Written by: Paul Rubin
Primary Source: OR in an OB World
(Should I have spelled the last word in the title “ResouRces” or “resouRces”? The R community has a bit of a fascination about capitalizing the letter “r” as often as possible.)
Anyway, getting down to business, I thought I’d post links to a few resources related to the R statistical language/system/ecology that I think may be either not terribly well known or perhaps a bit under-appreciated. As I come across new ones (or see comments rubbing my nose in some glaring omission), I will try to remember to update the list.
In no particular order, here is what I have so far. If anyone sees a better way to organize the list, feel free to suggest it in the comments. Also, do not assume my level of verbosity for any item is an indicator of my judgment of its importance or utility. It’s purely random.
- CRAN Task Views
- Every R user knows CRAN, the Comprehensive R Archive Network, because it is the go-to repository for downloading R packages (and R itself). Perhaps not as well known is that it maintains a collection of task views, curated lists of packages useful for particular categories of tasks (such as clinical trials, econometrics or machine learning). Not all task views are limited to statistics. In particular, the task views for numerical mathematics and optimization are interesting to me. The curators are volunteers, so how complete and up to date a given task view is contains an element of chance. As of this writing, I don’t see a task view for simulation. I just mention this in case a reader is looking for something to do with their copious idle time. There is also a package (“ctv”) you can apparently use to install task views on your PC, but I have not tried it yet.
- R Bloggers
- R Bloggers is pretty much what it sounds like: an aggregator for, per their subtitle, “R news and tutorials contributed by (580) R bloggers”. Given the 580 sources, you can safely assume I am not keeping up with all the reading. They provide the usual RSS feeds and such should you wish to subscribe.
- RStudio webinars
- RStudio is of course famous for the eponymous IDE (which I highly recommend), along with the Shiny package for interactive web documents and various other highly useful software (including server versions). They also produce a serious of high quality webinars. You can get on their email list for advanced warning of dates and topics, or drop by their webinar repository after the fact to replay previous webinars (all of which are recorded) and optionally download supporting materials.
- The R Podcast
- I just came across this podcast series recently. The host/auteur, Eric Nantz, is a statistician with over a decade of experience using R. He starts out with the very basics (what is R and how do I install it) and gradually moves into more advanced topics. Again, you can subscribe by various means (including RSS feed), and all episodes can be replayed from the web site. He also has some screencasts (which I have not yet checked out), and some ancillary materials you can download. Each episode includes a segment where he responds to user feedback (provided by various methods, including voicemail, so that you can hear yourself ask your own question in the next episode). The site lists podcasts in reverse chronological order (which is pretty standard), so if you want to start with episode 1 (or was it 0?) you’ll need to do some clicking to get there. My advice is to grab the RSS feed in some aggregator, such as Inoreader, where you can see links to all the episodes in a compact format. One nice feature: Eric provides “show notes” (bullet list of topics), so you know what you’re getting. One feature I miss: in the early episodes, he provided the times at which each topic started, which made it easy to skip over stuff I didn’t need to hear. That seems to have gone missing in more recent posts.
- Beginner’s Guide to R
- Sharon Machlis wrote this beginner’s guide for Computerworld. If you are brand new to R, this is a very good place to start. You might also want to follow Sharon to find other helpful articles relating to R, such as “Great R packages for data import, wrangling & visualization“.
- Google’s R Style Guide
- Okay, cards on the table: some people take coding style (and in particular consistency of coding style) quite seriously, and some pay it no attention at all. Also, some people view Google as the center of the digital universe, and some think it is the forerunner to either Big Brother or Skynet (or both). So maybe you will be interested in Google’s coding style guide for R, and maybe you won’t. At any rate, it’s there for the asking.
- Gallery of htmlwidgets for R
- Hadley Wickham’s Vocabulary page
- Hadley Wickham, besides being a data scientist, is one of the most prolific authors of high quality (and highly popular) R packages. His vocabulary page lists some of the commands, functions and packages he considers most essential, grouped by task category. Once you’ve gotten some experience using R, you might want to consult this page to see if you’ve missed anything useful to you.
- DataScience+ is home to free tutorials (115 as of this writing) relating to R. It should be a go-to location when you are looking to learn more about the uses of R. Tutorials range from the basic (getting started with R) to the somewhat esoteric (random forests, Bayesian regression, …).