R Programming Language
A small set of available resources.
- An Introduction to ggplot2. Ozancan Ozdemir.
- An Introduction to R. CRAN R Core Team.
- An Introduction to Statistical Learning with Applications in R, 2nd. Ed.. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
- A succint introduction to R. Steve Haroz.
- Advanced R, 2nd. Ed.. Hadley Wickham.
- Advanced R Solutions. 3rd. Ed. Malte Grosser, Henning Baumann, Hadley Wickham.
- Best Practices for Data Visualization. Andreas Krause, Nicola Rennie, Brian Tarran. Royal Statistical Society.
- Data Visualization. A practical introduction. Kieran Healy.
- Data Visualization with R. Martin Schweinberger.
- Efficient R programming. Colin Gillespie.
- Forecasting: Principles and Practice (3rd ed). Rob J Hyndman and George Athanasopoulos.
- Functional Programming with tidyverse. Sara Altman, Bill Behrman, Hadley Wickham.
- Fundamentals of Data Visualization. Claus O. Wilke.
- Geocomputation with R. Robin Lovelace, Jakub Nowosad, Jannes Muenchow.
- Geographic Data Science with R: Visualizing and Analyzing Environmental Change. Michael C. Wimberly.
- ggplot2: Elegant Graphics for Data Analysis.. Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen.
- Introduction to Data Science. Data Analysis and Prediction Algorithms with R. Rafael A. Irizarry.
- Introduction to R. Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau.
- Learning Statistics with R. Danielle Navarro.
- Tidy Modeling with R. Max Kuhn and Julia Silge.
- Mastering Shiny. Hadley Wickam.
- Mastering Shiny Solutions. Maya Gans and Marly Gotti.
- Mastering Software Development in R. Roger D. Peng, Sean Kross, and Brooke Anderson.
- Modern Data Science with R, 2ed. Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton.
- R for Data Analysis. Trevor French.
- R for Data Science. Hadley Wickham, Garrett Grolemund.
- R for Data Science. 2nd Edition. Hadley Wickhan, Mine Çentinkaya-Rundel, Garrett Grolemund.
- R for Data Science: Exercise Solutions. Jeffery B. Arnold.
- R Cookbook. James D. Long and Paul Teetor.
- R Graphics Cookbook, 2nd. Ed.. Winston Chang.
- R language for programmers. John D. Cook.
- R Packages (2e). Hadley Wickham and Jenny Bryan.
- R Programming for Data Science. Roger D. Peng.
- R Without Statistics. David Keys.
- Simple R. Using R for Introductory Statistics. John Verzani.
- Spatial Epidemiology Notes. Applications and Vignettes in R. Charles DiMaggio.
- Statistical Inference via Data Science. A ModernDive into R and the Tidyverse. Chester Ismay and Albert Y. Kim.
- Solutions to ggplot2: Elegant Graphics for Data Analysis. Howard Baek.
- Text Mining with R. Julia Silge and David Robinson.
- Visualize This (2ed). Nathan Yau.
- Awesome Quarto - Resources. Github Repo.
Updated: 06/29/2024 (C. Lizarraga)
- Datasets
- Julia Programming Language
- Python Programming Language
- R Programming Language
- UNIX/Linux Command Line Interface (CLI)
- General Data Science
- Machine Learning
- Probability & Statistics
- Time Series Analysis & Forecasting
- Open Science & Reproducible Research
- AI Tools Landscape
more ...
- UArizona Data Science Workshops
- UArizona Data Lab Workshops (Fall 2023)
- UArizona Data Lab Deep Learning Workshops (Fall 2023)
Carlos Lizárraga, UArizona Data Lab, Data Science Institute, University of Arizona, 2024.
