DSCI 531: Data Visualization I

Effective static data graphics using R and python for exploratory data analysis.

Course website: https://ubc-mds.github.io/DSCI_531_viz-1/

Course Learning Outcomes

By the end of the course, students are expected to:

• Implement python libraries and R’s ggplot2 to generate static graphics.
• Understand the key components/encodings of a statistical graphic.
• Describe key modern plot types and their uses for EDA.
• Analyze and draw conclusions from data using EDA.
• Make effective design choices when producing a data graphic.
• Understand where EDA falls short (Eg. Automatic decision making)

Assessments

You'll be evaluated as follows:

Assessment	Weight	Due Date	Location	Covers
"Half Lab" Assignment 1	6%	Saturday, Oct 13 at 18:00	Submit to Github	Lec 1
Lab Assignment 2	12%	Saturday, Oct 20 at 18:00	Submit to Github	Lec 2-3
Quiz 1	20%	Tuesday, Oct 23, 15:00-15:30	Your lab room; submit to canvas	Lec 1-3, Lab 1-2
Lab Assignment 3	12%	Saturday, Oct 27 at 18:00	Submit to Github	Lec 4-5
Lab Assignment 4	12%	Saturday, Nov 3 at 18:00	Submit to Github	Lec 6-7
Quiz 2	20%	Wed, November 7, 14:00 – 14:30	Your DSCI 512 (Wednesday) lab rooms	Lec 4-8, Lab 3-4
Lec 8 Hackathon	6%	Tuesday, Nov 6 at 18:00	Submit to GitHub	NA
Peer Review of Hackathon	5%	Friday Wednesday, Nov 7 at 18:00	Submit to GitHub	NA
Participation	7%	NA	NA	Seven lectures

Tip: Use the lecture learning objectives as beacons when studying for your quizzes!

Lecture Details

Lecture	Topic	Pre-readings/Resources
1 / complete worksheet	Basic plot "types" with ggplot2	r4ds: data-vis, esp. 3.6
2 / complete worksheet	EDA; aesthetic mappings in ggplot2	r4ds: data-vis, esp. 3.3
3 / complete worksheet	Dependence. Finish looking at ggplot2 as a tool.	stat545: colors; Jenny's theme tutorial
4 / complete worksheet	Special plot types: GGally, Interactivity and 3D plotting with plotly, ggmaps	ggobi's list of GGally extensions; r-bloggers on ggmap
5	Plotting for humans	stat545: dos-and-donts, Geckoboard tips
6	Plotting in Python, Part I: matplotlib	pyplot tut
7	Plotting in Python, Part II: seaborn	elitedatascience seaborn tut
8	Hackathon

Annotated Resources

Here are prominent course resources that we will be referring to.

1. R for Data Science (r4ds)
   • Overall good book on using R for data science -- including data vis, of course!
2. ggplot2 book
   • Readable, comprehensive resource for learning about ggplot2, by the main author of the ggplot2 package, Hadley Wickham.
3. STAT 545 "All the Graph Things" by Jenny Bryan.
   • Contains tutorials relevant to our subject matter.

Other resource that you might find useful:

1. R Graphics Cookbook
   • Good as a reference if you want to learn how to make a specific type of plot in ggplot2.
2. Jenny Bryan's ggplot2 tutorial
   • Has a lot of examples and less dialogue.
3. ggplot2 cheat sheet
   • Great for quick reference if you need something beyond tab-completion.
4. "Visualization Analysis and Design" by Tamara Munzner, CRC Press, 2014.
   • The go-to book for data vis theory.

Policies

Please see the general MDS policies.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.