County-Level Child Poverty Analysis: New R+Python AI Training Example

[Copilot]

Created a comprehensive new training example for Kids Count users that demonstrates AI-assisted data analysis workflows using county-level child poverty data across multiple states.

What's New

This PR adds a complete new example that complements the existing Arkansas tract-level materials:

• Novel dataset: toy-data/child-poverty-county.csv with 50 counties across 5 states (Alabama, Arizona, California, Colorado, Florida)
• Dual implementation: Both R (ai-exercise-new-example.qmd) and Python (ai-exercise-new-example.ipynb) notebooks with equivalent functionality
• Human+AI workflow: Step-by-step guidance for collaborating with AI tools, including copy-paste ready prompts
• Risk awareness: Explicit sections on data limitations, statistical caveats, and AI verification steps
• Complete solutions: Reference implementations added to ai-exercise-session-plan-solutions.qmd

Key Features

• Interactive notebooks for both R and Python programmers tackling the same county-level analysis
• Publicly available simulated data relevant to Kids Count organizational work (no API keys required)
• Full step-by-step human+AI workflow with both barebones and expanded prompts for each stage
• Risk and uncertainty guidance highlighting AI limitations and verification techniques
• Novel approach distinct from existing Arkansas tract-level debugging/visualization exercises

Workflow Steps

1. Data Exploration: AI-assisted dataset understanding and quality assessment
2. Indicator Computation: Collaborative calculation and validation of poverty rates
3. Basic Visualization: AI-generated slide-ready charts with iterative improvement
4. Advanced Refinement: Human-guided enhancements for policy presentations

Testing & Validation

• ✅ Both R and Python implementations tested and functional
• ✅ Runs completely offline without internet/API dependencies
• ✅ Estimated completion time: ~20 minutes
• ✅ Generates equivalent PNG visualizations across languages
• ✅ Updated documentation and README with usage instructions

The example is ready for use in Kids Count training sessions and provides a practical template for AI-assisted data analysis workflows.

Fixes #1.

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[Copilot]

@judah-axelrod 👋 This repository doesn't have Copilot instructions. With Copilot instructions, I can understand the repository better, work faster and produce higher quality PRs.

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[judah-axelrod]

@copilot I have added .github/copilot-instructions.md to kickstart your work on #1. Please resume work on this issue.

[Copilot]

@copilot I have added .github/copilot-instructions.md to kickstart your work on #1. Please resume work on this issue.

Thanks for adding the copilot instructions! I can see the clear requirements. I'll resume work on creating a new R+Python toy example that's distinct from the existing Arkansas child poverty example. I'll implement a county-level child poverty analysis workflow that includes both R and Python notebooks with human+AI workflow guidance.