Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Acknowledgments
About the Authors
I: Getting Started
	1 Setting Up Your Computer
		1.1 Setting up Command Line Tools
		1.2 Installing git
		1.3 Creating a GitHub Account
		1.4 Selecting a Text Editor
		1.5 Downloading the R Language
		1.6 Downloading RStudio
	2 Using the Command Line
		2.1 Accessing the Command Line
		2.2 Navigating the File System
		2.3 Managing Files
		2.4 Dealing with Errors
		2.5 Directing Output
		2.6 Networking Commands
II: Managing Projects
	3 Version Control with git and GitHub
		3.1 What Is git?
		3.2 Conguration and Project Setup
		3.3 Tracking Project Changes
		3.4 Storing Projects on GitHub
		3.5 Accessing Project History
		3.6 Ignoring Files from a Project
	4 Using Markdown for Documentation
		4.1 Writing Markdown
		4.2 Rendering Markdown
III: Foundational R Skills
	5 Introduction to R
		5.1 Programming with R
		5.2 Running R Code
		5.3 Including Comments
		5.4 Defining Variables
		5.5 Getting Help
	6 Functions
		6.1 What Is a Function?
		6.2 Built-in R Functions
		6.3 Loading Functions
		6.4 Writing Functions
		6.5 Using Conditional Statements
	7 Vectors
		7.1 What Is a Vector?
		7.2 Vectorized Operations
		7.3 Vector Indices
		7.4 Vector Filtering
		7.5 Modifying Vectors
	8 Lists
		8.1 What Is a List?
		8.2 Creating Lists
		8.3 Accessing List Elements
		8.4 Modifying Lists
		8.5 Applying Functions to Lists with lapply()
IV: Data Wrangling
	9 Understanding Data
		9.1 The Data Generation Process
		9.2 Finding Data
		9.3 Types of Data
		9.4 Interpreting Data
		9.5 Using Data to Answer Questions
	10 Data Frames
		10.1 What Is a Data Frame?
		10.2 Working with Data Frames
		10.3 Working with CSV Data
	11 Manipulating Data with dplyr
		11.1 A Grammar of Data Manipulation
		11.2 Core dplyr Functions
		11.3 Performing Sequential Operations
		11.4 Analyzing Data Frames by Group
		11.5 Joining Data Frames Together
		11.6 dplyr in Action: Analyzing Flight Data
	12 Reshaping Data with tidyr
		12.1 What Is “Tidy” Data?
		12.2 From Columns to Rows: gather()
		12.3 From Rows to Columns: spread()
		12.4 tidyr in Action: Exploring Educational Statistics
	13 Accessing Databases
		13.1 An Overview of Relational Databases
		13.2 A Taste of SQL
		13.3 Accessing a Database from R
	14 Accessing Web APIs
		14.1 What Is a Web API?
		14.2 RESTful Requests
		14.3 Accessing Web APIs from R
		14.4 Processing JSON Data
		14.5 APIs in Action: Finding Cuban Food in Seattle
V: Data Visualization
	15 Designing Data Visualizations
		15.1 The Purpose of Visualization
		15.2 Selecting Visual Layouts
		15.3 Choosing Effective Graphical Encodings
		15.4 Expressive Data Displays
		15.5 Enhancing Aesthetics
	16 Creating Visualizations with ggplot2
		16.1 A Grammar of Graphics
		16.2 Basic Plotting with ggplot2
		16.3 Complex Layouts and Customization
		16.4 Building Maps
		16.5 ggplot2 in Action: Mapping Evictions in San Francisco
	17 Interactive Visualization in R
		17.1 The plotly Package
		17.2 The rbokeh Package
		17.3 The leaflet Package
		17.4 Interactive Visualization in Action: Exploring Changes to the City of Seattle
VI: Building and Sharing Applications
	18 Dynamic Reports with R Markdown
		18.1 Setting Up a Report
		18.2 Integrating Markdown and R Code
		18.3 Rendering Data and Visualizations in Reports
		18.4 Sharing Reports as Websites
		18.5 R Markdown in Action: Reporting on Life Expectancy
	19 Building Interactive Web Applications with Shiny
		19.1 The Shiny Framework
		19.2 Designing User Interfaces
		19.3 Developing Application Servers
		19.4 Publishing Shiny Apps
		19.5 Shiny in Action: Visualizing Fatal Police Shootings
	20 Working Collaboratively
		20.1 Tracking Different Versions of Code with Branches
		20.2 Developing Projects Using Feature Branches
		20.3 Collaboration Using the Centralized Workflow
		20.4 Collaboration Using the Forking Workflow
	21 Moving Forward
		21.1 Statistical Learning
		21.2 Other Programming Languages
		21.3 Ethical Responsibilities
Index
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