📊 Data Visualization - Music Rankings Through Time: Analysis of Rolling Stone's Top 500 Albums

Overview

This project analyzes Rolling Stone magazine's Top 500 Albums dataset published as part of the TidyTuesday initiative on May 7, 2024.
We explore two main questions:

• How do an album's genre and release year influence its ranking changes from 2003 to 2020?
• How do artist gender and group size affect album popularity on Spotify?

The analysis employs statistical methods including ANOVA, regression analysis, and data visualization techniques to identify trends in music rankings across time, genres, and artist demographics.

Further details on methodology, findings, and discussions are available in the complete project documentation.

Repository Structure

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├── code/                # Jupyter notebooks for analyses
│   ├── q1+q2.ipynb      # Main analysis for Questions 1 and 2
│   └── q2_further.ipynb # Additional analysis for Question 2
├── data/                # Dataset files and metadata
│   ├── metadata.md      # Metadata for the dataset
│   └── rolling_stone.csv # Main dataset file
├── figures/             # Generated visualizations
│   ├── box_plot_q1.jpeg # Box plots for Question 1 analysis
│   ├── line_q1.jpeg     # Line graphs for Question 1 analysis
│   ├── rank_box_q1.jpeg # Box plots of rankings for Question 1
│   ├── q2_boxplot.png   # Box plots for Question 2 analysis
│   ├── q2_correlation.png # Correlation analysis for Question 2
│   ├── q2_peak_billboard.png # Billboard peak position analysis
│   └── q2_weeks_billboard.png # Billboard weeks analysis
├── presentation.pdf     # Project presentation slides
├── proposal.md          # Original project proposal
└── report.md            # Detailed project report

Getting Started

1. Clone this repository.
2. Install required Python libraries:

   pip install -r requirements.txt
   

3. Open the Jupyter notebooks in the code/ directory to reproduce the analysis:
   • q1+q2.ipynb for the main analysis of both research questions
   • q2_further.ipynb for additional analysis on Question 2

Dataset

• Source: Rolling Stone Magazine's Top 500 Albums, curated by TidyTuesday
• Details: Contains rankings from 2003, 2012, and 2020, along with album metadata, artist information, Billboard performance, and Spotify metrics.
• Full Documentation: Rolling Stone Data Documentation

Acknowledgement

This project is conducted as part of a Data Visualization course. We would like to express our gratitude to the course instructor and teaching assistants for their guidance and feedback.

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Proposal: Analysis of Rolling Stone's Top 500 Albums Dataset

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1. Dataset Overview

The dataset used in this project is the Rolling Stone Album Rankings, published on May 7, 2024, as part of the TidyTuesday initiative. It compiles the 500 Greatest Albums of All Time according to Rolling Stone magazine and provides insights into rankings across multiple years (2003, 2012, and 2020), as well as additional metadata on the albums and artists.

Provenance

• Source: TidyTuesday Rolling Stone Album Rankings Dataset
• Original Publisher: Rolling Stone Magazine
• Data Collection: Information compiled from Rolling Stone's published rankings, Billboard statistics, and Spotify metadata

Dataset Dimensions

• Number of Observations: 500 albums

• Number of Variables: 15+ (including numerical and categorical data)

• Key Variables:

  • Numerical Variables:
    • rank_2003, rank_2012, rank_2020 (album rankings over the years)
    • differential (ranking change between 2020 and 2003)
    • release_year (year the album was released)
    • spotify_popularity (Spotify popularity score)
    • weeks_on_billboard (number of weeks on Billboard charts)
    • peak_billboard_position (highest position on Billboard charts)
  • Categorical Variables:
    • album (album title)
    • artist (artist or band name)
    • genre (album genre)
    • artist_gender (gender classification of the artist)
    • type (album classification, e.g., studio, live, compilation)

• Full Data Dictionary: Rolling Stone Data Documentation

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2. Reason for Choosing This Dataset

Topic of Interest

• Music rankings have been a widely discussed topic for decades, shaping public opinion, artist legacies, and industry recognition. The Rolling Stone Top 500 Albums dataset presents a unique opportunity to analyze how factors such as release year, genre, artist type, and commercial success impact an album's standing in music history.

• Additionally, this dataset provides a rich combination of historical rankings, commercial performance (Billboard data), and modern streaming popularity (Spotify metrics), making it ideal for statistical analysis and data visualization.

Dataset Compliance with Project Requirements

Published after 2023 – The dataset was released on May 7, 2024, ensuring it meets the publication date requirement.
Contains both numerical and categorical variables – Includes:

• Numerical variables: ranking positions, album release years, streaming popularity.
• Categorical variables: album genres, artist names, artist gender.
  Sufficient complexity for analysis – Contains multiple dimensions of data (historical rankings, artist metadata, streaming trends), allowing for diverse and meaningful research questions.

Given these characteristics, this dataset is an excellent choice for our project, offering an opportunity to explore trends in music rankings over time and across various artist demographics.

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3. Two main questions to answer and

Question 1. How do an album's genre and release year influence its ranking changes from 2003 to 2020?

Question 2. How do artist gender and group size affect album popularity on Spotify?

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4. Plan for answering each of the questions including the variables involved, variables to be created (if any), external data to be merged in (if any).

1. How do an album's genre and release year influence its ranking changes from 2003 to 2020?

• Variables involved: genre, release_year, differential (rank_2020 - rank_2003)
• Plan: Use statistical methods like ANOVA or regression to assess the relationship between genre, release_year, and differential. Group albums by genre and decade of release, then compare average differentials (new variable) to identify trends.
• Visualizations: Box plots and line graphs to depict trends clearly over time.
• External data: None required

2. How do artist gender and group size affect album popularity on Spotify?

• Variables involved: artist_gender, artist_member_count, spotify_popularity
• Plan: Use regression analysis to model spotify_popularity with artist_gender and artist_member_count as predictors, then explore interactions, such as whether gender effects vary by group size.
• Visualizations: Faceted scatter plots and grouped bar charts for clearer depiction of interactions.
• External data: None required