In an effort to improve my data analysis skills, I’ve been learning and speaking about the R programming language. Even if you don’t want to be a data scientist, (whatever the hell that means this week) learning some analysis skills can pay dividends. Data literacy is an essential skill in our data soaked economy and R is a good learning tool for analysis skills.
One of the harder things to do when starting in a new area is finding useful resources. It’s tough to find the digital needle in the web powered haystack. To make your life a little easier, here’s a list of the R resources I found to be useful.
There are three paths to getting R setup on your machine. If you’re a Visual Studio 2017 user, the easiest way to get R is to install the Data Science workload in Visual Studio. This will get you the Microsoft flavor of R and R Tools for Visual Studio.
If you’re not into Visual Studio, you can also install an R interpreter and R Studio. R Studio is a free R IDE. For interpreters, you can go with either the Microsoft flavor or the standard CRAN flavor of R.
If neither of those options work for you, you can also run R in a Jupyter Notebook. Jupyter is a web-based environment that makes it really easy to mix text and code. It’s used in many contexts including scientific research and virtual textbooks. To setup a local copy, start off by installing Anaconda. Anaconda is a data science environment that includes a plethora of handy analysis tools. After you install Anaconda, you’ll need to install R using the conda package manager. Then you can run Jupyter using the “jupyter notebook” command.
conda install -c r r-essentials jupyter notebook
R Studio Cheat Sheets
A collection of useful R related guides in PDF format.
R Tutor Tutorials
This site came in handy a few times while trying to find specific R issues.
Flowing data has a variety of useful articles on R and other data topics.
Don’t forget about the built-in R help system. Prefix any command with a question mark and it’ll search the R documentation for you. (Example: “?kmeans”)
I skimmed through a bunch of books on R, but the one I really liked was R: Recipes for Analysis, Visualization and Machine Learning. The writing was clear and the content was pragmatic. The task based format was easy to follow and implement. Another book that I used was R for Data Science.
R: Recipes for Analysis, Visualization and Machine Learning
R for Data Science
This list of resources is enough to help you get started in learning R. Go forth and learn how to slice and dice your data.