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 the author'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, this book guides readers through the processes of correctly entering and analyzing data and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. The author covers testing data for normality, defining and identifying outliers, and working with non-normally distributed 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 outlining algorithms and the art of programming using R. --

Introducing our software team -- Getting data into R -- Working with your data -- Tell me about my data -- Visualizing your data -- An overview of science, hypothesis testing, experimental design, and inference -- Hypothesis tests : using one- and two-sample tests -- Hypothesis tests : differences among multiple samples -- Hypothesis tests : linear relationships -- Hypothesis tests : observed and expected values -- A few more advanced procedures -- An introduction to computer programming -- Final thoughts -- Appendix : Solutions to select problems.