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E-raamat: Getting Started with R: An Introduction for Biologists

(Department of Animal and Plant Science, University of Sheffield), (Department of Animal and Plant Science, University of Sheffield), (Department of Evolutionary Biology and Environmental Studies, University of Zurich)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 23-Feb-2017
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780191091926
  • Formaat - PDF+DRM
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 23-Feb-2017
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780191091926

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R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences. Getting Started with R is now the go-to introductory guide for biologists wanting to learn how to use R in their research. It teaches readers how to import, explore, graph, and analyse data, while keeping them focused on their ultimate goals: clearly communicating their data in oral presentations, posters, papers, and reports. It provides a consistent workflow for using R that is simple, efficient, reliable, and reproducible.

This second edition has been updated and expanded while retaining the concise and engaging nature of its predecessor, offering an accessible and fun introduction to the packages dplyr and ggplot2 for data manipulation and graphing. It expands the set of basic statistics considered in the first edition to include new examples of a simple regression, a one-way and a two-way ANOVA. Finally, it introduces a new chapter on the generalised linear model.

Getting Started with R is suitable for undergraduates, graduate students, professional researchers, and practitioners in the biological sciences.

Arvustused

Review from previous edition The book would make the ideal text for a short course on data management and presentation - it truly packs an amazing amount of wisdom and wit between slim covers. * Trends in Ecology and Evolution * I was engaged by the refreshing style of the authors, that while informal, gives the user clear step-by-step instructions for using the software. Apart from the clear biological leaning of the example data, this book is applicable to anyone learning R (even a statistician!). * Significance *

Preface ix
Introduction to the second edition ix
What this book is about xii
How the book is organized xiv
Why R? xvi
Updates xviii
Acknowledgements xviii
Chapter 1 Getting and Getting Acquainted with R
1(34)
1.1 Getting started
1(1)
1.2 Getting R
2(3)
1.3 Getting RStudio
5(1)
1.4 Let's play
6(2)
1.5 Using R as a giant calculator (the size of your computer)
8(7)
1.6 Your first script
15(6)
1.7 Intermezzo remarks
21(1)
1.8 Important functionality: packages
21(3)
1.9 Getting help
24(2)
1.10 A mini-practical---some in-depth play
26(2)
1.11 Some more top tips and hints for a successful first (and more) R experience
28(7)
Appendix 1a Mini-tutorial solutions
29(1)
Appendix 1b File extensions and operating systems
30(5)
Chapter 2 Getting Your Data into R
35(22)
2.1 Getting data ready for R
35(5)
2.2 Getting your data into R
40(5)
2.3 Checking that your data are your data
45(3)
2.4 Basic troubleshooting while importing data
48(1)
2.5 Summing up
49(8)
Appendix Advanced activity: dealing with untidy data
50(7)
Chapter 3 Data Management, Manipulation, and Exploration with dplyr
57(22)
3.1 Summary statistics for each variable
58(1)
3.2 Dplyr Verbs
59(1)
3.3 Subsetting
60(7)
3.4 Transforming
67(1)
3.5 Sorting
68(1)
3.6 Mini-summary and two top tips
69(1)
3.7 Calculating summary statistics about groups of your data
70(3)
3.8 What have you learned lots
73(6)
Appendix 3a Comparing classic methods and dplyr
73(1)
Appendix 3b Advanced dplyr
74(5)
Chapter 4 Visualizing Your Data
79(14)
4.1 The first step in every data analysis---making a picture
79(1)
4.2 ggplot2: a grammar for graphics
80(5)
4.3 Box-and-whisker plots
85(2)
4.4 Distributions: making histograms of numeric variables
87(3)
4.5 Saving your graphs for presentation, documents, etc.
90(1)
4.6 Closing remarks
91(2)
Chapter 5 Introducing Statistics in R
93(38)
5.1 Getting started doing statistics in R
93(2)
5.2 Χ2 contingency table analysis
95(8)
5.3 Two-sample t-test
103(5)
5.4 Introducing ... linear models
108(1)
5.5 Simple linear regression
109(9)
5.6 Analysis of variance: the one-way ANOVA
118(10)
5.7 Wrapping up
128(3)
Appendix Getting packages not on CRAN
128(3)
Chapter 6 Advancing Your Statistics in R
131(36)
6.1 Getting started with more advanced statistics
131(1)
6.2 The two-way ANOVA
131(14)
6.3 Analysis of covariance (ANCOVA)
145(19)
6.4 Overview: an analysis workflow
164(3)
Chapter 7 Getting Started with Generalized Linear Models
167(36)
7.1 Introduction
167(3)
7.2 Counts and rates---Poisson GLMs
170(3)
7.3 Doing it wrong
173(4)
7.4 Doing it right---the Poisson GLM
177(17)
7.5 When a Poisson GLM isn't good for counts
194(7)
7.6 Summary, and beyond simple Poisson regression
201(2)
Chapter 8 Pimping Your Plots: Scales and Themes in ggplot2
203(16)
8.1 What you already know about graphs
203(1)
8.2 Preparation
204(2)
8.3 What you may want to customize
206(1)
8.4 Axis labels, axis limits, and annotation
207(2)
8.5 Scales
209(3)
8.6 The theme
212(6)
8.7 Summing up
218(1)
Chapter 9 Closing Remarks: Final Comments and Encouragement
219(4)
General Appendices
223(4)
Appendix 1 Data Sources
223(1)
Appendix 2 Further Reading
224(1)
Appendix 3 R Markdown
225(2)
Index 227
Andrew leads a research team studying community and evolutionary ecology. He has been using R and teaching quantitative methods for over 16 years.

Owen leads a research team studying ecological forecasting. He has been using R and teaching quantitative methods for over 16 years.

Dylan leads a research team studying population biology. He has been using R and teaching quantitative methods for over 15 years.