Assignment #3 (HW #3)

Learning Objectives

HW #3 will have you focus on making more progress towards completing your final assignment (HW #4). As a reminder, your final assignment is meant to combine all of the course learning outcomes(!):

• identify which types of visualizations are most appropriate for your data and your audience
• prepare (e.g. clean, explore, wrangle) data so that its appropriately formatted for building data visualizations
• build effective, responsible, accessible, and aesthetically-pleasing visualizations using the R programming language, and specifically {ggplot2} + ggplot2 extension packages
• write code from scratch and read and adapt code written by others
• apply a DEI (Diversity, Equity & Inclusion) lens to the process of designing data visualizations
• assess, critique, and provide constructive feedback on data visualizations

Description

Please begin by reviewing HW #4 in full to refamiliarize yourself with the expectations for your final submission at the end of the quarter – you’ll again be making some incremental steps towards your final deliverables here in HW #3.

For HW #3, you must answer / complete the following:

• 1. Which option do you plan to pursue? It’s okay if this has changed since HW #1.
• 2. Restate your question(s). Has this changed at all since HW #1? If yes, how so?

Be mindful that the number of questions you list above will depend on which option you choose to pursue:

• If you choose option 1, you should have one overarching question and at least three sub-questions that each of your infographic components answer.
• If you choose option 2, you should have one question that each of your three visualizations works to answer, where each visualization is meant to reach / serve a different target audience and purpose.
• If you chose option 3, you should refer back to your approved proposal to determine how many questions you should list.

• 3. Explain which variables from your data set(s) you will use to answer your question(s), and how.

Important – be specific here!

For example, let’s say I’m interested in how Lyme disease incidence is changing over time for states X, Y and Z. An appropriate explanation of variables might look something like this:

“I have two data sets, one containing population estimates by state from the {tidycensus} package, and another from the CDC containing lyme disease case counts by county and year. After wrangling / joining these data sets, I was able to calculate disease incidence as cases per 100k people at the state level. Doing so provides me with three variables to visualize: time (years), disease incidence (cases per 100k people), and state.”

• 4. In HW #2, you created some exploratory data viz to better understand your data. You may already have some ideas of how you plan to formally visualize your data, but it’s incredibly helpful to look at visualizations by other creators for inspiration. Find at least two data visualizations that you could (potentially) borrow / adapt pieces from. Link to them or download and embed them into your .qmd file, and explain which elements you might borrow (e.g. the graphic form, legend design, layout, etc.).

Tip: Remember to check out the Resources page!!

It lists tons of really wonderful websites, tutorials, and links to the repos / websites of some seriously incredible data viz creators. This is an excellent place to start when looking for inspiration.

• 5. Hand-draw your anticipated visualizations, then take a photo of your drawing(s) and embed it in your rendered .qmd file – note that these are not exploratory visualizations, but rather your plan for your final visualizations that you will eventually polish and submit with HW #4. You should have:
  • a sketch of your infographic (which should include at least three component visualizations) if you are pursuing option 1
  • a sketch of all three separate visualizations if you are pursuing option 2
  • a sketch of the approved number of visualizations, as outlined in your proposal, if you are pursuing option 3

Tip: Embedding images into a .qmd file

There are a number of ways to render an image in .qmd file. Two ways I find easiest are:

1. Using knitr::include_graphics() in a code chunk. Here are the chunk options I often specify:

```{r}
#| eval: true
#| echo: false
#| fig-align: "center"
#| out-width: "100%"
#| fig-alt: "Alt text here"
knitr::include_graphics("path/to/image")
```

2. Using Markdown syntax:

![](file/path/to/image){fig-alt="Alt text goes here"}

Here is a minimal example of a hand-drawn plot – you don’t need to be an artist, but your vision (and handwriting) should be clear:

• 6. Mock up all of your hand drawn visualizations using code. We understand that you will continue to iterate on these into HW #4 (particularly after receiving feedback), but by the end of HW #3, you should:

  • have your data plotted (if you’re experimenting with a graphic form(s) that was not explicitly covered in class, we understand that this may take some more time to build; you should have as much put together as possible)
  • use appropriate strategies to highlight / focus attention on a clear message
  • include appropriate text such as titles, captions, axis labels
  • experiment with colors and typefaces / fonts
  • create a presentable / aesthetically-pleasing theme (e.g. (re)move gridlines / legends as appropriate, adjust font sizes, etc.)

• 7. Answer the following questions:

  • a. What challenges did you encounter or anticipate encountering as you continue to build / iterate on your visualizations in R?
  • b. What ggplot extension tools / packages do you need to use to build your visualizations? Are there any that we haven’t covered in class that you’ll be learning how to use for your visualizations?
  • c. What feedback do you need from the instructional team and / or your peers to ensure that your intended message is clear?

Rubric (specifications)

You must complete the following, as detailed below, to receive a “Satisfactory” mark for Assignment #3:

Complete the following steps under your lastName-eds240-HW4 repo, not in GitHub Classroom:

See details below.

• Complete all work in a file named, HW3-drafting-viz.qmd – you are welcome to draft / practice things in different files / scripts, but you must have all required elements of the assignment available in this file for us to reference and grade.
• Add any necessary YAML options (title, author, date, etc.). You must also include the following options:

---
# ~ additional YAML options omitted for brevity ~
format:
  html:
    embed-resources: true
---

• Complete all exercises and answer all questions under the Description section above (use the check boxes to ensure you’ve completed all 7 required parts).

A few notes on expectations

• Your plots don’t have to be perfect but the message you want to convey should be clear
• The more progress you make now, the more feedback we can provide ahead of HW #4
• There are no strict length requirements for the free-response questions above, however we expect that you answer them thoughtfully and fully
• Low-effort responses and plots will result in a “Not Yet” mark

• Ensure all code chunks have the correct options set so that (a) code and visualizations render, and (b) warnings and messages are suppressed (unless there is a reason to have them print). You should never print out entire data frames. Please note that rendered documents that don’t have appropriately specified chunk options will receive a “Not Yet” score.
• Code should be appropriately styled and annotated (please see this resource on the course website), and your .qmd file should be easy to navigate.
• When complete, render your HW3-drafting-viz.qmd file, verify that you can open the resulting HW3-drafting-viz.html file in your browser and that all formatting looks good. Rename your .html file so that it has your first initial / last name at the start (e.g. SCsik-HW3-drafting-viz.html), then send it to both Sloane and Sam on Slack via direct message by 11:59pm on Sat 02/22/2025.