Useful Resources

Texts

There are no required texts for this course, but we will be covering useful material from a number of the following books, all of which are excellent resources for learning basic to intermediate level statistics and R programming.

• Davies, T.M. (2026). The Book of R: A First Course in Programming and Statistics (2nd Edition). No Starch Press.
• Baumer, B.S., Kaplan, D.T., & Horton, N.J. (2021). Modern Data Science with R (2nd Edition). Chapman & Hall/CRC. link to web version
• Ismay, C. & Kim, A.Y. (2019). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Chapman & Hall/CRC. link to web version
• Irizarry, R.A. (2019). Introduction to Data Science. Lean Publishing.
• Kabacoff, R. (2022). R in Action: Data Analysis and Graphics with R (3rd Edition). Manning Publications Co.
• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2nd Edition). O’Reilly Media, Inc. link to web version

These are available in print or electronic format directly from the publishers - e.g., No Starch Press, O’Reilly Media, Inc., Manning Publications Co. - or from Amazon.com.

Other Resources

Cheatsheets

• Posit Cheatsheets Resource
• Base R
• Advanced R
• R Reference Card
• Markdown and GitHub Flavored Markdown
• RMarkdown 1
• RMarkdown 2
• RMarkdown Reference Guide
• Learning RMarkdown
• RStudio IDE
• Data Import
• Data Transformation with {dplyr}
• Data Wrangling with {dplyr} and {tidyr}
• Types of Regression (R in Action Table 8.1)
• Regression Syntax (R in Action Table 8.2)
• Useful Functions for Regression Models (R in Action Table 8.3)
• {leaflet} for Interactive Mapping
• Basics of Probability
• {shiny} Tutorial 1
• {shiny} Tutorial 2

Software Tools

Programming Languages

• R (MacOS, Windows, Linux)
• Python (MacOS, Windows, Linux)
• Julia (MacOS, Windows, Linux)

Text and Markdown Editors and Publishing Software

• BB Edit (MacOS)
• MarkdownPad2 (Windows)
• Notepad++ (Windows)
• Obsidian (MacOS)
• Pandoc (MacOS, Windows, Linux)
• Quarto (MacOS, Windows, Linux)
• Visual Studio Code (MacOS, Windows, Linux)

IDEs

• RStudio Desktop (R, Python) (MacOS, Windows, Linux)
• Posit Cloud (R, Python) (browser)
• JupyterLab (R, Python, Julia) (MacOS, Windows, Linux, browser)
• PyCharm (Python) (MacOS)

Version Control Tools

• git (MacOS, Windows, Linux)
• GitHub (Website)
• GitHub Desktop (MacOS, Windows)

Web Resources

• CRAN (Comprehensive R Archive Network)
• DataCamp
• Software Carpentry
• Data Carpentry
• rOpenSci
• Stack Overflow
• Online R Exercises
• R-bloggers
• Mockaroo

Books

Statistical Modeling in Biology

• Bolker, B.M. (2008). Ecological Models and Data in R. Princeton University Press.
• Irizarry, R.A. & Love, M.I. (2015). Data Analysis for the Life Sciences. Lean Publishing.
• Quinn, G.P. & Keough, M.J. (2002). Experimental Design and Data Analysis for Biologists. Cambridge University Press.

R and Basic Statistics

• Caffo, B. (2015). Statistical Inference for Data Science. Lean Publishing.
• Caffo, B. (2016). Regression Models for Data Science in R. Lean Publishing.
• Chihara, L.M. & Hesterberg, T.C. (2018). Mathematical Statistics with Resampling and R. John Wiley & Sons, Inc.
• Crawley, M.J. (2014). Statistics: An Introduction Using R. (2nd Edition). John Wiley & Sons, Inc.
• Dalgaard, P. (2008). Introductory Statistics with R (2nd Edition). Springer.
• Diez, D., Çetinkaya-Rundel, M., & Barr, C.D. (2019). OpenIntro Statistics (Fourth Edition). OpenIntro.org.
• Irizarry, R.A. (2019). Introduction to Data Science. Lean Publishing.
• Ismay, C. & Kim, A.Y. (2019). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Chapman & Hall/CRC.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
• Shahbaba, B. (2012). Biostatistics with R. Springer.
• Wolfe, D.A. & Schneider, G. (2017). Intuitive Introductory Statistics. Springer.

