Telling stories with data visualization

Transcript

1. TELLING STORIES WITH DATA VISUALIZATION Chris Keathley / @ChrisKeathley /

   c@keathley.io
2. None

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
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12. Alberto Cairo @AlbertoCairo thefunctionalart.com

13. Anscombe’s Quartet

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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
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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”
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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
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32. http responses

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34. FICTION

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36. Unicorn

37. The real world Unicorn

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39. VISUALIZATION IS ONE VIEWPOINT

40. “THIS IS HOW THE SERVER IS DOING”

41. 
 “THIS IS HOW OUR USERS ARE DOING”

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43. (UNCONCIOUS) BIAS

44. Common mistakes and how to solve them

45. Pie Charts

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47. Don’t use pie charts

48. Colors

49. Color is filled with subtle bias

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51. Use color deliberately and sparingly

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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

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64. Use labels to provide clarity

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66. Pattern Matching

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69. Correlation and Causation

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73. http://tylervigen.com/spurious-correlations tyler vigen

74. The clustering illusion and confirmation bias

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80. “That looks great!” - Our client

81. “That looks wrong” -Us

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83. Apophenia

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85. “I want this to be true and it looks like

    it is” Confirmation bias

86. The clustering illusion

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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