This document summarizes the key expectations and challenges when visualizing data or building visual analytics tools. There are several main points: 1. Expect potential mismatches between what clients think they need versus what the data and visualization actually require, requiring clear communication and compromise. 2. Different projects will have different goals that require flexibility in the types of visualizations created, whether for presentation, exploration, or both. 3. A significant amount of time, often 70-80%, will be spent cleaning and preparing data prior to visualization due to issues like missing values, formatting inconsistencies, and data quality problems. 4. Iteration is essential to work out bugs and refine visualizations to best meet requirements and dead

1. WHAT TO EXPECT
WHEN YOU ARE
VISUALIZING
Krist Wongsuphasawat / @kristw
Based on true stories
Forever querying
Never-ending cleaning
Hopelessly prototyping
Last minute coding
and many more…

2. Computer Engineer
Bangkok, Thailand
Chulalongkorn University
Krist Wongsuphasawat / @kristw

3. Programming + Soccer
Computer Engineer
Bangkok, Thailand
Krist Wongsuphasawat / @kristw

4. Programming + Soccer
Computer Engineer
Bangkok, Thailand
Krist Wongsuphasawat / @kristw

5. (P.S. These are actually not my robots, but our competitors’.)
Krist Wongsuphasawat / @kristw
Computer Engineer
Bangkok, Thailand

6. Krist Wongsuphasawat / @kristw
Computer Engineer
Bangkok, Thailand
PhD in Computer Science
Information Visualization
Univ. of Maryland

7. Krist Wongsuphasawat / @kristw
Computer Engineer
Bangkok, Thailand
IBM
Microsoft
PhD in Computer Science
Information Visualization
Univ. of Maryland

8. PhD in Computer Science
Information Visualization
Univ. of Maryland
IBM
Microsoft
Data Visualization Scientist
Twitter
Krist Wongsuphasawat / @kristw
Computer Engineer
Bangkok, Thailand

9. #interactive visualizations
Open-source projects
Visual Analytics Tools

10. DATA =ME+ VIS

11. Me
clients, data, requirements, etc.

12. WHAT TO EXPECT?

13. 1. EXPECT POTENTIAL MISMATCHES

14. INPUT (DATA)
What clients think they have

15. INPUT (DATA)
What clients think they have What they usually have

16. YOU
What clients think you are

17. YOU
What clients think you are What they will get

18. OUTPUT (VIS)
What clients ask for

19. OUTPUT (VIS)
What clients ask for What they really need

20. COMMUNICATE

21. I need this. Take this.

22. I need this. Here you are.
I need this. Take this.

23. & COMPROMISE

24. 2. EXPECT DIFFERENT REQUIREMENTS

25. DIFFERENT GOALS
Present
Communicate information effectively
Explore
Exploratory analysis, Reusable tools for exploration
Explore + Present
Analyze data + tell story
Enjoy
More flexible

26. DIFFERENT GOALS
Present
Communicate information effectively
Explore
Exploratory analysis, Reusable tools for exploration
Explore + Present
Analyze data + tell story
Enjoy
More flexible

27. 3. EXPECT TO CLEAN DATA

28. DATA SOURCES
Open data
Publicly available
Internal data
Private, owned by clients’ organization
Self-collected data
Manual, site scraping, etc.
Combine the above

29. MANY FORMS OF DATA
Standalone files
txt, csv, tsv, json, Google Docs, …, pdf*
APIs
better quality with more overhead
Databases
doesn’t necessary mean they are organized
Big data
bigger pain

30. HAVING ALL TWEETS
How people think I feel.

31. How people think I feel. How I really feel.
HAVING ALL TWEETS

32. CHALLENGES
Get relevant Tweets
hashtag: #oscars
keywords: “spotlight” (movie name)
Too big
Need to aggregate & reduce size
Slow
Long processing time (hours)

