How Vienna found a unique model for low rent
May 25th, 2023
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Hi. This is Zara. I work as a marketing and customer support intern at Datawrapper and this is my first weekly chart entry. Without further ado, let’s dive in!
This is not one of those blog posts where we begin by stating that musical tastes are subjective. There’s a very indirect link between musical preference and cognitive abilities as showcased in a 2015 study by Greenberg et al. The study showed that people who scored high on empathy preferred music that had low arousal, negative valence, and emotional depth while more pragmatic, systematic thinking individuals showed a preference for thrilling, lively and complex music.
The study, though very interesting, is quite simplistic and any effort to create a reliable impression about someone’s personality based on their musical taste would be foolish. You may not listen to the same songs five years from now (or five days from now, if you’re me). You may not listen to the same songs if you’re born in a Cuban town or in Berlin. And finally, you may not listen to a sad song when you’re happy or a mellow song when you’re hyped. Musical preference seems to be connected to how we’re feeling at any moment (ever heard of that meme about walking to the farmer’s market listening to Jay-Z rapping about “Money, Cash, Hoes”?).
Alright, enough of these academic style gymnastics. This research and some clever music visualizations compelled me to visualize my Spotify playlist:
Here’s what it says: All data points colored orange in the visualization above are tracks from my 2018 playlist, while the ones colored blue correspond to tracks from Spotify’s 2018 top tracks playlist. Not surprisingly, the tracks on Spotify’s top 100 playlist score quite high in terms of popularity index. But what pops out is that these tracks tend to be quite loud as well.
I know what you’re thinking. This is not newsflash either: Music critics and regular folks have complained about the steadily increasing loudness in music over time. What struck me was that I could quickly see that loud songs are played more frequently than quiet songs, with just a few clicks in a charting tool, without having to read through long reports or getting lost in comparison tables (though if you’re that type, I have the averages of both playlists below).
To create the scatter plot up there, I first had to get all the songs from my playlist and the Spotify top tracks in a structured data table. To do so, I used the tool Exportify. I had to connect my Spotify account to get a CSV, which I then imported it into Google Spreadsheets. Next, I wanted to find out details about all these tracks, such as their audio features (which includes attributes like acousticness, tempo, valence, loudness, speechiness, and instrumentalness).
Enter Spotify API. With this cool resource, one can request information such as audio features and the popularity score about audio tracks, artists and playlists. The result is one long JSON file , which I converted into a CSV file and imported it into my main spreadsheet.
But now I wanted to understand how my music taste compares to popular taste. After doing some research about which playlists I could compare my tracks to, I settled on the 2018 top tracks playlist by Spotify. I replicated the same steps for this playlist- namely convert the playlist to a CSV, request audio features and popularity score from the Spotify’s API and convert them to CSV, and finally import everything back to my spreadsheet.
Once I had all the data in one place, I cleaned it up and settled on a scatter plot to visualize it. I tried other chart types, but the scatter plot was the perfect chart type to show information about every single track. I had a lot of fun playing around with the data
and the visualization provided conclusive evidence that my musical preference was superior to stuff that was most popular across Spotify (just kidding).
That’s it! If you’re looking for a data set to get started with creating data visualizations, you might already have it – like your personal playlist. I hope the how-to above helps you to get started. If you want to learn more about the scatter plot and its settings, hover over the chart and click Edit this chart in the top-right. Also, if you have any feedback, let me know in the comments. See you next week!
The API only lets you request information about 100 tracks at a time. I downloaded information about the 1000 tracks I listened to between 2015 and 2019, so it took quite a while. If your playlist is not as crazy long as mine is, it should take you far less time.↩︎