Visualizing the Changing Sound of Music
An experiment to understand how genres and collaborations have shifted over the last decade
As part of a week-long course on information visualisation at National Institute of Design by Amit Kapoor, I chose to analyse the top 100 songs from Billboard Music charts of 2008 and 2018. I wanted to understand the dominant genres of 2008 and 2018. Using a web scraper, I compiled the top 100 songs into a Google Sheet through Wikipedia.
Initial experiments with the dataset were done using an online data visualisation tool called Charticulator and a few basic sketches.
The sketches and outputs from Charticulator were modified and cleaned up on Adobe Illustrator.
Each dot represents a song. The colour represents its year. The genre of the song is represented on the Y axis and the Billboard ranking on the X axis. By comparing the position of the dots against each other, you can find which genre topped the charts between both the years.
Rap was a top charting genre in 2008 but has been taken over by pop in 2018. Country and rock music are losing their spots in the top 100 songs on the Billboard charts.
The chart below represents the number of singles, collaborations, and group songs that hit the Billboard Top 100 chart in 2008 and 2018. There were no artist-collaborated songs in the 2008 Billboard charts. However, they hit the charts in 2018. As social media plays an important role on how we discover new things in the recent times, collaborations have helped reach a wider audience.
Collaborations among artists were especially popular in pop and rap music. Singles in pop music hit the top 100 charts during both years. Group songs and collaborations were fairly low with R&B.
Our experiments were printed on an A4 sheet for the final class presentation. Here is what it looked like!
This was my first attempt at visualising data through the week-long course and here are a few things that I learned through the process.
- Finding datasets, cleaning datasets, and web data scrapping
- Encoding and decoding charts
I realised that showcasing and comparing different parameters of the dataset brought out a better story. Here are a some things I’d do differently:
- Bring a wider range of comparison between decades starting from the 1950s, 1960s, and so on to 2020s
- Adding richness to the dataset by using parameters like lyrics, song tempo, beats, etc.
- Having a hypothesis to test or selecting a topic that is slightly familiar reduces the time taken for initial research.
Thanks for reading. I’m Sasha Cherian, a student at National Institute Of Design, Bangalore India, learning about data viz and information design. If you’re passionate about data viz and music, I would love you hear your thoughts and inputs at sashacherian06@gmail.com or in the comments section below to refine this project further!
