Practical R for Biologists: An Introduction
R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense.

This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyze their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R.
1137482317
Practical R for Biologists: An Introduction
R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense.

This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyze their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R.
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Practical R for Biologists: An Introduction

Practical R for Biologists: An Introduction

Practical R for Biologists: An Introduction

Practical R for Biologists: An Introduction

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Overview

R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense.

This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyze their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R.

Product Details

ISBN-13: 9781789245349
Publisher: CABI
Publication date: 03/09/2021
Pages: 400
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Dr. Donald Quicke has had more than 40 years' experience teaching undergraduate and postgraduate biology students, initially at Sheffield University, UK and then at Imperial College London. He retired in 2013 and is now a teaching research fellow at the Department of Biology, Chulalongkorn University, Bangkok, Thailand. He co-authored Practical R for Biologists: An Introduction (2021). He is one of the world's leading experts on the taxonomy, systematics, and biology of parasitoid wasps.

Dr. Buntika A. Butcher has worked on parasitoid wasps (plus some forensic entomology) since gaining her PhD in the Department of Biology, Imperial College London in 2004. On returning to Thailand she was appointed to a lectureship at Chulalongkorn University Bangkok and was subsequently promoted to Associate Professor in 2015. She has published more than 60 papers; co-authored Practical R for Biologists: An Introduction (2021); and has supervised numerous entomology students to masters and doctoral degree levels.

Dr. Rachel Kruft Welton got her master's degree at Imperial College, London and a PhD at University of Birmingham, UK before qualifying as a teacher. She has been a professional biology and science tutor for nearly 20 years, including mentoring undergraduates as part of Birmingham University's alumni scheme.

Table of Contents

About the Authors xv

Preface xix

Acknowledgements xxxi

1 How to Use This Book 1

Setting Up Your Computer 1

Running Code as You Go Along 1

Chapter Structure 2

2 Installing and Running R 3

Downloading and Installing R onto Your Computer 3

Installing Packages 7

3 Very Basic R Syntax 9

4 First Simple Programs and Graphics 13

Basic R Features 13

Commas, Brackets and Concatenation 14

The Colon Character 15

Raise to the Power of Symbol 15

Exiting from R 16

Help Pages 16

Beginning with Simple R Code to Get Used to the Command Line System 16

Playing with Graphics 19

Working with Character Variables 23

Built-in R Datasets 27

The table Function 27

Ragged Data 28

5 The Dataframe Concept 31

Combining Sets of Tables for Data Collected on Different Dates 34

Converting Factors in a Dataframe to Numeric or Character 34

6 Plotting Biological Data in Various Ways 37

Example 1 Bryophytes up a Mountain 37

Troubleshooting 1 41

Adding a Legend to a Plot 43

Troubleshooting 2 - Vector Lengths Differ 45

Troubleshooting 3 - Missing Data and NAs 46

Incorporating More Types of Data on the Same Graph 48

Example 2 Tropical Forests, Rural Population, Logarithmic Axes and Installing Packages 49

Example 3 Creating a Barplot: Bryophytes Side-by-side 52

Example 4 Stacked Bar Chart, with Different Colours, Fills and Legends 53

Example 5 Dietary Differences between Hornbill Species - Entering Data as a Table 57

Example 6 Horizontal Bar Plot of Camera Trap Data and More Troubleshooting 60

Example 7 Adding Error Bars to a Barplot or Plot: Fly Ommatidea 62

Example 8 Creating Pie Charts Using pie and circlize 64

Example 9 Fish Metacercarial Load and Box and Whisker Plots 69

Adding Notches to a Boxplot 73

Tukey's Honest Significant Difference Test 74

7 The Grammar of Graphics Family of Packages 79

8 Sets and Venn Diagrams 85

9 Statistics: Choosing the Right Test 95

Explanatory and Response Variables, Experiments and Surveys 97

Parametric versus Non-parametric Tests 98

Difference between Linear Models and Generalized Linear Models 98

Our Basic Aim Is to Achieve a Near-linear QQ Plot and Even Variance 102

10 Commonly Used Measures and Statistical Tests 103

Normality, Skew and Kurtosis 103

Testing Whether Proportions Agree with Null Expectations 104

The Special Case of Contingency Tables 106

Hardy-Weinberg Equilibrium 107

Alternatives to the Chi-squared Test under Some Circumstances 110

Testing Whether Two Means Are Significantly Different 111

Single-sample t-test 111

Two-sample t-test 112

Paired t-test 113

Testing Whether Three or More Means Differ from One Another 113

Comparing Two Variances 114

Non-normally Distributed Data with Small Sample Sizes - Mann-Whitney U Test 114

Non-parametric Two-sample Tests 116

Binomial Test 117

11 Regression and Correlation Analyses 119

Linear versus Non-linear Regression 120

Log-log Plot Example Correlation of Numbers of Species with Area 121

Linearizing Data with No Known Underlying Model 123

Errant Points and Leverage 125

QQ Model Plot from the car Library 129

Comparing Regression Slopes and Intercepts Using t-test 130

Non-linear Regression 134

Multiple Regression 137

Pairwise Plots of Explanatory Variables to Visually Inspect Interactions 138

Polynomial Regression and Model Simplification 140

Model Simplification 143

12 Count Data as Response Variable 147

Example 1 Fledgling Numbers in Relation to Clutch Initiation Date 148

Example 2 Pollinator Flower Visits in Passiflora in Relation to Flower Size 151

13 Analysis of Variance (ANOVA) 155

Example 1 A One-way ANOVA, the InsectSprays Dataset 155

Example 2 ANOVA with Proportion Data as Response Variable Using Arcsine Transformation 157

