Paul A. Mastering Health Data Science Using R 2025

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Paul A. Mastering Health Data Science Using R 2025

Textbook in PDF format This book provides a practical, application-driven guide to using R for public health and health Data Science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to apply R skills to their own research needs. Practical case studies in public health are provided throughout to reinforce learning. Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an introduction to probability, statistical inference and regression analysis. Since R is designed for statisticians, it is built with data in mind. This comes in handy when we want to streamline how we process and analyze data. It also means that many statisticians working on new methods are publishing user-created packages in R, so R users have access to most methods of interest. R is also an interpreted language, which means that we do not have to compile our code into Machine Language first; this allows for simpler syntax and more flexibility when writing our code, which also makes it a great first programming language to learn. When working with data frames, we often use the Tidyverse package, which is actually a collection of R packages for data science applications. An R package is a collection of functions and/or sample data that allow us to expand on the functionality of R beyond the base functions. You can check whether you have the tidyverse package installed by going to the Package tab in the Output Pane in RStudio or by running the following command, which displays all your installed packages. Preface I Introduction to R Getting Started with R Data Structures in R Working with Data Files in R II Exploratory Analysis Intro to Exploratory Data Analysis Data Transformations and Summaries Case Study: Cleaning Tuberculosis Screening Data Merging and Reshaping Data Visualization with ggplot2 III Distributions and Hypothesis Testing Probability Distributions in R Hypothesis Testing Case Study: Analyzing Blood Lead Level and Hypertension IV Regression Linear Regression Logistic Regression Model Selection Case Study: Predicting Tuberculosis Risk V Writing Larger Programs LogicandLoops Functions Case Study: Designing a Simulation Study Writing Efficient Code VI Extra Topics Expanding Your R Skills Writing Reports in Quarto