This document discusses principles of effective data visualization. It outlines different types of visualizations like bar charts, line charts, and scatter plots that effectively convey relationships in data. It emphasizes designing visualizations that maximize the data-ink ratio to clearly present information while minimizing non-essential elements. Guidelines are provided for proper use of different chart types and ensuring visualizations are designed accessibly and avoid distortion or deception.

1. Data Visualization
Christian Stade-Schuldt
Project-A Ventures
BI Team Knowledge Transfer

2. Outline
Motivation
Principles of Data Visualization
Types of Visualization
Summary
,
Project-A, Data Visualization, 2015 2

3. Why visualizing data?
Visualization lets you see things that would rather go unnoticed
Visualization gives answers faster
Color pictures are pretty and fun to look at
Simple example: Anscombe’s Quartet
,
Project-A, Data Visualization, 2015 3

4. Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.10 4.74 5.0 5.73 8.0 6.89
,
Project-A, Data Visualization, 2015 4

5. Anscombe’s Quartet II
,
Project-A, Data Visualization, 2015 5

6. Edward Tufte
Professor emeritus at Yale
University
Pioneer in the field of data
visualization
Notable works: The Visual
Display of Quantitative
Information
,
Project-A, Data Visualization, 2015 6

7. Principles of graphical excellence and
integrity
1. Serve a purpose
2. Make large data sets coherent
3. Present many numbers in a small space
4. Don’t lie
5. Use clear labels to defeat ambuigity and graphical distortion
6. Show entire scales
7. Show in context
,
Project-A, Data Visualization, 2015 7

8. Small multiples
,
Project-A, Data Visualization, 2015 8

9. The Lie factor
Lie factor =
Size of effect in graphic
Size of effect in data
(1)
=
5.3 − 0.6
0.6
/
27.5 − 18
18
= 14.8 (2)
,
Project-A, Data Visualization, 2015 9

10. Scale contorsions
,
Project-A, Data Visualization, 2015 10

11. Scale contorsions
,
Project-A, Data Visualization, 2015 11

12. Scale contorsions and context
,
Project-A, Data Visualization, 2015 12

13. Principle of data graphics
1. Above all else show the data
2. Maximize the data-ink ratio
3. Erase non-data-ink
4. Erase redundant data-ink
5. Revise and edit
,
Project-A, Data Visualization, 2015 13

14. Data-Ink
Data-ink the non-erasable ink used for the presentation of data
If removed the graphic would lose the content
Non-Data-Ink is accordingly the ink that does not transport the
information
Data-ink ratio = (data ink)/(total ink used to print the graphic)
Chartjunk: unecessary to comprehend the information represented or
distractive
,
Project-A, Data Visualization, 2015 14

15. Data-Ink-Ratio Example
Low Data-Ink-Ratio High Data-Ink-Ratio
,
Project-A, Data Visualization, 2015 15

16. Chart types
The good
Bar charts
Line charts
Scatter plots
Boxplots
The bad
Pie charts
Area charts
The ugly
All 3d charts
,
Project-A, Data Visualization, 2015 16

17. Bar charts
Go-to graph for comparing accross categories, discrete data or
continous data
Proximity: Set white space width separating contiguous bars equal to
50%-150% width of bars
Fills: Avoid pattern lines, use soft but distinct colors
Borders: Avoid
Tick marks: Do not overdo
,
Project-A, Data Visualization, 2015 17

18. Bar charts
,
Project-A, Data Visualization, 2015 18

19. Line chart
Used for continous data
Intervals should be equal in size
Values should only direct connect values in adjacent intervals
Indicate missing data
,
Project-A, Data Visualization, 2015 19

20. Line chart
,
Project-A, Data Visualization, 2015 20

21. Scatter plots
Great for correlation between two quantitave dimensions
,
Project-A, Data Visualization, 2015 21

22. Pie charts
Pie charts are the Aquaman of data visualization
,
Project-A, Data Visualization, 2015 22

23. Pie charts
The same data represented as a column chart
,
Project-A, Data Visualization, 2015 23

24. 3d Pie charts
What is worse than a pie chart? Meet the 3d pie chart
,
Project-A, Data Visualization, 2015 24

25. Maps
provide specific
information
about particular
locations
provide general
information
about spatial
patterns
can be used to
compare
patterns on two
or more maps
,
Project-A, Data Visualization, 2015 25

26. Maps
Relevant xkcd
,
Project-A, Data Visualization, 2015 26

27. Remember Color Blindness
Approximately 10% of males and 1% of females suffer color vision
deficiency
Original colors Perceived colors
,
Project-A, Data Visualization, 2015 27

28. Summary
Visualize your data
Choose the right type for your visualization
Aim for a high Data-Ink-Ratio
,
Project-A, Data Visualization, 2015 28

29. For Further Reading I
Tufte, Edward R
The Visual Display of Quantitative Information.
Graphics Press, 2001.
,
Project-A, Data Visualization, 2015 29