33. Hadoop Cluster
GETTING BIG DATA
Data Storage

34. Pig / Scalding (slow)
GETTING BIG DATA
Hadoop Cluster
Data Storage
Tool

35. Hadoop Cluster
Pig / Scalding (slow)
GETTING BIG DATA
Data Storage
Tool

36. Pig / Scalding (slow)
GETTING BIG DATA
Hadoop Cluster
Data Storage
Tool
Your laptop Smaller dataset

37. Hadoop Cluster
Pig / Scalding (slow)
Data Storage
Tool
Final dataset
Tool node.js / python / excel (fast)
Your laptop
GETTING BIG DATA
Smaller dataset

38. CLEANING
Data come in different formats.
tsv to json
Quality of data collection.
null, missing data, typos, timestamp
Filter
Remove unnecessary data
Conversion
Change country code from 3-letter (USA) to 2-letter (US)
Correct time of day based on users’ timezone
Convert lat/lon to county
etc.

39. 4. EXPECT TO CLEAN DATA A LOT

40. 70-80% of time cleaning data
“DATA JANITOR”

41. WHY?
Definition of “clean” depends on the task.
e.g. Restaurant reviews

42. USER RESTAURANT RATING
========================
A MCDONALD’S 3
B MCDONALDS 3
C MCDONALD 4
D MCDONALDS 5
E IHOP 4
F SUBWAY 4

43. WHY?
Definition of “clean” depends on the task.
e.g. Restaurant reviews
Data issue can present itself anytime.
in the project timeline

44. RAMSAY & RAMSEY

45. WHY?
Definition of “clean” depends on the task.
e.g. Restaurant reviews
Data issue can present itself anytime.
in the project timeline
It takes time to process data.
Run. Wait… Oops! Re-run. Wait…

46. RECOMMENDATIONS
Always think that you will have to do it again
document the process, automation
Reusable scripts
break a gigantic do-it-all function into smaller ones
Reusable data
keep for future project

47. 5. EXPECT TO TRY AND BREAK THINGS

48. https://twitter.com/hashtag/
d3brokeandmadeart
#D3BROKEANDMADEART

49. 6. EXPECT TO ITERATE UNTIL IT WORKS

50. 7. EXPECT DEADLINE

51. EXAMPLE PROJECTS

52. EXAMPLE 1: STORYTELLING

53. WHAT TO EXPECT
timely
Deadline is strict. Also can be unexpected events.
wide audience
easy to explain and understand, multi-device support
one-off projects
content screening

54. from fans’ conversations
Reveal the talking points
of every episode of

55. Problem is coming.
CHAPTER I

56. Problem
Want to know what the audience
talk about a TV show
from Tweets

57. HBO’s Game of Thrones
Based on a book series “A Song of Ice and Fire”
Medieval Fantasy. Knights, magic and dragons.

58. Brief Story

59. A King dies.
A lot of contenders wage a war
to reclaim the throne.

60. Minor characters with no claim to the throne
set their own plans in action to gain power
when all the major characters end up killing each other.

61. Brave/Honest/Honorable characters die.
Intelligent but shady characters
and characters who know nothing
continue to live.

62. While humans are busy killing each other,
ice zombies “White walkers” are invading from the North.
The only group who seems to care about this
is neutral group called the Night’s Watch.

63. HBO’s Game of Thrones
Based on a book series “A Song of Ice and Fire”
Medieval Fantasy. Knights, magic and dragons.
Many characters.
Anybody can die.
6 seasons (60 episodes) so far
Multiple storylines in each episode

64. Problem
Want to know what the audience
talk about a TV show
from Tweets

65. Ideas
Common words
Too much noise

66. Ideas
Common words
Too much noise
Characters
How o!en each character were mentioned?

67. I demand a trial by prototyping.
CHAPTER II

68. Prototyping
Pull sample data
from Twitter API
Entity recognition and counting
naive approach

69. List of names
Daenerys Targaryen,Khaleesi
Jon Snow
Sansa Stark
Tyrion Lannister
Arya Stark
Cersei Lannister
Khal Drogo
Gregor Clegane,Mountain
Margaery Tyrell
Joffrey Baratheon
Bran Stark
Theon Greyjoy
Jaime Lannister
Brienne
Eddard Stark,Ned Stark
Ramsay Bolton
Sandor Clegane,Hound
Ygritte
Stannis Baratheon
Petyr Baelish,Little Finger
Robb Stark
Bronn
Varys
Catelyn Stark
Oberyn Martell
Daario Naharis
Davos Seaworth
Jorah Mormont
Melisandre
Myrcella Baratheon
Tywin Lannister
Tommen Baratheon
Grey Worm
Tyene Sand
Rickon Stark
Missandei
Roose Bolton
Robert Baratheon
Jojen Reed
Jeor Mormont
Tormund Giantsbane
Lysa Arryn
Yara Greyjoy,Asha Greyjoy
Samwell Tarly,Sam
Hodor
Victarion Greyjoy
High Sparrow
Dragon
Winter
Dothraki

