form of forum answers and tutorials.
When you run into problems https://stackoverflow.com/ can be an excellent resource. As I mentioned though, sometimes this site can be of little help with more basic problems. Particularly when you don’t know specifically the question that you need to ask. If posting a question on stackoverflow, people within the community will ask that you create a MWE (minimum working example).
Wikipedia: “In computing, a minimal working example is a collection of source code and other data files which allow a bug or problem to be demonstrated and reproduced. The important feature of a minimal working example is that it is as small and as simple as possible, such that it is just sufficient to demonstrate the problem, but without any additional complexity or dependencies which will make resolution harder.” This is good practice in finding problems on your own too.
DataCamp, Code School, and Lynda all offer online classes for learning R. I’ve used DataCamp a lot. It is, however, a paid service, though you can do the first chapter of any class free. There are also many free tutorials if you look around the site more.
For Lynda.com, it was the case that you could get access for free with an Edmonton Public Library card.
Places to find good tutorials:
http://www.cookbook-r.com/ A really excellent place to start.
http://www.r-tutor.com/r-introduction
https://www.r-bloggers.com/how-to-learn-r-2/
Jenny Bryan’s STAT 545 webpage: http://stat545.com/
For Text Mining:
Coursera offers many classes in statistics that use R. This is how I started out. Be careful about the Johns Hopkins classes, though. I find that though they bill themselves as basic, the level of programming skill required increases too rapidly. This could be true for any MOOC. The point is, that if you find that things get too complicated too quickly don’t get frustrated - just drop the class. (I’ve done this lots.)
You won’t really learn anything from Twitter, but you can find a lot of R news there, and there are many interesting accounts to follow. Here are just a few.
Mara Averick @dataandme
Roger D. Peng @rdpeng
/r/DataIsBeautiful @DataIsBeautiful
boB Rudis @hrbrmstr
Julia Silge @juliasilge
David Robinson @drob
Hilary Parker @hspter
Jenny Bryan @JennyBryan
Hadley Wickham @hadleywickham