R

This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. This book is based on the Coursera Class "Developing Data Products" as part of the Data Science Specialization. Particular emphasis is paid to developing Shiny apps and interactive graphics.

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.

This book provides a practical guide to critical data science methods, focusing on their application in credit risk management. Using examples in R and Python, it presents step-by-step processes for applying various analytical techniques while highlighting the importance of aligning methods with the specific characteristics of the data. Designed for practitioners and those with foundational data science and banking knowledge, the book bridges theory and practice with real-world examples.

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!

Disassemble and assemble R bytecode

This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

For every exercise I did my best to connect the specific statistical concepts with R code, and every time I use linear algebra I connect it with a concrete R example. In this book you will not find something such as "this is left as an exercise to the reader".