data-handling

What problem does it solve?

Data handling best practices provide a clear framework to craft transparent, reproducible data analysis code in R and Python, reducing errors and making workflows auditable.

Core Features & Use Cases

• Organize inputs at the top of scripts to make data sources and dependencies explicit.
• Track data through key steps with summaries and checks to improve traceability.
• Annotate analytical decisions to capture rationale, enabling peer review and reproduction.
• Validate data integrity at critical junctures (joins, filters, transformations) to prevent silent data loss.
• Document common pitfalls and guardrails for end-to-end pipelines, including validation patterns and error handling.

Quick Start

Apply these conventions to your next R or Python script to improve reproducibility.