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

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Lets talk about…

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Lets talk about… Visualizations

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

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

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Visualizations are important

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

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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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Alberto Cairo @AlbertoCairo thefunctionalart.com

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Anscombe’s Quartet

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Tons of tools

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D3 graphviz matplotlib R Canvas Emacs org mode

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“telling stories”

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

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I live here

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“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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“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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http responses

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FICTION

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Unicorn

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The real world Unicorn

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VISUALIZATION IS ONE VIEWPOINT

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“THIS IS HOW THE SERVER IS DOING”

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 “THIS IS HOW OUR USERS ARE DOING”

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(UNCONCIOUS) BIAS

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Common mistakes and how to solve them

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Pie Charts

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Don’t use pie charts

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Colors

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Color is filled with subtle bias

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Use color deliberately and sparingly

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Avoid using red and green in the same display

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ChartJunk and Noise

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Initial design goals

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Initial design goals

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small multiples

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Initial design goals

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Sparklines

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Initial design goals

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Labels and Scales

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Use labels to provide clarity

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Pattern Matching

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Correlation and Causation

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

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The clustering illusion and confirmation bias

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“That looks great!” - Our client

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“That looks wrong” -Us

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Apophenia

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“I want this to be true and it looks like it is” Confirmation bias

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The clustering illusion

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Case Study: Lunch attendance

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THE PROBLEM:

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HYPOTHESIS

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AN HYPOTHESIS MUST…

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AN HYPOTHESIS MUST… MAKE AN ASSERTION

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AN HYPOTHESIS MUST… MAKE AN ASSERTION BE FALSIFIABLE

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

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HYPOTHESIS PEOPLE ATTEND TOPICS THEY’RE INTERESTED IN

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The whole story

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Lets Remove the chart junk

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TODO: BASIC ATTENDANCE

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Maxima

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Minima Maxima

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TODO: ADD EXAMPLE OF ATTENDANCE

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TODO: ADD EXAMPLE OF ATTENDANCE Frontend

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TODO: ADD EXAMPLE OF ATTENDANCE Technology

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CONSIDER THE VIEWPOINT

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TODO: MEMBERS WITH ATTENDANCE

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HYPOTHESIS PEOPLE ATTEND TOPICS THEY’RE INTERESTED IN

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TODO: MEMBERS WITH ATTENDANCE Team Pizza

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TODO: MEMBERS WITH ATTENDANCE Team Pizza Long tail

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HYPOTHESIS PEOPLE ATTEND TALKS THAT THEIR FRIENDS ATTEND

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TODO: MEMBERS GROUPED

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TODO: MEMBERS GROUPED

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TODO: MEMBERS GROUPED

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TODO: MEMBERS GROUPED Friends

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Conclusion

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Consider your biases

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Iterate

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Let the data speak

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Resources Edward Tufte Alberto Cairo informationisbeautiful.net

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