Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R
- By Michael Freeman, Joel Ross
- Published Nov 23, 2018 by Addison-Wesley Professional. Part of the Addison-Wesley Data & Analytics Series series.
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Description
- Copyright 2019
- Pages: 384
- Edition: 1st
- eBook (Watermarked)
- ISBN-10: 0-13-515910-5
- ISBN-13: 978-0-13-515910-1
The Foundational Hands-On Skills You Need to Dive into Data Science
“Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.”
–From the foreword by Jared Lander, series editor
Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience.
Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you’ve uncovered. Step by step, you’ll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales.
Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything’s focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to
- Install your complete data science environment, including R and RStudio
- Manage projects efficiently, from version tracking to documentation
- Host, manage, and collaborate on data science projects with GitHub
- Master R language fundamentals: syntax, programming concepts, and data structures
- Load, format, explore, and restructure data for successful analysis
- Interact with databases and web APIs
- Master key principles for visualizing data accurately and intuitively
- Produce engaging, interactive visualizations with ggplot and other R packages
- Transform analyses into sharable documents and sites with R Markdown
- Create interactive web data science applications with Shiny
- Collaborate smoothly as part of a data science team
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Author's Site
Visit this site for code listings from the authors: https://programming-for-data-science.github.io/
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Table of Contents
Foreword xi
Preface xiii
Acknowledgments xvii
About the Authors xix
Part I: Getting Started 1
Chapter 1: Setting Up Your Computer 3
1.1 Setting up Command Line Tools 4
1.2 Installing git 5
1.3 Creating a GitHub Account 6
1.4 Selecting a Text Editor 6
1.5 Downloading the R Language 7
1.6 Downloading RStudio 8
Chapter 2: Using the Command Line 9
2.1 Accessing the Command Line 9
2.2 Navigating the File System 11
2.3 Managing Files 15
2.4 Dealing with Errors 18
2.5 Directing Output 20
2.6 Networking Commands 20
Part II: Managing Projects 25
Chapter 3: Version Control with git and GitHub 27
3.1 What Is git? 27
3.2 Configuration and Project Setup 30
3.3 Tracking Project Changes 32
3.4 Storing Projects on GitHub 36
3.5 Accessing Project History 40
3.6 Ignoring Files from a Project 42
Chapter 4: Using Markdown for Documentation 45
4.1 Writing Markdown 45
4.2 Rendering Markdown 48
Part III: Foundational R Skills 51
Chapter 5: Introduction to R 53
5.1 Programming with R 53
5.2 Running R Code 54
5.3 Including Comments 58
5.4 Defining Variables 58
5.5 Getting Help 63
Chapter 6: Functions 69
6.1 What Is a Function? 69
6.2 Built-in R Functions 71
6.3 Loading Functions 73
6.4 Writing Functions 75
6.5 Using Conditional Statements 79
Chapter 7: Vectors 81
7.1 What Is a Vector? 81
7.2 Vectorized Operations 83
7.3 Vector Indices 88
7.4 Vector Filtering 90
7.5 Modifying Vectors 92
Chapter 8: Lists 95
8.1 What Is a List? 95
8.2 Creating Lists 96
8.3 Accessing List Elements 97
8.4 Modifying Lists 100
8.5 Applying Functions to Lists with lapply() 102
Part IV: Data Wrangling 105
Chapter 9: Understanding Data 107
9.1 The Data Generation Process 107
9.2 Finding Data 108
9.3 Types of Data 110
9.4 Interpreting Data 112
9.5 Using Data to Answer Questions 116
Chapter 10: Data Frames 119
10.1 What Is a Data Frame? 119
10.2 Working with Data Frames 120
10.3 Working with CSV Data 124
Chapter 11: Manipulating Data with dplyr 131
11.1 A Grammar of Data Manipulation 131
11.2 Core dplyr Functions 132
11.3 Performing Sequential Operations 139
11.4 Analyzing Data Frames by Group 142
11.5 Joining Data Frames Together 144
11.6 dplyr in Action: Analyzing Flight Data 148
Chapter 12: Reshaping Data with tidyr 155
12.1 What Is “Tidy” Data? 155
12.2 From Columns to Rows: gather() 157
12.3 From Rows to Columns: spread() 158
12.4 tidyr in Action: Exploring Educational Statistics 160
Chapter 13: Accessing Databases 167
13.1 An Overview of Relational Databases 167
13.2 A Taste of SQL 171
13.3 Accessing a Database from R 175
Chapter 14: Accessing Web APIs 181
14.1 What Is a Web API? 181
14.2 RESTful Requests 182
14.3 Accessing Web APIs from R 189
14.4 Processing JSON Data 191
14.5 APIs in Action: Finding Cuban Food in Seattle 197
Part V: Data Visualization 205
Chapter 15: Designing Data Visualizations 207
15.1 The Purpose of Visualization 207
15.2 Selecting Visual Layouts 209
15.3 Choosing Effective Graphical Encodings 220
15.4 Expressive Data Displays 227
15.5 Enhancing Aesthetics 229
Chapter 16: Creating Visualizations with ggplot2 231
16.1 A Grammar of Graphics 231
16.2 Basic Plotting with ggplot2 232
16.3 Complex Layouts and Customization 238
16.4 Building Maps 248
16.5 ggplot2 in Action: Mapping Evictions in San Francisco 252
Chapter 17: Interactive Visualization in R 257
17.1 The plotly Package 258
17.2 The rbokeh Package 261
17.3 The leaflet Package 263
17.4 Interactive Visualization in Action: Exploring Changes to the City of Seattle 266
Part VI: Building and Sharing Applications 273
Chapter 18: Dynamic Reports with R Markdown 275
18.1 Setting Up a Report 275
18.2 Integrating Markdown and R Code 279
18.3 Rendering Data and Visualizations in Reports 281
18.4 Sharing Reports as Websites 284
18.5 R Markdown in Action: Reporting on Life Expectancy 287
Chapter 19: Building Interactive Web Applications with Shiny 293
19.1 The Shiny Framework 293
19.2 Designing User Interfaces 299
19.3 Developing Application Servers 306
19.4 Publishing Shiny Apps 309
19.5 Shiny in Action: Visualizing Fatal Police Shootings 311
Chapter 20: Working Collaboratively 319
20.1 Tracking Different Versions of Code with Branches 319
20.2 Developing Projects Using Feature Branches 329
20.3 Collaboration Using the Centralized Work[1]ow 331
20.4 Collaboration Using the Forking Work[1]ow 335
Chapter 21: Moving Forward 341
21.1 Statistical Learning 341
21.2 Other Programming Languages 342
21.3 Ethical Responsibilities 343
Index 345
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