A primer in biological data analysis and visualization using R

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.

Machine generated contents note: 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.

Contents note continued: 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 …