Getting Started with R: An Introduction for Biologists

✍ Scribed by Beckerman, Andrew P;Petchey, Owen L

• Publisher: Oxford University Press, USA
• Year: 2012
• Tongue: English
• Leaves: 124
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover......Page 1
Table of Contents......Page 6
What this book is about......Page 8
What you need to know to make this book work for you......Page 9
How the book is organized......Page 10
Chapter 1: Why R?......Page 12
Chapter 2: Import, Explore, Graph I—Getting Started......Page 16
2.1 Where to put your data......Page 18
2.3 How to get your data into R and where it is stored in R’s brain......Page 21
2.4 Working with R—hints for a successful first (and more) interaction......Page 22
2.5 Make your first script file......Page 26
2.6 Starting to control R......Page 29
2.7 Making R work for you—developing a workflow......Page 30
2.8 And finally . . .......Page 32
3.1 Getting your data into R......Page 34
3.2 Checking that your data is your data......Page 37
3.4 How to isolate, find, and grab parts of your data—I......Page 39
3.5 How to isolate, find, and grab parts of your data—II......Page 41
3.6 Aggregation and how to use a help file......Page 42
3.7 What your first script might look like (what you should now know)......Page 46
4.1 The first step in data analysis—making a picture......Page 50
4.2 Making a picture—bar graphs......Page 51
4.2.1 Pimp my barplot......Page 55
4.3 Making a picture—scatterplots......Page 61
4.3.1 Pimp my scatterplot: axis labels......Page 64
4.3.2 Pimp my scatterplot: points......Page 65
4.3.3 Pimp my scatterplot: colours (and groups)......Page 67
4.3.4 Pimp my scatterplot: legend......Page 70
4.4 Plotting extras: pdfs, layout, and the lattice package......Page 75
Chapter 5: Doing your Statistics in R—Getting Started......Page 76
5.1 Chi-square......Page 77
5.2 Two sample t-test......Page 81
5.2.1 The first step: plot your data......Page 83
5.2.2 The two sample t-test analysis......Page 87
5.3 General linear models......Page 88
5.3.1 Always start with a picture......Page 89
5.3.2 Potential statistical and biological hypotheses—it’s all about lines......Page 91
5.3.3 Specifying the model......Page 94
5.3.4 Plot, model, then assumptions......Page 95
5.3.5 Interpretation......Page 97
5.3.7 Interpretation......Page 100
5.4 Making a publication quality figure......Page 103
5.4.1 Coefficients, lines, and lines()......Page 104
5.4.2 Expanded grids, prediction, and a more generic model plotting method......Page 105
5.4.3 The final picture......Page 110
5.4.4 An analysis workflow......Page 112
Chapter 6: Final Comments and Encouragement......Page 116
Appendix: References and Datasets......Page 120
D......Page 122
P......Page 123
Y......Page 124

✦ Subjects

Nonfiction;Textbooks