Building reproducible analytical pipelines with R
Building reproducible analytical pipelines with R
About the Book
This book will not teach you about the R programming language, machine learning, statistics or visualisation. The goal is to teach you a set of tools, practices and project management techniques that should make your projects easier to reproduce, replicate and retrace. These tools and techniques can be used right from the start of your project at a minimal cost, such that once you’re done with the analysis, you’re also done with making the project reproducible. Your projects are going to be reproducible simply because they were engineered, from the start, to be reproducible.
Building on your knowledge of R, you will learn about several packages to build reproducible analytical pipelines: {renv}, {targets}, {fusen} but also about trunk-based development with Git and Github, and Docker.
You can read the online version for free here: https://raps-with-r.dev/
About the Author
Reader Testimonials
Olumide Oyalola
The author of "Building Reproducible Analytical Pipelines with R" has truly excelled in delivering an indispensable guide for data analysts and scientists. The book not only covers the fundamentals of reproducibility but also provides comprehensive examples and best practices using R. It's a well-structured and accessible resource, making complex concepts easy to understand. Whether you're a beginner or an experienced R user, this book is a must-have. Highly recommended!
Soundarya S Rajan
Grateful for the insights gained as a beginner from your book on building reproducible pipelines with R. Upholding reproducibility in research is crucial, and this is a valuable resource for anyone aiming to enhance their skills.
Kevin Rosamont
I wanted to express my appreciation for the valuable insights this book provides: the book goes from software engineering principles to Docker and continuous integration, provides a clear roadmap for enhancing code reliability in data science. The accessible language and practical examples make complex concepts approachable. The emphasis on documentation and reproducibility is commendable. Overall, it's a valuable resource bridging the gap between data science and software engineering.
Olivia Angelin-Bonnet
An excellent book that every data scientist working in R should read! Full of excellent and practical advice that can be applied straight away to your current projects. It changed the way I write code, and how I think about the reproducibility of my work.
Table of Contents
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