Part 2: The Mechanics of Visualization
Objective
Explain how the elements of a data visualization can represent the characteristics of a data set.
Why study the mechanics?
When you write up results in language, you need to have an understanding of structure and grammar to communicate clearly.
The same thing applies when communicating visually. We need an understand of the “grammar” of graphics if we are to think critically about visualizations.
Claus Wilke
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Mapping data onto aesthetics
Fundamentally, “data visualization map data values into quantifiable features.”
- We are taking data values and turning them into “blobs of ink or colored pixels on screen.”
What is an aesthetic?
Aesthetics are “every aspects of a given graphical element.” These include:
- Position
- Shape
- Size
- Color
Illustration of aesthetics
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What kinds of data are there?
- Continuous quantitative
- Discrete quantitative
- Categorical ordered
- Categorical unordered
- Dates and times
How do we connect data and aesthetics?
Scaling is the process to mapping data to aesthetics.
In other words, changing aesthetic values in ways that correspond with changes in the underlying values.
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Choices about aesthetics can change perceptions
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Coordinate systems and axes
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Choices about axes scales
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Color for qualitative distinction
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Color for quantitative distinction
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Color for highlighting
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A directory of visualizations
With the tools of aesthetics, scales, coordinate systems, many visualizations become possible
Exercise
Take an example data visualization task from your work and explicitly describe the choices you made with respect to the different aesthetic features of the figure, including position, shape, size, and color.
Part 4: Making Attractive Visualizations
“The purpose of visualization is insight, not pictures.” - Ben Shneiderman
We should be ruthless about making visualizations that are informative in all aspects of their design.
Whose advise should we take?
Edward Tufte - a highly opinionated but influential voice Stephen Few - practical business intelligence Nathan Yau - modern data visualization Alberto Cairo - The Truthful Art
What makes for a good graphic?
Tufte’s principles
- Graphical excellence is the well-designed presentation of interesting data - a matter of substance, of statistics, and design.
- Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency.
- Graphical excellence is that which gives to the viewer the greater number of ideas in the shortest time with the least ink in the smallest space.
- Graphical excellence is nearly always multivariate.
- Graphical excellence is about telling the truth with data.
Tufte’s Rules - The Data-Ink Ratio
“A large share of ink on a graphic should present data-information.”
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Tufte’s Rules - The Lie Factor
\[ \text{Lie Factor} = \frac{\text{size of effect shown in graphic}}{\text{size of effect in data}} \]
A large lie factor means that the size of the effect in the graphic is much greater than the size of the effect in the data itself.
Lie factor illustration
Consider a situation where you see 25% growth in revenue over a 5 year period. Here’s an honest plot, where the change in the data (25%) is the same as the change in the visualization (25%)
Actual revenue increase: 25 %
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A dishonest chart
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Overlapping Points
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Don’t use too much color
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(these data are fake)
A better plot
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Checklist
- Labels
- Avoid 3D
- Choose clear professional typography
- Make sure they are large enough to viewable, but not so big as to overstate the content. Maintain whitespace.
- Include citations and notes!
- Remember the basics:
- Position
- Shape
- Size
- Color
Make visualization part of your workflow
Iterate and experiment!
Develop good skills with a modern, powerful piece of visualization software (R, Python, PowerBI, Tableau). (Excel is ok but not as robust).
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Exercise
Evaluate a visualization from your own work using the principles and pointers from this part of the course, like Tufte’s data-ink ratio or the best practices for uses of color. Describes some ways you might adjust your visualization following these recommendations. Alternatively, find a figure online and critique it in the same way.
