Textbooks and other resources

R Programming

• R for Data Science – Garrett Grolemund and Hadley Wickham

  • Open-source online version is available for free; Available for purchase online
  • No official solution manual for the book exercises exists, but several can be found online, like this version by Jeffrey B. Arnold. Your exact solutions may vary, but these are a good starting point.

• Hands-On Programming with R by Garrett Grolemund. This is a non-statistical introduction to R programming with many hands-on examples.

• Advanced R – Hadley Wickham

  • Hardcover available online, but the online version is free
  • A deeper dive into R as a programming language, not just a tool for data science. Most of this material is best covered on your own after you are familiar with R.

• R Programming for Data Science

• Modern Data Science with R – Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton

Statistics with R

• Modern Dive: A moderndive into R and the tidyverse by Chester Ismay and Albert Y. Kim

• Learning Statistics with R by Danielle Navarro

• OpenIntro Statistics Open-source online version is available for free

• An Introduction to Statistical Learning: with Applications in R – Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

  • Each chapter includes code that demonstrates how to implement different methods. Unfortunately, their code use a lot of base R functions and syntax, whereas our emphasis is on getting things done with the tidyverse collection of R packages. However, this is still a great book and the code provided is useful.
  • You can download a free PDF of the entire book from the authors’ site
    

• Broadening Your Statistical Horizons is an applied textbook on generalized linear models, with all of the examples / code in R.

• Forecasting: Principles and Practice

• Tidy Modeling with R The purpose of this book is to demonstrate how the tidyverse and tidymodels can be used to produce high quality models.

• Text Mining with R by Julia Silge and David Robinson. What happens if your data is text, rather than numbers? What if you wanted to do sentiment analysis?

Visualisations

The de-facto standard for visualisations in R is the ggplot2 package. If you want to read Hadley Wickham’s paper that implemented the grammar of graphics into R, you can find it here

• Data Visualization: A Practical Introduction by Kieran Healy.

• Fundamentals of Data Visualization by Claus O. Wilke.

• R Graphics Cookbook A practical guide by Winston Chang that provides any specific examples/ recipes to help you generate high-quality graphs quickly. I use it as quick reference to get my ggplot working.

• The BBC Visual and Data Journalism cookbook for R graphics

• Interactive web-based data visualization with R, plotly, and shiny

Spatial Visualisations

• Tidy spatial data in R: using dplyr, tidyr, and ggplot2 with sf

• Spatial Data Science

• Geocomputation with R