My latest idea is to attempt to map top rated beers, based on the average rating given by users at RateBeer.com. Using the GraphiQL interface, I can create a query that pulls these beers (100 at a time) into a .json format that can then be copied into any code editing software. The ultimate goal is to display these beers in some sort of geographic format to understand if high quality beers are evenly spread across the globe (or within the US), or if there are significant geographic patterns at work. Along the way we’re also likely to discover some interesting and perhaps unexpected patterns.
The basic workflow is as follows:
- Develop a query to pull the data using the RateBeer API
- Copy results into a code editor (I use Brackets)
- Merge the results into a single .json file (the API limits each query to 100 results)
- Open the final .json file in Exploratory
- Analyze the results using many of Exploratory’s R-based features
- Map the results using one or more platforms (Exploratory, Mapbox, and Carto are three possible options)
I’ll post about this process in a series, divided into three (or more) posts, not including this overview. Here’s what we’ll cover in each part of the series:
- The initial post will cover the data retrieval and processing work (Steps 1-3 above)
- The second post will focus on the data analysis portion (Steps 4-5 above)
- Our third post will examine any and all mapping efforts
Stay tuned for the next entry in the series, and may all your beers be good ones.