[ENHANCEMENT] Enhanced Problem-Solving Statistics and Analytics Dashboard

[rahul-s-bhatt]

Feature Description

Implement comprehensive problem-solving statistics with advanced analytics and interactive visualizations to provide users with deeper insights into their LeetCode progress.

Problem Statement

The current statistics implementation is basic, showing only solved problem counts. Users need more detailed analytics about their problem-solving patterns, progress over time, and comparative performance to make informed decisions about their learning journey.

Proposed Solution

Create an enhanced statistics system that provides:

1. Detailed performance metrics
2. Interactive visualizations
3. Comparative analytics
4. Time-based progress tracking

Technical Details

1. Backend Enhancements

from typing import Dict, List, Any
from datetime import datetime, timedelta

class EnhancedStatisticsManager:
    def __init__(self, user_data: Dict[str, Any]):
        self.user_data = user_data
        self.metrics: Dict[str, Any] = {}

    def calculate_performance_metrics(self) -> Dict[str, Any]:
        return {
            "success_rates": {
                "easy": self._calculate_success_rate("Easy"),
                "medium": self._calculate_success_rate("Medium"),
                "hard": self._calculate_success_rate("Hard")
            },
            "time_analysis": {
                "average_solve_time": self._calculate_avg_solve_time(),
                "best_time_of_day": self._analyze_optimal_time(),
                "consistency_score": self._calculate_consistency()
            },
            "topic_performance": self._analyze_topic_performance(),
            "language_stats": self._analyze_language_usage()
        }

    def generate_progress_data(self) -> Dict[str, List[Any]]:
        return {
            "weekly_progress": self._calculate_weekly_trends(),
            "topic_growth": self._analyze_topic_growth(),
            "difficulty_progression": self._analyze_difficulty_progression()
        }

2. Frontend Visualizations

// Progress Tracking Chart
const progressChart = new Chart(ctx, {
    type: 'line',
    data: {
        datasets: [{
            label: 'Problems Solved',
            data: weeklyProgress,
            fill: false,
            borderColor: 'rgb(75, 192, 192)'
        }, {
            label: 'Success Rate',
            data: successRates,
            fill: false,
            borderColor: 'rgb(153, 102, 255)'
        }]
    },
    options: {
        responsive: true,
        scales: {
            x: {
                type: 'time',
                time: {
                    unit: 'week'
                }
            },
            y: {
                beginAtZero: true
            }
        }
    }
});

// Topic Performance Radar
const topicRadar = new Chart(ctx, {
    type: 'radar',
    data: {
        labels: topicLabels,
        datasets: [{
            label: 'Success Rate',
            data: topicSuccessRates,
            fill: true,
            backgroundColor: 'rgba(75, 192, 192, 0.2)'
        }]
    }
});

Required Skills

• Python
• Data Analysis
• JavaScript
• Data Visualization
• Other: Statistical Analysis

Difficulty Level

• Beginner Friendly
• Intermediate
• Advanced

Estimated Time

• Small (< 2 hours)
• Medium (2-4 hours)
• Large (4-8 hours)
• Extra Large (> 8 hours)

Implementation Checklist

Phase 1: Backend Enhancement

• Implement enhanced metrics calculation
• Add success rate analysis
• Create time-based analysis
• Add language performance tracking
• Implement topic performance analysis
• Add data caching for performance
• Write unit tests

Phase 2: Data Structure Updates

• Update data models
• Optimize data retrieval
• Implement efficient caching
• Add new GraphQL queries
• Update API endpoints

Phase 3: Frontend Implementation

• Create progress tracking charts
• Add topic performance radar
• Implement success rate displays
• Add comparative visualizations
• Create interactive filters
• Implement responsive design

Phase 4: Testing & Optimization

• Write frontend tests
• Perform performance testing
• Optimize data loading
• Add error handling
• Update documentation

Additional Context

This enhancement will provide users with:

• Detailed performance metrics
• Visual progress tracking
• Topic-wise analysis
• Time-based insights
• Comparative statistics
• Goal tracking capabilities

Getting Started

1. Review current analytics implementation in:
   • api/core/analytics/analytics_manager.py
   • api/data_formatter.py
2. Study the GraphQL query structure
3. Plan database optimizations
4. Design new UI components

Resources

• Chart.js Documentation: https://www.chartjs.org/docs/
• Python Analytics Libraries: pandas, numpy
• GraphQL Query Optimization: https://graphql.org/learn/best-practices/
• Frontend Performance: https://web.dev/fast/

Labels: enhancement, priority: high, analytics, frontend, backend, data-visualization, good-first-issue