Git Analytics Scripts

This directory contains scripts for analyzing Git repository statistics and user contributions.

🚀 Quick Start

Choose the script that best fits your needs:

1. Quick Stats (quick_git_stats.sh) - Fast & Simple

# From repository root
./quick_git_stats.sh <username>
./quick_git_stats.sh <username> --start-date 2024-01-01
./quick_git_stats.sh <username> --start-date 2024-01-01 --end-date 2024-12-31

# From any subdirectory
cd utils && ./quick_git_stats.sh <username>

2. Full Analytics (git_analytics.sh) - Comprehensive Bash Script

# From repository root
./git_analytics.sh <username>
./git_analytics.sh <username> --start-date 2024-01-01
./git_analytics.sh <username> --start-date 2024-01-01 --end-date 2024-12-31

# From any subdirectory
cd utils && ./git_analytics.sh <username>

3. Advanced Analytics (git_analytics.py) - Python with JSON Output

# From repository root
python3 git_analytics.py <username> --format json --output report.json
python3 git_analytics.py <username> --start-date 2024-01-01 --format json
python3 git_analytics.py <username> --start-date 2024-01-01 --end-date 2024-12-31 --format json

# From any subdirectory
cd utils && python3 git_analytics.py <username> --format json

📊 Git Analytics Script (git_analytics.sh)

A comprehensive script that analyzes Git repository statistics for a specific user, including:

• Merged Pull Request Count - Number of merged PRs/MRs
• Commit Statistics - Total commits across all branches
• Lines of Code - Lines added, deleted, and net contribution
• Activity Analysis - Commit patterns, time periods, and activity scores

🚀 Quick Start

# Basic usage (run from repository root)
./git_analytics.sh <username>

# Example
./git_analytics.sh "username"

# Run from different directory
./git_analytics.sh "username" /path/to/repo

🔧 Pull Request Detection

The scripts now automatically detect pull requests by parsing merge commit messages, making them independent of external CLI tools or API access:

• Automatic Detection - Parses "Merge pull request #X" messages from git log --merges
• No External Dependencies - Works without GitHub CLI or API access
• Accurate Counting - Correctly identifies merged pull requests by the specified user

Note: The scripts use the Git commit author name for both commits and pull request detection. Make sure to use the exact author name as it appears in your Git commits.

📋 Features

1. Merged Pull Request Analysis

• Automatic Detection - Parses merge commit messages to identify PR numbers
• Self-contained - No external CLI tools or API access required
• Accurate Counting - Correctly counts pull requests merged by the specified user
• Cross-platform - Works on any Git repository with merge commits

2. Commit Statistics

• Total Commits - All commits by user across all branches
• Unique Commits - Deduplicated commit count
• Branch Analysis - Commits per branch breakdown
• Recent Activity - Commits in last 30 days
• Time Analysis - Commits by year and day of week

3. Lines of Code Analysis

• Files Modified - Total number of files touched
• Current LOC - Lines of code in files currently in repository
• Lines Added - Total lines added by user (including merge commits)
• Lines Deleted - Total lines deleted by user (including merge commits)
• Net Contribution - Net lines added (additions - deletions)
• Merge Commit Support - Accurately counts lines from merged pull requests

4. Detailed Statistics

• Activity Timeline - First and last commit dates
• Days Active - Total days between first and last commit
• Yearly Breakdown - Commits per year
• Activity Patterns - Most active day of week
• Activity Score - Calculated engagement metric

🔧 Requirements

Required Dependencies

• git - Git version control system
• awk - Text processing utility
• wc - Word count utility
• sort - Sorting utility
• uniq - Unique line filtering

No External Dependencies Required

The scripts are now self-contained and don't require GitHub CLI, GitLab CLI, or API access for pull request detection.

📊 Sample Output

================================
Git Analytics Summary
================================
User: username
Repository: example-repo
Generated: 2024-12-19 14:30:25

Quick Stats:
Total Commits: 45
Unique Commits: 42
Recent Activity (30 days): 12
Activity Score: 1050

Counting Pull Requests...
Pull Requests (from merge commits): 4
PR Details:
  PR #42: a1b2c3d Merge pull request #42 from feature/user-authentication
  PR #38: e4f5g6h Merge pull request #38 from feature/api-endpoints
  PR #25: i7j8k9l Merge pull request #25 from feature/database-migration
  PR #12: m0n1o2p Merge pull request #12 from feature/unit-tests

Counting Commits...
Total Commits: 45
Unique Commits: 42
Commits by Branch:
  main: 25 commits
  feature/auto-save: 15 commits
  bugfix/auth: 5 commits
Recent Activity (Last 30 days):
Commits in last 30 days: 12

Counting Lines of Code...
Files Modified: 23
Total Lines of Code: 15420
Lines Added: 2847
Lines Deleted: 1234
Net Lines: 1613

Detailed Commit Statistics...
First Commit: 2024-01-15
Last Commit: 2024-12-19
Days Active: 338
Commits by Year:
  2024: 45 commits
Most Active Day of Week:
  Wednesday: 12 commits

================================
Analysis Complete
================================
All statistics have been generated for user: username

🎯 Use Cases

For Project Managers

• Team Performance - Track individual contributions
• Project Health - Monitor activity levels
• Resource Planning - Identify most active contributors

For Developers

• Personal Analytics - Track your own contributions
• Portfolio Building - Generate statistics for resumes
• Code Review - Understand your impact on projects

For Open Source Projects

• Contributor Recognition - Acknowledge top contributors
• Community Health - Monitor project activity
• Documentation - Generate contribution reports

