Programming Skills for Data Science Start Writing Code to Wrangle Analyze and Visualize Data with R 1st Edition by Michael Freeman, Joel Ross ISBN 0135159083 9780135159088

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 Configuration 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

Credits

Code Snippets