A Primer in Biological Data Analysis and Visualization Using R / / Gregg Hartvigsen.

• Superior document: Title is part of eBook package: De Gruyter Columbia University Press Complete eBook-Package 2021
• VerfasserIn: Hartvigsen, Gregg,
• Place / Publishing House: New York, NY : : Columbia University Press, , [2021]; ©2021
• Year of Publication: 2021
• Language: English
• Physical Description: 1 online resource :; 65 figures
• Other title: Frontmatter --; Contents --; Preface to the Second Edition --; Acknowledgments --; Introduction --; 1. Introducing Our Software Team --; 2. Getting Data intro R --; 3. Working with Your Data --; 4. Tell Me About My Data --; 5. Visualizing Your Data --; 6. An Overview of Science, Hypothesis Testing, Experimental Design, and Inference --; 7. Hypothesis Tests: Using One- and Two-Sample Tests --; 8. Hypothesis Tests: Differences Among Multiple Samples --; 9. Hypothesis Tests: Linear Relationships --; 10. Hypothesis Tests: Observed and Expected Values --; 11. A Few More Advanced Procedures --; 12. An Introduction to Computer Programming --; 13. Final Thoughts --; APPENDIX: Solutions to Select Problems --; Bibliography --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9780231554404; 9783110739077; 9783110754001; 9783110753776; 9783110754148; 9783110753912
• DOI: 10.7312/hart20212
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Gregg Hartvigsen.

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 correctly entering and analyzing data 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-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.This second edition has been revised to be current with the versions of R software released since the book’s original publication. It features updated terminology, sources, and examples throughout.