Critique and Create Project 3: Proportions

Introduction

As with previous projects, you’re being asked to critique a series of visualizations and then to apply best practices as you create your own. Follow along below, but ultimately do the work using the corresponding .qmd file on Posit Cloud

This project’s data

The code chunk above gets you started by loading the required packages and data. Run it (ignoring any warnings), then take a look at rheum to understand what it shows.

rheum

This data is modified slightly from the Edward Gracely’s “Arthritis Treatment Dataset,” here: causeweb.org/tshs/arthritis-treatment/.

Assignment

Respond to the three Critique questions, then finish the two Create code chunks below, and finish with a Self-Critique.

Critique

As an example, I’ll show two visualizations of proportions that can be drawn from this data set. The polished visualization doesn’t show you the code used to create it; instead, much of the relevant code is sprinkled throughout the proportions methods page. As you critique, consider how the visualizations were made, and try to think about why each visualization is well made or poorly well made.

Rough figure

Start by considering this rough figure, including how clear and useful it might be:

rheum |> 
  count(sex) |> 
  ggplot(aes(x = "data",
             y = n,
             fill = sex)) +
  geom_col() +
  theme_minimal() +
  coord_polar(theta = "y")

This chart starts shows the proportion of males and females with rheumatoid arthritis. In what ways is it a good visualization? In what ways is it lacking? (No need to answer these!)

Compare this chart to the polished version to answer questions in the next two sections.

Polished figure

This polished figure makes some clear decisions in shaping the narrative around the data being shown. As you compare it to the previous chart, think about the ways it is changed and how these changes affect the viewer’s ability to understand and respond to the story being told about the data.

Critique the visualization

Many of the changes between these two figures are subtle, but they do make a difference.

You respond

When you answer the following questions, it might be helpful to consider not only Wilke’s chapters on proportions, including chapters 10 and 11, but also other chapters on aesthetics and argument:

These two pie charts present the same information two different ways. In what noticeable ways has the polished version improved upon the rough draft?
Which of these changes do you think made the biggest difference? How do they affect the viewer’s experience?
Is there anything you see that could still be improved?

Create

Recreating a visualization

Taking inspiration from the above visualization on age, use the rheum data set to recreate a polished version of the chart comparing proportions of Male and Female patients. By studying the file called 05-proportions-functions.qmd, you should be able to make something of nearly the same polish.

You code

Write code to recreate this polished figure as closely as you can.

Creating your own visualization

Create another chart of proportions by looking at other strategies explained in the file called 05-proportions-functions.qmd. You might do any one of the following:

Create multiple bar charts showing the proportions of patients’ sex and age.
Use the ggmosaic package to create a mosaic plot showing the nested proportions of sex and age.
Use the ggforce package to create a parallel sets plot showing the nested proportions of sex and age.

You code

Write code to make your own figure, and polish it as much as you’re able.

Self-Critique

It’s helpful to learn how to acknowledge the things in our own work that we’re proud of, just as it is good to be able to point out weaker points. Reflecting through self critique is a valuable way to grow in any skill.

You respond

Explain your process and goals for the visualization you created. Critique it, including describing things that you wish you could do but that you don’t yet know how. Write at least a paragraph.