Data Analysis and Visualization for Communication Science
Introduction
This class will be the intersection of data analysis, visual design, and communication science. We’ll examine some good and bad data visualizations, and make plenty of our own.
No prior knowledge of R will be required to take this course, but students should be prepared to learn a new programming language and to work with data.
What we will cover
- Weeks 1-4: R & the Tidyverse
- Weeks 5-6: Data visualization with ggplot
- Week 8: Midterm presentations
- Week 9: Aesthetics
- Week 10: Editorial standards
- Week 11: Tables & statistical results
- Week 12: Maps & geospatial data
- Week 13: Interactivity & the internet
- Week 14: Final presentations
Some ways to use LLMs in this course
With a university email address, you can sign up for the GitHub Student Developer Pack, which will let you use the GitHub Copilot AI tool for free. UZH also offers access to Copilot, which will get you the same thing. Copilot has integration with RStudio, and will act as sort of an autocomplete for your code.
Figure 0.1: Copilot predicting the next line of code I’m going to write.
If you get stuck on a step, you can type what you want to do with a comment and Copilot will suggest code for you on the next line.
Figure 0.2: I’ve added a comment, and now Copilot is suggesting the next step for me.
Of course, there are many more tools out there for you to use, and in this course you’re free to use any of them as you see fit. This course will focus on teaching you the broader concepts of what you need to know, not whether you’ve memorized every single function in R.
I’ve used copilot to write many of the code snippets in this book, but am hardly dependent on it to create a plot.