Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, and programming in the biological sciences.
This book provides a functional introduction for biologists new to R. While teaching how to import, explore, graph, and analyse data, it keeps readers focused on their ultimate goals - communicating their data in oral presentations, posters, papers, and reports. It also provides a consistent method (workflow) for using R that is simple, efficient, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.
It is really, really good for total beginners like me. It will dedicate sentences to tell you which button to click, which pane to look at and provide you a massive dataset instead of having you experiment on one you have to make yourself. However, some of the codes don't seem to work.
I never thought I would learn my first programming language by reading a book, rather than taking an online course, a workshop, or by bugging a colleague who has some strong coding muscles. This book is tailored for biologists and it's an excellent step-by-step guide for building the fundamental skills in using R for data analysis and visualization. Midway through the book, I had already figured how to learn by myself, and that's the greatest skill in any learning process. The "ggplot2" section was the most important module for me, but that said, the other three sections are equally useful and important.
This book provides a very nice, basic introduction to R with accessible biological examples. You can work along with the authors, which is very nice. Having tried & somewhat failed to dabble in R with just a background of basic programming skills but nothing R-specific, this book was a perfect grounding point. Now I can approach R very logically and with the statistical mindset that I need to conduct my analyses. I would recommend this book! It's also pretty short & easy to read, so that's good.