70. Sample Tweet

71. Sample Tweet

72. Sample data
Character Count
Hodor 10000
Jon Snow 5000
Daenerys 4000
Bran Stark 3000
… …
*These numbers are made up for presentation, not real data.

73. When you play the game of vis,
you iterate or you die.
CHAPTER III

74. Where to go from here?

75. + episodes
The Guardian & Google Trends
http://www.theguardian.com/news/datablog/ng-interactive/2016/apr/22/game-of-thrones-the-most-googled-characters-episode-by-episode

76. + emotion

77. + connections

78. + connections

79. Gain insights from a single episode
emotion & connections

80. Sample data
Character Count
Jon Snow+Sansa 1000
Tormund+Brienne 500
Bran Stark+Hodor 300
… …
Character Count
Hodor 10000
Jon Snow 5000
Daenerys 4000
… …
INDIVIDUALS CONNECTIONS
+ top emojis + top emojis
*These numbers are made up for presentation, not real data.

81. Graph
NODES LINKS
+ top emojis + top emojis
Character Count
Jon Snow+Sansa 1000
Tormund+Brienne 500
Bran Stark+Hodor 300
… …
Character Count
Hodor 1000
Jon Snow 500
Daenerys 400
… …
*These numbers are made up for presentation, not real data.

82. Network Visualization
Node-link diagram
Force-directed layout
http://blockbuilder.org/kristw/762b680690e4b2b2666dfec15838a384

83. Issue: Hairball

84. Why?
Too many nodes & edges
nodes = nodes.filter(n => n.count > 100)
links = links.filter(l => l.count > 100)
The force is (too) strong.
force
.charge(…)
.gravity(…)
.linkDistance(…)
.linkStrength(…)

85. Issue: Occlusions

86. Tried: Fixed positions

87. + Collision Detection
http://blockbuilder.org/kristw/2850f65d6329c5fef6d5c9118f1de6e6

88. + Community Detection
https://github.com/upphiminn/jLouvain

89. + Collision Detection (with clusters)
https://bl.ocks.org/mbostock/7881887

90. Tormund + Brienne

91. Issue: Convex hull
http://bl.ocks.org/mbostock/4341699
d3.geom.hull(vertices)

92. x & y only, no radius

93. Example

94. Fix it

95. Fix it

96. Let’s get other episodes.

97. Hadoop remembers.
CHAPTER IV

98. More data
Hadoop
Rewrite the scripts in Scalding
to get archived data

99. How much data do we need?
Whole week?
5 days?
2 days?
A day?
etc.

100. How much data do we need?

101. Transitions

102. not so smooth

103. A#er switching episode
1. Store old positions for existing objects.
2. Assign new initial positions.*

104. Initial positions
Default: random
Better starting points
Heuristics based on degree of nodes

106. A#er switching episode
1. Store old positions for existing objects.
2. Assign new initial positions.*
3. Run simulation without updating <svg> for n rounds
4. Animate objects from old to new positions.
5. Resume simulation and update <svg> every tick.

