Abstract
So far, we discussed the best ways to visually think data. This chapter is a sort of a “data visualization FailCon.” FailCon is a conference for startup founders to learn from their own and others' failures and prepare for success. And, indeed, we all can identify countless excellent data visualizations. They all look just perfect, so you need to be excellent at reverse-engineering to seamlessly learn how to achieve such quality. Thankfully, we can always call abysmal visuals to the rescue. The idea is not to bash on the people who have put those together but, rather, to learn from their errors in order not to repeat them.
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Notes
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http://www.thedailybeast.com/articles/2015/12/07/does-standing-lead-to-weight-loss.html . Greatly recommended is also Fung's blog, Junk Charts ( http://junkcharts.typepad.com/ ).
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The data visualization and the underlying data are from Information Is Beautiful http://www.informationisbeautiful.net/visualizations/what-is-the-worlds-biggest-cash-crop/ .
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UNHCR Global Trends report 2014, http://unhcr.org/2014trends/ .
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Image source: https://commons.wikimedia.org/wiki/File:Spider_Chart.svg .
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Image Source: https://bl.ocks.org/mbostock/4062006 .
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© 2016 Tarek Amr and Rayna Stamboliyska
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Amr, T., Stamboliyska, R. (2016). Getting the Facts Right. In: Practical D3.js. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-1928-7_3
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DOI: https://doi.org/10.1007/978-1-4842-1928-7_3
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