Getting Started with R An Introduction for Biologists

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Format: Paperback
Pub. Date: 2012-07-22
Publisher(s): Oxford University Press
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Summary

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

Prefacep. vii
What this book is aboutp. vii
What you need to know to make this book work for youp. viii
How the book is organizedp. ix
Why R?p. 1
Import, Explore, Graph I-Getting Startedp. 5
Where to put your datap. 7
Make a folder for your instructions (code, script)p. 10
How to get your data into R and where it is stored in R's brainp. 10
Working with R-hints for a successful first (and more) interactionp. 11
Make your first script filep. 15
Starting to control Rp. 18
Making R work for you-developing a workflowp. 19
And finally …p. 21
Import, Explore, Graph II-Importing and Exploringp. 23
Getting your data into Rp. 23
Checking that your data is your datap. 26
Summarizing your data-quick versionp. 28
How to isolate, find, and grab parts of your data-Ip. 28
How to isolate, find, and grab parts of your data-IIp. 30
Aggregation and how to use a help filep. 31
What your first script might look like (what you should now know)p. 35
Import, Explore, Graph III-Graphsp. 39
The first step in data analysis-making a picturep. 39
Making a picture-bar graphsp. 40
Pimp my barplotp. 44
Making a picture-scatterplotsp. 50
Pimp my scatterplot: axis labelsp. 53
Pimp my scatterplot: pointsp. 54
Pimp my scatterplot: colours (and groups)p. 56
Pimp my scatterplot: legendp. 59
Plotting extras: pdfs, layout, and the lattice packagep. 64
Doing your Statistics in R-Getting Startedp. 65
Chi-squarep. 66
Two sample t-testp. 70
The first step: plot your datap. 72
The two sample t-test analysisp. 76
General linear modelsp. 77
Always start with a picturep. 78
Potential statistical and biological hypotheses-it's all about linesp. 80
Specifying the modelp. 83
Plot, model, then assumptionsp. 84
Interpretationp. 86
Treatment contrasts and coefficientsp. 89
Interpretationp. 89
Making a publication quality figurep. 92
Coefficients, lines, and lines()p. 93
Expanded grids, prediction, and a more generic model plotting methodp. 94
The final picturep. 99
An analysis workflowp. 101
Final Comments and Encouragementp. 105
Appendix: References and Datasetsp. 109
Indexp. 111
Table of Contents provided by Ingram. All Rights Reserved.

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