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E-raamat: Getting Started with R: An Introduction for Biologists [Oxford Scholarship Online e-raamatud]

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  • Formaat: 160 pages
  • Ilmumisaeg: 24-May-2012
  • Kirjastus: Oxford University Press
  • ISBN-13: 9780199601615
Teised raamatud teemal:
  • Oxford Scholarship Online e-raamatud
  • Raamatu hind pole hetkel teada
Getting Started with R: An Introduction for BiologistsSuurem pilt
  • Formaat: 160 pages
  • Ilmumisaeg: 24-May-2012
  • Kirjastus: Oxford University Press
  • ISBN-13: 9780199601615
Teised raamatud teemal:
Wiley OnlineRohkem infot Oxford Scholarship Online e-raamatute kohta
Raamatu kodulehekülg: http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199601615.001.0001/acprof-9780199601615
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.
Preface vii
What this book is about vii
What you need to know to make this book work for you viii
How the book is organized ix
Chapter 1 Why R?
1(4)
Chapter 2 Import, Explore, Graph I---Getting Started
5(18)
2.1 Where to put your data
7(3)
2.2 Make a folder for your instructions (code, script)
10(1)
2.3 How to get your data into R and where it is stored in R's brain
10(1)
2.4 Working with R---hints for a successful first (and more) interaction
11(4)
2.5 Make your first script file
15(3)
2.6 Starting to control R
18(1)
2.7 Making R work for you---developing a workflow
19(2)
2.8 And finally...
21(2)
Chapter 3 Import, Explore, Graph II---Importing and Exploring
23(16)
3.1 Getting your data into R
23(3)
3.2 Checking that your data is your data
26(2)
3.3 Summarizing your data---quick version
28(1)
3.4 How to isolate, find, and grab parts of your data---I
28(2)
3.5 How to isolate, find, and grab parts of your data---II
30(1)
3.6 Aggregation and how to use a help file
31(4)
3.7 What your first script might look like (what you should now know)
35(4)
Chapter 4 Import, Explore, Graph III---Graphs
39(26)
4.1 The first step in data analysis---making a picture
39(1)
4.2 Making a picture---bar graphs
40(10)
4.2.1 Pimp my barplot
44(6)
4.3 Making a picture---scatterplots
50(14)
4.3.1 Pimp my scatterplot: axis labels
53(1)
4.3.2 Pimp my scatterplot: points
54(2)
4.3.3 Pimp my scatterplot: colours (and groups)
56(3)
4.3.4 Pimp my scatterplot: legend
59(5)
4.4 Plotting extras: pdfs, layout, and the lattice package
64(1)
Chapter 5 Doing your Statistics in R---Getting Started
65(40)
5.1 Chi-square
66(4)
5.2 Two sample t-test
70(7)
5.2.1 The first step: plot your data
72(4)
5.2.2 The two sample t-test analysis
76(1)
5.3 General linear models
77(15)
5.3.1 Always start with a picture
78(2)
5.3.2 Potential statistical and biological hypotheses---it's all about lines
80(3)
5.3.3 Specifying the model
83(1)
5.3.4 Plot, model, then assumptions
84(2)
5.3.5 Interpretation
86(3)
5.3.6 Treatment contrasts and coefficients
89(1)
5.3.7 Interpretation
89(3)
5.4 Making a publication quality figure
92(13)
5.4.1 Coefficients, lines, and lines()
93(1)
5.4.2 Expanded grids, prediction, and a more generic model plotting method
94(5)
5.4.3 The final picture
99(2)
5.4.4 An analysis workflow
101(4)
Chapter 6 Final Comments and Encouragement
105(4)
Appendix: References and Datasets 109(2)
Index 111
Andrew Beckerman and Owen Petchey are Evolutionary Ecologists with over 20 years of combined experience using R for data analysis and visualisation. Andrew is a Senior Lecturer at the University of Sheffield, UK and Owen is an Assistant Professor at the University of Zurich, Switzerland.
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