Due: 16 February, 11:59 pm

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

Purpose: learn some of the technical details of how to write code to create “good” information visualizations that follow the design principles we discussed last week. We will focus on graphing amounts and proportions.

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. Optional Exercises: You don’t have to do these, but they can be really helpful for extra practice. This week, take a look at the DataCamp course “Categorical Data in the Tidyverse”. This will introduce how to work with and manipulate factors, which we’ll be doing a lot of in class next week.

  3. Reflection: When you have completed all of the readings, download and edit this template to write a ~150 word (or more) 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
  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.


The readings listed below are broken into two groups:

  1. Design principles
  2. Coding techniques to implement those principles

Design principles

The design principles discussed in the following readings repeat many of the concepts we saw last week, except focused on the particular subset of charts for this week:

Coding techniques

The readings below discuss two important components that we will run into a lot in making charts: factors & facets. Factors are categorical variables, but dealing with them in R can be somewhat messy. Fortunately, we have the forcats package to help us tackle these! Facets, on the other hand, are rather straight forward to implement and offer a handy technique for creating charts when you have many different variables to consider at once:

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