Beer-in-hand Data Science

Beer-in-hand Data Science

Obviously, analysing beer data in high on everybody’s list of favourite things to do in your weekend. Amanda Dobbyn wanted to examine whether she could provide us with an informative categorization the 45.000+ beers in her data set, without having to taste them all herself.

You can find the full report here but you may also like to interactively discover beer similarities yourself in Amanda’s Beer Clustering Shiny App. Or just have a quick look at some of Amanda’s wonderful visualizations below.

A density map of the bitterness (y-axis) and alcohol percentages (x-axis) in the most popular beer styles.
A k-means clustering of each of the 45000 beers in 10 clusters. Try out other settings in Amanda’s Beer Clustering Shiny App.
The alcohol percentages (x), bitterness (y) and cluster assignments of some popular beer styles.

 

Modelling beer’s bitterness (y) by the number of used hops (x).

 

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