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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