Exploring Medical Data Science with R · Essential Table of Contents

Start here – the essential Table of Contents and roadmap for all lessons in this R for medical data science course.

This page is the central Table of Contents and learning roadmap for the “Exploring Medical Data Science with R” series, designed to help clinicians, researchers, and colleagues in the pharmaceutical industry quickly locate the chapters and case studies most relevant to their current needs. At a glance, you can see the topics covered in each chapter, the real-world datasets used (such as clinical trial and public health databases), and links to hands-on R code examples, making it easier to study systematically and to return later when you need to revisit a specific method or workflow.

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Foundations in Medical Data Science

1. Sec.1-Ch.1-Subsec.1: The History and Development of Medical Data Science

2. Sec.1-Ch.1-Subsec.2: An Overview of Machine Learning, Statistics, and ChatGPT — and Their Relationship with R

3. Sec.1-Ch.1-Subsec.3: The R Language, From Its Origins to a Global Data Science Ecosystem

4. Sec.1-Ch.1-Subsec.4: Getting Started with Pharmaceutical Data Science – Understanding Data Visualization

5. Sec.1-Ch.1-Subsec.5: Learning the Basics and Workflow of Data Analysis

6. Sec.1-Ch.2-Subsec.1: Downloading and Installing R and RStudio (Windows & Mac)

7. Sec.1-Ch.2-Subsec.2: A Practical Guide to the RStudio Interface

8. Sec.1-Ch.2-Subsec.3: Working Smarter with RStudio – Practical Tips for Customizing Your Environment

9. Sec.1-Ch.2-Subsec.4: Managing R Projects in RStudio and Visualizing Data with ggplot2 & gganimate

10. Sec.1-Ch.2-Subsec.5: Mastering R Package Management

11. Sec.1-Ch.2-Subsec.6: Working with Datasets in R

12. Sec.1-Ch.2-Subsec.7: Mastering Data File Handling in R – RDS, RData, CSV & TXT

13. Sec.1-Ch.2-Subsec.8: Mastering Data Files in R – Excel, SPSS, SAS, Stata & Minitab

14. Sec.1-Ch.2-Subsec.9: Running SQL in R

15. Sec.1-Ch.2-Subsec.10: Quick Start with Web Scraping in R

16. Sec.1-Ch.3-Subsec.1: Notebooks and Markdown

17. Sec.1-Ch.3-Subsec.2: Dynamic Reporting with R Markdown & R Notebook

18. Sec.1-Ch.3-Subsec.3: R Markdown for Academic Writing

19. Sec.1-Ch.3-Subsec.4: Mastering R Markdown YAML

20. Sec.1-Ch.3-Subsec.5: Mastering Headings, Lists, Text Formatting, and Links in R Markdown

21. Sec.1-Ch.3-Subsec.6: Mastering the Integration of Code, Plots, and Mathematical Formulas in R Markdown

22. Sec.1-Ch.3-Subsec.7: From Knit to Integrated Multi-Document Workflows

23. Sec.1-Ch.3-Subsec.8: Integrating JavaScript Visualization for Interactive Data Reporting

24. Sec.1-Ch.3-Subsec.9: Creating Interactive Data Reports with R Markdown and Shiny

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Basic Data Handling & Missing Data

