Thomas W. MacFarland, Ed.D., is Senior Research Associate (Office of Institutional Effectiveness) and Associate Professor (College of Computing and Engineering) at Nova Southeastern University, Fort Lauderdale, Florida. From his first experience with agricultural research as an Integrated Pest Management scout, Dr. MacFarland has been actively involved with research and statistical analyses for more than 40 years and has been a proponent for use of the R language since it first grew out of S.

Jan Yates, Ph.D., is Professor Emerita of Educational Media and Computer Science Education at Nova Southeastern University's Abraham S. Fischler College of Education in Fort Lauderdale, Florida. Since 2001, she has worked as associate professor and program professor in these content areas and in the areas of curriculum development, program assessment and review, and accreditation.

Includes step-by-step processes on the use of R for data organization using multiple data formats: comma-separated values, Internet-hosted spreadsheets, tab-separated values, white space

Gives attention to both parametric and nonparametric issues when selecting R functions for statistical analysis

Provides numerous examples of how the ggplot2 package is used to make beautiful graphics for biostatistics

Explores how data in multiple file formats and locations can be imported into R

Features a new chapter on Working with Large and Complex Datasets, presented to introduce the reader to the complexity of data organization

Concludes each chapter with multiple addenda, providing an opportunity to either introduce or reinforce important topics and associated functions that extend beyond R-based fundamentals

Provides all external datasets used in the book, for practice and retention of included concepts

Includes supplementary material: [...]

