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R for Data Science 2nd Edition By Hadley Wickham
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Binding: Paperback
Language: English
ISBN-13: 978-9355429827
Reader’s Age: Adults 18+ | Students & Professionals
Ships Within: 5–10 Business Days
Authors: Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund
Release Date: July 18, 2023
Genre: Educational & Academic
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Learn Data Science with R: The Complete Tidyverse Guide for Data Analysis and Visualization
About the Book
R for Data Science 2nd Edition is your comprehensive roadmap to becoming proficient in modern data analysis. Written by the creators and core contributors of the tidyverse, this updated edition teaches you how to turn data into understanding using R’s most powerful and intuitive tools. You’ll learn the complete data science workflow: importing data from various sources, transforming messy datasets into clean structures, visualizing patterns that reveal insights, and building models that answer real-world questions. With practical examples and exercises throughout, this book bridges the gap between knowing R syntax and actually doing data science.
About the Author
Hadley Wickham is Chief Scientist at Posit (formerly RStudio) and creator of the tidyverse—a collection of R packages that revolutionized data science workflows. Mine Çetinkaya-Rundel is Professor of the Practice at Duke University and Developer Educator at Posit, bringing extensive teaching experience to complex concepts. Garrett Grolemund is a renowned data scientist and educator who specializes in making statistical computing accessible to learners worldwide. Together, they bring decades of experience teaching data science and developing the very tools you’ll use.
Who Is This Book For?
This book is perfect for aspiring data analysts, business intelligence professionals, researchers, and anyone who works with data and wants to extract meaningful insights. If you’re switching careers into data science or looking to upgrade from spreadsheets to scalable analysis tools, this guide meets you where you are. Students in statistics, economics, social sciences, and STEM fields will find it invaluable for both coursework and real-world applications. No prior programming experience is required—just curiosity and a willingness to learn by doing.
Frequently Asked Questions (FAQs)
Q: Is R for Data Science suitable for complete beginners with no coding experience?
Yes, the book starts from the basics and assumes no prior programming knowledge. It introduces R concepts gradually with hands-on examples, making it ideal for beginners who want to learn data analysis from scratch.
Q: What’s new in the 2nd edition compared to the first edition?
The 2nd edition includes updated tidyverse packages, new chapters on workflow and project management, expanded coverage of data visualization, and modern best practices for data science. All code examples have been refreshed to work with current R versions.
Q: Do I need to buy RStudio or any paid software to follow this book?
No, R and RStudio are completely free and open-source. You can download both at no cost and follow every example in the book without any software purchases.
Q: How long does it take to complete R for Data Science?
Most learners complete the book in 8-12 weeks when dedicating 5-10 hours per week. However, you can work through it at your own pace, and each chapter includes exercises that reinforce learning through practice.
Q: Can I use this book for learning data visualization and ggplot2 specifically?
Absolutely. The book dedicates significant coverage to data visualization using ggplot2, teaching you how to create publication-quality charts, customize aesthetics, and design visualizations that communicate insights effectively.
| Weight | 870 g |
|---|---|
| Dimensions | 22.9 × 17.8 × 2.8 cm |
You'll master essential data science skills including importing data from databases, spreadsheets, and APIs; tidying and transforming datasets with dplyr and tidyr; creating professional visualizations with ggplot2; and applying statistical models to make predictions. The book emphasizes reproducible workflows, communication of results, and practical problem-solving rather than abstract theory. By the end, you'll confidently handle the entire data science pipeline from raw data to compelling insights that drive decisions.
