R

Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications and describe the theory as it becomes necessary to solve specific challenges. The book includes links to computer code that readers can use to follow along as they program.

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. Printed copies of this book are available through Lulu.

Learn to use R, the tidyverse collection of packages and functional programming concepts to write efficient and readable code.

Modern Computational Statistics with R teaches statistics as a disciplined way of thinking: start with the scientific question, design, data, and uncertainty before reaching for formulas. Through real examples, simulations, and R, readers learn how to turn data into defensible evidence and build the statistical foundations needed for modern data science, machine learning, and AI.

This book gives a brief, but rigorous, treatment of regression models intended for practicing Data Scientists.

This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization.

Biological Data Science with R covers data manipulation with dplyr, visualization with ggplot2, essential statistics, survival analysis, RNA-seq analysis, phylogenetic trees, predictive modeling and infectious disease forecasting, text mining and natural language processing, and more.

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)

A rigorous treatment of linear models for self learning data scientists. This book is only available in pdf form.

This book teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever. The material for this book was developed as part of the industry-leading Johns Hopkins Data Science Specialization. Printed versions are available through Lulu (see link below).

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

The Working Notes complement Applied Data Science for Credit Risk and Probability of Default Rating Modeling with R, offering practice-oriented insights. Based on the author’s GitHub repository, they address real-world challenges and are regularly updated to reflect ongoing developments.

Longitudinal Data Analysis Using R is a practical, end-to-end guide to longitudinal data analysis that helps you move from raw data to credible substantive conclusions. It tackles the real pain points researchers face in longitudinal analysis, such as data preparation, exploring change over time, model choice, missing data, and interpretation, using clear explanations, real-world data, and fully reproducible R code.

Develop insights from data with tidy tools. Import, wrangle, visualize, and model data with the Tidyverse R packages.

Go beyond running MR code. Learn how to design, analyze, interpret, and report a Mendelian Randomization study—from GWAS data to a publication-ready manuscript. Built for graduate researchers in epidemiology, biostatistics, genetics, and public health.