A Few of My Favorite R Resources

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.

Setting Up R

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.

Installing 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.

R Windows Installer
Microsoft R (optional)
R Studio

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.

Anaconda Download


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
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.

About the Author Dustin Ewers

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