Its been a while since I did something with Spark. Its one of Apache’s most contributed to open source projects, so I was keen to see what had been developed since I last had a play. One of the big changes has been the development of a side project Zeppelin. Zeppelin is an interactive development environment (IDE) for Spark, much like Hue is to Hive. My aim here was to try have a play with Zeppelin and see if I could use it to develop a machine learning process. I needed some data, and the obvious dataset would be something to do with Led Zeppelin. So I used the Spotify API to download Echo Nest audio features for the songs on all Led Zeppelin’s studio albums. My plan was to do some unsupervised clustering to group songs with similar audio features together.
In the past I’ve built apps with R Shiny, and I’ve also developed a few data visualisations with d3.js. Given that R Shiny is an R based Back End Server that renders a Front End in Java Script, it seemed like it would be possible to integrate a d3.js visualisation into an R Shiny App. After some quick research, it turns out that it is possible, this blog explains how to do it, and here is an example (please note this is hosted on Shiny.io and sometimes runs out of free hours each month)
This is my first post, so I needed some data to play with. I’ve been wanting to learn more about APIs so tackling the Spotify API seemed like a great place to start. I soon came across the related artists function in the API and that gave me a great idea. What if you could map out and visualise how your favourite artists relate to each other according to Spotify. It could be a useful way to discover new similar artists. A visual recommendation engine.