As a graduate student taking a digital humanities course, I explored the dataset titled Spotify – Top Songs by Country Charts from Kaggle, containing the top 50 songs for each country on Spotify as of May 2020 with columns such as Country, Continent, Rank, Title, Artists, Album, Explicit, and Duration, this data offers a snapshot of global music preferences, highlighting how tastes vary across regions and continents. This dataset, spanning 29 European countries, 11 Asian countries, 1 African country, 10 North American countries, 9 South American countries, and 1 country in Australia, provided a rich ground for examining global music trends through Tableau Public. My analysis focused on three key visualizations: Top Songs by Country and Rank, Top Artist by Country, and Top Artist/Song by Map.
Top Songs by Country and Rank
In the first worksheet, I created a heat map to visualize the distribution of song ranks by country. Each cell represented a rank (1–50) for a specific country, with the color intensity corresponding to the duration of the song. This visualization revealed intriguing regional preferences: European countries, for instance, displayed a broader range of top-ranked songs compared to the Americas, where songs like Safaera by Bad Bunny, Jowell & Randy, and Nengo Flow dominated other North & South American charts in May. At the same time, Rockstar by Dababy & Roddy Ricch topped the chart in the US and Canada. The heat map provided an intuitive way to compare how different countries favored certain ranks and song durations.
Top Artist by Country
The second worksheet highlighted the most prominent artists in each country using a Crosstab (text table). By filtering the dataset by Rank and Country, I discovered that local artists heavily influenced charts in Europe and South America, while North America and Asia leaned toward global superstars. This visualization emphasized the intersection of cultural identity and music consumption, illustrating how countries’ diversity shapes global trends.
Top Artist/Song by Map
The third worksheet utilized a Mapbox to represent the average rank of songs by country. This visualization underscored the geographic spread of Spotify’s influence, with darker colors indicating higher-ranked songs. Filtering by Continent, Country, and Rank provided further insights—for example, South Africa showed a strong love for TheWeeknd in artist popularity. While Europe’s charts reflected diverse tastes across its countries. I tried adding explicit content as a filter revealing how regions like South America and Europe were more likely to feature explicit songs in their top charts.
Reflections
Having used Tableau before but not consistently with the tool, I can say it was both rewarding and instructive. Its interactive features allowed me to craft dynamic visualizations that conveyed the global reach and regional nuances of Spotify’s top charts. However, challenges like cleaning data (e.g., converting song durations into total seconds) and balancing interactivity with clarity highlighted the importance of thoughtful design in this digital humanities project.
This project aligns with our readings on the storytelling power of data visualization, showing how tools like Tableau can illuminate cultural and social patterns. By mapping music’s global influence, I gained a deeper appreciation for the intersection of technology, data, and human expression. This praxis not only improved my technical skills but also underscored the potential of digital tools to explore and share complex cultural narratives.
Kelechi Iwuagwu (Data Analytics & Viz, CUNY Grad Center)
