Chapter 7: Standardization — Refactor to Lab Template

Once you have a functional script that performs the desired tasks correctly, the next step is to ensure it aligns with the standards and best practices of your research lab. This chapter focuses on Refactor to Lab Template, a crucial phase for promoting consistency, readability, and maintainability of code within a collaborative research environment [LLM refactoring study].

The first aspect of this standardization process involves enforcing consistent code structure. Most research labs have established templates or preferred structures for their scripts. This might include a standard header section with information about the script’s purpose, author, and date, followed by logical sections for loading libraries, importing data, performing the main processing steps, and outputting results. Reorganizing your AI-generated code to fit this established structure ensures that it is familiar and easy to navigate for other members of your lab, promoting better collaboration and understanding across projects.

In addition to the overall structure, it is important to ensure that the code adheres to your lab’s style guidelines [Tidyverse style guide]. This includes aspects such as variable naming conventions (e.g., using snake_case or camelCase), consistent indentation (e.g., using tabs or a specific number of spaces), and the use of comments to explain complex logic or non-obvious steps. Following these style guidelines not only makes the code more readable but also reduces cognitive load for anyone who needs to work with it in the future.

Standardization is not cosmetic. A shared structure makes LLM-assisted scripts easier for humans to review, compare, test, and reuse.

flowchart TB
    accTitle: Lab-template refactoring pipeline
    accDescr: A vertical refactoring pipeline showing a working script moving through template application, diff review, testing, and commit.
    A[Working script] --> B[Apply lab R code template]
    B --> C[Normalize style and structure]
    C --> D[Review Git diff]
    D --> E[Test unchanged behavior]
    E --> F{Behavior preserved?}
    F -->|No| B
    F -->|Yes| G[Commit standardized script]

Integrating Version Control into the Refactoring Process

As discussed in Chapter 4, Git-based version control (e.g., using GitHub or GitLab) is an excellent way to manage the iterative improvements made to LLM-generated scripts. When refactoring to align with your lab template, you can:

  1. Commit Each Refactored Draft
    • After the LLM or developer applies the lab template and style guidelines, commit those changes.
    • Include a short message describing what was updated (e.g., “Refactored to lab template—added standardized header, consistent naming conventions”).
  2. Use Branches for Experimental Changes
    • If you want to compare multiple refactoring approaches, put each one in its own Git branch.
    • This ensures you can cleanly merge or discard any approach without losing your main code.
  3. Diff and Merge
    • Git’s built-in diff tools let you see exactly what the LLM or developer changed.
    • Reviewing diffs keeps you aware of whether your code is truly converging on your standard template.

Refer back to Chapter 4 (“Using Version Control to Track LLM-Generated Code Iterations”) for a more detailed outline of best practices for versioning each new iteration. Employing Git alongside your refactoring efforts helps maintain a clear history of all changes, ensuring you can roll back to earlier versions if needed.

Using the Provided Refactoring Prompt and R Code Template

If you want to automate part of the refactoring process, we recommend taking advantage of the following resources in this repository:

  1. R Code Refactoring Prompt Located at docs/templates/R_CodeRefactoringPromptExample.md.
    • This file contains a prompt you can give to your LLM (e.g., ChatGPT, Claude) to guide it in refactoring an existing script.
    • The prompt clearly specifies how to handle styling, code structure, and robust error handling.
    • It’s especially useful if you have large or complex R scripts that need to adhere to your lab’s conventions.
  2. Lab R Code Template Located at docs/templates/R_CodeTemplate.R.
    • This file is a starting skeleton with a header, dependency checks, command-line argument parsing (via optparse), main logic sections, and session info logging.
    • Adapt it to the actual input contract, failure handling, and tests. A shared structure improves consistency; the template alone does not establish production readiness.

Example Refactoring Workflow

  1. Review your existing R script (generated by the LLM or otherwise) to ensure it meets basic functional requirements.
  2. Open docs/templates/R_CodeRefactoringPromptExample.md and copy the prompt text.
  3. Paste only sanitized code + prompt into your chosen LLM environment. Remove private paths, credentials, embedded records, sensitive logs, and protected output examples first.
  4. Show or link the LLM to R_CodeTemplate.R, or provide a summary of your styling rules, so the model knows the structure to follow.
  5. Generate the refactored script and inspect the diff for unintended behavior changes.
  6. Run the agreed checks to confirm it still produces the desired output, then commit the reviewed changes.
  7. Iterate as needed, refining or editing until it meets your lab’s standards for clarity, reliability, and performance.

Double-Checking Functionality

Even after a script is refactored, it is absolutely crucial to double-check functionality [Google code review]. Thoroughly test the refactored script to confirm that it still works exactly as it did before the refactoring. The goal of refactoring is to improve the code’s organization and readability without altering its underlying functionality or introducing any new errors.

Refactoring should preserve behavior. If outputs change, treat that as a functional change that needs review, tests, and documentation.

Reproduce the Environment and the Workflow

renv and targets address different parts of an R analysis:

Tool What it records or tracks What you still need to specify
renv Project package dependencies in a lockfile, with snapshot and restore workflows R version, system libraries, and environment setup outside package restoration
targets Dependencies among pipeline steps, so affected work can be rerun when tracked inputs or code change Explicit input and output file targets, parameters, random seeds, and external dependencies

Restore the reviewed package environment as a separate setup step, then run the analysis. Analysis scripts should report missing dependencies instead of installing or upgrading them during execution [renv]. Add targets when several dependent steps or expensive reruns justify it; a short script can remain a short script. Files that determine whether a step is current must be declared as file targets, rather than only appearing as path strings inside code [targets walkthrough], [targets file tracking]. See the Agentic Coding Playbook for how these checks fit an agent-assisted change.

Conclusion

By combining version control practices described in Chapter 4 with a lab-wide code template, you can ensure that your LLM-generated scripts evolve into polished, consistent, and maintainable tools for your research workflows. This process promotes collaboration, enhances maintainability, and ultimately contributes to the overall quality and reproducibility of your lab’s work. Employing AI assistance (e.g., the R refactoring prompt) while diligently verifying functionality helps you streamline the refactoring process without sacrificing rigor.