107. Animate Nodes & Links
Remove
delay
Move & Change size/thickness
Add new

108. const selection = svg.selectAll('g.node')
.data(nodes, d => d.entity.id);
selection.exit()
.transition()
.duration(1000)
.style('opacity', 0)
.remove();
const sEnter = selection.enter().append('g')
.classed('node', true)
.attr('transform', d => `translate(${d.x},${d.y})`)
.style('opacity', 0)
.call(force.drag);
sEnter.append('circle')
.attr('r', d=>d.r)
.style('fill', d => options.colorScale(d.entity.group));
const sTrans = selection.transition()
.delay(1000)
.duration(2000)
.attr('transform', d => `translate(${d.x},${d.y})`)
.style('opacity', 1)
sTrans.select('circle')
.attr('r', d=>d.r)
Add “enter” nodes
with opacity 0
After 1s delay,
use transition to move nodes
and fade in new nodes
Fade “exit” nodes to opacity 0
and remove
Create selection

109. Animate Communities
Remove
delay
Move & Change shape*
Add new
http://blockbuilder.org/kristw/f9ffe87dd8b4038b5867e853c27cebb7

110. Default
t=0 t=1

111. Smoother
t=0 t=1t=0.5 t=0.51

112. Code
// original
path.attr('d', hull);
// with custom interpolation
path.attrTween('d', (d,i,currentAttr) =>
interpolateHull(d, currentAttr)
)

113. Colors
Default: d3.category10()
Distinct but nothing about the context
Custom palette
Colors related to the groups/houses.
Black = Night’s Watch
Blue = North
Red = Daenerys
Gold = Lannister
…

114. Hold the vis.
CHAPTER V

115. The vis is not enough.

117. Legend

118. Navigation

119. Top 3

120. Adjust threshold

121. Recap

122. Filtered Recap
Tooltip

123. Demo
https://interactive.twitter.com/game-of-thrones

124. Mobile Support

125. A visualizer always evaluates his work.
CHAPTER VI

126. “Feedback is the breakfast of champion.”
— Ken Blanchard

127. Self & Peer
Does it solve the problem?

128. Google Analytics
Pageviews
Visitors
Actions
Referrals
Sites/Social

129. Feedback

130. Feedback

131. EXAMPLE 2: VISUAL ANALYTICS TOOLS

132. Data sources
Output
explore
analyze
present
get
*
*

133. Data sources
Output
explore
analyze
present
get
*
*
ad-hoc scripts

134. Data sources
Output
explore
analyze
present
get
*
*
ad-hoc scripts tools for exploration

135. WHAT TO EXPECT
richer, more features
to support exploration of complex data
more technical audience
product managers, engineers, data scientists
accuracy
designed for dynamic input
long-term projects

136. USER ACTIVITY LOGS

137. UsersUseTwitter

138. UsersUse
Product Managers
Curious
Twitter

139. UsersUse
Curious
Engineers
Log data
in Hadoop
Write Twitter
Instrument
Product Managers

140. WHAT ARE BEING LOGGED?
tweet
activities

141. WHAT ARE BEING LOGGED?
tweet from home timeline on twitter.com
tweet from search page on iPhone
activities

142. WHAT ARE BEING LOGGED?
tweet from home timeline on twitter.com
tweet from search page on iPhone
sign up
log in
retweet
etc.
activities

143. ORGANIZE?

144. LOG EVENT A.K.A. “CLIENT EVENT”
[Lee et al. 2012]

145. LOG EVENT A.K.A. “CLIENT EVENT”
client : page : section : component : element : action
web : home : timeline : tweet_box : button : tweet
1) User ID
2) Timestamp
3) Event name
4) Event detail
[Lee et al. 2012]

146. LOG DATA

147. UsersUse
Curious
Engineers
Log data
in Hadoop
Twitter
Instrument
Write
Product Managers
bigger than
Tweet data

148. UsersUse
Curious
Engineers
Log data
in Hadoop
Data Scientists
Ask
Twitter
Instrument
Write
Product Managers

149. UsersUse
Curious
Engineers
Log data
in Hadoop
Data Scientists
Find
Ask
Twitter
Instrument
Write
Product Managers

150. LOG DATA

151. UsersUse
Curious
Engineers
Log data
in Hadoop
Data Scientists
Find, Clean
Ask
Twitter
Instrument
Write
Product Managers

152. UsersUse
Curious
Engineers
Log data
in Hadoop
Data Scientists
Find, Clean
Ask
Monitor
Twitter
Instrument
Write
Product Managers

153. UsersUse
Curious
Engineers
Log data
in Hadoop
Data Scientists
Find, Clean, Analyze
Ask
Monitor
Twitter
Instrument
Write
Product Managers

