I’ll never forget the first time I heard a Metallica song. It was “Until it Sleeps” off the Load album (Yes, my introduction to Heavy Metal music was Metallica’s most controversial album at the time. DON’T JUDGE!). It was 1996, I was 14 going 15 and the heavy metal bug bit me.

Heavy Metal is one of my favorite music genres. It’s intense, high-energy, and complex. If you are a frequent reader of my blog then you know that music is important to me.

But enough about me. I was very excited to see that Rolling Stone magazine had released their list of the Greatest Metal Albums of All-Time. I was already a big fan of their other lists (500 Greatest Songs, 500 Greatest Albums, etc). I sat down to read it and quickly after starting, I didn’t just want to read about it, I wanted to listen to it as well.

Spotify to the rescue! Spotify is amazing and it turned out that they had all the albums on the list with the exception of a FIVE! Oh, happy day! Here I had all the metal I could muster.

I didn’t take me long to start thinking about this as a dataset and how I could visualize it. And then I remember the Metallica studio album viz I made several months ago. In it, I used Echonest song values to visualize the album composition. I wanted to something similar to that but due to the scale of this list, I knew I would need to approach it differently. Also, if you want to read about how to go about doing this exercise for your music, read this.

Echonest gives you access to access several song attributes, I used the ones in BOLD:

  1. Beats per Minute
  2. Energy
  3. Danceability
  4. Loudness
  5. Valence
  6. Length
  7. Acoustic
  8. Popularity

I know what a lot of you may be thinking. “What? You did a viz about heavy metal and you didn’t use ‘Loudness’? What are you thinking?” Well, I’ll tell you. This collection of albums spans decades. Think of how we consume music has changed since 1970:

  • Vinyl
  • Cassette
  • Compact Disc
  • Digital

And as we’ve embraced these changes to our music media, music engineers & music labels have over-compensating for this by making songs louder. Don’t believe me? Go and google “the Loudness Wars” So as to not throw people off, I did not use that attribute.

So there you have it, I had the foundation for my viz. Please see the screenshot of the viz here and click here for the interactive version

More Metal (1)

Here’s why I like viz:

  • You get the whole picture in one view with no internal scrolling. This allows you to see some interesting trends.
    • For example, when looking at the Popularity column, you can see that the middle of the list a bit more obscure with the more popular albums at the beginning and end
  • Putting Energy and Valence next to each other was a conscious decision.
    • This makes it really easy to see the albums that very high in energy and very low in positivity (case in point, Reign in Blood by Slayer at no. 6)
  • The DNA plot works very well here because it’s simply a modified box and whisker. You can quickly & easily see distribution and outliers
  • IT’S 100% PURE HEAVY METAL!!!

If you like this viz or have any comments or questions, shoot me a tweet or drop a comment below.

Keep head bangin’

If you want to play with this dataset, feel free to download it at data.world. If you do make something, please let me know. I’d love to see it.

Until next time!

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