Practical R for Biologists by Quicke, Donald -- Read -- Imperial Library of Trantor

Index

Cover Half Title Title Copyright Dedication Contents About the Authors Preface Acknowledgements 1. How to Use This Book

Setting Up Your Computer Running Code as You Go Along Chapter Structure

2. Installing and Running R3

Downloading and Installing R onto Your Computer Installing Packages

3. Very Basic R Syntax 4. First Simple Programs and Graphics

Basic R Features Commas, Brackets and Concatenation The Colon Character Raise to the Power of Symbol Exiting from R Help Pages Beginning with Simple R Code to Get Used to the Command Line System Playing with Graphics Working with Character Variables Built-in R Datasets The table Function Ragged Data

5. The Dataframe Concept

Combining Sets of Tables for Data Collected on Different Dates Converting Factors in a Dataframe to Numeric or Character

6. Plotting Biological Data in Various Ways

Example 1 – Bryophytes up a Mountain Troubleshooting Adding a Legend to a Plot Troubleshooting 2 – Vector Lengths Differ Troubleshooting 3 – Missing Data and NAs Incorporating More Types of Data on the Same Graph Example 2 – Tropical Forests, Rural Population, Logarithmic Axes and Installing Packages Example 3 – Creating a Barplot: Bryophytes Side-by-side Example 4 – Stacked Bar Chart, with Different Colours, Fills and Legends Example 5 – Dietary Differences between Hornbill Species – Entering Data as a Table Example 6 – Horizontal Bar Plot of Camera Trap Data and More Troubleshooting Example 7 – Adding Error Bars to a Barplot or Plot: Fly Ommatidea Example 8 – Creating Pie Charts Using pie and circlize Example 9 – Fish Metacercarial Load and Box and Whisker Plots Adding Notches to a Boxplot Tukey’s Honest Significant Difference Test

7. The Grammar of Graphics Family of Packages 8. Sets and Venn Diagrams 9. Statistics: Choosing the Right Test

Explanatory and Response Variables, Experiments and Surveys Parametric versus Non-parametric Tests Difference between Linear Models and Generalized Linear Models Our Basic Aim Is to Achieve a Near-linear QQ Plot and Even Variance

10. Commonly Used Measures and Statistical Tests

Normality, Skew and Kurtosis Testing Whether Proportions Agree with Null Expectations The Special Case of Contingency Tables Hardy-Weinberg Equilibrium Alternatives to the Chi-squared Test under Some Circumstances Testing Whether Two Means Are Significantly Different Single-sample t-test Two-sample t-test Paired t-test Testing Whether Three or More Means Differ from One Another Comparing Two Variances Non-normally Distributed Data with Small Sample Sizes – Mann-Whitney U Test Non-parametric Two-sample Tests Binomial Test

11. Regression and Correlation Analyses

Linear versus Non-linear Regression Log-log Plot Example Correlation of Numbers of Species with Area Linearizing Data with No Known Underlying Model Errant Points and Leverage QQ Model Plot from the car Library Comparing Regression Slopes and Intercepts Using t-test Non-linear Regression Multiple Regression Pairwise Plots of Explanatory Variables to Visually Inspect Interactions Polynomial Regression and Model Simplification Model Simplification

12. Count Data as Response Variable

Example 1 – Fledgling Numbers in Relation to Clutch Initiation Date Example 2 – Pollinator Flower Visits in Passiflora in Relation to Flower Size

13. Analysis of Variance (ANOVA)

Example 1 – A One-way ANOVA, the InsectSprays Dataset Example 2 – ANOVA with Proportion Data as Response Variable Using Arcsine Transformation Example 3 – Analysis with Proportion Data as Response Variable Using Logit Transformation

14. Analysis of Covariance (ANCOVA)

Example 1 – Growth of Tagged Gobies Example 2 – Fitting through the Origin and Count Data as Response Variable

15. More Generalized Linear Modelling

Model Inspection Binary Response Variable with One Continuous Explanatory Variable

