Название: Building reproducible analytical pipelines with RАвтор: Bruno Rodrigues
Издательство: Leanpub
Год: 2023-06-04
Страниц: 522
Язык: английский
Формат: pdf (true)
Размер: 18.9 MB
Build reproducible analytical pipelines to output consistent, high-quality data products using R, Github and Docker. Learn about functional and literate programming to keep your code concise, easier to test and share and easily understandable by others by packaging it. Run your pipelines on Github Actions and focus on what matters: analysing data!
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.
Who is this book for?
This book is for anyone that uses raw data to build any type of output based on that raw data. This can be a simple quarterly report for example, in which the data is used for tables and graphs, or a scientific article for a peer reviewed journal or even an interactive web application. It doesn’t matter, because the process is, at its core, always very similar:
• Get the data;
• Clean the data;
• Write code to analyse the data;
• Put the results into the final product.
This book will already assume some familiarity with programming, and in particular the R programming language. However, if you’re comfortable with another programming language like Python, you could still learn a lot from reading this book. The tools presented in this book are specific to R, but there will always be an alternative for the language you prefer using, meaning that you could apply the advice from this book to your needs and preferences.
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