17 Effective Viz

🚩 Pre-Class Learnings

To prepare for this lesson, do the followings:

🔥 Data Story Critique

Go to http://s.telegraph.co.uk/graphics/projects/Africa-in-100-years/index.html then answer the following questions:

  • What is the data story?
  • What is effective?
  • What could be improved?

Presenting a Data Visual

When preparing to present a visualization, consider the following:

Motivation & Context

  • What is the question you are answering, and why is it important?
  • What data context does the audience need to understand the visual? (W’s?)

Orientation

  • What aspects of the visual should you explain to provide necessary orientation?
  • Walk through guides (axes, color legend, etc.)

Highlights

  • Hone in on one or two interesting data points and tell the story behind them.
  • Explain how the visual aspects of the viz reflect that story (this reinforces how they should interpret the viz).

Big Picture

  • What are the overall trends or takeaways?
  • What are the implications for them? Why does it matter?
  • What comparison are you wanting to highlight?

Improving Data Visual

Section 4.2: Customizing ggplot2 Plots from Mastering Software Development in R presents 6 guidelines for creating great plots:

  1. Aim for high data density.
  2. Use clear, meaningful labels.
  3. Provide useful references.
  4. Highlight interesting aspects of the data.
  5. Consider using small multiples.
  6. Make order meaningful.

Although it’s not explicitly stated, an overarching theme is to facilitate comparisons:

  • When you present your visualizations, what aspects is the viewer drawn to, and what do they want to compare?
  • Make it as easy as possible to compare those things.

Exercise: Present Your Visual

For about 2 minutes, each member presents their EDA visualizations to their project team. After that, for about 5 minutes, the team discuss how the visualizations might be improved and/or how the data story might be refined. When discussing the visualizations,

  • Consider the 6 guidelines for creating great plots.
  • What questions do you have as the audience?
  • What addition information might provide important context to understand the comparisons being drawn?

Exercise: Human-Centered Data Science

Explore The Pudding’s 30 Years of American Anxieties then answer the following questions:

  • In what ways do these letters reveal essential context that would never be found in a dataset?
  • What hidden context can you imagine for your dataset?
    • What additional information could accompany your dataset to provide a more full picture of the lived experiences of all those who may have been connected to the data?
    • Who collected this data? Why? What might have been their agenda?
    • How might the agendas of the data collectors affected what data are available? In terms of:
      • What cases are present in and absent from the data?
      • What variables are available and in what format (e.g., categories)?
    • Think about the labor involved in collecting your data. Whose labor is most visible and applauded? Whose labor is invisible?

Exercise: Project in 3 Visuals

In your project team, discuss the following question: if your digital deliverable could only show 3 visuals, what would they be?

  • What ideas do you have about the order of your visuals?
  • What might you do to combine multiple visuals into one?