Resources for a course on advanced topics in R programming for environmental data science
In this seminar, we will discuss advanced topics in data science using the R statistical programming language, with biological and ecological applications. Topics will vary based on student interests, but will likely include code efficiency, functional programming, and reproducible workflows.
By the end of this course, students will be able to:
- Demonstrate an advanced understanding of R fundamentals (e.g., object types, environments, data storage)
- Predict which operations in R will be slow or memory-intensive, and describe ways of detecting and addressing these bottlenecks
- Identify technologies that enable collaborative coding and reproducibility
- Collaboratively create best practices for code efficiency, reproducibility, and documentation
- Explore and understand other advanced R topics as desired based on student interest
This class is based around the Advanced R textbook, written by Hadley Wickham, which is a great resource for diving in depth into some advanced concepts in R. The book is freely available online here (with solutions available here). Throughout the semester, we will likely also draw from other resources, depending on student interest.
Other useful resources related to the material in this course:
- Course on R debugging and robust programming by Laurent Gatto & Robert Stojnic
- Data Challenge Lab by Hadley Wickham
- R for Data Science by Garrett Grolemund & Hadley Wickham, and some solutions
- R packages by Hadley Wickham
- Efficient R programming by Colin Gillespie & Robin Lovelace
- R Programming for Data Science by Roger D. Peng
- Mastering Software Development in R by Roger D. Peng, Sean Kross and Brooke Anderson