Clustering Zeppelin on Zeppelin

track-clusters

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.

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Integrating D3.js into R Shiny

Spotify Interactive Discography Shiny App

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)

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The life of the 7+ song

plot3

Following on from this blog post on how to access the million song dataset I decided to do some analysis of the data. I focussed in particular on the length of songs. I performed the analysis using R and Hive and Hadoop through AWS. This first gist uses R to construct some hadoop code to import all the data into the HDFS. Once the data was in the HDFS I ran the following Hive code to mine the data. Then finally, more R code to analyse and visualise the results. For this I mainly used the ggplot2 package which is great for producing good looking graphs.

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Plotting BPI data with Plotly

plotly image 2

Plotly is a new tool I’ve recently come across for sharing interactive graphs. Its effectively an API that allows you to pump graphs to the web using various coding languages; currently R, Python and Matlab. It automatically adds an element of interactivity to the graphs using java script. Its then easy to share those graphs with other people. Its business model works in a similar way to github and bitbucket. It’s free if you are happy to share your output with the world but you have to pay for privacy. So for bloggers and open source enthusiasts its a useful free tool.

There are plenty of instructions on the site to guide you through using it, but here’s a quick overview of using it in R from me.

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Spotify Artists Discography

shuggie discography

Following on from my last post I’ve added a few more functions to my SpotifyAPI package.

It now includes getArtistsAlbums which takes the output from a getArtists search and finds the albums by that artist and outputs a data.frame. This can be followed up by a getAlbumsTracks which will find all the tracks from those albums and create a data.frame. Finally I’ve added visDiscography. This uses both those functions along with the get artist function to create an interactive visualisation of an artists discography.

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Spotify Related Artists Visualisation

Otis Shuggie Related Artists

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.

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