A Primer in Biological Data Analysis and Visualization Using R by Hartvigsen, Gregg -- Read -- Imperial Library of Trantor

Index

Cover Title Page Copyright Contents 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

Samples are normally distributed Samples are not normally distributed

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 χ2 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

Get and use the derivative

11.6 An introduction to mathematical modeling 11.7 Problems

12. An introduction to computer programming

12.1 What is a “computer program”?

An example: the central limit theorem

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