Example 1 – Logistic regression of gall former predation LD50s Example 2 – Pollinator counts – showing importance of deviance Example 3 – Proportion data with N known

16. Monte Carlo Tests and Randomization

Random Number Generator Code Example 1 – Flower Visits by Thai Honey Bee Species Randomizing Cells in a Matrix

17. Principal Components Analysis

Example 1 – Rock Oyster Allozymes Example 2 – The Iris Dataset

18. Species Abundance, Accumulation and Diversity Data

Species Accumulation Data Species Accumulation Curves and Randomization Species Richness Estimation Species Diversity Indices A Note to Be Cautious about Logarithms in Functions Broken-stick Models A Much Faster Approach Using Vectorization

19. Survivorship

Example 1 – Survival of Killdeer Nests

20. Dates and Julian Dates

Problem with Two-digit Dates and POSIX: A Date of Burial Example Phenology and the density Function Extracting Day and Month from Julian Days Seasonal Patterns and Other Smoothing Curves

21. Mapping and Parsing Text Input for Data

Creating Our Own Map from Digitized Coordinates

22. More on Manipulating Text

Example 1 – Standardizing Names in a Phylogenetic Tree Description Method 1 with Wildcards Method 2 Based on Fixed Character String Length Method 3 Using a Vector of Positions Example 2 – Substrings of Unknown Length Trimming White Spaces and/or Tabs Using Wildcards to Locate Internal Letter Strings Finding Suffixes, Prefixes and Specifying Letters, Numbers and Punctuation Manipulating Character Case Ignoring Character Case Specifying Particular and Modifiable Character Classes

23. Phylogenies and Trees

Branch Lengths Random Trees Different Types of Plots in ape

24. Working with DNA Sequences and Other Character Data

Sequential Runs of Base Types Downloading DNA Sequences from GenBank Translating DNA to Amino Acids Prettifying a Table Easy Ways to Extract Taxon Names from a Phylogenetic Matrix Replacing Specified Ambiguity Codes with a Question Mark

25. Spacing in Two Dimensions 26. Population Modelling Including Spatially Explicit Models

Example 1 – Ricker Population Growth Model, Plotting as You Go Example 2 – Host–Parasitoid Population Modelling – Discrete Time Version Example 3 – Spatial Host–Parasitoid Model Example 4 – Genetic Drift, a Program Aimed at Teaching Students about Evolution

27. More on apply Family of Functions – Avoid Loops to Get More Speed

Using apply Using tapply to Calculate Values Based on Factors

28. Food Webs and Simple Graphics

A Parasitoid foodweb Example Foodweb and Community Packages

29. Adding Photographs 30. Standard Distributions in R

The Normal Distribution Student’s t Distribution Lognormal Distribution Logistic Distribution Poisson Distribution Gamma Distribution The Chi-squared Distribution

31. Reading and Writing Data to and from Files

Appending Data to an Existing File Using read.delim with Non-tab Separator Choosing a File to Read Interactively Using Excel for Data Entry The readxl Function and Tibbles Reading PDF Files for Data Mining Writing Graphics Directly to Disc

Appendix 1: Summary of Graphical Parameters

Arguments Passed Directly to par Function Arguments Applied Directly to the plot Function as well as in Some Others Arguments for the lines Function Having Multiple Graphics Windows Open at the Same Time Macintosh-specific Graphics Using the layout Function Using the split.screen Function

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

General Housekeeping Functions Setting or Changing the Working Directory Finding What Files Are in a Directory Graphical Functions and Parameters Interaction with User Mathematical Functions Writing Concatenated Data Straight to File (in the Working Directory) Using cat Troubleshooting Package Installation

Appendix 3: Some Useful Statistical and Mathematical Equations

Logical Mathematical Operators Descriptive Statistics Distributions Correlation Coefficients Statistical Tests Logarithms and Exponents Logistic Functions Weibull and Gompertz Equations Trigonometric Functions Convert Radians and Degrees Functions

Bibliography Web Resources Index Cabi Back

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