🔍 Advanced Usage

Custom Date Ranges

# The script automatically shows recent activity, but you can modify it
# Edit the script to change the "30 days ago" period

Multiple Users

# Run for multiple users
for user in user1 user2 user3; do
    ./git_analytics.sh "$user" > "report_${user}.txt"
done

Export to File

# Save output to file
./git_analytics.sh username > analytics_report.txt

# Save with timestamp
./git_analytics.sh username > "analytics_$(date +%Y%m%d_%H%M%S).txt"

⚠️ Limitations

1. Merged PR Counting - Requires GitHub/GitLab CLI or API access
2. Private Repos - API calls may fail for private repositories
3. Large Repositories - May be slow for very large codebases
4. Git History - Only analyzes current Git history (not deleted branches)

🛠️ Troubleshooting

Common Issues

"Not a Git repository"

# Make sure you're in a Git repository
cd /path/to/your/repo
./utils/git_analytics.sh username

"No commits found for user"

# Check if the username matches Git author names
git log --pretty=format:"%an" | sort -u | grep -i "username"

"Could not determine PR count"

# This usually means no merge commits were found for the user
# Check if the user has merged any pull requests
git log --merges --author="username" --oneline

Performance Tips

• Large Repositories: The script may take time for large codebases
• Network Issues: API calls may timeout on slow connections
• Memory Usage: Very large repositories may use significant memory

📈 Activity Score Calculation

The activity score is calculated as:

Activity Score = (Total Commits × 10) + (Recent Commits × 50)

This gives more weight to recent activity while considering overall contribution.

🔄 Updates and Maintenance

The script is designed to be:

• Self-contained - Minimal external dependencies
• Cross-platform - Works on macOS, Linux, and Windows (with Git Bash)
• Extensible - Easy to add new metrics or modify existing ones

🐍 Python Analytics Script (git_analytics.py)

Advanced Git analytics with JSON output support and enhanced data processing.

Features

• JSON Output - Machine-readable format for further processing
• Enhanced Statistics - More detailed analysis than bash version
• File Extension Analysis - Breakdown by file types
• Largest Files - Top 10 largest files modified
• Export Support - Save reports to files

Usage Examples

# Basic usage
python3 git_analytics.py "username"

# With date range
python3 git_analytics.py "username" --start-date 2024-01-01 --end-date 2024-12-31

# JSON output
python3 git_analytics.py "username" --format json

# Save to file
python3 git_analytics.py "username" --format json --output report.json

# Analyze different repository
python3 git_analytics.py "username" --repo /path/to/repo

Python Dependencies

• Standard library modules: os, sys, json, subprocess, datetime, collections, argparse
• No external dependencies required

⚡ Quick Stats Script (quick_git_stats.sh)

Lightweight script for fast statistics overview.

Features

• Fast Execution - Minimal processing time
• Essential Stats - Core metrics only
• Simple Output - Clean, readable format
• No Dependencies - Uses only standard Unix tools

Usage

# From repository root
./utils/quick_git_stats.sh <username>
./utils/quick_git_stats.sh <username> --start-date 2024-01-01
./utils/quick_git_stats.sh <username> --start-date 2024-01-01 --end-date 2024-12-31

# From any subdirectory (scripts auto-detect Git root)
cd utils && ./quick_git_stats.sh <username>
cd example-repo && ../quick_git_stats.sh <username>

Sample Output

Quick Git Stats for: username
==================================
Total Commits: 45
Recent Commits (30 days): 12
Files Modified: 23
Lines Added: 2847
Lines Deleted: 1234
Net Lines: 1613
==================================

📅 Date Filtering

All scripts support date range filtering to analyze contributions within specific time periods:

Date Format

• Format: YYYY-MM-DD (e.g., 2024-01-01)
• Start Date: --start-date - Filter commits from this date (inclusive)
• End Date: --end-date - Filter commits until this date (inclusive)

Examples

# Analyze commits from January 1, 2024 onwards
./quick_git_stats.sh "username" --start-date 2024-01-01

# Analyze commits in 2024 only
./quick_git_stats.sh "username" --start-date 2024-01-01 --end-date 2024-12-31

# Analyze commits until December 31, 2023
./git_analytics.sh "username" --end-date 2023-12-31

# Python with date range and JSON output
python3 git_analytics.py "username" --start-date 2024-01-01 --end-date 2024-12-31 --format json

Benefits

• Time-based Analysis: Focus on specific periods (quarters, years, sprints)
• Performance: Faster processing for large repositories
• Trend Analysis: Compare activity across different time periods
• Project Phases: Analyze contributions during specific project phases
• Flexible Execution: Scripts automatically detect and navigate to Git repository root

📊 Comparison Table

Feature	Quick Stats	Full Analytics	Python Analytics
Speed	⚡ Fast	🐌 Medium	🐌 Medium
Output Format	Text	Text	Text/JSON
Merged PR Counting	❌ No	✅ Yes	✅ Yes
File Analysis	❌ No	✅ Yes	✅ Yes
Dependencies	Minimal	Medium	Python + libs
Export Support	❌ No	❌ No	✅ Yes
Customization	❌ No	✅ Yes	✅ Yes

🎯 Use Cases

Quick Stats - When to Use

• Daily Check-ins - Quick overview of your contributions
• Team Standups - Fast team member statistics
• CI/CD Integration - Automated reporting in pipelines

Full Analytics - When to Use

• Detailed Reports - Comprehensive analysis
• Project Reviews - Full contribution assessment
• Documentation - Complete user activity reports

Python Analytics - When to Use

• Data Processing - Further analysis with other tools
• API Integration - Programmatic access to statistics
• Custom Reports - Tailored analytics for specific needs