Data visualization is often used to bring clarity to complex or diffuse subjects, but this vision often comes at the expense of showing uncertainty and dispersion to readers. This talk will cover methods for indicating model uncertainty and sources of error as well as dispersion in data without sacrificing clarity of presentation. Examples (both good and bad) will be drawn from coverage of the past presidential election and we'll have fun exploring solutions in R and ggplot2.
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Adam Hyland is an economist and software developer based out of Boston. He has been working with R since 2010 and when he isn't writing software he makes cool articles on Wikipedia.