8 documentation
8.1 Best practices
(produced with assistance from claude.ai)
By following these recommendations, your R code and analysis will be well-documented, easy to understand, and more likely to be reproducible by others (or even your future self). This not only aids in learning but also prepares you for collaborating on research projects and disseminating your work transparently.
8.1.1 Use R Markdown
R Markdown allows you to seamlessly integrate code, output, visualizations, and explanatory text into a single document. This makes it easy to document your code and tell a coherent story about your analysis.
8.1.2 Follow a Clean Code Style Guide
Adopt a consistent style guide for your R code, such as the tidyverse style guide (https://style.tidyverse.org/). This ensures that your code is readable, maintainable, and follows best practices.
8.1.3 Use Meaningful and Descriptive Names
Choose variable and function names that are clear and descriptive of their purpose. This makes your code self-documenting and easier to understand.
8.1.5 Chunk Your Code and Explain Each Chunk
In R Markdown, organize your code into logical chunks (e.g., data import, cleaning, analysis, visualization). For each chunk, provide a preceding comment that explains the purpose of that chunk and any necessary context.
8.1.6 Use Inline Code Comments
Within each code chunk, use inline (#) comments to explain complex or non-obvious operations. This helps future readers (including your future self) understand the rationale behind your code.
8.1.7 Interpret Output and Visualizations
After each code chunk that produces output or visualizations, include text that interprets the results and explains their significance in the context of your analysis.
8.1.8 Document Data Transformation Steps
When cleaning or transforming data, document each step in detail, including the rationale behind your choices and any potential implications or limitations.
8.1.9 Provide an Overview and Narrative
At the beginning of your R Markdown document, include an overview section that explains the research question, the dataset being used, and a high-level narrative of your analysis approach.
8.1.10 Follow Best Practices for Reproducibility
Document any external dependencies (e.g., packages, data sources), include session information (e.g., version numbers), and consider using techniques like relative file paths to enhance the reproducibility of your work.
8.1.4 Comment Liberally
While good naming conventions help, you should still liberally comment your code. Use comments to explain the purpose of each code section, describe the logic behind your approach, and document any potential quirks or gotchas.