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 (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. Contents Note Continued: 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 -- 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? Gregg Hartvigsen. Includes Bibliographical References (pages [229]-230) And 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. Book jacket Contents INTRODUCTION CHAPTER 1. INTRODUCING OUR SOFTWARE TEAM CHAPTER 2. GETTING DATA INTO R CHAPTER 3. WORKING WITH YOUR DATA CHAPTER 4. TELL ME ABOUT MY DATA CHAPTER 5. VISUALIZING YOUR DATA CHAPTER 6. THE INTERPRETATION OF HYPOTHESIS TESTS CHAPTER 7. HYPOTHESIS TESTS: ONE- AND TWO-SAMPLE COMPARISONS CHAPTER 8. TESTING DIFFERENCES AMONG MULTIPLE SAMPLES CHAPTER 9. HYPOTHESIS TESTS: LINEAR RELATIONSHIPS CHAPTER 10. HYPOTHESIS TESTS: OBSERVED AND EXPECTED VALUES CHAPTER 11. A FEW MORE ADVANCED PROCEDURES CHAPTER 12. AN INTRODUCTION TO COMPUTER PROGRAMMING CHAPTER 13. FINAL THOUGHTS ACKNOWLEDGMENTS SOLUTIONS TO ODD-NUMBERED PROBLEMS BIBLIOGRAPHY INDEX