R FOR DATA SCIENCE

1. Chapter 1 Data Visualization with ggplot2
2. Chapter 2 Workflow: Basics
3. Chapter 3 Data Transformation with dplyr
4. Chapter 4 Workflow: Scripts
5. Chapter 5 Exploratory Data Analysis
6. Chapter 6 Workflow: Projects
7. Chapter 7 Tibbles with tibble
8. Chapter 8 Data Import with readr
9. Chapter 9 Tidy Data with tidyr
10. Chapter 10 Relational Data with dplyr
11. Chapter 11 Strings with stringr
12. Chapter 12 Factors with forcats
13. Chapter 13 Dates and Times with lubridate
14. Chapter 14 Pipes with magrittr
15. Chapter 15 Functions
16. Chapter 16 Vectors
17. Chapter 17 Iteration with purrr
18. Chapter 18 Model Basics with modelr
19. Chapter 19 Model Building
20. Chapter 20 Many Models with purrr and broom
21. Chapter 21 R Markdown
22. Chapter 22 Graphics for Communication with ggplot2
23. Chapter 23 R Markdown Formats
24. Chapter 24 R Markdown Workflow

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