Welcome
Advanced R takes a deeper dive into statistical computing in R, building your communication and iteration skills. We will explore different modalities for communicating statistical ideas—dynamic visualizations and dashboards, while also exploring ways to integrate R code into these environments. We will also continue to expand your skills as a developer—writing functions and iterating over those functions to query data from APIs and scrape the web. We’re going to have a lot of fun!
How to Use This Text
Watch out sections contain things you may want to look out for - common errors, etc.
Example sections contain code and other information. Don’t skip them!
Note sections contain clarification points (anywhere I would normally say “note that ….). Make sure to read them to avoid any common pitfalls or misconceptions.
Consider these sections to be required readings. This is where we will direct you to existing materials to explain or introduce a concept.
Similarly, consider these sections to be required viewing for the course material.
Check-in sections contain small tasks that you need to do throughout the reading, to practice or prepare. Although they are not graded, please treat them as required!
Each chapter will have a longer practice exercise to complete and turn in. These are intended to be done with help from instructors and peers.
Consider these to be optional readings / viewings. The world of R programming has so many interesting tidbits, we can’t possibly teach them all - but we want to share them with you nonetheless!
We will usually make these “click to expand” so that they don’t distract from your reading.
These are personal opinion comments from the authors. Take them with a grain of salt; we aren’t the only R programmers worth listening to, we are just sharing what has worked for us.
We will usually make these “click to expand” so that they don’t distract from your reading.
Acknowledements
The design (and redesign) of this course was generously supported by the Noyce School of Applied Computing at Cal Poly through a teaching innovation grant. We thank both our collaboration team at Cal Poly—Charlotte Mann, Emily Robinson, Zoe Rehnberg, Julia Schedler—and our external collaborator—Tyson Barrett. This class wouldn’t have been possible without the generous support we received!
A huge shout out goes the the amazing women at Macalester College—Leslie Myint, Brianna Heggeseth, and Alicia Johnson—for making their Intermediate Data Science course materials public. And finally, a massive thank you to Sam Shanny-Csik for their incredible resources for making and styling Quarto websites!
This website is built with Quarto, the lovely icons are all by Allison Horst, and none of this would be possible without the tidyverse.
License
This online work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International. Visit here for more information about the license.