R for Health Data Science  book cover
1st Edition

R for Health Data Science

ISBN 9780367428198
Published November 17, 2020 by Chapman and Hall/CRC
364 Pages 72 Color & 43 B/W Illustrations

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Book Description

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care.

R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses.


  • Provides an introduction to the fundamentals of R for healthcare professionals
  • Highlights the most popular statistical approaches to health data science
  • Written to be as accessible as possible with minimal mathematics
  • Emphasises the importance of truly understanding the underlying data through the use of plots
  • Includes numerous examples that can be adapted for your own data
  • Helps you create publishable documents and collaborate across teams

With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.

Table of Contents

I Data wrangling and visualisation
1. Why we love R
2 R basics
3 Summarising data
4 Different types of plots
5 Fine tuning plots

II Data analysis
6 Working with continuous outcome variables
7 Linear regression
8 Working with categorical outcome variables
9 Logistic regression
10 Time-to-event data and survival

III Workflow
11 The problem of missing data
12 Notebooks and Markdown
13 Exporting and reporting
14 Version control
15 Encryption

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Ewen is a surgeon and Riinu is a physicist. And they’re both data scientists too. They dabble with a few programming languages and are generally all over technology. They are most enthusiastic about the R statistical programming
language and have a combined experience of 25 years using it. They work at the University of Edinburgh and have taught R to hundreds of healthcare professionals and researchers.