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Category: "R"

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  1. Developing Data Products in R
    Brian Caffo and Sean Kross

    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.

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

  3. Not So Short Introduction to R
    A Practical Learning Journey from First Contact to Reproducible Data Systems
    James Daniel

    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.

  4. The Excel Refugee Migration
    Stop Babysitting Spreadsheets. Start Commanding Data
    James Daniel

    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.

  5. Predicting Persistence: Population Models for Conservation
    With examples and exercises in R
    Jacob Koella

    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.

  6. Data Science with R
    Data Analysis and Prediction Algorithms with R - Third Edition
    Daniel Paredes

    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.

  7. SIG avec R
    Gérer ses données spatiales avec R
    Jerome Mathieu

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

  8. Biochemical Networks
    By Simulations in R
    Adonis Cedeño

    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.

  9. No Description Available
  10. Single Cell RNA-seq Analysis Using Public Data
    Getting Started with Single Cell RNA-seq Analysis in R
    LabCode
    No Description Available
  11. 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!

  12. R Bytecode
    An exploration of R's stack-based virtual machine and its bytecode.
    mikefc

    Disassemble and assemble R bytecode

  13. Probability of Default Rating Modeling with R
    Comprehensive overview of the modeling processes, principles, and designs
    Andrija Djurovic

    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.

  14. The Hitchhiker's Guide to Linear Models
    Based on the famous R programming language
    Mauricio 'Pacha' Vargas Sepúlveda

    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".

  15. Discover the thrilling world of Reinforcement Learning (RL) in our engaging eBook! Learn the fundamentals of RL, from AI agents and environments to rewards and actions. Explore real-world applications like robotics, healthcare, and personalized recommendations, where RL is transforming industries.