{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# US Military & Veteran Analysis - Interactive Demo\n",
        "\n",
        "Explore **state-level data** on veteran demographics, firearm ownership, mental health indicators, and VA healthcare.\n",
        "\n",
        "**Dataset Highlights:**\n",
        "- 6 authoritative sources integrated\n",
        "- Veteran vs civilian suicide rates\n",
        "- PTSD prevalence estimates\n",
        "- VA healthcare utilization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "plt.style.use('seaborn-v0_8-darkgrid')\n",
        "sns.set_palette('husl')\n",
        "\n",
        "print('Libraries loaded')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df = pd.read_csv('military_firearm_merged_analysis.csv')\n",
        "print(f'Total states: {len(df)}')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Dataset Overview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print('Dataset Statistics:')\n",
        "print('=' * 60)\n",
        "print(f\"States: {len(df)}\")\n",
        "print(f\"\\nVeteran Statistics:\")\n",
        "print(f\"  Total veteran population: {df['veteran_population'].sum():,.0f}\")\n",
        "print(f\"  Avg veteran %: {df['veteran_percentage'].mean():.1f}%\")\n",
        "print(f\"\\nMental Health:\")\n",
        "print(f\"  Avg veteran suicide rate: {df['veteran_suicide_rate'].mean():.1f} per 100K\")\n",
        "print(f\"  Avg civilian suicide rate: {df['civilian_suicide_rate'].mean():.1f} per 100K\")\n",
        "print(f\"  Avg veteran risk ratio: {df['veteran_risk_ratio'].mean():.2f}x\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. Veteran Risk Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Top 10 states by veteran risk ratio\n",
        "high_risk = df.nlargest(10, 'veteran_risk_ratio')[['NAME', 'veteran_risk_ratio', 'veteran_suicide_rate', 'civilian_suicide_rate']]\n",
        "print('States with Highest Veteran Risk Ratio:')\n",
        "print(high_risk.to_string(index=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Compare veteran vs civilian suicide rates\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "\n",
        "sorted_df = df.sort_values('veteran_suicide_rate', ascending=True).tail(20)\n",
        "y_pos = range(len(sorted_df))\n",
        "\n",
        "ax.barh(y_pos, sorted_df['veteran_suicide_rate'], height=0.4, label='Veteran', color='steelblue', alpha=0.8)\n",
        "ax.barh([y + 0.4 for y in y_pos], sorted_df['civilian_suicide_rate'], height=0.4, label='Civilian', color='coral', alpha=0.8)\n",
        "\n",
        "ax.set_yticks([y + 0.2 for y in y_pos])\n",
        "ax.set_yticklabels(sorted_df['NAME'])\n",
        "ax.set_xlabel('Suicide Rate per 100,000', fontsize=12)\n",
        "ax.set_title('Veteran vs Civilian Suicide Rates by State', fontsize=14, fontweight='bold')\n",
        "ax.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. Firearm Ownership Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print('Firearm Ownership Statistics:')\n",
        "print('=' * 50)\n",
        "print(f\"Mean ownership rate: {df['ownership_percentage'].mean():.1f}%\")\n",
        "print(f\"Highest: {df.loc[df['ownership_percentage'].idxmax(), 'NAME']} ({df['ownership_percentage'].max():.1f}%)\")\n",
        "print(f\"Lowest: {df.loc[df['ownership_percentage'].idxmin(), 'NAME']} ({df['ownership_percentage'].min():.1f}%)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Correlation between firearm ownership and veteran suicide\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "\n",
        "ax.scatter(df['ownership_percentage'], df['veteran_suicide_rate'], alpha=0.7, s=100)\n",
        "\n",
        "# Add correlation line\n",
        "z = np.polyfit(df['ownership_percentage'], df['veteran_suicide_rate'], 1)\n",
        "p = np.poly1d(z)\n",
        "ax.plot(df['ownership_percentage'].sort_values(), p(df['ownership_percentage'].sort_values()), \n",
        "        'r--', alpha=0.8, label=f'r = {df[\"ownership_percentage\"].corr(df[\"veteran_suicide_rate\"]):.3f}')\n",
        "\n",
        "ax.set_xlabel('Firearm Ownership Rate (%)', fontsize=12)\n",
        "ax.set_ylabel('Veteran Suicide Rate (per 100K)', fontsize=12)\n",
        "ax.set_title('Firearm Ownership vs Veteran Suicide Rate', fontsize=14, fontweight='bold')\n",
        "ax.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. VA Healthcare Utilization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print('VA Healthcare Utilization:')\n",
        "print('=' * 50)\n",
        "print(f\"Mean utilization: {df['va_utilization_pct'].mean():.1f}%\")\n",
        "print(f\"\\nTop 5 States (highest VA use):\")\n",
        "top_va = df.nlargest(5, 'va_utilization_pct')[['NAME', 'va_utilization_pct', 'veteran_population']]\n",
        "print(top_va.to_string(index=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# PTSD prevalence by state\n",
        "if 'ptsd_prevalence_pct' in df.columns:\n",
        "    fig, ax = plt.subplots(figsize=(14, 6))\n",
        "    sorted_ptsd = df.sort_values('ptsd_prevalence_pct')\n",
        "    ax.barh(sorted_ptsd['NAME'], sorted_ptsd['ptsd_prevalence_pct'], color='teal')\n",
        "    ax.set_xlabel('PTSD Prevalence (%)', fontsize=12)\n",
        "    ax.set_title('PTSD Prevalence Among Veterans by State', fontsize=14, fontweight='bold')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. Correlation Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Key correlations\n",
        "metrics = ['veteran_suicide_rate', 'ownership_percentage', 'ptsd_prevalence_pct', 'va_utilization_pct', 'veteran_percentage']\n",
        "corr_matrix = df[metrics].corr()\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='RdBu_r', center=0, ax=ax, fmt='.2f')\n",
        "ax.set_title('Correlation Matrix', fontsize=14, fontweight='bold')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Conclusion\n",
        "\n",
        "This notebook demonstrated:\n",
        "- Loading and exploring veteran analysis data\n",
        "- Comparing veteran vs civilian suicide rates\n",
        "- Analyzing firearm ownership correlations\n",
        "- VA healthcare utilization patterns\n",
        "\n",
        "**Important Note**: RAND firearm data is from 2016 (most recent publicly available). Use findings as exploratory, not causal.\n",
        "\n",
        "**Author**: Luke Steuber | luke@lukesteuber.com | @lukesteuber.com (Bluesky)"
      ]
    }
  ],
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