Menu Expand
Statistics for Ecologists Using R and Excel

Statistics for Ecologists Using R and Excel

Mark Gardener

(2017)

Abstract

This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs.

Statistical approaches covered include: data exploration; tests for difference – t-test and U-test; correlation – Spearman’s rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal–Wallis test; and multiple regression.

Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results.

New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises.

Praise for the first edition:

This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. – Sue Townsend, Biodiversity Learning Manager, Field Studies Council

[M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel – Mark Edwards, EcoBlogging

A must for anyone getting to grips with data analysis using R and excel. – Amazon 5-star review

It has been very easy to follow and will be perfect for anyone. – Amazon 5-star review

A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. – Goodreads, 4-star review


This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. (This review refers to the first edition.)


Sue Townsend, Biodiversity Learning Manager, Field Studies Council

The text that I have found most helpful in getting back to using R has been Mark Gardener's Statistics for Ecologists Using R and Excel. This excellent little book leads the reader nicely through the basics. Starting with how to down load R and getting data into the programme through exploratory statistics and into basic analysis with a section on reporting results which includes visualising data. It also makes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel. (This review refers to the first edition.)


Mark Edwards

I truly love the book and think it fills an important niche.


Bradley E. Carlson, Wabash College

Mark Gardener began his career as an optician but returned to science and trained as an ecologist. His research is in the area of pollination ecology. He has worked extensively in the UK as well as Australia and the United States. Currently he works as an associate lecturer for the Open University and also runs courses in data analysis for ecology and environmental science.

Table of Contents

Section Title Page Action Price
Support files ix
1. Planning 1
1.1 The scientific method 1
1.2 Types of experiment/project 3
1.3 Getting data – using a spreadsheet 4
1.4 Hypothesis testing 4
1.5 Data types 5
1.6 Sampling effort 8
1.7 Tools of the trade 13
1.8 The R program 13
1.9 Excel 15
2. Data recording 19
2.1 Collecting data – who, what, where, when 19
2.2 How to arrange data 21
3. Beginning data exploration – using software tools 26
3.1 3.1 Beginning to use R 26
3.2 Manipulating data in a spreadsheet 34
3.3 Getting data from Excel into R 50
4. Exploring data – looking at numbers 54
4.1 Summarizing data 55
4.2 Distribution 63
4.3 A numerical value for the distribution 73
4.4 Statistical tests for normal distribution 82
4.5 4.5 Distribution type 83
4.6 Transforming data 88
4.7 When to stop collecting data? The running average 91
4.8 Statistical symbols 96
5. Exploring data – which test is right? 100
5.1 Types of project 100
5.2 Hypothesis testing 101
5.3 Choosing the correct test 102
6. Exploring data – using graphs 107
6.1 Introduction to data visualization 107
6.2 Exploratory graphs 117
6.3 Graphs to illustrate differences 123
6.4 Graphs to illustrate correlation and regression 148
6.5 6.5 Graphs to illustrate association 162
6.6 Graphs to illustrate similarity 176
6.7 Graphs – a summary 178
7. Tests for differences 182
7.1 Differences: t-test 182
7.2 7.2 Differences: U-test 191
7.3 Paired tests 197
8. Tests for linking data – correlations 206
8.1 Correlation: Spearman’s rank test 207
8.2 Pearson’s product moment 213
8.3 Correlation tests using Excel 216
8.4 Correlation tests using R 222
8.5 Curved linear correlation 226
9. Tests for linking data – associations 230
9.1 Association: chi-squared test 230
9.2 Goodness of fit test 236
9.3 Using R for chi-squared tests 237
9.4 Using Excel for chi-squared tests 241
10. Differences between more than two samples 247
10.1 Analysis of variance 248
10.2 Kruskal–Wallis test 272
11. Tests for linking several factors 285
11.1 Multiple regression 285
11.2 Curved-linear regression 304
11.3 Logistic regression 314
12. Community ecology 329
12.1 Diversity 329
12.2 Similarity 341
13. Reporting results 359
13.1 Presenting findings 359
13.2 Publishing 360
13.3 13.3 Reporting results of statistical analyses 360
13.4 Graphs 362
13.5 Writing papers 365
13.6 Plagiarism 367
13.7 References 368
13.8 Poster presentations 369
13.9 Giving a talk (PowerPoint) 370
14. Summary 373
Glossary 375
Appendices 381
Index 393