R Programming: Statistical Data Analysis in Research

✍ Scribed by Kingsley Okoye, Samira Hosseini

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 314
• Edition: 2024
• Category: Library

✦ Synopsis

This book is written for statisticians, data analysts, programmers, researchers, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using R object-oriented programming language and RStudio integrated development environment (IDE). R is an open-source software with a development environment (RStudio) for computing statistics and graphical displays through data manipulation, modeling, and calculation. R packages and supported libraries provide a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical software, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system. Therefore, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the users. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and nonparametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for the reliability and validity of the available datasets. Different research experiments, case scenarios, and examples are explained in this book. The book provides a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations thus congruence of Statistics and Computer programming in Research.

✦ Table of Contents

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