1. Sec.B-Ch.1-Subsec.1: Fundamentals of Data and Core Data Structures for Clinical Data Science

2. Sec.B-Ch.1-Subsec.2: Continuing Fundamentals of Data and Core Data Structures for Clinical Data Science

3. Sec.B-Ch.1-Subsec.3: Assignment Operations and Arithmetic Operations in R

4. Sec.B-Ch.1-Subsec.4: Logical Operations and Matrix Operations in R

5. Sec.B-Ch.1-Subsec.5: A Complete Analysis of Loops and Iteration Functions in R

6. Sec.B-Ch.2-Subsec.1: Exploring Data Using Basic R Functions

7. Sec.B-Ch.2-Subsec.2: From Data Sorting to Grouping and Filtering

8. Sec.B-Ch.2-Subsec.3: Processing Raw Medical Data in R

9. Sec.B-Ch.2-Subsec.4: Handling Missing Data in Clinical Research Using R

10. Sec.B-Ch.2-Subsec.5: Deletion Strategies and Imputation Methods in R

11. Sec.B-Ch.2-Subsec.6: Random Regression Imputation for Handling Missing Data in Medical Data Science

12. Sec.B-Ch.2-Subsec.7：Data Reshaping, Deduplication, and Data Matching in R

13. Sec.B-Ch.2-Subsec.8：Using R for Data Reshaping, Row/Column Naming, and Data Type Conversion

14. Sec.B-Ch.2-Subsec.9：Data Transformation and Variable Engineering in R

15. Sec.B-Ch.2-Subsec.10：A Practical Guide to dplyr Variant Functions and Interactive Data Editing

16. Sec.B-Ch.2-Subsec.11：Data Reshaping in R, Feature Scaling, and Feature Visualization

17. Sec.B-Ch.2-Subsec.12：Mastering String Processing and Regular Expressions in R

18. Sec.B-Ch.2-Subsec.13：Mastering String Manipulation in R with stringr

19. Sec.B-Ch.2-Subsec.14：Mastering String Processing in R

20. Sec.B-Ch.2-Subsec.15：Pattern Matching and Text Manipulation with the stringr Package

21. Sec.B-Ch.2-Subsec.16：Mastering Date and Time Data Handling in R

Analysis of Quantitative Data

1. Sec.B-Ch.3-Subsec.1：Describing Central Tendency of Quantitative Variables with R

2. Sec.B-Ch.3-Subsec.2：Analyze 20 Outcomes Simultaneously with One Simple Function

3. Sec.B-Ch.3-Subsec.3：Understanding Hypothesis Testing Through Cases

4. Sec.B-Ch.3-Subsec.4：Understanding Normal Distribution and Normality Testing

5. Sec.B-Ch.3-Subsec.5：Understanding Variance and Homogeneity of Variance Tests (Complete Coverage of Three Methods)

6. Sec.B-Ch.3-Subsec.6：Independent Samples t-Test in R

7. Sec.B-Ch.3-Subsec.7：One-Sample and Paired t-Tests

8. Sec.B-Ch.3-Subsec.8：Analysis of Variance (ANOVA) and Its Applications

9. Sec.B-Ch.3-Subsec.9：Non-Parametric Tests for Group Differences — Wilcoxon Rank-Sum Test and Kruskal-Wallis Test

Analysis of Qualitative Data

1. Sec.B-Ch.4-Subsec.1：From One-Way to Multi-Way Cross-Tabulations with Odds Ratio Calculation in R

2. Sec.B-Ch.4-Subsec.2：Working with the birthwt Dataset in R

3. Sec.B-Ch.4-Subsec.3：Independence Tests in Statistical Analysis

4. Sec.B-Ch.4-Subsec.4：Understanding Correlation Relationships

5. Sec.B-Ch.4-Subsec.5：Advanced Correlation Analysis Methods

6. Sec.B-Ch.4-Subsec.6：Advanced Categorical Data Analysis in R

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Regression Analysis

1. Sec.B-Ch.5-Subsec.1：From Galton to Modern Medicine

2. Sec.B-Ch.5-Subsec.2：Mastering Linear Regression Modeling

3. Sec.B-Ch.5-Subsec.3：Dummy Variables in Clinical Regression Analysis

4. Sec.B-Ch.5-Subsec.4：Regression Modeling Syntax in R

5. Sec.B-Ch.5-Subsec.5：Mastering Regression Model Evaluation

6. Sec.B-Ch.5-Subsec.6：Plot Diagnostics for Linear Regression Models

7. Sec.B-Ch.5-Subsec.7：Building Binary Logistic Regression Models

8. Sec.B-Ch.5-Subsec.8：Model Selection in Logistic Regression

9. Sec.B-Ch.5-Subsec.9：Ordered Logistic Regression in Clinical Research

10. Sec.B-Ch.5-Subsec.10：Conditional Logistic Regression in Clinical Research

Advanced Regression Analysis

1. Sec.B-Ch.6-Subsec.1：Understanding Generalized Additive Models

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Meta-Analysis & Bibliometrics

1. Sec.2-Ch.1-Subsec.1: Understanding Meta-Analysis in Evidence-Based Medicine

2. Sec.2-Ch.1-Subsec.2: A Concise Guide to the 7 Key Steps of Meta-Analysis

3. Sec.2-Ch.1-Subsec.3: Fixed-Effect Dose–Response Modeling in R

4. Sec.2-Ch.1-Subsec.4: Detecting Publication Bias in Binary Outcome Meta-Analysis with R

5. Sec.2-Ch.1-Subsec.5: Sensitivity and Subgroup Analyses in Meta-Analysis of Binary Outcomes Using R

6. Sec.2-Ch.1-Subsec.6: Meta-Analysis of Continuous Variables in R

7. Sec.2-Ch.1-Subsec.7: Single-Rate Meta-Analysis in R

8. Sec.2-Ch.2-Subsec.1: Mastering Network Meta-Analysis with R

9. Sec.2-Ch.2-Subsec.2: Dose–Response Meta-analysis in R

10. Sec.2-Ch.2-Subsec.3: Continuous Dose-Response Analysis and Prediction with R

11. Sec.2-Ch.2-Subsec.4: Best Linear Unbiased Prediction in Dose-Response Meta-Analysis

12. Sec.2-Ch.2-Subsec.5: Multivariate Meta-Analysis and Meta-Regression in R

13. Sec.2-Ch.2-Subsec.6: A Practical Guide to Bayesian Diagnostic Meta-Analysis in R (I)

14. Sec.2-Ch.2-Subsec.7: A Practical Guide to Bayesian Diagnostic Meta-Analysis in R (II)

15. Sec.2-Ch.2-Subsec.8: A Practical Guide to Bayesian Diagnostic Meta-Analysis in R (III)

16. Sec.2-Ch.2-Subsec.9: Application of Cumulative Meta-Analysis in Evidence-Based Medicine

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Public Datasets

1. Sec.3-Ch.1-Subsec.1: Secondary Data Analysis in Medical Research – Concepts, Methods, and Applications

2. Sec.3-Ch.1-Subsec.2: Downloading and Exploring NHANES Data Using R

3. Sec.3-Ch.1-Subsec.3: Mastering NHANES Data in R – From Download to Analysis

4. Sec.3-Ch.1-Subsec.4: Understanding Sampling Weights and Complex Survey Design in NHANES

5. Sec.3-Ch.1-Subsec.5: Understanding Survey Weights in NHANES

6. Sec.3-Ch.1-Subsec.6: NHANES Sample Weight Selection and Implementation in R

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Q&A Column

1. Column Q&A: Making R Work for Medical Researchers

2. Column Q&A: Working with Multiple R Versions in RStudio

3. Column Q&A: Trouble Installing R Packages? A Step-by-Step Guide to Fix It

4. Column Q&A: How to Write a Solid Medical Review: What It Is, How to Do It, and How to Search the Literature

5. Column Q&A: How to Do a Proper Literature Search, A Step-by-Step Guide

6. Column Q&A: Can Research Based on Public Databases Be Published in International Journals?