Assignments

Weekly reflection memo

You will get the most out of this class if you (1) attend class, (2) complete all the readings, and (3) engageTake detailed notes, work through the example code and try to understand it, have vivid dreams about graphs, etc.

with the readings.Also (4) ask for help!

To encourage engagement with the readings, you’ll need to submit a 250–500 word reflection memo by 11:59 PM each Monday (the day before class). You can do a lot of different things with this memo: discuss something you learned from the readings, write about the best or worst data visualization you saw that week, connect the readings or projects from that week to your own work, etc. These memos essentially let you explore and answer some of the key questions of this course, including:

• What is truth? How is truth related to visualization?
• Why do we visualize data?
• What makes a great visualization? What makes a bad visualization?
• How do you choose which kind of visualization to use?
• What is the role of stories in presenting analysis?
• How can we communicate uncertainty?
• How can you lie with statistics? How do you tell the truth with statistics?
• How do you make sure people don’t think you’re lying with statistics?

The readings for each week will also include a set of questions specific to that week. You do not have to answer all of these questions. That would be impossible. They exist to guide your thinking, that’s all.

These memos are also to help me see what you glean from each week’s reading so I can prepare class discussions to be most useful and interesting to you. I will grade these memos using a check system:

• ✔+:⊕ 110% in gradebook
  
  Memo shows phenomenal thought and engagement with the readings. I will not assign these often.
• ✔:⊕ 90% in gradebook
  
  Memo is thoughtful, well-written, and shows engagement with the readings. This is the expected level of performance.
• ✔−:⊕ 50% in gradebook
  
  Memo is hastily composed, too short, and only cursorily engages with the readings. This grade signals that you need to improve next time. I will hopefully not asisgn these often.

I will rescale everyone’s memo grades at the end of the semester.

Problem sets

To practice working with ggplot and making data-based graphics, you will complete a series of 5 problem sets. You need to show that you made a good faith effort to work each question. The problem sets will also be graded using a check system:

• ✔+:⊕ 110% in gradebook
  
  Problem set is 100% completed. Every question was attempted and answered, and most answers are correct. Document is clean and easy to follow. Work is exceptional. I will not assign these often.
• ✔:⊕ 90% in gradebook
  
  Problem set is 70–99% complete and most answers are correct. This is the expected level of performance.
• ✔−:⊕ 50% in gradebook
  
  Problem set is less than 70% complete and/or most answers are incorrect. This indicates that you need to improve next time. I will hopefully not asisgn these often.

I will rescale everyone’s problem set grades at the end of the semester.

You may (and should!) work together on the problem sets, but you must turn in your own answers. You cannot work in groups of more than three people, and you must note who participated in the group in your assignment.

Code-through

The objectives of this class include “Share data and graphics in open forums” and “Be curious and confident in consuming and producing data.” To help you with this, you will write a short code-through tutorial of some data visualization principle, technique, or example.

One of the reasons R is so popular is because the R community is exceptionally generous and open and sharing.So are Python and other modern open source languages too.

The internet is full of tutorials and code-throughs where people explain how to do something interesting with R.

You will write one code-through or tutorial during the semester on a topic of your choice. Complete details for the assignment (along with a lot of examples to look at) will be given later. You will complete this on your own, but you can get help from others (but you can’t all write about the same topic).

The R-Weekly e-mail newsletter includes dozens of these every week, and Mara Averick (chief tidyverse advocate at RStudio) regularly tweets out links to different posts as well. Here are some others examples to give you a jist of what you’ll be doing:⊕ Yours won’t be nearly as complicated as these, by the way. Nor do they need to be. You’ll illustrate and explain something simple.

• The Greatest Twitter Scheme of All Time?
• Mapping Fall Foliage with sf
• Exploring Minard’s 1812 plot with ggplot2
• Animations in R
• Drone sightings in the US, visualized
• Quickly play with Polity IV and OECD data (and see the danger of US democracy)

This assignment will also be graded with a check system.

Mini projects

To give you practice with the data and design principles you’ll learn in this class, you will complete two smaller projects. I will provide you with real-world data and pose one or more questions—you will make a pretty picture to answer those questions.

I will also grade these with a check system, and I will provide substantial feedback on your design and code.

Final project

At the end of the course, you will demonstrate your data visualization skills by completing a final project.

Project details will be posted later once I settle on the best form for it. This much is certain so far:

• You will use some sort of real-world, public management-related data to explore some sort of research question with multiple graphics
• It will be so much fun!

There is no final exam. This project is your final exam.