Below is a collection of project plans and code templates referenced throughout this textbook. Rather than embedding all content here, each item is split into its own file for easier navigation and reuse.
A basic outline covering data ingestion, cleaning, representative record selection, and categorization. It serves as the starting point for iterative improvements.
- See file: ProjectPlan_InitialDraft.md
In this iteration, the project plan is enhanced with more detailed steps for filtering, validation, and output generation. It refines the initial draft for better clarity and completeness.
- See file: ProjectPlan_Improved.md
This comprehensive plan integrates additional datasets (e.g., demographics), applies race-specific BMI categorizations, and details output summary reporting. It represents a mature workflow for processing clinical EHR data.
- See file: ProjectPlan_Advanced.md
This expert-level prompt instructs an AI assistant on generating a production-quality R script using data.table and optparse. It emphasizes thorough planning, asking clarifying questions, and following best practices for modular and reproducible code.
- See file: CodingPromptForRScript.md
This document demonstrates how to instruct an AI assistant to reformat an existing R script to match a prescribed template’s structure and style guidelines. It emphasizes code readability and robust error handling.
- See file: R_CodeRefactoringPromptExample.md
This production-quality R script template includes metadata, dependency management, command-line argument parsing, core logic, and output saving sections. It is designed as a starting point for building robust, reproducible R scripts.
- See file: R_CodeTemplate.md
This file provides best practices for generating a well-structured GitHub repository, including a detailed README.md, tool-specific documentation, and environment setup using the mamba (Conda) package manager.
- See file: GitHubRepoDocumentationGuidelines.md