234 pages, b/w illustrations
R is a popular programming language that statisticians use to perform a variety of statistical computing tasks. Rooted in Gregg Hartvigsen's extensive experience teaching biology, 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 to the organization, computation, and visualization of biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to express data in histograms, boxplots, barplots, scatterplots, before/after line plots, pie charts, and graphs. He covers data normality, outliers, and nonnormal data and examines frequently used statistical tests with one value and one sample; paired samples; more than two samples across a single factor; correlation; and linear regression. A Primer in Biological Data Analysis Using R also includes a section on advanced procedures and a final chapter on possible extensions into programming, featuring a discussion of algorithms, the art of looping, and combining programming and output.
"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 colleagues and students."
– Dr. Andy Conway, Princeton University
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
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
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
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
4.6. Dealing with Non-normal Data
5. Visualizing Your Data
5.6. Bump Charts (Before and After Line Plots)
5.7. Pie Charts
5.8. Multiple Graphs (Using Par and Pairs)
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
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
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
9. Hypothesis Tests: Linear Relationships
9.2. Linear Regression
10. Hypothesis Tests: Observed and Expected Values
10.1. The X2 Test
10.2. The Fisher Exact Test
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
12. An Introduction to Computer Programming
12.1. What Is a “Computer Program”?
12.2. Introducing Algorithms
12.3. Combining Programming and Computer Output
13. Final Thoughts
13.1. Where Do I Go from Here?
Solutions to Odd-numbered Problems
There are currently no reviews for this product. Be the first to review this product!
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.