Advanced Features Example
=========================

This example demonstrates the advanced features of UpSet plots including interactive
filtering, statistical analysis, and animated transitions.

Setup
-----

First, let's import our libraries and create some sample data with engagement metrics:

.. altair-plot::
    :output: none

    import altair_upset as au
    import pandas as pd
    import numpy as np

    # Create sample data with metrics
    np.random.seed(42)
    n_users = 1000

    # Generate platform usage data with engagement metrics
    platforms = ['Facebook', 'Instagram', 'Twitter', 'LinkedIn', 'TikTok']
    data = pd.DataFrame({
        platform: np.random.choice([0, 1], size=n_users, p=[0.3, 0.7])
        for platform in platforms
    })

    # Add engagement metrics
    data['daily_time_spent'] = np.random.lognormal(3, 1, n_users)  # minutes
    data['posts_per_week'] = np.random.poisson(5, n_users)
    data['engagement_rate'] = np.random.beta(2, 5, n_users)

Basic UpSet Plot with Metrics
-----------------------------

Create a basic UpSet plot showing platform usage with engagement metrics:

.. altair-plot::

    au.UpSetAltair(
        data=data,
        sets=platforms,
        title="Social Media Platform Usage Analysis",
        subtitle="Interactive analysis of user engagement patterns",
        width=800,
        height=500
    ).chart

Engagement Analysis
-------------------

Let's analyze the engagement patterns across different platform combinations:

.. code-block:: python

    # Calculate average engagement metrics for each platform
    for platform in platforms:
        platform_data = data[data[platform] == 1]
        print(f"\n{platform} Metrics:")
        print(f"Users: {len(platform_data)}")
        print(f"Average daily time: {platform_data['daily_time_spent'].mean():.1f} minutes")
        print(f"Average posts per week: {platform_data['posts_per_week'].mean():.1f}")
        print(
            f"Average engagement rate: {platform_data['engagement_rate'].mean()*100:.1f}%"
        )


    # Find most engaged platform combinations
    def get_engagement_metrics(group):
        return pd.Series(
            {
                "users": len(group),
                "avg_time": group["daily_time_spent"].mean(),
                "avg_posts": group["posts_per_week"].mean(),
                "avg_engagement": group["engagement_rate"].mean() * 100,
            }
        )


    # Calculate metrics for all combinations
    combinations = data.groupby(platforms).apply(get_engagement_metrics).reset_index()

    # Sort by average engagement
    top_engaged = combinations.sort_values("avg_engagement", ascending=False).head(3)
    print("\nTop 3 Most Engaged Platform Combinations:")
    for _, row in top_engaged.iterrows():
        active_platforms = [p for p, v in zip(platforms, row[platforms]) if v == 1]
        print(f"\n{' & '.join(active_platforms)}:")
        print(f"Users: {row['users']}")
        print(f"Avg Time: {row['avg_time']:.1f} minutes")
        print(f"Avg Posts: {row['avg_posts']:.1f} per week")
        print(f"Avg Engagement: {row['avg_engagement']:.1f}%")