1 Introduction: Biostatistics and R.- 1.1 Purpose of this Text.- 1.2 Development of Biostatistics.- 1.3 Development of R.- 1.4 How R is Used in this Text.- 1.5 Import Data into R.- 1.6 Addendum1: Efficient Programming with R, Project Workflow, and Good Programming Practices (gpp).- 1.7 Addendum2: Preview of Descriptive Statistics and Graphics Using R.- 1.8 Addendum3: R and Beautiful Graphics.- 1.9 Addendum4: Research Designs Used in Biostatistics.- 1.10 Prepare to Exit, Save, and Later Retrieve this R Session.- 1.11 External Data and/or Data Resources Used in this Lesson.- 2 Data Exploration, Descriptive Statistics, and Measures of Central Tendency.- 2.1 Background.- 2.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 2.3 Organize the Data and Display the Code Book.- 2.4 Conduct a Visual Data Check Using Graphics (e.g., Figures).- 2.5 Descriptive Statistics for Initial Analysis of the Data.- 2.6 Quality Assurance, DataDistribution, and Tests for Normality.- 2.7 Statistical Test(s).- 2.8 Summary.- 2.9 Addendum1: Specialized External Packages and Functions.- 2.10 Addendum2: Parametric v Nonparametric.- 2.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns.- 2.12 Prepare to Exit, Save, and Later Retrieve this R Session.- 2.13 External Data and/or Data Resources Used in this Lesson.- 3 Student's t-Test for Independent Samples.- 3.1 Background.- 3.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 3.3 Organize the Data and Display the Code Book.- 3.4 Conduct a Visual Data Check Using Graphics (e.g., Figures).- 3.5 Descriptive Statistics for Initial Analysis of the Data.- 3.6 Quality Assurance, Data Distribution, and Tests for Normality.- 3.7 Statistical Test(s).- 3.8 Summary of Outcomes.- 3.9 Addendum1: t-Statistic v z-Statistic.- 3.10 Addendum2: Parametric v Nonparametric.- 3.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns.- 3.12 Prepare to Exit, Save, and Later Retrieve This R Session.- 3.13 External Data and/or Data Resources Used in this Lesson.- 4 Student's t-Test for Matched Pairs.- 4.1 Background.- 4.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 4.3 Organize the Data and Display the Code Book.- 4.4 Conduct a Visual Data Check Using Graphics(e.g., Figures).- 4.5 Descriptive Statistics for Initial Analysis of the Data.- 4.6 Quality Assurance, Data Distribution, and Tests for Normality.- 4.7 Statistical Test(s).- 4.8 Summary of Outcomes.- 4.9 Addendum1: R-Based Tools for Unstacked (e.g. Wide) Data.- 4.10 Addendum2: Stacked Data and Student's t-Test for Matched Pairs.- 4.11 Addendum 3: The Impact of N on Student's t-Test.- 4.12 Addendum 4: Parametric v Nonparametric.- 4.13 Addendum5: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns.- 4.14 Prepare to Exit, Save, and Later Retrieve This R Session.- 4.15 External Data and/or Data Resources Used in this Lesson.- 5 Oneway Analysis of Variance (ANOVA).- 5.1 Background.- 5.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 5.3 Organize the Data and Display the Code Book.- 5.4 Conduct a Visual Data Check Using Graphics(e.g., Figures).- 5.5 Descriptive Statistics for Initial Analysis of the Data.- 5.6 Quality Assurance, Data Distribution, and Tests for Normality.- 5.7 Statistical Test(s).- 5.8 Summary of Outcomes.- 5.9 Addendum1: Other Packages for Display of Oneway ANOVA.- 5.10 Addendum2: Parametric v Nonparametric.- 5.11 Addendum3: Additional Practice Data Sets.- 5.12 Prepare to Exit, Save, and Later Retrieve This R Session.- 5.13 External Data and/or DataResources Used in this Lesson.- 6 Twoway Analysis of Variance (ANOVA).- 6.1 Background.- 6.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 6.3 Organize the Data and Display the Code Book.- 6.4 Conduct a Visual Data Check Using Graphics (e.g., Figures).- 6.5 Descriptive Statistics for Initial Analysis of the Data.- 6.6 Quality Assurance, Data Distribution, and Tests for Normality.- 6.7 Statistical Test(s).- 6.8 Summary of Outcomes.- 6.9 Addendum 1: Other Packages for Display of Twoway ANOVA.- 6.10 Addendum 2: Parametric v Nonparametric.- 6.11 Addendum 3: Additional Practice Data Sets.- 6.12 Prepare to Exit, Save, and Later Retrieve This R Session.- 6.13 External Data and/or Data Resources Used in this Lesson.- 7 Correlation, Association, Regression, Likelihood, and Prediction.- 7.1 Background.- 7.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 7.3 Organize the Data and Display the Code Book.- 7.4 Quality Assurance, Data Distribution, and Tests for Normality.- 7.5 Statistical Test(s).- 7.6 Summary of Outcomes.- 7.7 Addendum 1: Multiple Regression.- 7.8 Addendum 2: Likelihood and Odds Ratio.- 7.9 Addendum 3:Parametric v Nonparametric.- 7.10 Addendum 4: Additional Practice Data Sets.- 7.11 Prepare to Exit, Save, and Later Retrieve This R Session.- 7.12 External Data and/or Data Resources Used in this Lesson.- 8 Working with Large and Complex Datasets.- 8.1 Background.- 8.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions.- 8.3 Organize the Data and Display the Code Book.- 8.4 Conduct a Visual Data Check Using Graphics (e.g., Figures).- 8.5 Descriptive Statistics for Initial Analysis of the Data.- 8.6 Quality Assurance, Data Distribution, and Tests for Normality.- 8.7 Statistical Test(s).- 8.8 Summary of Outcomes.- 8.9 Addendum1: Additional Graphics, to Show Relationships Between and Among Data.- 8.10 Addendum2: Graphics Using the lattice Package.- 8.11 Addendum3: Graphics Using the ggplot2 Package.- 8.12 Addendum 4: Beyond an Introduction to R - Use the tidyverse to Create Subsets of Original Datasets.- 8.13 Prepare to Exit, Save, and Later Retrieve This R Session.- 8.14 External Data and/or Data Resources Used in this Lesson.- 9 Future Actions and Next Steps.- 9.1 Use of This Text.- 9.2 R and Beautiful Reporting with R Markdown.- 9.3 Future Use of R for Biostatistics.- 9.4 Big Data and Bio Informatics.- 9.5 External Resources.- 9.6 Contact the Authors.