Over the last 2-3 months I’ve been playing with hops data from the Beer Maverick website, where they have profiles of hundreds of hops based on aroma/flavor data. This type of data is a natural for a network graph, where we can see the relationships between hops and the aroma characteristics; hops with similar profiles…
Category: data visualization
Hop Network, Part 2
I recently posted about developing a hop network using data from the Beer Maverick site. As I slowly add data the network graph can be refreshed and tweaked; eventually I’ll land on a combination of styles, colors, and sizes that optimize the graph for interactive exploration. For the latest rendition, we’re up to 10 hops,…
Developing a Hop Network
In my last post I talked enthusiastically about the Beer Maverick site and their detailed information about hops (> 300 types). That information got me thinking about creating a hop network where each hop would be positioned based on their relationship to each of the nine aroma types identified on the Beer Maverick site. Here’s…
The Search for Beer Data, Part 1
One of the challenges to providing interesting content for this site lies in the scarcity of interesting data sources on the beer industry, especially at the craft beer level. There is certainly data in the beer universe (think Untappd, Beer Advocate, BreweryDB, etc.) but not a lot that is accessible via API or traditional databases….
Beer Types and Glassware Styles, Part 3
Now we’re on to the conclusion of this 3 part series, where we deliver the network graph showing the linkages between beer types and glassware styles. As noted in the prior post, Gephi is our tool of choice for creating the network graph. The key elements required for most network graphs are nodes and edges….
Beer Styles and Glassware – Part 2
In the last post, we used the RateBeer API and Exploratory to gather and analyze data that matched beer styles to specific types of glassware. This gave us the ability to see some interesting patterns and to especially understand which glass styles are tied to the most popular (or obscure) beer types. In this follow-up,…
Beer Styles and Glassware
We’re back after a long break! In this post, the focus is on using RateBeer API data to analyze patterns in beer volumes by style, and look at the recommended glassware for each style. Our initial analysis will be of the basic statistical variety, using our old friend Exploratory, the powerful R-based software suite. As…
What are the Top 500 Beers?
Now it’s time for the fun part of this series – taking our data into Exploratory and finding out about the Top 500 beers according to RateBeer users. What patterns will we see? Which styles will dominate the ratings? Will high ABV styles rule the day? We’ll be able to answer these and other questions…
Top 500 Beers – Data Retrieval
In Part 1 of this series, I’m going to share how to retrieve the data that will allow us to analyze the Top 500 beers as rated by RateBeer users. As noted in the prior post, this article will focus on the steps where we retrieve and format the data for upcoming analysis. Here are…
Top 500 Beers Mapping Project
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…