A slightly updated version of the talk, now with Choose Your Own Adventure Ending.

1. How Humans
See Data
John Rauser
@jrauser
November 2017

2. How Humans
See Data
John Rauser
@jrauser
November 2017

3. visualization

4. visualization
is
communication

5. how to make better visualizations

6. help humans solve analytical
problems quickly and accurately
with visualization

7. Part I: Why visualize data at all?

9. x
1.972
y
1.236
x y
0.111 0.542
1.112 1.994 0.902 0.005
0.000 1.009 0.598 0.085
0.665 1.942 1.613 1.790
0.235 0.356 1.298 1.955
0.247 1.658 0.651 1.937
1.275 1.961 1.949 1.316
0.702 0.045 0.099 0.567
1.760 0.350 0.862 0.010
1.691 0.277 0.027 0.768
1.628 1.778 0.706 1.956
1.957 1.290 1.042 1.999

11. pre-attentive processing

12. A graph is an encoding
of the data.

13. x
1.972
y
1.236
x y
0.111 0.542
1.112 1.994 0.902 0.005
0.000 1.009 0.598 0.085
0.665 1.942 1.613 1.790
0.235 0.356 1.298 1.955
0.247 1.658 0.651 1.937
1.275 1.961 1.949 1.316
0.702 0.045 0.099 0.567
1.760 0.350 0.862 0.010
1.691 0.277 0.027 0.768
1.628 1.778 0.706 1.956
1.957 1.290 1.042 1.999

15. n x y n x y
1 1.972 1.236 13 0.111 0.542
2 1.112 1.994 14 0.902 0.005
3 0.000 1.009 15 0.598 0.085
4 0.665 1.942 16 1.613 1.790
5 0.235 0.356 17 1.298 1.955
6 0.247 1.658 18 0.651 1.937
7 1.275 1.961 19 1.949 1.316
8 0.702 0.045 20 0.099 0.567
9 1.760 0.350 21 0.862 0.010
10 1.691 0.277 22 0.027 0.768
11 1.628 1.778 23 0.706 1.956
12 1.957 1.290 24 1.042 1.999

18. Good visualizations optimize
for the human visual system.

19. How does the human
visual system work?

20. How does the human visual
system decode a graph?

22. Cleveland’s three visual
operations of pattern perception:
1. Detection
2. Assembly
3. Estimation

23. Part II: estimation

24. Three levels of estimation
a. discrimination X=Y X!=Y
b. ranking X>Y X<Y
c. ratioing X / Y = ?

25. At the heart of quantitative
reasoning is a single question:
Compared to what?
- Tufte, Envisioning Information

28. Three levels of estimation
a. discrimination X=Y X!=Y
b. ranking X>Y X<Y
c. ratioing X / Y = ?

31. the most
important
thing

33. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

34. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

35. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

36. “The first rule of color:
do not talk about color!”
- Tamara Munzner

37. luminance
saturation
hue

39. luminance
saturation
hue

44. Observation: Alphabetical is
almost never the correct ordering
of a categorical variable.

47. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

53. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

57. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

62. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

63. 11 mpg

64. 11 mpg

65. 11 mpg

67. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned
scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

71. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

74. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

75. Observation: Stacked
anything is nearly always
a mistake.

81. Stacking makes the reader
decode lengths, not position
on a common scale.

82. 11 mpg

84. Observation: Stacked
anything is nearly always
a mistake.

86. Observation: Pie charts are
ALWAYS a mistake.

87. Piecharts are the information visualization
equivalent of a roofing hammer to the
frontal lobe. They have no place in the world
of grownups, and occupy the same semiotic
space as short pants, a runny nose, and
chocolate smeared on one’s face. They are
as professional as a pair of assless chaps.
http://blog.codahale.com/2006/04/29/google-analytics-the-goggles-they-do-nothing/

88. Piecharts are the information visualization
equivalent of a roofing hammer to the frontal
lobe. They have no place in the world of
grownups, and occupy the same semiotic
space as short pants, a runny nose, and
chocolate smeared on one’s face. They are
as professional as a pair of assless chaps.
http://blog.codahale.com/2006/04/29/google-analytics-the-goggles-they-do-nothing/

89. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

92. Tables are preferable to graphics for many small
data sets. A table is nearly always better than a
dumb pie chart; the only thing worse than a pie
chart is several of them, for then the viewer is
asked to compared quantities located in spatial
disarray both within and between pies… Given
their low data-density and failure to order
numbers along a visual dimension, pie charts
should never be used.
-Edward Tufte, The Visual Display of Quantitative Information

93. Tables are preferable to graphics for many
small data sets. A table is nearly always better
than a dumb pie chart; the only thing worse than
a pie chart is several of them, for then the viewer
is asked to compared quantities located in spatial
disarray both within and between pies… Given
their low data-density and failure to order
numbers along a visual dimension, pie charts
should never be used.
-Edward Tufte, The Visual Display of Quantitative Information

94. Clinton Trump
Among Democrats 99% 1%
Among Republicans 53% 47%
Who do you think did a better
job in tonight’s debate?

96. Afghanistan
Albania
Algeria
Angola
Argentina
Australia
Austria
Bahrain
Bangladesh
Belgium
Benin
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon

97. All good pie charts are jokes.

100. Observation: Comparison is trivial
on a common scale.

106. the dashboard metaphor is
fundamentally flawed

110. Observation: Scatterplots
show relationships directly.

113. Observation: Growth charts
usually aren’t.

115. If growth (slope) is
important, plot it directly.

117. Observation: Growth charts
usually aren’t.
If growth (slope) is important,
plot it directly.

118. The most important measurement should exploit
the highest ranked encoding possible.
• Position along a common scale
• Position on identical but nonaligned scales
• Length
• Angle or Slope
• Area
• Volume or Density or Color saturation
• Color hue

119. Cleveland’s three visual operations
of pattern perception:
1. Detection
2. Assembly
3. Estimation

120. Part three: assembly

121. Gestalt Psychology

123. reification

124. emergence

126. emergence

128. Prägnanz

129. Law Of Closure

131. Law Of Continuity

134. Observation: Good plots
leverage the law of continuity
to assist with assembly.

137. Law of Similarity

144. Law of Proximity

147. Observation: dodged bar
charts are a bad idea

148. Cleveland’s three visual operations
of pattern perception:
1. Detection
2. Assembly
3. Estimation

149. Part IV: detection

157. excel’s defaults are pretty bad

158. -
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
1 2 3 4 5 6

159. Observation: Detection isn’t
as trivial as it seems.

161. “Above all else, show the data.”
-Tufte

162. R/GGplot2 code for every plot in this
presentation available at http://goo.gl/xH5PLV
The rendered document is at
http://rpubs.com/jrauser/hhsd_notes
This presentation is at
https://goo.gl/LuDNje

163. interlude

164. visualization
is
communication

165. rhetoric

166. Logos
Ethos
Pathos

168. visualization
is
communication

169. art
is
communication

170. visualization
is
art

176. why does it make you
feel that way?

177. visualization has as much to
learn from art as from science

178. Part V: Choose your
own adventure

179. Choose you own adventure
A. What are gridlines for?
B. Humans are bad at vertical distance
C. Do I have to include 0 on my y-axis?
D. What aspect ratio should I use?

180. Weber’s law: The “Just Noticeable
Difference” is proportional to the
size of the initial stimuli.

181. 10 20

182. 10 20
100 110

185. 12 units
12 units

186. Observation: Weber’s Law is
why gridlines are useful

190. “Erase non-data ink.”
-Tufte

191. “Erase non-data ink,
within reason.”
-Tufte

192. “Erase non-data ink that interferes
with detection or doesn’t assist
assembly and estimation.”
-Rauser

194. You are bad at estimating
the difference between lines.

199. Observation: If a difference is
important, plot it directly.

200. You are best at detecting variation
in slope near 45 degrees.

202. banking to 45

205. Observation: Banking to 45
best shows variation in slope

206. Q: Should I include 0 on my scale?

209. Q: Should I include 0 on my scale?
A: It depends.

210. Q: Should I include 0 on my scale?
A: Relying on the pre-attentive
perception of size or intensity?
Yes, otherwise you will mislead.
Using position? It’s up to you.

217. “Above all else, show the data.”
-Tufte

218. “Above all else, show
the variation in the data.”
-Rauser (via Tufte)

219. R/GGplot2 code for every plot in this
presentation available at http://goo.gl/xH5PLV
The rendered document is at
http://rpubs.com/jrauser/hhsd_notes
This presentation is at
https://goo.gl/LuDNje

220. end