R

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.

If you are an independent learner or an instructor for a data science, statistics, or public health course, check out the open case studies project (www.opencasestudies.org) and this guide which will describe the variety of ways our case studies can be used for hands-on data science activities.

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.

This book presents a modern, unified, and language-agnostic workflow for creating analytical pipelines that are truly reproducible, from environment to execution.

Tidyplots (an R package) is easier for many common plotting tasks.

If R has ever felt powerful but confusing, this book gives you the missing map. Not So Short Introduction to R guides researchers, analysts, graduate students, Excel/SPSS/STATA users, and emerging data workers from fragile spreadsheets and copied scripts into reproducible analytical workflows they can understand, explain, and trust.

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.

How long will a population persist, and why do some populations go extinct while others survive? 'Predicting Persistence' will help you to get a grasp of the fundamental aspects of the theory underlying these questions, and it will show you how to simulate them with the programming language R.

Discover how to become a Data Scientist at your own pace with updated content and real-world examples. From data analysis to prediction algorithms with machine learning. This Third Edition includes new chapters on Generative AI, Ethics, and modern Machine Learning workflows.

Utilisez R comme un SIG pour gérer et cartographier vos données spatiales

Learn to model, analyze, and visualize biochemical networks using R. This book guides you through reaction systems, stoichiometry, dynamical simulations, and data-driven workflows to understand complex molecular behavior. Designed for students, researchers, and anyone exploring computational biochemistry.

Unveil the Secrets of the grapes in the Vineyard! Join us on a journey through the world of wine like never before. Discover the magic of data science and machine learning as we uncork the mysteries hidden in the wine dataset. From predicting to redefining winemaking, get ready for a revolution. Stay tuned for a taste of tomorrow, today!