Data visualization on differing tastes in beer across the US

Recently, I worked with an customer to extract market data about small craft breweries, which got me wondering about the diversity of beers across the United States and which types of beer were most popular. Living in the Bay Area, I’m lucky to get exposed to a wide variety of beers, but I was curious to know what the rest of the country is drinking.  Here is what I found.

  • Not surprisingly, California has the most unique beers, but when weighted against its state population its looks more in line with the rest of the country. Instead, the honor of the highest beer diversity per capita goes to Hawaii (excluding the nation’s capital).
  • American IPA by far is the most popular beer type, being found on 15,350 taps across the United States in Week 14 and 3,142 unique beers. Over the course of 4 weeks, this number had increased from 13,749 taps, which seems likely due to the transition from winter to spring.
  • Of the top fifty beer types, Double IPA are the bitterest, with an average IBU of 84. If you’re looking for a beer with to get you tipsy, an American Barleywine is going to best bet, averaging around 11.28 ABV. The popular American IPA meanwhile has an average ABV of 6.68 and IBU of 62.
  • Despite the rise of craft breweries in recent years, national brands still reign king, with Bud Light on the most taps, followed by Coors Light and Miller Lite. Some California breweries have beers that make an appearance in the top twenty-five beers, including Sierra Nevada, Ballast Point, and Lagunitas.
  • Stone Brewing Company can lay claim to being the most diverse brewery in the nation, with 321 unique beers on tap. Of those, 59 were of the Double IPA variety and another 28 falling under the American IPA family.

Here’s the link to the data visualization in Tableau and what follows is an explanation of how I got the data.


How I got the data

Wanting to approach this subject from a different perspective, I chose to instead go to a source that has a wide variety of different locations serving beers, TapHunter.


Using, I created extractors to pull in all of the locations using TapHunter, as well as what they have on tap.


From there, I could set my extractors to run on weekly schedule to get updated data.



With the clean CSVs that outputs, I was able to load the data into Tableau Public and then make a data visualization.


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