Resources for 431

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.

A Few Books

There are many free R books available online. Here are a few that some students in past versions of this course have liked.

• Introduction to Modern Statistics, 2nd edition by Mine Çetinkaya-Rundel and Johanna Hardin.
• Introduction to Data Science: Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry
• Fundamentals of Data Visualization by Claus O. Wilke
• Data Visualization: A practical introduction by Kieran Healy

Getting Better at R, RStudio and Quarto

• R Studio Educational Resources at https://education.rstudio.com/learn/
• Learn the tidyverse at https://www.tidyverse.org/learn/
  • The tidyverse style guide including styler
• easystats packages: https://easystats.github.io/easystats/index.html and then select the easystats package you’re interested in from the top right and select Articles or Reference.
• janitor: https://sfirke.github.io/janitor/index.html
• patchwork: https://patchwork.data-imaginist.com/
• rstanarm: https://mc-stan.org/rstanarm/
• MKinfer vignettes: https://cran.r-project.org/web/packages/MKinfer/vignettes/MKinfer.html

Also, see Appendix A of Dr. Love’s textbook.

Other R and Quarto references I recommend

• R Graphics Cookbook, 2nd Edition
• R for Data Science, 2nd Edition
  • Sections on Workflow: scripts and projects and getting help
• Posit’s Cheat Sheets
• Quarto main page, especially Get Started for R Studio and Guide
  • Also worth looking at R for Data Science’s chapters on Quarto and on Quarto formatting.
• R Colors list from R Charts
• Introduction to Regression Methods for Public Health Using R by Ramzi W. Nahhas