R Programming: Statistical Data Analysis in Research

Preface
	Structure and Organization
	Part I
	Part II
Acknowledgments
Contents
Abbreviations
Part I Fundamental Concepts of R Programming and Statistical Data Analysis in Research
1 Introduction to R Programming and RStudio Integrated Development Environment (IDE)
	1.1 What is R Programming Language?
	1.2 RStudio Integrated Development Environment (IDE)
	1.3 Installing and Configuring R and RStudio Software
		1.3.1 Downloading and Installing R Language
		1.3.2 Downloading and Installing RStudio Software
	1.4 Running Your First R Project in R Using RStudio
	1.5 Tips and Technical Guidelines
		1.5.1 Tips About a New R Project
		1.5.2 Opening Existing R Projects and R Scripts
		1.5.3 Working with Multiple R Projects
		1.5.4 Closing or Quitting R
	1.6 Summary
	References
2 Working with Data in R: Objects, Vectors, Factors, Packages and Libraries, and Data Visualization
	2.1 Introduction
	2.2 Preparing RStudio and Script for Working with Data in R
	2.3 Working with Data in R
		2.3.1 Pre-loaded Sample Data in R
		2.3.2 Creating Your Own Data in R
		2.3.3 Import and Using External Data in R
	2.4 R Objects
	2.5 R Vectors: Vectorization and Factorization
		2.5.1 Creating and Working with Vectors in R
		2.5.2 Understanding Sequence in Vectors
		2.5.3 Extracting and Replacing Elements in Vectors
		2.5.4 Vectorization in R
		2.5.5 Factorization in R
	2.6 R Packages and Libraries
	2.7 Plots and Data Visualization
	2.8 Summary
	References
3 Test of Normality and Reliability of Data in R
	3.1 Introduction
	3.2 Test of Data Normality in R: Kolmogorov–Smirnov (K-S) and Shapiro–Wilk (S-W) Test
	3.3 Test of Data Reliability in R: Cronbach’s Alpha Test
	3.4 Summary
	References
4 Choosing Between Parametric and Non-parametric Tests in Statistical Data Analysis
	4.1 Introduction
	4.2 Parametric Versus Non-parametric Tests
		4.2.1 Parametric Test
		4.2.2 Non-parametric Tests
	4.3 Choosing Between Parametric and Non-parametric Test
		4.3.1 Types of Parametric Versus Non-parametric Tests in Statistical Analysis
		4.3.2 Examples and Use Case Scenarios: Parametric Versus Non-parametric Tests
	4.4 Differences Between Parametric Versus Non-parametric Tests
	4.5 Advantages and Disadvantages of Parametric Versus Non-parametric Tests
	4.6 Summary
	References
5 Understanding Dependent and Independent Variables in Research Experiments and Hypothesis Testing
	5.1 What Are Variables in Scientific Research?
		5.1.1 Types of Variables in Scientific Research
		5.1.2 Examples and Use Case Scenarios of Independent Versus Dependent Variables
	5.2 Summary
	References
6 Understanding the Different Types of Statistical Data Analysis and Methods
	6.1 Introduction to Statistical Data Analysis
	6.2 Statistical Data Analysis and Methods in Scientific Research
		6.2.1 Linear Regression
		6.2.2 Logistic Regression
		6.2.3 Linear-Log Model
		6.2.4 T-test
		6.2.5 Analysis of Variance—ANOVA (F-test)
		6.2.6 Mann–Whitney U Test
		6.2.7 Chi-Squared (χ2)
		6.2.8 Kruskal–Wallis H Test
		6.2.9 Correlation
		6.2.10 Wilcoxon Test (Signed-Rank and Rank-Sum)
	6.3 Summary
	References
Part II Application and Implementation of Advanced Methods for Statistical Data Analysis in Research Using R
7 Regression Analysis in R: Linear Regression and Logistic Regression
	7.1 Introduction to Regression Analysis
	7.2 Linear Regression Analysis in R: Simple Regression and Multiple Regression
	7.3 Logistic Regression Analysis in R
	7.4 Summary
	References
8 T-test Statistics in R: Independent Samples, Paired Sample, and One Sample T-tests
	8.1 Introduction
	8.2 Independent Samples T-test in R
	8.3 Paired (Dependent) Sample T-test in R
	8.4 One Sample T-test in R
	8.5 Summary
	References
9 Analysis of Variance (ANOVA) in R: One-Way and Two-Way ANOVA
	9.1 Introduction
	9.2 One-Way ANOVA Test in R
	9.3 Two-Way ANOVA Test in R
	9.4 Summary
	References
10 Chi-Squared (X2) Statistical Test in R
	10.1 Introduction
	10.2 Chi-Squared (X2) Test in R
	10.3 Conclusion
	References
11 Mann–Whitney U Test and Kruskal–Wallis H Test Statistics in R
	11.1 Introduction
	11.2 Mann–Whitney U Test in R
	11.3 Kruskal–Wallis H Test in R
	11.4 Summary
	References
12 Correlation Tests in R: Pearson Cor, Kendall’s Tau, and Spearman’s Rho
	12.1 Introduction
	12.2 Pearson Correlation Test in R
	12.3 Kendall’s Tau and Spearman’s Rho Correlation Tests in R
	12.4 Summary
	References
13 Wilcoxon Statistics in R: Signed-Rank Test and Rank-Sum Test
	13.1 Introduction
	13.2 Signed-Rank Wilcoxon Test in R
	13.3 Rank-Sum Wilcoxon Test in R
	13.4 Summary
	References
Epilogue and Conclusion
Index