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.
Not strictly music related; hence its posted on another blog, but I’ve been working on an R package that takes R objects and converts them into interactive visualisations using the d3 library in java script.
Learn More about R2D3 Here
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.
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.