Designing Data and Analytical Workflows

A guide for implementing reproducible, maintainable, and optimal workflows in R and Python

Reproducibility

Build workflows that can be independently verified. Same inputs, same code, same environment -> always the same results.

Write code that ages well. Clear, modular, and idiomatic workflows that anyone can read, extend, and debug without heroic effort.

Use resources deliberately. Design workflows that are efficient today and scalable tomorrow, without over-engineering upfront (maybe a little bit).