\( \def\e{\operatorname{E}} \def\p{\operatorname{P}} \def\var{\operatorname{Var}} \def\sd{\operatorname{SD}} \def\bin{\operatorname{Bin}} \def\n{\operatorname{N}} \def\se{\operatorname{SE}} \def\asim{\mathrel{\dot\sim}} \def\obs{\text{obs}} \def\ep{\varepsilon} \def\new{\text{new}} \)

C Additional Resources

Here’s a page with additional loosely organized resources that you may find useful.

• Harvard’s online R programming course: https://cs50.harvard.edu/r/2024 which has more info on general R programming like conditionals, loops, functions etc.

• R-charts: https://r-charts.com which has a fairly comprehensive list of plots in both base R and ggplot2, with great code examples.

• R-statistics.co: https://r-statistics.co which has great tutorials on everything from ggplot2 and regression to time series models and cluster computing.

• An Introduction to Statistical Learning (ISLR): https://www.statlearning.com great high level survey of some key statistical learning concepts like regression, deep learning, and even survival analysis. Code syntax is sometimes slightly old-fashioned since it uses only base R, with no tidyverse at all, but a great intro nonetheless to some advanced topics.

more will be added in the future…