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tubern: Comprehensive YouTube Analytics Examples

tubern: Enhanced YouTube Analytics for R

This vignette demonstrates the enhanced features of tubern, showing you how to efficiently retrieve, analyze, and visualize YouTube Analytics data.

Quick Setup

library(tubern)

# First-time setup - shows setup guide
yt_oauth()

# Or with your credentials
yt_oauth("your-client-id.apps.googleusercontent.com", "your-client-secret")

New Features Overview

1. Relative Date Ranges

No more manual date calculations! Use intuitive relative dates:

# Get common date range options
get_common_date_ranges()

# Use relative dates in reports
report <- get_report(
  ids = "channel==MINE",
  metrics = "views,likes",
  start_date = "last_30_days"  # End date calculated automatically
)

# More examples
get_report(ids = "channel==MINE", metrics = "views", start_date = "this_month")
get_report(ids = "channel==MINE", metrics = "views", start_date = "last_quarter")
get_report(ids = "channel==MINE", metrics = "views", start_date = "yesterday")

2. Helper Functions for Common Reports

Channel Overview

# Get comprehensive channel performance
overview <- get_channel_overview("last_30_days")

# Customize what metrics to include
overview <- get_channel_overview(
  "this_month", 
  include_engagement = TRUE,
  include_subscribers = TRUE
)

Top Performing Videos

# Get top 10 videos by views
top_videos <- get_top_videos("last_7_days")

# Get more videos with custom metrics
top_videos <- get_top_videos(
  "last_30_days",
  max_results = 25,
  metrics = c("views", "likes", "comments", "shares", "estimatedMinutesWatched")
)

Audience Demographics

# Full demographic breakdown
demographics <- get_audience_demographics("last_90_days")

# Age groups only
age_data <- get_audience_demographics("this_month", dimension = "ageGroup")

# Gender breakdown only
gender_data <- get_audience_demographics("this_quarter", dimension = "gender")

Geographic Performance

# Top countries by views
geo_performance <- get_geographic_performance("last_30_days")

# US states/provinces (requires filtering to US)
us_states <- get_geographic_performance(
  "this_month", 
  dimension = "province",
  filters = "country==US"
)

Device and Platform Analysis

# Performance by device type - use get_report directly
device_performance <- get_report(
  ids = "channel==MINE",
  metrics = "views,estimatedMinutesWatched",
  dimensions = "deviceType",
  start_date = "last_30_days"
)

# Operating system breakdown
os_performance <- get_report(
  ids = "channel==MINE",
  metrics = "views,estimatedMinutesWatched", 
  dimensions = "operatingSystem",
  start_date = "this_month"
)

# Both device and OS
device_os <- get_report(
  ids = "channel==MINE",
  metrics = "views,estimatedMinutesWatched",
  dimensions = "deviceType,operatingSystem",
  start_date = "last_7_days"
)

Daily Time Series Data

# Daily performance trends
daily_data <- get_daily_performance("last_30_days")

# With engagement metrics
daily_engagement <- get_daily_performance(
  "this_month",
  metrics = c("views", "likes", "comments", "shares")
)

Revenue Reports (Requires Monetary Scope)

# Set up OAuth with monetary scope first
yt_oauth("your-client-id", "your-client-secret", scope = "monetary")

# Get revenue data
revenue <- get_revenue_report("last_month")

# Revenue in different currency
revenue_eur <- get_revenue_report("this_quarter", currency = "EUR")

3. Enhanced Parameter Validation

tubern now provides helpful validation and suggestions:

# Invalid metrics get helpful suggestions
# get_report(ids = "channel==MINE", metrics = "vews", start_date = "last_7_days")
# Error: Invalid metric(s): vews
# Did you mean: 'vews' -> 'views'

# Check available metrics and dimensions
get_available_metrics()
get_available_metrics("view")  # Filter by pattern

get_available_dimensions() 
get_available_dimensions("country|city")  # Geographic dimensions

4. Data Transformation and Analysis

Convert to Data Frames

# Get data and convert to clean data.frame
report <- get_daily_performance("last_30_days")
df <- yt_to_dataframe(report)
head(df)

# Convert to tibble (if tibble package installed)
tbl <- yt_to_tibble(report)

# Keep original column names
df_orig <- yt_to_dataframe(report, clean_names = FALSE)

Data Export

# Export to CSV
report <- get_top_videos("last_7_days")
file_path <- yt_export_csv(report, "top_videos.csv")

# Auto-generated filename with timestamp
file_path <- yt_export_csv(report)

Quick Visualization

# Automatic plot creation (requires ggplot2)
daily_report <- get_daily_performance("last_30_days")
yt_quick_plot(daily_report)  # Line plot over time

