**Book Title:** R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

**Publisher:** O'Reilly Media

**ISBN:** 1491910399

**Author:** Hadley Wickham

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**Book Title:** R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

**Publisher:** O'Reilly Media

**ISBN:** 1491910399

**Author:** Hadley Wickham

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- R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
- R Graphics Cookbook: Practical Recipes for Visualizing Data
- Practical Statistics for Data Scientists: 50 Essential Concepts
- Text Mining with R: A Tidy Approach
- The Art of R Programming: A Tour of Statistical Software Design
- ggplot2: Elegant Graphics for Data Analysis (Use R!)
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
- Hands-On Programming with R: Write Your Own Functions and Simulations
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
- Python Data Science Handbook: Essential Tools for Working with Data

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.

You’ll learn how to:

**Wrangle**—transform your datasets into a form convenient for analysis**Program**—learn powerful R tools for solving data problems with greater clarity and ease**Explore**—examine your data, generate hypotheses, and quickly test them**Model**—provide a low-dimensional summary that captures true "signals" in your dataset**Communicate**—learn R Markdown for integrating prose, code, and results