In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

This month, I wanted to share some resources specifically for learning to visualize data using R.

1) R is a free, open source programming language that is heavily used for statistical analysis, but has also expanded to encompass nearly any kind of data analysis you would want to do. In the Scholarly Commons, we have R and RStudio (a user-friendly R development environment) installed on all of our lab computers. RStudio’s website provides links to a lot of ways for you to get started with R.

2) R guru Hadley Wickham gave a public lecture at the University of Notre Dame last August. (Note that his talk starts about 37 minutes into the video.) In the lecture, he walks through a simple example of the iterative process of data visualization in R, and gives additional related advice for doing data science. You can learn from his lecture without knowing any R, but you will find it easier to understand if you have basic experience with programming in general.

3) If you want a book to help you learn more in depth, Wickham and a colleague wrote R for data science: Import, tidy, transform, visualize, and model data. You can read R for data science online, or you can come in to the Scholarly Commons to read the physical book while practicing on one of our lab computers.

4) You can also find a number of specific R courses at Lynda.com, such as “Data Visualization in R with ggplot2.” Just make sure to log in with your U of I credentials so you can access the courses for free.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.