R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R.
Rezensionen / Stimmen
An excellent, easy-to-read introduction to biostatistics and the software program R. Simple but rigorous, with top-notch coverage of R. I would recommend this book to both colleagues and students. -- Andy Conway, Princeton University A recommendation for any college-level course strong in biostatistics and modeling...a fine guide for science and R programming students alike. Midwest Book Review Hartvigsen has succeeded in accomplishing his stated objectives. Buy the book and share the knowledge with students... the book is relevant, timely, and just what is needed with current trends in science education. Ecology A well-written overview of both biostatistics and R programming... this volume will fill an important niche for undergraduate biology. Quarterly Review of Biology
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Illustrationen
Maße
Höhe: 254 mm
Breite: 178 mm
Gewicht
ISBN-13
978-0-231-16698-0 (9780231166980)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Gregg Hartvigsen is a professor in the Department of Biology at the State University of New York at Geneseo. He taught a workshop on network analysis using R at the National Institute for Mathematical and Biological Synthesis at the University of Tennessee, Knoxville, and was a visiting scientist and site reviewer for the Mathematical Biosciences Institute at Ohio State University. He also served as co-PI on a National Science Foundation training grant for undergraduate biology and mathematics.
Introduction 1. Introducing Our Software Team 1.1. Solving Problems with Excel and R 1.2. Install R and RStudio 1.3. Getting Help with R 1.4. R as a Graphing Calculator 1.5. Using Script Files 1.6. Extensibility 1.7. Problems 2. Getting Data Into R 2.1. Using C( ) for Small Datasets 2.2. Reading Data from an Excel Spreadsheet 2.3. Reading Data from a Website 2.4. Problems 3. Working with Your Data 3.1. Accuracy and Precision of Our Data 3.2. Collecting Data Into Dataframes 3.3. Stacking Data 3.4. Subsetting Data 3.5. Sampling Data 3.6. Sorting an Array of Data 3.7. Ordering Data 3.8. Sorting a Dataframe 3.9. Saving a Dataframe to a File 3.10. Problems 4. Tell Me About My Data 4.1. What Are Data? 4.2. Where's the Middle? 4.3. Dispersion About the Middle 4.4. Testing for Normality 4.5. Outliers 4.6. Dealing with Non-normal Data 4.7. Problems 5. Visualizing Your Data 5.1. Overview 5.2. Histograms 5.3. Boxplots 5.4. Barplots 5.5. Scatterplots 5.6. Bump Charts (Before and After Line Plots) 5.7. Pie Charts 5.8. Multiple Graphs (Using Par and Pairs) 5.9. Problems 6. The Interpretation of Hypothesis Tests 6.1. What Do We Mean by "Statistics"? 6.2. How to Ask and Answer Scientific Questions 6.3. The Difference Between "Hypothesis" and "Theory" 6.4. A Few Experimental Design Principles 6.5. How to Set Up a Simple Random Sample for an Experiment 6.6. Interpreting Results: What is the "P-value"? 6.7. Type I and Type II Errors 6.8. Problems 7. Hypothesis Tests: One- and Two-Sample Comparisons 7.1. Tests with One Value and One Sample 7.2. Tests with Paired Samples (Not Independent) 7.3. Tests with Two Independent Samples 7.4. Problems 8. Testing Differences Among Multiple Samples 8.1. Samples Are Normally Distributed 8.2. One-way Test for Non-parametric Data 8.3. Two-way Analysis of Variance 8.4. Problems 9. Hypothesis Tests: Linear Relationships 9.1. Correlation 9.2. Linear Regression 9.3. Problems 10. Hypothesis Tests: Observed and Expected Values 10.1. The X2 Test 10.2. The Fisher Exact Test 10.3. Problems 11. A Few More Advanced Procedures 11.1. Writing Your Own Function 11.2. Adding 95% Confidence Intervals to Barplots 11.3. Adding Letters to Barplots 11.4. Adding 95% Confidence Interval Lines for Linear Regression 11.5. Non-linear Regression 11.6. An Introduction to Mathematical Modeling 11.7. Problems 12. An Introduction to Computer Programming 12.1. What Is a "Computer Program"? 12.2. Introducing Algorithms 12.3. Combining Programming and Computer Output 12.4. Problems 13. Final Thoughts 13.1. Where Do I Go from Here? Acknowledgments Solutions to Odd-Numbered Problems Bibliography Index
