Telling stories with data visualization

A discussion about best practices in information design. Presented at oredev.org

Transcript

1. TELLING STORIES WITH DATA VISUALIZATION Chris Keathley / @ChrisKeathley / c@keathley.io
3. Lets talk about…
4. Lets talk about… Visualizations
5. Lets talk about… Visualizations Common mistakes and how to fix them
6. Lets talk about… Visualizations Common mistakes and how to fix them Case Study
7. Visualizations are important
8. “Numerical calculations are exact, but graphs are rough” - other people
9. 55.3846,97.1795 51.5385,96.0256 46.1538,94.4872 42.8205,91.4103 40.7692,88.3333 38.7179,84.8718 35.641,79.8718 33.0769,77.5641 28.9744,74.4872 26.1538,71.4103 23.0769,66.4103 22.3077,61.7949 22.3077,57.1795 23.3333,52.9487 25.8974,51.0256 29.4872,51.0256 32.8205,51.0256 35.3846,51.4103 40.2564,51.4103 44.1026,52.9487 46.6667,54.1026 50,55.2564 53.0769,55.641 56.6667,56.0256 59.2308,57.9487 61.2821,62.1795 61.5385,66.4103 61.7949,69.1026 57.4359,55.2564 54.8718,49.8718 52.5641,46.0256 48.2051,38.3333 49.4872,42.1795 51.0256,44.1026 45.3846,36.4103 42.8205,32.5641 38.7179,31.4103 35.1282,30.2564 32.5641,32.1795 30,36.7949 33.5897,41.4103 36.6667,45.641 38.2051,49.1026 29.7436,36.0256 29.7436,32.1795 30,29.1026 32.0513,26.7949 35.8974,25.2564 41.0256,25.2564 44.1026,25.641 47.1795,28.718 49.4872,31.4103 51.5385,34.8718 53.5897,37.5641 55.1282,40.641 56.6667,42.1795 59.2308,44.4872 62.3077,46.0256 64.8718,46.7949 67.9487,47.9487 70.5128,53.718 71.5385,60.641 71.5385,64.4872 46.9231,79.8718 48.2051,84.1026 50,85.2564 53.0769,85.2564 55.3846,86.0256 56.6667,86.0256 56.1538,82.9487 53.8462,80.641 51.2821,78.718 50,78.718 47.9487,77.5641 29.7436,59.8718 29.7436,62.1795 31.2821,62.5641 57.9487,99.4872 61.7949,99.1026 64.8718,97.5641 68.4615,94.1026 70.7692,91.0256 72.0513,86.4103 73.8462,83.3333 76.6667,75.2564 77.6923,71.4103 79.7436,66.7949 81.7949,60.2564 83.3333,55.2564 85.1282,51.4103 86.4103,47.5641 87.9487,46.0256 89.4872,42.5641 93.3333,39.8718 95.3846,36.7949 98.2051,33.718 56.6667,40.641 59.2308,38.3333 60.7692,33.718 63.0769,29.1026 64.1026,25.2564 64.359,24.1026 74.359,22.9487 71.2821,22.9487 67.9487,22.1795 76.6667,75.2564 77.6923,71.4103 79.7436,66.7949 81.7949,60.2564 83.3333,55.2564 85.1282,51.4103 86.4103,47.5641 87.9487,46.0256 89.4872,42.5641 93.3333,39.8718 95.3846,36.7949 98.2051,33.718 56.6667,40.641 59.2308,38.3333 60.7692,33.718 63.0769,29.1026 64.1026,25.2564 64.359,24.1026 74.359,22.9487 71.2821,22.9487 67.9487,22.1795 65.8974,20.2564 63.0769,19.1026 61.2821,19.1026 58.7179,18.3333 55.1282,18.3333 52.3077,18.3333 49.7436,17.5641 47.4359,16.0256 44.8718,13.718 48.7179,14.8718 51.2821,14.8718 54.1026,14.8718 56.1538,14.1026 52.0513,12.5641 48.7179,11.0256 47.1795,9.8718 46.1538,6.0256 50.5128,9.4872 53.8462,10.2564 57.4359,10.2564 60,10.641
12. Alberto Cairo @AlbertoCairo thefunctionalart.com
13. Anscombe’s Quartet
17. Tons of tools
18. D3 graphviz matplotlib R Canvas Emacs org mode
19. “telling stories”
20. “The viewer can see the entire narrative” - me just now
22. I live here
23. “Some people are less than a mile from [the utility’s] service area but, by law, can't get its broadband”
25. “Facebook’s offer price was $38 a share, giving the company a valuation of $104 billion, nearly four times larger than google in 2004” - Washington Post
32. http responses
34. FICTION
36. Unicorn
37. The real world Unicorn
39. VISUALIZATION IS ONE VIEWPOINT
40. “THIS IS HOW THE SERVER IS DOING”
41. 
 “THIS IS HOW OUR USERS ARE DOING”
43. (UNCONCIOUS) BIAS
44. Common mistakes and how to solve them
45. Pie Charts
47. Don’t use pie charts
48. Colors
49. Color is filled with subtle bias
51. Use color deliberately and sparingly
53. Avoid using red and green in the same display
54. ChartJunk and Noise
55. Initial design goals
56. Initial design goals
57. small multiples
58. Initial design goals
59. Sparklines
60. Initial design goals
61. Labels and Scales
64. Use labels to provide clarity
66. Pattern Matching
69. Correlation and Causation
73. http://tylervigen.com/spurious-correlations tyler vigen
74. The clustering illusion and confirmation bias
80. “That looks great!” - Our client
81. “That looks wrong” -Us
83. Apophenia
85. “I want this to be true and it looks like it is” Confirmation bias
86. The clustering illusion
88. Case Study: Lunch attendance
89. THE PROBLEM:
90. HYPOTHESIS
91. AN HYPOTHESIS MUST…
92. AN HYPOTHESIS MUST… MAKE AN ASSERTION
93. AN HYPOTHESIS MUST… MAKE AN ASSERTION BE FALSIFIABLE
94. HYPOTHESIS HOW DO WE GET MORE PEOPLE TO SHOW UP TO OUR MEETUP?
95. HYPOTHESIS PEOPLE ATTEND TOPICS THEY’RE INTERESTED IN
96. The whole story
97. Lets Remove the chart junk
98. TODO: BASIC ATTENDANCE
99. Maxima
100. Minima Maxima
101. TODO: ADD EXAMPLE OF ATTENDANCE
102. TODO: ADD EXAMPLE OF ATTENDANCE Frontend
103. TODO: ADD EXAMPLE OF ATTENDANCE Technology
104. CONSIDER THE VIEWPOINT
105. TODO: MEMBERS WITH ATTENDANCE
106. HYPOTHESIS PEOPLE ATTEND TOPICS THEY’RE INTERESTED IN
107. TODO: MEMBERS WITH ATTENDANCE Team Pizza
108. TODO: MEMBERS WITH ATTENDANCE Team Pizza Long tail
109. HYPOTHESIS PEOPLE ATTEND TALKS THAT THEIR FRIENDS ATTEND
110. TODO: MEMBERS GROUPED
111. TODO: MEMBERS GROUPED
112. TODO: MEMBERS GROUPED
113. TODO: MEMBERS GROUPED Friends
114. Conclusion
115. Consider your biases
116. Iterate
117. Let the data speak
118. Resources Edward Tufte Alberto Cairo informationisbeautiful.net
119. Thanks! Chris Keathley / @ChrisKeathley / c@keathley.io