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
Biography
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
