I was toying around with the idea of multivariate beer, along the same lines as Data Cuisine. I wanted to represent county demographics with beer ingredients. The higher a value, say, population density, the more hops I use, or the higher the median household income the more of a particular grain.
For contrast I brewed county ales for Aroostook, Maine (low population density), Arlington, Virginia (high median household income), Bronx, New York (high population density), and Marin, California (high education rate).
Could I taste and see the difference? In short, yes. But I’d adjust the recipe code and brewing process the next time around. Here’s how it went down.
I wrote a quick function in R that spits out basic recipes. Enter the county name and state, and you get a list of ingredients and suggestions on how to use them.
Download the code if you want to mess around with it.
Nothing too fancy. It uses data from the 2013 American Community Survey and translates county values to ingredient amounts. Here’s what I eventually settled on.
For example, here’s the recipe for Los Angeles County, California.
> make_recipe("Los Angeles", "California")
Hop addition times decided by brewer. Suggestion: Continuous hopping every 10 minutes during a 60-minute boil. That's 0.8 ounces per interval, which includes the hop addition at the beginning of the boil.
Add half of aroma hops at flameout. Use the rest for dry-hopping.