Example 3 Analysis with Proportion Data as Response Variable Using Logit Transformation 163

14 Analysis of Covariance (ANCOVA) 166

Example 1 Growth of Tagged Gobies 166

Example 2 Fitting through the Origin and Count Data as Response Variable 168

15 More Generalized Linear Modelling 171

Model Inspection 171

Binary Response Variable with One Continuous Explanatory Variable 172

Example 1 Logistic regression of gall former predation 172

LD50s 176

Example 2 Pollinator counts - showing importance of deviance 177

Example 3 Proportion data with N known 182

16 Monte Carlo Tests and Randomization 187

Random Number Generator Code 187

Example 1 Flower Visits by Thai Honey Bee Species 188

Randomizing Cells in a Matrix 191

17 Principal Components Analysis 194

Example 1 Rock Oyster Allozymes 194

Example 2 The Iris Dataset 197

18 Species Abundance, Accumulation and Diversity Data 200

Species Accumulation Data 200

Species Accumulation Curves and Randomization 202

Species Richness Estimation 208

Species Diversity Indices 208

A Note to Be Cautious about Logarithms in Functions 210

Broken-stick Models 211

A Much Faster Approach Using Vectorization 214

19 Survivorship 218

Example 1 Survival of Killdeer Nests 218

20 Dates and Julian Dates 227

Problem with Two-digit Dates and POSIX: A Date of Burial Example 232

Phenology and the density Function 234

Extracting Day and Month from Julian Days 236

Seasonal Patterns and Other Smoothing Curves 238

21 Mapping and Parsing Text Input for Data 240

Creating Our Own Map from Digitized Coordinates 247

22 More on Manipulating Text 257

Example 1 Standardizing Names in a Phylogenetic Tree Description 257

Method 1 With Wildcards 259

Method 2 Based on Fixed Character String Length 262

Method 3 Using a Vector of Positions 262

Example 2 Substrings of Unknown Length 264

Trimming White Spaces and/or Tabs 268

Using Wildcards to Locate Internal Letter Strings 268

Finding Suffixes, Prefixes and Specifying Letters, Numbers and Punctuation 269

Manipulating Character Case 271

Ignoring Character Case 272

Specifying Particular and Modifiable Character Classes 273

23 Phylogenies and Trees 275

Branch Lengths 279

Random Trees 280

Different Types of Plots in ape 281

24 Working with DNA Sequences and Other Character Data 284

Sequential Runs of Base Types 288

Downloading DNA Sequences from GenBank 290

Translating DNA to Amino Acids 292

Prettifying a Table 293

Easy Ways to Extract Taxon Names from a Phylogenetic Matrix 295

Replacing Specified Ambiguity Codes with a Question Mark 296

25 Spacing in Two Dimensions 297

26 Population Modelling Including Spatially Explicit Models 303

Example 1 Ricker Population Growth Model, Plotting as You Go 303

Example 2 Host-Parasitoid Population Modelling- Discrete Time Version 306

Example 3 Spatial Host-Parasitoid Model 310

Example 4 Genetic Drift, a Program Aimed at Teaching Students about Evolution 318

27 More on apply Family of Functions - Avoid Loops to Get More Speed 322

Using apply 323

Using tapply to Calculate Values Based on Factors 324

28 Food Webs and Simple Graphics 326

A Parasitoid foodweb Example 326

Foodweb and Community Packages 328

29 Adding Photographs 332

30 Standard Distributions in R 335

The Normal Distribution 335

Student's t Distribution 338

Lognormal Distribution 340

Logistic Distribution 341

Poisson Distribution 342

Gamma Distribution 343

The Chi-squared Distribution 344

31 Reading and Writing Data to and from Files 348

Appending Data to an Existing File 349

Using read.delim with Non-tab Separator 350

Choosing a File to Read Interactively 350

Using Excel for Data Entry 351

The readxl Function and Tibbies 352

Reading PDF Files for Data Mining 354

Writing Graphics Directly to Disc 354

Appendix 1 Summary of Graphical Parameters 357

Arguments Passed Directly to par Function 357

Arguments Applied Directly to the plot Function as well as in Some Others 357

Arguments for the lines Function 358

Having Multiple Graphics Windows Open at the Same Time 358

Macintosh-specific Graphics 359

Using the layout Function 359

Using the split.screen Function 359

Appendix 2 General Housekeeping R Functions and Others Not Covered in the Main Text 360

General Housekeeping Functions 360

Setting or Changing the Working Directory 360

Finding What Files Are in a Directory 361

Graphical Functions and Parameters 361

Interaction with User 361

Mathematical Functions 361

Writing Concatenated Data Straight to File (in the Working Directory) Using cat 362

Troubleshooting Package Installation 362

Appendix 3 Some Useful Statistical and Mathematical Equations 364

Logical Mathematical Operators 364

Descriptive Statistics 364

Distributions 365

Correlation Coefficients 365

Statistical Tests 365

Logarithms and Exponents 366

Logistic Functions 366

Weibull and Gompertz Equations 366

Trigonometric Functions 367

Convert Radians and Degrees Functions 367

Bibliography 369

Web Resources 375

Index 377

Online Supplementary Appendices

1 Online Resources: Data Files

2 Online Resources: Complete R Codes Used for Graphs, Analyses and Simulations

3 Online Resource: Suggested Answers to Exercises

These Online Resources can be found at: cabi.org/openresources/45349

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