5  Required Texts

5.1 Course Notes

Professor Love maintains a book for the course entitled Applied Statistics and R Foundations for 431.

Although this document shares some of the features of a textbook, it is by no means comprehensive. The main purpose is to give 431 students a set of common materials on which to draw during the course, providing a series of examples using R to work through issues that are likely to come up during the semester, and in later work.

In addition, Professor Love’s slides and other class materials will be posted for your use throughout the semester. Once class begins, you’ll be able to access all materials (including the Course Book) through the main course website at https://thomaselove.github.io/431-2026/.

5.2 Buy This Book!

During the course, we will read David Spiegelhalter’s The Art of Statistics. You can purchase any of the available versions (hard-cover, paperback or e-reader) online or in your local bookstore for about $20.

• The Art of Statistics website contains R code, corrections and other materials.
• A reading plan for this text is part of the Course Calendar.

5.3 Other Books to Download

There are three additional free books that you will definitely need to obtain during the semester and may be interested in looking at before class begins. Simply visit the links below.

1. R for Data Science (2nd edition) by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund.

• Solutions to the exercises in R4DS can be found here and may be very helpful to you.

2. R Graphics Cookbook (2nd edition) by Winston Chang.
3. Biostatistics for Biomedical Research by Frank E. Harrell Jr. 

• A related set of YouTube videos can be found here.

5.4 Key Articles and Posts

While I will recommend dozens of articles, blog posts and the like to you over the course of the year, these are especially important in 431.

1. Several of the guides prepared by Jeff Leek and his group, including:
   • Reading academic (scientific) papers,
   • Writing your first academic paper
   • Write papers like a modern scientist
   • How to Share Data for Collaboration by Shannon E. Ellis and Jeffrey T. Leek in The American Statistician, 2018 Special Issue on Data Science, or you can read the PeerJ preprint version here.
2. Data Organization in Spreadsheets by Karl W. Broman and Kara H. Woo in The American Statistician, 2018 Special Issue on Data Science, or you can read the PeerJ preprint version.
   • The Ellis/Leek and Broman/Woo papers are part of the Practical Data Science for Stats collection, which may be of interest.
3. Project-oriented workflow at tidyverse.org from Jenny Bryan.
4. From the Ten Simple Rules series at PLOS Computational Biology:
   • Ten Simple Rules for Effective Statistical Practice by Kass RE et al. 2016
   • Ten Simple Rules for Graduate Students by Gu J Bourne PE 2007
   • Ten Simple Rules for Better Figures by Rougier NP Droettboom M Bourne PE 2014
   • Ten Simple Rules for Creating a Good Data Management Plan by Michener WK 2015
5. Statistical Inference in the 21st Century: A World Beyond p < 0.05 from 2019 in The American Statistician
6. The American Statistical Association’s 2016 Statement on p-Values: Context, Process and Purpose.

Professor Love’s class-specific READMEs will provide links to these articles and other recommended readings as the semester goes on.