Whether you’re new to statistical analysis or looking to enhance your analytical skills with the R programming language, this textbook provides comprehensive and practical guidance for understanding fundamental statistical concepts through healthcare examples in R. It is an ideal resource for students, educators, and healthcare researchers seeking a step-by-step first approach to effectively applying R in the analysis of healthcare data.
Readers are introduced to the fundamentals of base R, along with practical methods for data import, preprocessing, and transformation using functions from standard R packages such as base and stats, as well as pipe-friendly functions from the tidyverse collection of packages. Additionally, a chapter is devoted to visualization fundamentals, providing step-by-step guidance on creating data visualizations using the ggplot2 package and its extensions.
This textbook covers the most common statistical tests (e.g., t-test, one-way ANOVA, chisquare test, correlation, and non-parametric tests) and introduces more specialized analyses (e.g., linear regression, survival analysis, reliability of measurement analysis, diagnostic test accuracy, and ROC analysis) with examples from the biomedical field. Basic mathematical equations for these statistical tests and techniques are provided to enhance understanding. Statistical functions from both Base R and the rstatix add-on package are often presented side by side, fostering engagement and enriching the reader’s coding experience. Designed to be self-contained, this textbook does not require any prior experience with the R programming language, though it assumes a basic understanding of mathematics. (Note: Multivariable modeling and advanced statistical techniques are beyond the scope of this introductory textbook.)
Whether you're new to statistical analysis or looking to enhance your analytical skills with the R programming language, this textbook provides comprehensive and practical guidance for understanding fundamental statistical concepts through healthcare examples in R.
Preface About the Author 1 R via RStudio 2 RStudio Projects 3 R as
calculator 4 R functions 5 R packages 6 R objects 7 Atomic vectors 8 Matrices
and arrays 9 Lists and data frames 10 Data import, preprocessing, and
transformation 11 Data visualization with ggplot2 12 Introduction to
Statistics 13 Basic concepts of probability 14 Probability distributions 15
Descriptive statistics 16 Populations and samples 17 Confidence intervals 18
Hypothesis testing 19 Independent samples t-test 20 Wilcoxon-Mann-Whitney
test 21 Paired samples t-test 22 Wilcoxon Signed-Rank test 23 One-way
Analysis of Variance 24 Kruskal-Wallis test 25 Categorical data analysis 26
Correlation methods 27 Simple linear regression 28 Survival analysis 29
Reliability of measurement 30 Measures of diagnostic test accuracy 31
Receiver Operating Characteristic (ROC) curve Bibliography Index
Konstantinos I. Bougioukas received his PhD in Biostatistics and Research Methodology from the Faculty of Medicine at Aristotle University of Thessaloniki, Greece, in 2021. He has extensive experience teaching both basic and advanced Statistics. He has mentored and guided students and researchers from diverse scientific backgroundsincluding mathematics, medicine, biology, psychology, and health policyin applying statistical methodologies and R programming. As a research methodologist and data analyst, he specializes in evidence synthesisincluding systematic reviews and meta-analyses, overviews of reviews, and meta-epidemiological studiesas well as advanced data analysis and data visualization techniques. He has authored over 40 peer reviewed research articles published in high-impact biomedical journals. Additionally, he has contributed to the development of three open-source R packages: ccaR, amstar2Vis, and musicolor.
