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.
The textbook covers the most common statistical tests (e.g., t-test, one-way ANOVA, chi-square 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 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.)
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