154. Log data
EngineersData Scientists
Usersin Hadoop
Find, Clean, Analyze
Use
Monitor
Ask
Curious
1 2
Twitter
Instrument
Write
Product Managers

155. Scribe Radar
Project / Find & Monitor client events

156. GOALS
Search for client events
Explore client event collection
Monitor changes

157. CLIENT EVENT HIERARCHY
iphone home -
- - impression
tweet tweet click
iphone:home:-:-:-:impression
iphone:home:-:tweet:tweet:click

158. DETECT CHANGES
iphone home -
- - impression
tweet tweet click
iphone home -
- - impression
tweet tweet click
TODAY
7 DAYS AGO
compared to

159. CALCULATE CHANGES
+5% +5% +5%
+10% +10% +10%
-5% -5% -5%
DIFF

160. DISPLAY CHANGES
iphone home -
- - impression
tweet tweet click
Map of the Market [Wattenberg 1999], StemView [Guerra-Gomez et al. 2013]

161. DISPLAY CHANGES
home -
- - impression
tweet tweet click
iphone

162. Demo Demo Demo
Demo / Scribe Radar

163. Twitter for Banana

165. WORKFLOW
Requested / Identify needs
Design & Prototype
Make it work for sample dataset
Refine & Generalize
Productionize
Document & Release
Maintain & Support
Keep it running, Feature requests & Bugs fix

166. 8. EXPECT TO REFINE AND POLISH

167. REFINE & POLISH
UX / UI
Color
Animation
Mobile support
Performance
Loading time, Data file size
“The little of visualisation design” by Andy Kirk
http://www.visualisingdata.com/2016/03/little-visualisation-design/

168. 9. EXPECT TO GET FEEDBACK

169. FEEDBACK
Logging
User study
Forum, User group
Office hours

170. 10. EXPECT TO IMPROVE

171. HOW TO BE BETTER?
Time is limited.
Grow the team
Expand skills
Improve tooling
Solve a problem once and for all
Automate repetitive tasks

172. http://twitter.github.io/labella.js
Demo / Labella.js

173. https://github.com/twitter/d3kit
Demo / d3Kit
http://www.slideshare.net/kristw/d3kit

174. yeoman.io
Demo / Yeoman

175. SUMMARY

176. INPUT YOU OUTPUT

177. EXPECT
1) potential mismatches
2) different requirements
3) to clean data
4) to clean data a lot
5) to try and break things
Krist Wongsuphasawat / @kristw
kristw.yellowpigz.com
6) to iterate until it works
7) deadline
8) to refine and polish
9) to get feedback
10) to improve

178. #VOTE

179. Nicolas Garcia Belmonte, Robert Harris, Miguel Rios,
Simon Rogers, Jimmy Lin, Linus Lee, Chuang Liu,
and many colleagues at Twitter.
ACKNOWLEDGEMENT

180. RESOURCES
Images
Banana phone http://goo.gl/GmcMPq
Bar chart https://goo.gl/1G1GBg
Boss https://goo.gl/gcY8Kw
Champions League http://goo.gl/DjtNKE
Database http://goo.gl/5N7zZz
Fishing shark http://goo.gl/2fp4zW
Globe visualization http://goo.gl/UiGMMj
Harry Potter http://goo.gl/Q9Cy64
Holding phone http://goo.gl/It2TzH
Kiwi orange http://goo.gl/ejQ73y
Kiwi http://goo.gl/9yk7o5
Library https://goo.gl/HVeE6h
Library earthquake http://goo.gl/rBqBrs
Minion http://goo.gl/I19Ijg
NBA http://goo.gl/p7HBdG
NFL http://goo.gl/feQMZs
Orange & Apple http://goo.gl/NG6RIL
Pile of paper http://goo.gl/mGLQTx
Premier League http://goo.gl/AqIINO
Scrooge McDuck https://goo.gl/aKv8D7
The Sound of Music https://goo.gl/dqHlzj
Trash pile http://goo.gl/OsFfo3
Tyrion http://goo.gl/WaBonl
Watercolor Map by Stamen Design

181. THANK YOU

182. QUESTIONS?