Due: 09 February, 11:59 pm

Weight: This assignment is worth 0.5% of your final grade.

Purpose: The purpose of this assignment is to learn what makes a “good” information visualization, which is not entirely a subjective judgment. There are a wide variety of design principles available to help guide the creation of clear, effective information visualizations, many of which are rooted in research on human psychology.

Assessment: This assignment is graded using a check system:

  • ✔+ (110%): Reflection shows phenomenal thought and engagement with the course content. I will not assign these often.
  • ✔ (100%): Reflection is thoughtful, well-written, and shows engagement with the course content. This is the expected level of performance.
  • ✔− (50%): Reflection is hastily composed, too short, and/or only cursorily engages with the course content. This grade signals that you need to improve next time. I will hopefully not assign these often.

Notice that this is essentially a pass/fail or completion-based system. I’m not grading your writing ability, I’m not counting the exact number of words you write, and I’m not looking for encyclopedic citations of every single reading to prove that you did indeed read everything. I’m looking for thoughtful engagement, that’s all. Do good work and you’ll get a ✓.


  1. Read: Open up a notebook (physical, digital…whatever you take notes in best), and take notes while you go through the readings below.

  2. Reflection: When you have completed all of the readings, download and edit this template to write a ~150 word reflection about on what you’ve read (be sure to edit the YAML at the top). That’s fairly short - there are ~250 words on a typical double-spaced page in Microsoft Word (500 when single-spaced). You can do a lot of different things with this memo, for example:

    • Discuss something you learned from the course content
    • Write about the best or worst data visualization you saw recently
    • Connect the course content to your own work
    • Discuss some of the key insights or things you found interesting in the readings
  3. Optional Show and Tell: If you’ve seen a particularly good or bad visualization that you’d like to share, add it to your reflection. You can insert it as an image, or just link to it. If there is time, I may choose some to use for a “show and tell” in class.

  4. Submit Everything: Knit your document to a html page, then create a zip file of everything in your R Project folder. Go to the “Assignment Submission” page on Blackboard and submit your zip file.


“Data visualization is part art and part science. The challenge is to get the art right without getting the science wrong and vice versa.”

– Claus O. Wilke in Fundamentals of Data Visualization

These two chapters cover a lot of excellent examples of what makes a chart “good” and “bad”:

Optional Video

If you have time, watch this 40-minute video titled “How humans see data”, by John Rauser. He discusses how we can exploit our understanding of human psychology to design effective charts (I’ll cover much of the same information in the video in class).

EMSE 4575: Exploratory Data Analysis (Spring 2021)
Wednesdays | 12:45 - 3:15 PM | Dr. John Paul Helveston | jph@gwu.edu |