R Programming

• Davies, T.M. (2026). The Book of R: A First Course in Programming and Statistics (2nd Edition). No Starch Press.
• Kabacoff, R. (2022). R in Action: Data Analysis and Graphics with R (3rd Edition). Manning Publications Co.
• Matloff, N. (2011). The Art of R Programming. No Starch Press.
• Peng, R. (2020). R Programming for Data Science. Lean Publishing.
• Peng, R. (2016). Exploratory Data Analysis with R. Lean Publishing.
• Wickham, H. (2015). Advanced R. Chapman & Hall/CRC.
• Wickham, H. (2019). Advanced R. (2nd Edition). Chapman & Hall/CRC.
• Zuur, A.F., Ieno, E.N., & Meesters, E.H.W.G. (2009). A Beginner’s Guide to R. Springer.

R Reference

• Adler, J. (2009). R in a Nutshell. O’Reilly Media, Inc.
• Jones, E., Harden, S., & Crawley, M.J. (2022). The R Book (3rd Edition). Wiley.
• Ekstrøm, C. T. (2016). The R Primer (2nd Edition). Chapman & Hall/CRC.
• Gardener, M. (2012). The Essential R Reference. John Wiley & Sons, Inc.
• Long, J.D. & Teetor, P. (2019). R Cookbook (2nd Edition). O’Reilly Media, Inc.

R Graphics

• Chang, W. (2013). R Graphics Cookbook. O’Reilly Media, Inc.
• Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (2nd Edition). Springer.

Data Science

• Baumer, B.S., Kaplan, D.T., & Horton, N.J. (2017). Modern Data Science with R. Chapman & Hall/CRC.
• Bruce, P. & Bruce, A. (2017). Practical Statistics for Data Scientists. O’Reilly Media, Inc.
• Cady, F. (2017). The Data Science Handbook. John Wiley & Sons, Inc.
• Grus, J. (2015). Data Science from Scratch. O’Reilly Media, Inc.
• Ismay, C. & Kim, A.Y. (2019). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Chapman & Hall/CRC.
• Larose, C.D. & Larose D.T. (2019). Data Science Using Python and R. John Wiley & Sons, Inc.
• Maillund, T. (2016). Introduction to Data Science and Statistical Programming in R. Lean Publishing.
• McNicholas, P.D. & Tait, P.A. (2019). Data Science with Julia. Chapman & Hall/CRC.
• Pearson, R.K. (2018). Exploratory Data Analysis Using R. Chapman & Hall/CRC.
• Peng, R.D. & Matsui, E. (2015). The Art of Data Science. Lean Publishing.
• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (2nd Edition). O’Reilly Media, Inc.
• Williams, G.J. (2017). The Essentials of Data Science. Chapman & Hall/CRC.
• Zumel, N. & Mount, J. (2020). Practical Data Science with R (2nd Edition). Manning Publications Co.

Data Visualization

• Dale, K. (2016). Data Visualization with Python and JavaScript. O’Reilly Media, Inc.
• Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.
• Thomas, S.A. (2015). Data Visualization with JavaScript. No Starch Press.
• Wilke, C.O. (2019) Fundamentals of Data Visualization. O’Reilly Media, Inc.

Spatial Data Analysis

• Bivand, R.S., Pebesma, E., & Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R (2nd Edition). Springer.
• Brundson, C. & Comber, L. (2019). An Introduction to R for Spatial Analysis and Mapping (2nd Edition). SAGE.
• Brunsdon, C. & Singleton, A.D. (Eds.). (2015). Geocomputation: A Practical Primer. Los Angeles: SAGE.
• Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. Chapman & Hall/CRC.

R and Bayesian Statistics

• Bolstad, W.M. & Curran, J.M. (2017). Introduction to Bayesian Statistics (3rd Edition). John Wiley & Sons, Inc.
• Kruschke, J.K. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd Edition). Elsevier.
• McElreath, R. (2019). Statistical Rethinking: A Bayesian Course with Examples in R and Stan (2nd Edition). Chapman & Hall/CRC.

General and Generalized Regression, Mixed Effects, and Multilevel/Hierarchical Modeling

• Burnham, K.P. & Anderson, D.R. (2002). Model Selection and Multimodel Inference. Springer.
• Dunn, P.K. & Smyth, G.K. (2018). Generalized Linear Models With Examples in R. Springer.
• Fox, J. (2016). Applied Regression Analysis and Generalized Linear Models (3rd Edition). SAGE.
• Fox, J. & Weisberg, S. (2019). An R Companion to Applied Regression. SAGE.
• Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• James, G,, Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
• Hoffman, J.P. (2022). Linear Regression Models: Applications in R. Chapman & Hall/CRC.
• Zuur, A.F., Ieno, E.N., Walker, N.J., Savaliev, A.A., & Smith, G.M. (2009). Mixed Effects Models and Extensions in Ecology with R. Springer.

