My goal for this project was to use the Spotify data given to us to see how streaming changed over different music genres between 2010 and 2023. I wanted to make a clear visualization that would make it easier to compare genres and notice the changes over time. I also wanted to understand how the way data is organized affects what we can learn from it. The dataset already puts music into categories like as Pop, Rock, Hip-Hop/Rap, Metal, and R&B/Soul, so those decisions shape the kinds of questions I can ask about music and popularity.

I put the Spotify CSV data into Flourish and looked at different graphs to display it. I thought that a line chart made the most sense because the data is organized by year and I wanted people to be able to follow changes over time. I also spent time changing the colors so that each genre had its own color and adding a legend at the bottom so the colors were easier to understand. One problem I noticed was that some genres, especially Metal, had much smaller stream totals than genres with much larger numbers. It made some of the smaller categories harder to see. I had to think about how to make the visualization easier to read without changing the scale in a misleading way.

My final visualization shows total Spotify streams for different music genres from 2010 through 2023. I used a line chart because it allows the viewer to follow each genre across the years and see how streaming patterns changed. I used different colors for the genres and placed the key at the bottom so viewers can identify each line. I also used “year” as the x-axis and “total streams” as the y-axis. My goal was to make the graph simple enough that someone who has never seen the dataset could understand what it is showing without needing a lot of explanation.

This taught me that choosing a visualization is not just about making a graph look good. The type of graph can change what a audience notices and how they take in the data. I also realized that the categories in the dataset affect the questions I can ask. For example, music can be categorized in different ways, and different genre definitions could give different results. Removing some of the categories also helped make my visualization less crowded and easier to focus on. At the same time, I learned that removing information can affect what the audience is able to see, so those choices need to have a reason behind them. If I did this project again, I would spend more time testing different designs and finding ways to make smaller genres easier to compare while keeping the graph accurate.

Data source: https://www.kaggle.com/datasets/irynatokarchuk/top-streamed-spotify-songs-by-year-2010-2023