11 Useful Resources for R Programmers

By Joseph Schmuller

Here, you learn about books and websites that help you learn more about R programming. Without further ado. . .

Interacting with users

If you want to delve deeper into R applications that interact with users, start with this tutorial by shiny guiding force Garrett Grolemund.

For a helpful book on the subject, consider Chris Beeley’s web Application Development with R Using Shiny, 2nd Edition (Packt Publishing, 2016).

Machine learning

For the lowdown on all things Rattle, go directly to the source: Rattle creator Graham Williams has written Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Springer, 2011). Check out the companion website.

The University of California-Irvine Machine Learning Repository plays such a huge role in the R programming world. Here’s how its creator prefers that you look for the material:

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Thank you, UCI Anteaters!

If machine learning interests you, take a comprehensive look at the field (under its other name, “statistical learning”): Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani’s An Introduction to Statistical Learning with Applications in R (Springer, 2017).

An Introduction to Neural Networks, by Ben Krose and Patrick van der Smagt, is a little dated, but you can get it for the low, low price of nothing:

After you download a large PDF, it’s a good idea to upload it into an ebook app, like Google Play Books. That turns the PDF into an ebook and makes it easier to navigate on a tablet.


The R-bloggers website has a nice article on working with databases.

Of course, R-bloggers has terrific articles on a lot of R-related topics!

You can learn quite a bit about RFM (Recency Frequency Money) analysis and customer segmentation at www.putler.com/rfm-analysis.

Maps and images

The area of maps is a fascinating one. You might be interested in something at a higher level. If so, read Introduction to visualising spatial data in R by Robin Lovelace, James Cheshire, Rachel Oldroyd (and others).

David Kahle and Hadley Wickham’s ggmap: Spatial Visualization with ggplot2 is also at a higher level.

Fascinated by magick? The best place to go is the primary source. Check it out.