Books

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.

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.

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.

En el manual expongo, de forma clara y sencilla, los conceptos básicos de un análisis exploratorio de datos a nivel descriptivo y cómo llevarlo a la práctica con el software estadístico R y datos reales. El libro está pensado para que el lector avance paso a paso en su proceso de auto-aprendizaje, por lo que se proporcionan muchos ejemplos.

You are not bad at data. Your work has outgrown the shape of Excel. This book helps finance, HR, operations, reporting, and business analysis professionals replace manual spreadsheet survival with calmer, reusable workflows in R.

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.

The book provides a modern look at introductory Biostatistical concepts and the associated computational tools using the latest developments in computation and visualization in the R language environment. The book includes practical data analysis based on datasets that can be downloaded here: https://github.com/muschellij2/biostatmethods.

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

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

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).

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.

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

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!)

Are you confident your statistical results are correct? To answer this, you need to know the concepts behind statistics. This book, and R code, focus on data with correlated errors that arise in mixed models and phylogenetic models. It will hopefully give you confidence to judge statistical methods and the results they produce. The book is free!

Greenhouse gas emissions have caused considerable changes in climate, including increased surface air temperatures and rising sea levels. This e-textbook presents a series of laboratory exercises in R that teach the Earth science and statistical concepts needed for assessing climate-related risks. These exercises are intended for upper-level undergraduates, beginning graduate students, and professionals in other areas who wish to gain insight into academic climate risk analysis.