Mastering Health Data Science Using R

1. Auflage 2025
ISBN: 978-1-032-72936-7
Verlag: Taylor & Francis Ltd

Buch, Englisch, 372 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 453 g

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.

Key features:

· Includes practical case studies.

· Explains how to write larger programmes.

· Contains additional information on Quarto.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Paul, Alice

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Preface Acknowledgments 1 Getting Started with R 1.1 Why R? 1.1.1 Installation of R and RStudio  1.2 The R Console  1.2.1 Basic Computations and Objects 1.2.2 Naming Conventions 1.3 RStudio and Quarto 1.3.1 Panes 1.3.2 Calling Functions 1.3.3 Working Directories and Paths 1.3.4 Installing and Loading Packages 1.4 RStudio Projects and RStudio Global Options 1.5 Tips and Reminders 2 Data Structures in R 2.1 Data Types  2.2 Vectors  2.2.1 Indexing a Vector  2.2.2 Modifying a Vector and Calculations  2.2.3 Practice Question  2.2.4 Common Vector Functions  2.3 Factors  2.4 Matrices  2.4.1 Indexing a Matrix  2.4.2 Modifying a Matrix  2.4.3 Practice Question  2.5 Data Frames  2.5.1 Indexing a Data Frame  2.5.2 Modifying a Data Frame  2.5.3 Practice Question  2.6 Lists  2.7 Exercises  3 Working with Data Files in R  3.1 Importing and Exporting Data  3.2 Summarizing and Creating Data Columns  3.2.1 Column Summaries  3.2.2 Practice Question  3.2.3 Other Summary Functions  3.2.4 Practice Question  3.2.5 Missing, Infinite, and NaN Values  3.2.6 Dates in R  3.3 Using Logic to Subset, Summarize, and Transform  3.3.1 Practice Question  3.3.2 Other Selection Functions  3.4 Exercises  I Introduction to R 4 Intro to Exploratory Data Analysis  4.1 Univariate Distributions  4.1.1 Practice Question  4.2 Bivariate Distributions  4.2.1 Practice Question  4.3 Autogenerated Plots  4.4 Tables  4.5 Exercises  5 Data Transformations and Summaries  5.1 Tibbles and Data Frames  5.2 Subsetting Data  5.2.1 Practice Question  5.3 Updating Rows and Columns  5.3.1 Practice Question  5.4 Summarizing and Grouping  5.4.1 Practice Question  5.5 Exercises  6 Case Study: Cleaning Tuberculosis Screening Data  7 Merging and Reshaping Data  7.1 Tidy Data 7.2 Reshaping Data  7.2.1 Practice Question  7.3 Merging Data with Joins  7.3.1 Practice Question  7.4 Exercises  8 Visualization with ggplot2  8.1 Intro to ggplot  8.1.1 Practice Question  8.2 Adjusting the Axes and Aesthetics  8.3 Adding Groups  8.3.1 Practice Question  8.4 Extra Options  8.5 Exercises  9 Case Study: Exploring Early COVID-19 Data  9.1 Pre-processing  9.2 Mobility and Cases Over Time  II Exploratory Analysis  10 Probability Distributions in R  10.1 Probability Distributions in R  10.1.1 Random Samples  10.1.2 Density Function  10.1.3 Cumulative Distribution  10.1.4 Quantile Distribution  10.1.5 Reference List for Probability Distributions  10.1.6 Practice Question  10.2 Empirical Distributions and Sampling Data  10.2.1 Practice Question  10.3 Exercises  11 Hypothesis Testing  11.1 Univariate Distributions and One-Sample Tests  11.1.1 Practice Question  11.2 Correlation and Covariance  11.3 Two-Sample Tests for Continuous Variables  11.3.1 Practice Question  11.3.2 Two-Sample Variance Tests  11.4 Two-Sample Tests for Categorical Variables  11.4.1 Practice Question  11.5 Adding Hypothesis Tests to Summary Tables 11.6 Exercises  12 Case Study: Analyzing Blood Lead Level and Hypertension  III Distributions and Hypothesis Testing  13 Linear Regression  13.1 Simple Linear Regression  13.1.1 Practice Question  13.2 Multiple Linear Regression  13.3 Diagnostic Plots and Measures  13.3.1 Normality  13.3.2 Homoscedasticity, Linearity, and Collinearity  13.3.3 Practice Question  13.3.4 Leverage and Influence  13.4 Interactions and Transformations  13.4.1 Practice Question  13.5 Evaluation Metrics  13.6 Stepwise Selection  13.7 Exercises  14 Logistic Regression  14.1 Generalized Linear Models in R  14.1.1 Practice Question  14.2 Residuals, Discrimination, and Calibration  14.2.1 Receiver Operating Characteristic (ROC) Curve  14.2.2 Calibration Plot  14.2.3 Practice Question  14.3 Variable Selection and Likelihood Ratio Tests  14.4 Extending Beyond Binary Outcomes  14.5 Exercises  15 Model Selection 15.1 Regularized Regression  15.2 Elastic Net  15.3 Best Subset  15.4 Exercises  16 Case Study: Predicting Tuberculosis Risk  16.1 Model Selection  16.2 Evaluate Model on Validation Data  IV Regression  17 Logic and Loops  17.1 Logic and Conditional Expressions 17.1.1 Practice Question 17.2 Loops  17.2.1 Practice Question  17.3 Avoiding Control Flows with Functions  17.4 Exercises  18 Functions  18.1 Components of a Function  18.1.1 Arguments  18.1.2 Practice Question  18.1.3 Return Values  18.1.4 Scope of Objects  18.1.5 Functions within Functions and Returning Functions 18.2 Documenting Functions  18.2.1 Practice Question  18.3 Debugging and Testing 18.3.1 Unit tests  18.3.2 Practice Question  18.4 Exercises  19 Case Study: Designing a Simulation Study  19.1 Outlining Our Approach  19.2 Coding Our Simulation Study  19.3 Results  20 Writing Efficient Code 20.1 Use Fast and Vectorized Functions  20.1.1 Practice Question  20.2 Avoid Copies and Duplicates objects!copies  20.2.1 Practice Question  20.3 Parallel Programming  20.4 Exercises V Writing Larger Programs 21 Expanding your R Skills 21.1 Reading Documentation for New Packages  21.2 Trying Simple Examples  21.3 Deciphering Error Messages and Warnings  21.3.1 Debugging Code  21.4 General Programming Tips  21.5 Exercises  22 Writing Reports in Quarto  22.1 Starting a Quarto file  22.1.1 Adding Code Chunks  22.1.2 Customizing Chunks  22.2 Formatting Text in Markdown  22.3 Formatting Figures and Tables  22.3.1 Using References  22.4 Adding in Equations  22.5 Exercises References

Über Autor(innen)

Alice Paul is an Assistant Professor of Biostatistics and Teaching Scholar, holding a Ph.D. in Operations Research from Cornell University. With six years of teaching experience at the undergraduate, master’s, and Ph.D. levels, she instructed students in diverse fields, including biostatistics, engineering, computer science, and data science at both Brown University and Olin College of Engineering.