# Bar chart for top videos
top_videos <- get_top_videos("last_7_days")
yt_quick_plot(top_videos, chart_type = "bar")

# Custom x and y columns
geo_data <- get_geographic_performance("last_30_days")
yt_quick_plot(geo_data, x_col = "country", y_col = "views")

Data Summary

# Get summary statistics
report <- get_channel_overview("last_30_days")
summary <- yt_extract_summary(report)
print(summary)

5. Improved Error Handling and Diagnostics

Diagnostic Tools

# Check if everything is set up correctly
diagnose_tubern()

# Check API quota status
check_api_quota()

Error Recovery

# tubern now provides helpful error messages and suggestions
# for authentication, API quota, parameter errors, etc.

# Example: If OAuth token expires, tubern will suggest running yt_oauth() again
# Example: If API quota is exceeded, tubern will suggest ways to reduce usage

Complete Workflow Examples

Example 1: Channel Performance Dashboard

# Set up authentication
yt_oauth("your-client-id", "your-client-secret")

# Get overview data
overview <- get_channel_overview("last_30_days")
overview_df <- yt_to_dataframe(overview)

# Get daily trends
daily <- get_daily_performance("last_30_days")
daily_df <- yt_to_dataframe(daily)

# Get top videos
top_videos <- get_top_videos("last_30_days", max_results = 10)
videos_df <- yt_to_dataframe(top_videos)

# Get geographic performance
geo <- get_geographic_performance("last_30_days")
geo_df <- yt_to_dataframe(geo)

# Create visualizations
yt_quick_plot(daily)      # Daily trend
yt_quick_plot(top_videos) # Top videos
yt_quick_plot(geo)        # Geographic performance

# Export data
yt_export_csv(overview, "channel_overview.csv")
yt_export_csv(daily, "daily_performance.csv")
yt_export_csv(top_videos, "top_videos.csv")

Example 2: Audience Analysis

# Demographic analysis
demographics <- get_audience_demographics("last_90_days")
demo_df <- yt_to_dataframe(demographics)

# Device analysis  
devices <- get_report(
  ids = "channel==MINE",
  metrics = "views,estimatedMinutesWatched",
  dimensions = "deviceType",
  start_date = "last_90_days"
)
device_df <- yt_to_dataframe(devices)

# Geographic analysis
geography <- get_geographic_performance("last_90_days", max_results = 50)
geo_df <- yt_to_dataframe(geography)

# Combined analysis with dplyr (if available)
if (require(dplyr)) {
  # Top countries by watch time
  top_countries <- geo_df %>%
    arrange(desc(estimated_minutes_watched)) %>%
    head(10)
  
  print(top_countries)
}

Example 3: Content Performance Analysis

# Analyze video performance over time
videos_monthly <- get_top_videos("this_month", max_results = 50)
videos_df <- yt_to_dataframe(videos_monthly)

# Compare with previous month
videos_prev <- get_top_videos("last_month", max_results = 50) 
videos_prev_df <- yt_to_dataframe(videos_prev)

# Daily performance for trend analysis
daily_trends <- get_daily_performance(
  "last_30_days",
  metrics = c("views", "estimatedMinutesWatched", "likes", "comments")
)
trends_df <- yt_to_dataframe(daily_trends)

# Visualize trends
yt_quick_plot(daily_trends, x_col = "day", y_col = "views")

Best Practices

1. Authentication

  • Use scope = "monetary" only when you need revenue data
  • Keep your OAuth credentials secure
  • Use diagnose_tubern() to troubleshoot authentication issues

2. Data Retrieval

  • Use relative dates for convenience: "last_30_days", "this_month"
  • Start with helper functions like get_channel_overview() before using get_report() directly
  • Use max_results parameter to limit data when testing

3. Error Handling

  • Always check diagnose_tubern() if you encounter issues
  • Use check_api_quota() to monitor your API usage
  • Enable setup_guide = TRUE for first-time setup help

4. Data Analysis

  • Convert API responses to data.frames with yt_to_dataframe()
  • Use yt_quick_plot() for rapid data exploration
  • Export data with yt_export_csv() for external analysis

Troubleshooting

Common Issues

  1. Authentication Problems

    # Run diagnostics
diagnose_tubern()

# Re-authenticate
yt_oauth("your-client-id", "your-client-secret")

  2. API Quota Exceeded

    # Check quota status
check_api_quota()

# Reduce data scope
get_report(ids = "channel==MINE", metrics = "views", 
          start_date = "yesterday", max_results = 10)

  3. Invalid Parameters

    # Check available options
get_available_metrics()
get_available_dimensions()

# Use helper functions for validation
get_channel_overview("last_7_days")  # Pre-validated parameters

More Resources

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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