364 Pages 72 Color & 43 B/W Illustrations
    by Chapman & Hall

    364 Pages 72 Color & 43 B/W Illustrations
    by Chapman & Hall

    364 Pages 72 Color & 43 B/W Illustrations
    by Chapman & Hall

    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.

    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


    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.

    "This book is unique in that it is written in a step-by-step format. Every subsequent tutorial builds on what we have already learned and takes us 1 step farther. In addition, the book presents data and programs to replicate the models developed and offers new methods that are ready to use. In my opinion, the book is a must-have for the interested biostatistical audience."
    Luca Bertolaccini, International Society for Clinical Biostatistics, 72, 2021

    "This is a real gem of a book, a completely self-contained introduction to R, to data visualization and to the basics of statistical analysis and modelling, written in an easy style, with lots of graphics, good advice and useful R code. In fact, it is one of the best introductions to R that I have seen, written throughout in a simple and conversational style, and with complementary material not generally found in R textbooks, such as Markdown and interfacing project versions to GitHub.

    [. . .] All in all, this book is a unique and comprehensive treatment of the use of R in the context of health science, but it is useful for any application discipline. The style is supremely accessible and the use of graphics is pervasive to the explanation of concepts throughout the book. The authors [. . . ] are to be congratulated in putting together such a useful guide to R and the basics of statistics and statistical modelling. Perhaps it is because they are not primarily statisticians by training that they have produced such an easy-to-follow text, directed by practitioners with a long experience in data analysis towards other practitioners seeking a painless learning experience. Highly recommended!
    Michael Greenacre, Journal of the Royal Statistical Society, Serie A