**by Garrett Grolemund, Hadley Wickham**

DescriptionTable of ContentsDetailsHashtagsReport an issue ### Book Description

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. ### Table of Contents

### Book Details

### Related Books

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.

This open book is licensed under a Creative Commons License (CC BY-NC-ND). Free download in PDF format is not available. You can read R for Data Science book online for free.

Chapter 1

Introduction

Chapter 2

Introduction

Chapter 3

Data visualisation

Chapter 4

Workflow: basics

Chapter 5

Data transformation

Chapter 6

Workflow: scripts

Chapter 7

Exploratory Data Analysis

Chapter 8

Workflow: projects

Chapter 9

Introduction

Chapter 10

Tibbles

Chapter 11

Data import

Chapter 12

Tidy data

Chapter 13

Relational data

Chapter 14

Strings

Chapter 15

Factors

Chapter 16

Dates and times

Chapter 17

Introduction

Chapter 18

Pipes

Chapter 19

Functions

Chapter 20

Vectors

Chapter 21

Iteration

Chapter 22

Introduction

Chapter 23

Model basics

Chapter 24

Model building

Chapter 25

Many models

Chapter 26

Introduction

Chapter 27

R Markdown

Chapter 28

Graphics for communication

Chapter 29

R Markdown formats

Chapter 30

R Markdown workflow

Title

R for Data Science

Subject

Computer Science

Publisher

O'Reilly Media

Published

2016

Pages

520

Edition

1

Language

English

ISBN13 Digital

9781491910399

ISBN10 Digital

1491910399

License

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