Getting Started with GitHub Copilot

  • A GitHub account is required to use GitHub Copilot
  • Students can get free GitHub Copilot access through the GitHub Student Developer Pack
  • GitHub Copilot integrates directly into RStudio as an autocompletion tool
  • Test Copilot by writing descriptive comments and observing the suggestions
  • Copilot can be customized through RStudio’s settings

Where Do We Need AI Support?

  • AI assistants excel at generating boilerplate code, completing syntax, and helping with documentation
  • Use caution with complex logic, project-specific requirements, and security-critical code
  • Always review and test AI-generated code thoroughly
  • AI assistants are tools to augment, not replace, human expertise
  • Develop a balanced approach that leverages AI strengths while maintaining code quality and security
  • Stay open to learning and adapting as AI technologies evolve

Risks, Drawbacks and Responsibilities with AI Usage

  • AI can generate incorrect, insecure, or inefficient code - always verify
  • You are fully responsible for all code you commit, regardless of AI involvement
  • AI usage is inappropriate for critical systems, novel algorithms, and learning fundamentals
  • Heavy AI reliance can lead to skill atrophy and reduced code understanding
  • LLMs produce wrong code because they pattern-match rather than understand
  • Comprehensive testing, code review, and iterative refinement are essential
  • Balance AI productivity gains with maintaining your programming skills
  • Consider legal, ethical, and privacy implications of AI tool usage
  • Treat AI as a junior assistant requiring supervision, not an expert to trust blindly
  • Responsible AI usage requires continuous learning and critical thinking

RStudio Autocompletion with Copilot

  • Copilot generates suggestions based on context from your code and comments
  • Write clear, descriptive comments to get better suggestions
  • Use Tab to accept, and Esc to dismiss suggestions
  • Break complex tasks into smaller steps for more accurate suggestions
  • Always review and test AI-generated code before using it
  • Combine Copilot assistance with your own coding expertise for best results

Context Definition and the AGENTS.md Concept

  • Context definitions help AI assistants generate code that matches your project standards
  • Context improves consistency: AI-generated code matches your project standards
  • AGENTS.md provides a standardized, version-controlled, machine-readable way to specify coding guidelines
  • Multiple AGENTS.md files can exist at different hierarchy levels; AI assistants use the closest file
  • Effective AGENTS.md files include clear examples of preferred and forbidden patterns
  • Start simple and iterate: Begin with core principles and expand based on needs
  • Combining AGENTS.md with inline comments, chat instructions, and tooling creates robust context
  • R projects benefit from explicit tidyverse usage and piping conventions in context definitions

Using AI within Pipelines via ellmer

  • ellmer provides a unified interface for integrating LLMs into R workflows
  • Set up authentication using environment variables for security
  • Integrate AI prompts seamlessly into tidyverse pipelines
  • Design clear, constrained prompts for consistent results
  • Implement error handling and caching for robust pipelines
  • Document models and prompts for reproducibility
  • Consider rate limits, costs, and ethical implications when using AI in data processing
  • Local model options are available for high-volume use cases

Understanding and Setting LLM Parameters

  • LLMs generate text through repeated token probability calculations and random selection
  • Temperature controls the randomness of outputs: low values are more deterministic, high values are more creative
  • Top-K and top-P sampling limit token selection to reduce nonsensical outputs
  • Seeds enable reproducible results when using the same prompt and parameters
  • Use params() in ellmer to configure temperature, top_p, seed, and other parameters
  • For data processing, use low temperature (0.1-0.3) and set seed for reproducibility
  • Document parameter choices and model versions for transparent, reproducible research
  • Test parameter settings on sample data before processing full datasets
  • Combine appropriate parameters with clear prompts for best results

Revise Your Code with AI

  • AI chat interfaces provide valuable code review assistance
  • Use iterative refinement: review, improve, and re-review
  • Ask specific questions about correctness, performance, and style
  • Always validate and test AI suggestions before accepting them
  • Combine AI review with human peer review and automated tools
  • Be aware of AI limitations and seek human expertise for critical code
  • Document the review process and improvements made

GitHub Issue-Driven Coding with Copilot

  • Issue-driven development provides structure and context for AI-assisted coding
  • Detailed issues with clear requirements help AI assistants generate better code
  • Reference issue numbers in commits, comments, and code to maintain traceability
  • Use issues to guide Copilot for autonomous code generation and review
  • Effective use of GitHub repositories and RStudio projects enhances collaboration and version control
  • If not familiar with git, read “Happy Git and GitHub for the useR” for a gentle introduction

Wrap-Up and Next Steps

  • Always verify AI suggestions—you are responsible for all code you commit
  • Use AI strategically: excel at boilerplate and documentation, avoid critical algorithms
  • Write clear prompts and provide context for better AI suggestions
  • Structure work with GitHub issues to guide AI effectively
  • AI applications extend beyond code completion to testing, documentation, and project management
  • The AI landscape evolves rapidly—stay current through communities and experimentation
  • Human expertise, judgment, and creativity remain essential
  • Balance AI assistance with skill maintenance to avoid over-dependence
  • Treat AI as a powerful tool that amplifies your capabilities, not a replacement