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



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



Examples of amplified cognition



More examples of amplified cognition

WTF Visualizations: http://viz.wtf/

https://www.google.com/url?q=http://viz.wtf/&sa=D&ust=1477923773334000&usg=AFQjCNF8Bva_JlzZchWRfbw-LbD7w9pc5Q



Data visualization: science or art?

https://www.google.com/url?q=http://chrisharrison.net/projects/clusterball/v2high/mathv2.jpg&sa=D&ust=1477923779216000&usg=AFQjCNFq_AOMzz7VdHQUpWklD0bUizOFlA



Data visualization theory

Quantitative communication



Quantitative communication

Quantitative values (measures)

Categorical information (groups)



Quantitative communication: example

Categorical information (name)

Categorical information (group)

Quantitative values



Quantitative communication: example

Both

Categorical

Quantitative



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




Graphical Integrity



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



Graphical integrity: principles

● Proportionality

● Utility

● Clarity



Proportionality: Lie Factor

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



Proportionality: Lie Factor

Data Visualization

5.3/0.6 = 8.83

27.5/18 = 1.53

LF=5.77



Utility: data ink

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



Utility: data ink



Clarity




Information Encoding



How to encode information in a graph

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



Points



Lines



Bars

Width plays no role!

Zero-based scale (LF)



Columns



2D shapes (the infamous pie chart)

Always put percentages!



2D shapes (the infamous pie chart)



How to encode information in a graph

● Categorical information

○ Position (along an axis)

○ Color

○ Shape

○ Fill

○ Linestyle



Position, color



Shape



Next lab session: ggplot tutorial

● R advanced graphics package

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

● REMINDER: Wednesday 9am!!

https://www.google.com/url?q=https://cran.r-project.org/web/packages/ggplot2/index.html&sa=D&ust=1477923789328000&usg=AFQjCNFI72-HCxWylXmUbqOyV0JEerLFNQ

https://www.google.com/url?q=https://cran.r-project.org/web/packages/ggplot2/index.html&sa=D&ust=1477923789329000&usg=AFQjCNGKs9-YPVAnuFg3HFG7FfYp3_acLg



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

https://www.google.com/url?q=http://www.ted.com/talks/manuel_lima_a_visual_history_of_human_knowledge%23t-701247&sa=D&ust=1477923789675000&usg=AFQjCNHejFDuFW5e2EDwqArKLjEG_yPPIw

https://www.google.com/url?q=http://www.ted.com/talks/manuel_lima_a_visual_history_of_human_knowledge%23t-701247&sa=D&ust=1477923789675000&usg=AFQjCNHejFDuFW5e2EDwqArKLjEG_yPPIw



Thank you!

[email protected]

[email protected]

mailto:[email protected]




The Green Lab - [11-A] Data Visualization

Education

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