R for data science: import, tidy, transform, visualize, and model data/
Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund.
- 2nd ed.
- Sebastopol: O'Reilly, 2023.
- xxiii, 550 pages; Color Illustrations; 23 cm.
1. Data Visualization 2. Workflow: Basics 3. Data Transformation 4. Workflow: Code Style 5. Data Tidying 6. Workflow: Scripts and Projects 7. Data Import 8. Workflow: Getting Help 9. Layers 10. Exploratory Data Analysis 11. Communication 12. Logical Vectors 13. Numbers 14. Strings 15. Regular Expressions 16. Factors 17. Dates and Times 18. Missing Values 19. Joins 20. Spreadsheets 21. Databases 22. Arrow 23. Hierarchical Data 24. Web Scraping 25. Functions 26. Iteration 27. A Field Guide to Base R 28. Quarto 29. Quarto Formats 29. Quarto Formats
Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.
You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to:
Visualize: Create plots for data exploration and communication of resultsTransform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto