R for health data science

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

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.