tubern provides an R interface to the YouTube Analytics API v2, allowing you to retrieve YouTube Analytics data for channels and content owners. The package supports authentication via OAuth 2.0 and provides functions for getting analytics reports and managing channel groups.

• 🚀 Enhanced User Experience: Simplified functions for common analytics tasks
• 📅 Smart Date Handling: Use intuitive relative dates like "last_30_days", "this_month"
• 🔍 Intelligent Validation: Get helpful suggestions for metrics, dimensions, and parameters
• 📊 Built-in Data Transformation: Convert API responses to data.frames, tibbles, and CSV exports
• 📈 Quick Visualization: Generate plots with one function call
• 🛠️ Advanced Error Handling: Informative error messages and diagnostic tools
• 🔐 Streamlined Authentication: Step-by-step OAuth setup with automatic scope detection
• ⚡ Pre-built Report Templates: Channel overview, top videos, demographics, and more
• 🌍 Geographic & Demographic Analysis: Built-in support for audience insights
• 💰 Revenue Analytics: Comprehensive monetization reporting (with proper OAuth scope)

install.packages("tubern")

# install.packages("devtools")
devtools::install_github("gojiplus/tubern", build_vignettes = TRUE)

library(tubern)

# First time? Get step-by-step setup guide
yt_oauth()

# With your credentials (basic analytics)
yt_oauth("your-client-id.apps.googleusercontent.com", "your-client-secret")

# For revenue data (requires special permission)
yt_oauth("your-client-id.apps.googleusercontent.com", "your-client-secret", scope = "monetary")

# Use intuitive relative dates - no more manual date calculations!
overview <- get_channel_overview("last_30_days")
daily_trends <- get_daily_performance("this_month") 
top_videos <- get_top_videos("last_7_days")

# Or use traditional dates
report <- get_report(
  ids = "channel==MINE",
  metrics = "views,likes,comments",
  start_date = "2024-01-01",
  end_date = "2024-01-31"
)

# Convert to data.frame with clean column names
df <- yt_to_dataframe(daily_trends)
head(df)

# Quick visualization
yt_quick_plot(daily_trends)    # Auto-detects best chart type
yt_quick_plot(top_videos, chart_type = "bar")

# Export to CSV
yt_export_csv(overview, "my_channel_overview.csv")

# Get summary statistics
summary <- yt_extract_summary(overview)

# Channel performance overview
overview <- get_channel_overview("last_30_days")

# Top performing videos
top_videos <- get_top_videos("last_7_days", max_results = 20)

# Audience demographics
demographics <- get_audience_demographics("last_90_days")

# Geographic performance
geo_performance <- get_geographic_performance("this_month")

# Device/platform breakdown
device_performance <- get_report(
  ids = "channel==MINE",
  metrics = "views,estimatedMinutesWatched", 
  dimensions = "deviceType",
  start_date = "last_30_days"
)

# Revenue analysis (requires monetary scope)
revenue <- get_revenue_report("last_month")

# Get available metrics and dimensions
get_available_metrics()
get_available_dimensions()

# Helpful error messages with suggestions
# get_report(ids = "channel==MINE", metrics = "vews", start_date = "last_7_days")
# → Error: Invalid metric(s): vews. Did you mean: 'vews' -> 'views'?

# Diagnostic tools
diagnose_tubern()  # Check setup
check_api_quota()  # Monitor API usage

• yt_oauth(): Set up OAuth 2.0 authentication
• get_report(): Retrieve YouTube Analytics reports
• list_groups(): List channel groups
• add_groups(): Create new channel groups
• update_group(): Modify existing groups
• delete_group(): Remove groups
• list_group_items(): List items in a group
• add_group_item(): Add channels to groups
• delete_group_item(): Remove channels from groups

Common metrics you can request include:

• views: Number of times videos were viewed
• likes, dislikes: Engagement metrics
• shares: Number of times videos were shared
• comments: Number of comments
• subscribersGained, subscribersLost: Subscriber changes
• estimatedMinutesWatched: Total watch time in minutes
• averageViewDuration: Average time spent watching

For monetary metrics (requires monetary-analytics scope):

• estimatedRevenue: Estimated revenue
• adEarnings: Revenue from ads
• impressionBasedCpm: Cost per thousand impressions

Dimensions allow you to segment your data:

• video: Individual video performance
• day: Daily breakdowns
• country: Geographic data
• deviceType: Desktop, mobile, tablet, etc.
• operatingSystem: OS breakdowns
• ageGroup, gender: Demographic data (limited availability)

1. Go to the Google Cloud Console
2. Create a new project or select an existing one
3. Enable the YouTube Analytics API
4. Create OAuth 2.0 credentials (Desktop application type)
5. Use the client ID and secret in yt_oauth()

For detailed authentication instructions, see the YouTube Analytics API documentation.

• API Changes: This package has been updated to use YouTube Analytics API v2. Some parameter names and behaviors may have changed from previous versions.
• Rate Limits: The YouTube Analytics API has usage quotas. See the quota usage documentation for details.
• Data Freshness: Analytics data typically has a delay of 24-72 hours.

• YouTube Analytics API Documentation
• YouTube Data API R Client (for video metadata, not analytics)
• Google APIs R Client (for general Google API authentication)

Released under the MIT License.

Contributions are welcome! Please read the Contributor Code of Conduct and submit issues or pull requests on GitHub.

For bug reports and feature requests, please use the GitHub issue tracker.