Primer in biological data analysis and visualization using R / Gregg Hartvigsen.

Includes bibliographical references (pages [229]-230) and index.

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.

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.