R is a free, open-source language built for working with data. It’s how a lot of statistics, analytics, and reproducible research actually gets done — at universities, in clinical trials, in finance, in sports analytics, everywhere. If you can get comfortable with the basics, you can do real work with it within a weekend.
This course is short, opinionated, and built around runnable examples. Every lesson ends with exercises (with hidden solutions) so you can practice as you go. You don’t need a math background. You don’t need a programming background. You just need to be willing to try things.
By the end of the course you’ll be able to:
dplyr,
tidyr)ggplot2The capstone lesson walks through a full mini-project: load a built-in dataset, explore it, model it, and write up the results.
What R is, how to install it, the RStudio tour, your first script, and how to install packages.
Variables, data types, vectors, comparisons, control flow, and writing your first function.
Read data, then reshape it with the tidyverse: filter,
select, mutate, group_by,
summarise, and the pipe.
The grammar of graphics with ggplot2: geoms, aesthetics,
facets, themes, and saving plots.
Descriptive stats, t-tests, correlation, and linear regression — with diagnostics and predictions.
R Markdown: write code and prose together, knit to HTML or PDF, and share something a colleague can rerun.
Put it all together: explore a real dataset, model it, and write up a short report — start to finish.
Lesson 1 walks you through the install if you haven’t done it.
Each lesson page has runnable code blocks. The recommended workflow is:
You’ll learn 5x faster if you have a real question you want to answer. Even something silly — “are NBA teams shooting more 3s than they used to?” — gives every concept a place to land. Pick one before you finish Lesson 2.
If something is unclear, broken, or could be better, email me at cavandonohoe@gmail.com or open an issue on GitHub. I genuinely want to make this better.