🎵 Spotify Data Visualization: Trends, Features & Time-Series Analysis

Author: Shrunali Salian & Team
Skills: R, Data Visualization, ggplot2, Time-Series Analysis, Correlation Analysis

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🚀 Project Overview

This project analyzes Spotify's extensive music dataset to uncover trends in track popularity, feature relationships, and user preferences over time.

📌 Project Highlights:
✅ Trend Analysis – Insights on track uploads, COVID impact & popularity factors
✅ Feature Analysis – Understanding relationships between song characteristics
✅ Time-Series Analysis – Examining trends in genre, artist popularity & explicit content

🔗 Dataset: Kaggle - Spotify Dataset (1921-2020)
📜 Medium Article (15,000+ Views!): Spotify Data Visualization

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🎯 Key Objectives

✔ Explore how Spotify has evolved over time
✔ Analyze key drivers of track popularity
✔ Understand song feature relationships & correlations
✔ Examine how music preferences change across decades

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📊 Trend Analysis

1️⃣ Spotify’s Performance Over the Years

📈 Tracks uploaded per year:

• Consistent increase in tracks uploaded
• 📌 2013 surge – Rising CD prices + Streaming platforms gaining traction
• 📌 2017 drop – Spotify’s global expansion costs skyrocketed before IPO

✅ Visualization: Number of tracks uploaded per year

ggplot(spotify_data, aes(x = year, y = track_count)) +
  geom_line() +
  labs(title = "Spotify Track Uploads Over Time")

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2️⃣ COVID Hypothesis: Did Artists Release More Emotional Songs?

🎶 Hypothesis: Artists may release more happy/sad songs during COVID.
❌ Result: No major change observed in song valence (mood).

✅ Visualization: Emotional content trends before & during COVID

ggplot(spotify_data, aes(x = year, y = valence, color = covid_period)) +
  geom_boxplot() +
  labs(title = "Emotional Content Trends During COVID")

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3️⃣ What Determines Track Popularity on Spotify?

⭐ Finding: Track popularity is driven by the artist’s follower count, NOT track characteristics.
📌 Artist popularity is the strongest predictor of a song’s success.

✅ Visualization: Correlation between track popularity & artist followers

ggplot(spotify_data, aes(x = artist_followers, y = track_popularity)) +
  geom_point(alpha = 0.5) +
  labs(title = "Artist Followers vs Track Popularity")

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🎵 Feature Analysis: Understanding Song Characteristics

1️⃣ How are Features of a Song Related?

📌 Strong Correlations Found Between:

• Valence & Energy
• Valence & Danceability
• Loudness & Energy

✅ Visualization: Heatmap of song feature correlations

library(ggcorrplot)
corr_matrix <- cor(spotify_data[, c("valence", "energy", "danceability", "loudness")])
ggcorrplot(corr_matrix, method="circle")

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2️⃣ Loudness vs Energy: Are Louder Songs More Energetic?

📌 Yes! Loud tracks tend to have higher energy levels.

✅ Visualization: Scatterplot of Loudness vs Energy

ggplot(spotify_data, aes(x = loudness, y = energy)) +
  geom_point(alpha = 0.5) +
  labs(title = "Loudness vs Energy in Spotify Tracks")

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3️⃣ Word Cloud: Most Popular Justin Bieber Tracks

📌 Finding: "Hold On" & "Anyone" are the most common song titles.

✅ Visualization: Word Cloud of Justin Bieber’s popular tracks

library(wordcloud)
wordcloud(spotify_data$track_name, max.words = 50, colors = brewer.pal(8, "Dark2"))

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⏳ Time-Series Analysis: How Music Preferences Change Over Time

1️⃣ Do Users Prefer Newer Artists Over Older Ones?

📌 Finding: Recent artists have higher popularity than early-decade artists.
📌 Possible reasons: Music industry evolution, genre shifts, & better production technology.

✅ Visualization: Artist popularity by year

ggplot(spotify_data, aes(x = artist_debut_year, y = artist_popularity)) +
  geom_line() +
  labs(title = "Popularity of Artists by Debut Year")

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2️⃣ Genre Preferences: Are Old Genres Still Popular?

📌 Finding: Genres from the 2000s remain the most popular today.
📌 Why? Older genres are re-adapted by modern artists to stay relevant.

✅ Visualization: Popularity of different genres by decade

ggplot(spotify_data, aes(x = genre_decade, y = genre_popularity)) +
  geom_bar(stat = "identity") +
  labs(title = "Genre Popularity Across Decades")

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3️⃣ Explicit Content in Music Over Time

📌 Finding: The cultural attitude toward explicit music has shifted, with 2000s music featuring the most explicit lyrics.

✅ Visualization: Proportion of explicit tracks over decades

ggplot(spotify_data, aes(x = decade, y = explicit_ratio)) +
  geom_line() +
  labs(title = "Increase in Explicit Content Over Time")

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🔮 Future Enhancements

🔹 Sentiment Analysis on Lyrics – Are sad songs more popular?
🔹 Machine Learning Model – Predicting song popularity based on features
🔹 Comparison with Competitors (Apple Music, YouTube Music, etc.)

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🎯 Why This Project Stands Out for Data Science & AI Roles

✔ Uses Real-World Music Data from Spotify – Popular dataset with strong industry relevance
✔ Applies R & ggplot2 for Advanced Data Visualization
✔ Performs Trend Analysis, Feature Relationships & Time-Series Analysis
✔ Explores the Evolution of Music Preferences & Popularity Factors

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🛠 How to Run This Project

1️⃣ Clone the repo:

git clone https://github.com/shrunalisalian/spotify-data-visualization.git

2️⃣ Install dependencies:

install.packages(c("ggplot2", "dplyr", "wordcloud", "ggcorrplot"))

3️⃣ Run the RMarkdown file:

rmarkdown::render("Hackathon.Rmd")

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📌 Connect with Me

• LinkedIn: Shrunali Salian
• Portfolio: https://portfolio-shrunali-suresh-salians-projects.vercel.app/
• Email: Email

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Collaborators:

1. Shrunali Salian
2. Vishnu Girish
3. Subhasree Vemprala Sathyanarayanan