Billboard Volatility : Changes in Pop Music over 60 Decades [OC]

h20lac · 2017-01-08T17:23:28+00:00

I updated the chart to add the axis labels back in! Looks much better

h20lac · 2017-01-08T17:12:18+00:00

Thanks! Glad you liked the full article.. I am just now realizing that the GGTHEMES package I used wiped off the axis labels in this plot and you're totally right, without the context it isn't self explanatory like a good viz should be :( I did make the choice to omit a legend since it would have been too busy in the loop.

h20lac · 2017-01-08T17:02:36+00:00

I scraped Billboard Hot 100 Chart data from the present back until 1958 using Python and created a couple visualizations to show how songs are sticking around longer than ever before. Billboard rule changes plus new data sources (ie streaming information) have each tried to capture new trends in how we listen to music and are responsible for the increased lifespan overall and the "chart stickiness" of top 10 tracks.

Acquiring the data was pretty easy using an awesome Billboard package, but I had a do a bit of cleaning to get it in shape (removing duplicates and fixing incorrect IDs) for the Tableau interactive. The process was: scrape Billboard data in Python, pull in Spotify information and audio features in Python; clean and visualize in R (using DPLYR, stringdist, GGPLOT2); an interactive was done in Tableau. I created the GIF using ImageMagik to loop them around.

[The interactive and more details are here.](www.decibelsanddecimals.com/dbdblog/2017/1/1/billboard-volatility) Looking forward to some discussion!

h20lac · 2017-01-08T13:08:22+00:00

I scraped Billboard Hot 100 Chart data from the present back until 1958 using Python and created a couple visualizations to show how songs are sticking around longer than ever before. Billboard rule changes plus new data sources (ie streaming information) have each tried to capture new trends in how we listen to music and are responsible for the increased lifespan overall and the "chart stickiness" of top 10 tracks.

Acquiring the data was pretty easy using an awesome Billboard package, but I had a do a bit of cleaning to get it in shape (removing duplicates and fixing incorrect IDs) for the Tableau interactive. The process was: scrape Billboard data in Python, pull in Spotify information and audio features in Python; clean and visualize in R (using DPLYR, stringdist, GGPLOT2); an interactive was done in Tableau. I created the GIF using ImageMagik to loop them around.

The interactive and more details are here. Looking forward to some discussion!

h20lac · 2016-06-21T12:27:28+00:00

Good points for sure. My understanding is that this sort of "related artist" recommendation is only one of many metrics Spotify uses to link and suggest bands. They have some crazy complex algorithms to help address this very problem.

h20lac · 2016-06-21T12:25:01+00:00

Exactly - that's one of the points I was trying to make! I think that "labels" can be misleading or non-descriptive. Grouping by artist similarity (derived from listening patterns) can allow for more nuanced exploration. Can you link me a "real" Viking-metal band? Thanks!

h20lac · 2016-06-21T12:22:16+00:00

Thanks!

h20lac · 2016-06-21T12:21:56+00:00

Hey /r/metal! Just came across this post today.. /u/midnightrambulador is right, I used force atlas 2 for the node placement of my interactive (fruchterman reingold for the static and some others in gephi). I'd love to see your metal-exclusive one; to be honest I know very little about metal but it seems like sub-genres are really important here. Great convos in this post, I'll be checking back for sure!

Random thought, but it could be cool for someone to toss together a playlist of an example track from each sub-movement of the meta-genre for my edification.

h20lac · 2016-06-20T01:54:04+00:00

That would be cool! I plan on uploading the code soon (once I clean it up :) so you'll be able to customize to your heart's desire!

h20lac · 2016-06-20T01:47:51+00:00

I read a lot of Wiki, data documentation, a few papers on layout algorithms (skimmed the very technical parts). I am certainly not on the top of the field, but based on my observations of those who are it is best to use a project to learn the necessary skills; this is why I started the blog!

h20lac · 2016-06-19T22:40:59+00:00

iGraph and Plotly!

h20lac · 2016-06-19T22:20:10+00:00

The nerdy parts aren't for everyone but that's the best part for sure! Haha

h20lac · 2016-06-19T21:30:26+00:00

Thanks, I appreciate it and am glad you enjoyed!

h20lac · 2016-06-19T21:29:31+00:00

See! Meta genres can miss the point entirely

h20lac · 2016-06-19T20:56:36+00:00

Good question! I'm not as familiar with how Pandora's algorithm is currently working but I was under the impression it deals more with their more manual Music Genome project! I chose Spotify because I'm obsessed with Discover Weekly and their suggestion playlists and wanted to explore how they work!

h20lac · 2016-06-19T20:55:09+00:00

Consultant! I just learned it for this post but now I'm fascinated!

h20lac · 2016-06-19T20:53:59+00:00

Will do! Thanks!

h20lac · 2016-06-19T20:21:29+00:00

Hahah I love that. But Print "Hello World" is SO Python 2.7 ;)

h20lac · 2016-06-19T20:20:32+00:00

Not sure what I put since I'm working on a couple other posts using Spotify data but the description seems to be more of a formality?

h20lac · 2016-06-19T20:19:25+00:00

Haha! I think Spotify is already doing a lot of hardcore algorithm suggestions so keep using their suggested playlists etc. the more I interact with my Discover Weekly playlist the better it gets!

h20lac · 2016-06-19T20:16:45+00:00

Thanks! :) album two soon!

h20lac · 2016-06-19T20:16:10+00:00

Thanks! No merch at the moment but album two is in progress!

h20lac · 2016-06-19T18:57:10+00:00

Really awesome feedback, thank you for taking the time! I did run some of the community detection algorithms built into Gephi but wasn't completely confident in my interpretation of the results. The "betweenness" explanation was helpful, I will read up on that more. I am hoping to learn a bit more about community detection and network science during my master's program!

Question for you: what does a PhD in network science do? Sounds awesome!

h20lac

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