Telling stories with data visualization

Transcript

1. TELLING STORIES WITH
   DATA VISUALIZATION
   Chris Keathley / @ChrisKeathley / [email protected]
   

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3. Lets talk about…
   

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4. Lets talk about…
   Visualizations
   

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5. Lets talk about…
   Visualizations
   Common mistakes and how to fix them
   

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6. Lets talk about…
   Visualizations
   Common mistakes and how to fix them
   Case Study
   

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7. Visualizations are
   important
   

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8. “Numerical calculations are
   exact, but graphs are
   rough”
   - other people
   

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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
    

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13. Anscombe’s
    Quartet
    

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17. Tons of tools
    

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18. D3
    graphviz
    matplotlib
    R
    Canvas
    Emacs org mode
    

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19. “telling stories”
    

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20. “The viewer can see the
    entire narrative”
    - me just now
    

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22. I live here
    

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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
    

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37. The real world
    Unicorn
    

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39. VISUALIZATION IS
    ONE VIEWPOINT
    

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40. “THIS IS HOW THE
    SERVER IS DOING”
    

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41. 

    “THIS IS HOW OUR
    USERS ARE DOING”
    

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43. (UNCONCIOUS)
    BIAS
    

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44. Common mistakes
    and how to solve them
    

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45. Pie Charts
    

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47. Don’t use pie charts
    

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48. Colors
    

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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
    

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54. ChartJunk and
    Noise
    

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55. Initial design goals
    

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56. Initial design goals
    

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57. small multiples
    

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58. Initial design goals
    

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59. Sparklines
    

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60. Initial design goals
    

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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
    

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74. The clustering illusion
    and confirmation bias
    

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80. “That looks great!”
    - Our client
    

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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
    

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86. The clustering illusion
    

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88. Case Study:
    Lunch attendance
    

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89. THE PROBLEM:
    

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90. HYPOTHESIS
    

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91. AN HYPOTHESIS MUST…
    

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92. AN HYPOTHESIS MUST…
    MAKE AN ASSERTION
    

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93. AN HYPOTHESIS MUST…
    MAKE AN ASSERTION
    BE FALSIFIABLE
    

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94. HYPOTHESIS
    HOW DO WE GET MORE PEOPLE
    TO SHOW UP TO OUR MEETUP?
    

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95. HYPOTHESIS
    PEOPLE ATTEND TOPICS THEY’RE
    INTERESTED IN
    

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96. The whole story
    

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97. Lets Remove the
    chart junk
    

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98. TODO: BASIC ATTENDANCE
    

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99. Maxima
    

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100. Minima
     Maxima
     

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101. TODO: ADD EXAMPLE OF
     ATTENDANCE
     

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102. TODO: ADD EXAMPLE OF
     ATTENDANCE
     Frontend
     

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103. TODO: ADD EXAMPLE OF
     ATTENDANCE
     Technology
     

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104. CONSIDER THE
     VIEWPOINT
     

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105. TODO: MEMBERS WITH
     ATTENDANCE
     

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106. HYPOTHESIS
     PEOPLE ATTEND TOPICS THEY’RE
     INTERESTED IN
     

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107. TODO: MEMBERS WITH
     ATTENDANCE
     Team Pizza
     

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108. TODO: MEMBERS WITH
     ATTENDANCE
     Team Pizza
     Long tail
     

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109. HYPOTHESIS
     PEOPLE ATTEND TALKS THAT
     THEIR FRIENDS ATTEND
     

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110. TODO: MEMBERS
     GROUPED
     

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111. TODO: MEMBERS
     GROUPED
     

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112. TODO: MEMBERS
     GROUPED
     

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113. TODO: MEMBERS
     GROUPED
     Friends
     

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114. Conclusion
     

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115. Consider your
     biases
     

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116. Iterate
     

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117. Let the data
     speak
     

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118. Resources
     Edward Tufte
     Alberto Cairo
     informationisbeautiful.net
     

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119. Thanks!
     Chris Keathley / @ChrisKeathley / [email protected]
     

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