ABSTRACT

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.

Features

  • 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.

part I|116 pages

Data wrangling and visualisation

chapter 1|11 pages

Why we love R

chapter 2|40 pages

R basics

chapter 3|24 pages

Summarising data

chapter 4|26 pages

Different types of plots

chapter 5|14 pages

Fine tuning plots

part II|152 pages

Data analysis

chapter 6|28 pages

Working with continuous outcome variables

chapter 7|42 pages

Linear regression

chapter 8|20 pages

Working with categorical outcome variables

chapter 9|40 pages

Logistic regression

chapter 10|18 pages

Time-to-event data and survival

part III|68 pages

Workflow

chapter 11|20 pages

The problem of missing data

chapter 12|14 pages

Notebooks and Markdown

chapter 13|14 pages

Exporting and reporting

chapter 14|8 pages

Version control

chapter 15|8 pages

Encryption