2.3: Data Visualization

Author

Ellen Bledsoe, Lily McMullen

A Visualization Primer

Learning Outcomes

  • Students will be able to explain why data visualization is important.
  • Students will be able to identify different types of data visualizations and describe when each is appropriate.
  • Students will be able to match a dataset’s structure to an appropriate visualization type.
  • Students will be able to critique a visualization.

Why Does Data Visualization Matter?

Small Groups

As a group, take 5 minutes to compare your notes and come to a group consensus on your top 3 reasons why data visualization matters. Be sure to choose someone to report out!

Instructor Note: Some strong answers include: patterns in data become visible that are invisible in a table; complex information can be communicated quickly; visualizations help identify outliers and anomalies; they are essential for communicating findings to non-technical audiences; and they support exploratory data analysis before formal testing.

Types of Visualization

Visual cues for communicating data

Here is a decent overview of some of the core concepts of data visualization.

This website is pretty great and goes into a lot of detail about good practices in data visualization. If this is something that really piques your interest, it is well worth exploring further!

In this course, our main take-away from this website is the use of visual cues to communicate data and which ones are better than others.

Small group activity

In groups, discuss as many types of visualizations as you can think of. It’s okay if you don’t know what they are called! Make a quick list or draw them out if you prefer.

This website is an amazing reference for data visualization methods and when to use what. It also has examples of each type plotted in ggplot2.

We will talk more about how to choose the right visualization for your data now and also in the rest of the module.

Instructor Note: Students commonly name: bar charts, pie charts, line graphs, scatter plots, histograms. Accept any reasonable answer and write them on the board (if you have one).

Data matching activity

Complete the data matching activity with your group. Your instructor will provide the worksheet.

Spend a few minutes seeing if you can match the data descriptions to the types of data visualizations.

Each “dataset” is pulled from the same overall dataset: measurements from 344 individual penguins from different species and on different islands over 3 years. Some datasets created multiple plots.

Data descriptions

  1. Penguin body mass and flipper lengths
  2. Penguin flipper lengths
  3. Penguin species and flipper lengths
  4. Penguins body mass, species, and flipper lengths

Answers:

  1. A
  2. C
  3. C
  4. B
  5. C
  6. D

Figure Critique

First, read through this blog post on the “Dos and Don’ts of Data Visualization”

Now that you have some insights, let’s critique these figures below. Yes, these are actual figures in the wild!

For each one, consider:

  • What aspects don’t work?
  • What aspects do work?
  • How would you present this data differently?

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Instructor Note: These figures share several problems that are worth talking about:

  • Truncated axes exaggerate differences by not starting the y-axis at zero. The GNH gender gap looks dramatic visually but is only 0.079 on a 0-1 scale.
  • Pie charts make proportion comparison difficult because humans are poor at judging angles. A bar chart is almost always clearer.
  • 3D effects are adding visual distortion without adding information. 3D bars and cylinders make it impossible to accurately read values off an axis.
  • Showing too many groups in the same overlapping plot makes it impossible to find meaning from any of them.
  • Bins of unequal length in a histogram.