Contents
1.1 Setting up Command Line Tools
1.5 Downloading the R Language
2.1 Accessing the Command Line
2.2 Navigating the File System
3 Version Control with git and GitHub
3.2 Configuration and Project Setup
3.4 Storing Projects on GitHub
3.6 Ignoring Files from a Project
4 Using Markdown for Documentation
6.5 Using Conditional Statements
8.5 Applying Functions to Lists with lapply()
9.1 The Data Generation Process
9.5 Using Data to Answer Questions
11 Manipulating Data with dplyr
11.1 A Grammar of Data Manipulation
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
12.2 From Columns to Rows: gather()
12.3 From Rows to Columns: spread()
12.4 tidyr in Action: Exploring Educational Statistics
13.1 An Overview of Relational Databases
13.3 Accessing a Database from R
14.3 Accessing Web APIs from R
14.5 APIs in Action: Finding Cuban Food in Seattle
15 Designing Data Visualizations
15.1 The Purpose of Visualization
15.3 Choosing Effective Graphical Encodings
16 Creating Visualizations with ggplot2
16.2 Basic Plotting with ggplot2
16.3 Complex Layouts and Customization
16.5 ggplot2 in Action: Mapping Evictions in San Francisco
17 Interactive Visualization in R
17.4 Interactive Visualization in Action: Exploring Changes to the City of Seattle
VI: Building and Sharing Applications
18 Dynamic Reports with R Markdown
18.2 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
19.2 Designing User Interfaces
19.3 Developing Application Servers
19.5 Shiny in Action: Visualizing Fatal Police Shootings
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