The Green Lab - [11-A] Data Visualization

1 Het begint met een idee

Data Visualization

Giuseppe Procaccianti

Vrije Universiteit Amsterdam

2 Giuseppe Procaccianti / S2 group / The Green Lab

Quick Recap: Experimental Process

Experiment scoping

Experiment planning

Idea

Experiment operation

Analysis & interpretation

Presentation & package

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3 Giuseppe Procaccianti / S2 group / The Green Lab

What is Data Visualization?

«The use of computer-supported, interactive, visual representations of abstract data to

amplify cognition».

Readings in Information Visualization: Using Vision to Think. S.K.Card, J.D.Mackinlay, and B.Shneiderman, Academic Press, 1999

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4 Giuseppe Procaccianti / S2 group / The Green Lab

Examples of amplified cognition

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5 Giuseppe Procaccianti / S2 group / The Green Lab

More examples of amplified cognition

WTF Visualizations: http://viz.wtf/

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Data visualization: science or art?

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Data visualization theory

Quantitative communication

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Quantitative communication

Quantitative values (measures)

Categorical information (groups)

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Quantitative communication: example

Categorical information (name)

Categorical information (group)

Quantitative values

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10 Giuseppe Procaccianti / S2 group / The Green Lab

Quantitative communication: example

Both

Categorical

Quantitative

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Tables vs. graphs

Tables

● Easy look-up of values (comparisons)

● Precise values

● Different units of measure are possible

Graphs

● Overall "shape" of data (trends)

● Reveal relationships between multiple variables

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Data visualization theory

Graphical Integrity

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13 Giuseppe Procaccianti / S2 group / The Green Lab

Graphical integrity [1]

● Data visualization must tell the truth

[1] Edward R. Tufte, 1983. The Visual Display of Quantitative Information. Graphics Press.

● Avoid misleading information and chartjunk

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14 Giuseppe Procaccianti / S2 group / The Green Lab

Graphical integrity: principles

● Proportionality

● Utility

● Clarity

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Proportionality: Lie Factor

● Ideal LF = 1○ if LF > 1 you are overstating an effect○ if LF < 1 you are understating an effect

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16 Giuseppe Procaccianti / S2 group / The Green Lab

Proportionality: Lie Factor

Data Visualization

5.3/0.6 = 8.83

27.5/18 = 1.53

LF=5.77

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17 Giuseppe Procaccianti / S2 group / The Green Lab

Utility: data ink

● Ideal Data-Ink ratio != 1○ a balance must be found between readability and utility

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18 Giuseppe Procaccianti / S2 group / The Green Lab

Utility: data ink

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19 Giuseppe Procaccianti / S2 group / The Green Lab

Utility: data ink

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Clarity

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Clarity

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Data visualization theory

Information Encoding

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How to encode information in a graph

● Quantitative information○ Points: relative position○ Lines: relative position, slope, length○ Bars: length (height)○ (2D) shapes: area

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Points

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Lines

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Lines

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Bars

Width plays no role!

Zero-based scale (LF)

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Columns

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2D shapes (the infamous pie chart)

Always put percentages!

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2D shapes (the infamous pie chart)

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How to encode information in a graph

● Categorical information

○ Position (along an axis)

○ Color

○ Shape

○ Fill

○ Linestyle

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Position, color

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Shape

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Next lab session: ggplot tutorial

● R advanced graphics package

● https://cran.r-project.org/web/packages/ggplot2/index.html

● REMINDER: Wednesday 9am!!

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References and further readings

● Graph design principles, M. Torchiano (slides) on BB

● Edward R. Tufte, 1983. The Visual Display of Quantitative Information. Graphics Press.

● Manuel Lima: A visual history of human knowledge

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36 Giuseppe Procaccianti / S2 group / The Green Lab

Thank you!

g.procaccianti@vu.nl

i.malavolta@vu.nl