This page is a standing resource to refer back to for any additional guidance as you work on your own data science projects after our classes (Intro Dat Sci, Data Sci - Water). I have tried to arrange the resources somewhat in “chronological” order, meaning in order of increasing complexity in your R journey.
If you think things are missing or you want to see more, please add an issue and I will add what resources I can.
These classes assumed some amount of R knowledge before you started, an assumption that turned out to be mostly wrong. However, there are plenty of Intro R materials out there! These include:
Stat 158 - Vectors, data frames, installing R, etc…
RStudio Materials - A series of videos, books, and more to get you started in R
RStudio Primers - Interactive online coding. Really excellent if you can make it through all of the primers.
R for Data Science - Covers all of the basic intro material, from a tidyverse perspective. As discussed, this is one way to find solutions in R, it happens to be my preferred way, but there are lots of Base R ways that work just fine! This is a big book, and should be thought of as a reference!
Stat 159 - A CSU specific course for an intro to the tidyverse
Happy Git With R - Phenomenal resource for learning git, GitHub, and RStudio integration. As discussed, using Git/GitHub/R together is a really important way to keep workflows open and robust
R Markdown - The primary book for learning more about R Markdown and all of its quirks
Cheatsheets - Short clear documents that cover so much material from dplyr to shiny apps. Great for quick references
Geocomputation with R - Intro to all things geospatial analysis, visualisation, etc… in R. Heavy on geometric operations and basics
Spatial Data Science - More deep investigation of spatial data science skills with a focus on the details of geometric and statistical operations. Written by the package creators of core geospatial packages sf, stars, terra and more!
Intro to probability/stats - Great intro to basic stats concepts and ideas about probability with an R focus
Hands on Machine Learning in R - Fantastic hands on approach to machine learning in R
Intro to Bayes - Nothing we discussed in class but a key approach to understand
Web Scraping - Clear and exhaustive introduction to webscraping including RSelenium, rvest, and other core packages.
Time Series - Primarily focused on forecasting, but generally useful for time series approaches.