Computing Essentials

A good workflow necessarily involves a reproducible analysis from raw data to published figure that is clear to outside inspectors and is easy to maintain. A healthier workflow automates this analysis and avoids duplication wherever possible. The best workflows are generic enough to allow for this process to evolve with the scope of the project and the number of collaborators.

The goal of this project is to help promote a good workflow by supplying tangible examples of automation useful to researchers and by supplying lesson exercises to reinforce that understanding. Optional challenges exist for those who wish to further refine these skills.

Introduction

The structural tone of these lessons is motivated by the following guidelines:

• Code and Data for the Social Sciences: A Practitioner’s Guide

• Best Practices for Scientific Computing

• Good Enough Practices in Scientific Computing

The above ideas are encapsulated nicely in a practical guide similar in scope to this project:

• The Good Research Code Handbook

This project attempts to supplement the above handbook with examples that bring a beginner up to speed with the above-mentioned guidelines. The lessons are structured around skill objectives that expand on results from previous lessons.