Data Science Workflows in R

An introduction to deploying production quality R code

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This guide is a resource for data analysts and data scientists looking to improve the way they write R code. While R remains a popular choice for statistical modelling and data analysis, its rapid development has enabled users to progress their work right through to being deployed into Production. However the type of work done when conducting experiments and developing models is very different to packaging up this work so it can reliably drive decisions in an organisation. This book will provide readers with an overview of contemporary frameworks for how data analysis is done in practice. It will cover how R projects are usually structured and how this can evolve based on project complexity. It will examine what is meant by experimental vs production analysis code and which principles need to be adopted. Finally it will show current tools and frameworks for taking experimental R code and strengthening it to align with best practice for reliable production grade software. Readers can step through a case study and download code to follow along.