H is for Help with R

April 9, 2018
By

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My goal this month is to offer some fun tutorials and introductions to the R statistical language and R Studio software. But you may find, at some point, that you need a little…

And that’s okay! R can have a very steep learning curve, but even for an expert, you need help figuring things out every once in a while. I constantly look things up while I’m using R – there are very few things I remember exactly how to do off the top of my head. R Studio is great in that it gives you some pop-up guidance when writing code and allows you to search for help in package manuals. But that won’t always cut it. To get that help, you need to have some idea of where to begin – which library, which function, and so on. Here are my favorite places to go when I need help doing something in R:

  • Stack Overflow R – This link takes you directly to questions tagged as R; at the top of the screen, you can type in details about your question to narrow it down.
  • Quick-R – This site is completely dedicated to how to do different things in R, in particular statistical analysis. When I can’t remember the syntax to create a specific kind of table or need a quick refresher on a plot, I go here.
  • R-bloggers – This page aggregates posts from other bloggers about R; it’s a curated feed of some of the best R posts out there. While I tend to go to the first two links on this list for quick help, this is where I go for in-depth tutorials.
  • Variance Explained – One of my favorite blogs – each post features walk-throughs and tons of code by R programmer David Robinson. If you want to behold the awesome things R can do, and maybe get some inspiration for your own awesome things, start here. He also has some great R tutorials.
  • Learning and Using R at Stanford – No, I didn’t go to Stanford. Fortunately, you don’t have to either to access some of these great resources. (Note, some do require a login, but many are free.) Once again, this is a great site to look for tutorials on specific topics.
  • All else fails, LMGTFY – But in all seriousness, if I can’t find the answers I need above, I just do a web search. 
And if you’re more of a book learner/lover, here are some of my favorite books on R:
  • R in a Nutshell – I’ve been carrying this book with me at almost all times for the last month or so, as I’ve prepared for this blog challenge.
  • A Beginner’s Guide to R – Sadly, this book might be outdated at this point, but this was the book I picked up in 2009 when I decided to try this crazy idea of dumping SPSS and going full R (without ever having used R before). Though it sounds like hubris (I was a 5th year doctoral student, so yes), it was also because I was broke and wanted to use something free (I was a 5th year doctoral student, after all). When I found I was in over my head, this book kept me from going back on my idea.
Finally, if you start feeling comfortable with R and want to take things a step farther:
  • Latent Variable Modeling with R – Especially if you’ll be doing a lot of psychometric or structural equation modeling work, this is a great book to have. It uses many of the packages I draw upon regularly (and would recommend above similar packages) like lavaan and ltm.
  • Text Mining with R: A Tidy Approach – Julia Silge and David Robinson (above) go through exactly how to use their R package, tidytext, which David frequently uses on his blog.

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