Web Scraping, Text Mining, and Text Analysis

• Friedl, J.E.F. (2000). Mastering Regular Expressions (3rd Edition). O’Reilly Media, Inc.
• Mitchell, R. (2015). Web Scraping with Python. O’Reilly Media, Inc.
• Nolan, D. & Temple Lang, D. (2014). XML and Web Technologies for Data Sciences with R. Springer.
• Silge, J. & Robinson, D. (2017). Text Mining with R: A Tidy Approach. O’Reilly Media, Inc.

R Packages, Quarto/RMarkdown, and Reproducible Research

• Gandrud, C. (2020). Reproducible Research with R and RStudio (3rd Edition). Chapman & Hall/CRC.
• Wickham, H. (2015). R Packages. O’Reilly Media, Inc.
• Xie, Y. (2017). Bookdown: Authoring Books and Technical Documents with RMarkdown. Chapman & Hall/CRC.
• Xie, Y., Allaire, J.J., & Grolemund, G. (2018). R Markdown: The Definitive Guide. Chapman & Hall/CRC.

git and Unix Shell Tools

• Albing, C., Vossen, J.P., & Newham, C. (2007). Bash Cookbook. O’Reilly Media, Inc.
• Barrett, D.J. (2016). Linux Pocket Guide: Essential Commands (3rd Edition). O’Reilly Media, Inc.
• Chacon, S. & Straub, B. (2014). Pro Git (2nd Edition). Apress.
• Dougherty, D. & Robbins, A. (1998). Sed and Awk (2nd Edition). O’Reilly Media, Inc.
• Newham, C. & Rosenblatt, B. (2005). Learning the bash Shell (3rd Edition). O’Reilly Media, Inc.
• Robbins, A. (2006). UNIX in a Nutshell (Fourth Edition). O’Reilly Media, Inc.

Data Science, Statistics, and Programming in Python

• Beazley, D. & Jones, B.K. (2013). Python Cookbook (3rd Edition). O’Reilly Media, Inc.
• Downey, A.B. (2012). Think Python. O’Reilly Media, Inc.
• Downey, A.B. (2014). Think Stats (2nd Edition). O’Reilly Media, Inc.
• Downey, A.B. (2023). Modeling and Simulation in Python. No Starch Press.
• Kazil, J. & Jarmul, K. (2016). Data Wrangling with Python. O’Reilly Media, Inc.
• Lubanovic, B. (2014). Introducing Python. O’Reilly Media, Inc.
• Lee, K.D. (2011). Python Programming Fundamentals. Springer.
• Lutz, M. (2013). Learning Python (Fifth Edition). O’Reilly Media, Inc.
• Lutz, M. (2014). Python Pocket Reference (Fifth Edition). O’Reilly Media, Inc.
• McKinney, W. (2013). Python for Data Analysis. O’Reilly Media, Inc.
• Rogel-Salazar, J. (2023). Statistics and Data Visualisation with Python. Chapman & Hall/CRC.
• VanderPlas, J. (2016). Python Data Science Handbook. O’Reilly Media, Inc.
• Vasiliev, Y. (2022). Python for Data Science. No Starch Press.
• Vaughan, L. (2023). Python Tools for Scientists: An Introduction to Coding, Anaconda, Jupyterlab, and the Scientific Libraries. No Starch Press.

Data Science, Statistics, and Programming in Julia

• McNicholas, P.D. & Tait, P.A. (2019). Data Science with Julia. Chapman and Hall/CRC.
• Phillips, L. (2024). Practical Julia: A Hands-on Introduction for Scientific Minds. No Starch Press.

Databases and SQL

• DeBarros, A. (2022). Practical SQL: A Beginner’s Guide to Storytelling with Data. (2nd Edition). No Starch Press.
• Kreibich, J.A. (2010). Using SQLite. O’Reilly Media, Inc.
• Obe, R.O. & Hsu, L.S. (2012). PostgreSQL: Up and Running. O’Reilly Media, Inc.
• Robinson, I., Webber, J., & Eifrem, E. (2015). Graph Databases (2nd Edition). O’Reilly Media, Inc.

Machine Learning/Deep Learning

• Boehmke, B. & Greenwall. B. (2020). Hands-On Machine Learning with R. Chapman & Hall/CRC.
• Chollet, F., Kalinowski, T., & Allaire, J.J. (2022). Deep Learning with R (2nd Edition). Manning Publications Co.
• Rhys, H.I. (2020). Machine Learning with R, tidyverse, and mlr. Manning Publications Co.