1 - Principles of Data Visualization

Week 1
8/20/2024
💻 🧰 📊 🥳

Jessica Cooperstone, Ph.D.

Introductions 👋

If you have found these slides, you’ve made it to the website! (Good job.)

Class will taught in a hybrid, synchronous manner, meaning I expect you to attend class during class time. This attendance can happen in person, or virtually via Zoom I have found that students who attend in person are more engaged, and tend to master material more quickly. But, it is up to you how you want to attend.
I will record class time for those who want to 1) revisit material or 2) can’t attend (this should be uncommon). These recordings are not to replace coming to class.

A combination of lecture, code run-throughs, live coding, and hands-on exercises.
Bring a laptop (not tablet) to class with R and RStudio downloaded
Come with your questions!
Engage as much as you can!

Module assignments: After each module, there will be an assignment to provide practice for the techniques learned in class.
Class reflections: After 10 of the 15 weeks, you will write a 1 paragraph reflection on the material that was presented in class. This can include your thoughts on how you will use these lessons in your own research and data visualizations, ways in which you have investigated this topic (or expect to) on your own, or what else you’d like to learn in this area. The purpose of this assignment is not to be burdensome, but to keep you engaged in the course material, and providing feedback to me on what parts you’ve found useful, what you’ve struggled with, and what you’d like to see more of in the future.
Capstone assignment: At the end of the semester, you will complete a capstone assignment where you create a series of visualizations based on your research data, data coming from your lab, or other data that is publicly available. I expect this assignment to be completed in R Markdown, annotated, and knitted into an easy-to-read .html file. I also expect your code to be fully commented such that I can understand what you are doing with each step, and why.

I expect you will turn assignments in on time. Late assignments are not accepted. If there are extenuating circumstances that prevent you from turning in an assignment on time, please connect with me as soon as possible after such a situation arises for discussion about a possible deadline extension.

It is fine for you to work with your classmates/labmates/whoever, but I expect you to turn in your own independent assignments representing your work
All assignments are open book, googling/investigating is required!

This is our tentative class schedule - but subject to change depending on our pacing, and your interests!

Date	Module	Topic
2024-08-20	1: Principles	Principles of data visualization
2024-08-27	1: Principles	Good and bad visualizations

Date	Module	Topic
2024-09-03	2: Coding fundamentals	R Markdown for reproducible research
2024-09-10	2: Coding fundamentals	Wrangling, the basics
2024-09-17	2: Coding fundamentals	ggplot 101
2024-09-24	2: Coding fundamentals	Themes, labels, facets (ggplot 102)

November 5 is the new OSU day of asynchronous learning

Date	Module	Topic
2024-10-29	4: Putting it together	Principal components analysis
2024-11-05	4: Putting it together	Manhattan plots and making lots of plots at once
2024-11-12	4: Putting it together	Interactive plots
2024-11-19	4: Putting it together	ggplot extension packages and complexheatmap
2024-11-26	No class, Thanksgiving	Relaxing and eating
2024-12-03	4: Putting it together	Capstone assignment open session