An R package for fetching, analyzing, and visualizing song lyrics. Connect to your Spotify account to pull playlists and listening history, retrieve lyrics from Genius, then run sentiment, complexity, emotion, and theme analysis.
Setup
1. Get Genius API Token (for lyrics analysis)
Step 1: Go to https://genius.com/api-clients
Step 2: Sign up or log in (free)
Step 3: Click “New API Client” and fill in: - App Name: lyricsLab - App Website: http://localhost - Redirect URI: (leave default)
Step 4: Copy your “Client Access Token”
Step 5: Set it in R:
# For current session only
Sys.setenv(GENIUS_API_TOKEN = "your_token_here")2. Get Spotify API Credentials (for listening history)
Step 1: Go to https://developer.spotify.com/dashboard
Step 2: Log in with your Spotify account
Step 3: Click “Create app” and fill in: - App name: lyricsLab - App description: R package for music analysis - Redirect URI: http://127.0.0.1:1410/
Step 4: Copy your Client ID and Client Secret
Step 5: Set them in R:
Sys.setenv(SPOTIFY_CLIENT_ID= "your_client_id")
Sys.setenv(SPOTIFY_CLIENT_SECRET= "your_client_secret")Quick start
library(lyricsLab)
# 1. Authenticate with Spotify
spotify_auth()
# 2. Get lyrics for a single song
lyrics <- get_lyrics("Ed Sheeran", "Drive")
# 3. Run analysis
sentiment_score(lyrics)
complexity_score(lyrics)
analyze_emotions(lyrics)
theme_detection(lyrics)
# 4. Plot
plot_sentiment_arc(lyrics)
plot_emotions_web(lyrics)
plot_themes(lyrics)Function reference
Authentication & Spotify data
| Function | Description |
|---|---|
spotify_auth() |
Authenticate with Spotify (opens browser) |
get_recently_played() |
Your 50 most recently played tracks |
get_top_tracks() |
Your top tracks over short, medium, or long term |
get_playlist_tracks() |
All tracks from a playlist URL or ID |
Lyrics
| Function | Description |
|---|---|
get_lyrics(artist, song) |
Fetch lyrics from Genius |
get_multiple_lyrics(artist, songs) |
Batch fetch lyrics for multiple songs |
preview_lyrics(lyrics) |
Print metadata and first few lines |
view_lyrics(lyrics) |
Print full lyrics to console |
Analysis
| Function | Description |
|---|---|
sentiment_score(lyrics) |
Single overall sentiment score (0–100) |
sentiment_arc(lyrics) |
Line-by-line sentiment scores and trajectory |
analyze_emotions(lyrics) |
8 NRC emotions with counts and percentages |
positive_negative_ratio(lyrics) |
Ratio of positive to negative lines |
complexity_score(lyrics) |
Vocabulary richness, reading level, complexity score |
theme_detection(lyrics) |
Keyword-based theme detection across 23 categories |
Visualization
| Function | Description |
|---|---|
plot_sentiment_arc(lyrics) |
Line chart of sentiment through the song |
plot_emotions_bar(lyrics) |
Bar chart of 8 NRC emotions |
plot_emotions_web(lyrics) |
Radar chart of 8 NRC emotions |
plot_themes(lyrics) |
Bar chart of top themes |
plot_listening_patterns(history) |
Hourly, daily, or weekly listening patterns |
Playlist analysis
| Function | Description |
|---|---|
analyze_playlist(playlist_url) |
Fetch and analyze all songs in a playlist |
plot_sentiment(analysis) |
Sentiment distribution across playlist |
plot_complexity(analysis) |
Complexity distribution across playlist |
plot_scatter(analysis) |
Sentiment vs complexity scatter |
plot_top_complex(analysis) |
Top 10 most complex songs |
Example: analyze a playlist
spotify_auth()
analysis <- analyze_playlist(
"https://open.spotify.com/playlist/37i9dQZF1DXcBWIGoYBM5M",
max_songs = 50
)
plot_sentiment(analysis)
plot_complexity(analysis)
plot_scatter(analysis)
plot_top_complex(analysis)
calculate_listening_diversity(analysis)Example: your listening habits
spotify_auth()
history <- get_recently_played()
plot_listening_patterns(history, type = "hourly")
plot_listening_patterns(history, type = "daily")
plot_listening_patterns(history, type = "weekly")
calculate_listening_diversity(history)