While I’m currently in Github mode (see previous post), I thought I’d share a list of resources I started putting together for learning and upskilling in the R programming language.
If you don’t know what R is, this probably won’t be of much use to you. But if you are a novice user, want to improve your skills, or just have access to a kick-arse list of cheatsheets, then this Github repository should be useful.
I started putting this list together for members of the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage, but I see no reason why it should be limited to that particular group of people.
I don’t claim that this list is exhaustive, nor do I vouch for the quality of any of the listed resources. Some of them are deprecated and fairly old too, so be warned.
The first section includes online resources such as short courses, reference guides, analysis demos, tips for more-efficient programming, better plotting guidelines, as well as some R-related mini-universes like markdown, ggplot, Shiny, and tidyverse.
The section following is a list of popular online communities, list-servers, and blogs that help R users track down advice for solving niggly coding and statistical problems.
The next section is a whopping-great archive of R cheatsheets, covering everything from the basics, plotting, cartography, databasing, applications, time series analysis, machine learning, time & date, building packages, parallel computing, resampling methods, markdown, and more.
Following this is a folder holding a few free books in PDF format, including Introductory Statistics with R and A Beginner’s Guide to R.
The last section is a whole raft of (mostly deprecated) contributed documentation for various R applications. This is a legacy list still present on the Comprehensive R Archive Network (CRAN), but as I warned above, a lot of is probably out-of-date now. There could still be a little gold left therein though, so keep it handy nonetheless.
I’d be happy to hear if you have other R-related instructional material that we could include in this repository. I’ve also started compiling an equivalent repository for Python, but this is still very much a work in progress.
I hope you find the resource useful.
