Book description
Data Wrangling with JavaScript is hands-on guide that will teach you how to create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies.
About the Technology
Why not handle your data analysis in JavaScript? Modern libraries and data handling techniques mean you can collect, clean, process, store, visualize, and present web application data while enjoying the efficiency of a single-language pipeline and data-centric web applications that stay in JavaScript end to end.
About the Book
Data Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you’ll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You’ll also build interactive visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit.
What's Inside
- Establishing a data pipeline
- Acquisition, storage, and retrieval
- Handling unusual data sets
- Cleaning and preparing raw data
- Interactive visualizations with D3
About the Reader
Written for intermediate JavaScript developers. No data analysis experience required.
About the Author
Ashley Davis is a software developer, entrepreneur, author, and the creator of Data-Forge and Data-Forge Notebook, software for data transformation, analysis, and visualization in JavaScript.
Quotes
A thorough and comprehensive step-by-step guide to managing data with JavaScript.
- Ethan Rive
Do you still think that you need R and Python skills to do data analysis? This mind-shifting book explains that JavaScript is enough!
- Ubaldo Pescatore
Does a fantastic job detailing the wrangling process, the tools involved, and the issues and concerns to expect without ever leaving the JavaScript domain.
- Alex Basile
Excellent real-world examples for full-stack JavaScript developers.
- Sai Kota
Table of contents
- Titlepage
- Copyright
- preface
- acknowledgments
- about this book
- about the author
- about the cover illustration
-
Chapter 1: Getting started: establishing your data pipeline
- 1.1 Why data wrangling?
- 1.2 What’s data wrangling?
- 1.3 Why a book on JavaScript data wrangling?
- 1.4 What will you get out of this book?
- 1.5 Why use JavaScript for data wrangling?
- 1.6 Is JavaScript appropriate for data analysis?
- 1.7 Navigating the JavaScript ecosystem
- 1.8 Assembling your toolkit
- 1.9 Establishing your data pipeline
- Summary
- Chapter 2: Getting started with Node.js
- Chapter 3: Acquisition, storage, and retrieval
- Chapter 4: Working with unusual data
-
Chapter 5: Exploratory coding
- 5.1 Expanding your toolkit
- 5.2 Analyzing car accidents
- 5.3 Getting the code and data
- 5.4 Iteration and your feedback loop
- 5.5 A first pass at understanding your data
- 5.6 Working with a reduced data sample
- 5.7 Prototyping with Excel
- 5.8 Exploratory coding with Node.js
- 5.9 Exploratory coding in the browser
- Putting it all together
- Summary
-
Chapter 6: Clean and prepare
- 6.1 Expanding our toolkit
- 6.2 Preparing the reef data
- 6.3 Getting the code and data
- 6.4 The need for data cleanup and preparation
- 6.5 Where does broken data come from?
- 6.6 How does data cleanup fit into the pipeline?
- 6.7 Identifying bad data
- 6.8 Kinds of problems
- 6.9 Responses to bad data
- Techniques for fixing bad data
- Cleaning our data set
- Preparing our data for effective use
- Building a data processing pipeline with Data-Forge
- Summary
- Chapter 7: Dealing with huge data files
-
Chapter 8: Working with a mountain of data
- 8.1 Expanding our toolkit
- 8.2 Dealing with a mountain of data
- 8.3 Getting the code and data
- 8.4 Techniques for working with big data
- 8.5 More Node.js limitations
- 8.6 Divide and conquer
-
8.7 Working with large databases
- 8.7.1 Database setup
- 8.7.2 Opening a connection to the database
- 8.7.3 Moving large files to your database
- 8.7.4 Incremental processing with a database cursor
- 8.7.5 Incremental processing with data windows
- 8.7.6 Creating an index
- 8.7.7 Filtering using queries
- 8.7.8 Discarding data with projection
- 8.7.9 Sorting large data sets
- 8.8 Achieving better data throughput
- Summary
- Chapter 9: Practical data analysis
- Chapter 10: Browser-based visualization
-
Chapter 11: Server-side visualization
- 11.1 Expanding your toolkit
- 11.2 Getting the code and data
- 11.3 The headless browser
-
11.4 Using Nightmare for server-side visualization
- 11.4.1 Why Nightmare?
- 11.4.2 Nightmare and Electron
- 11.4.3 Our process: capturing visualizations with Nightmare
- 11.4.4 Prepare a visualization to render
- 11.4.5 Starting the web server
- 11.4.6 Procedurally start and stop the web server
- 11.4.7 Rendering the web page to an image
- 11.4.8 Before we move on . . .
- 11.4.9 Capturing the full visualization
- Feeding the chart with data
- Multipage reports
- Debugging code in the headless browser
- Making it work on a Linux server
- 11.5 You can do much more with a headless browser
- Summary
-
Chapter 12: Live data
- 12.1 We need an early warning system
- 12.2 Getting the code and data
- 12.3 Dealing with live data
- 12.4 Building a system for monitoring air quality
- 12.5 Set up for development
- 12.6 Live-streaming data
- 12.7 Refactor for configuration
- 12.8 Data capture
- 12.9 An event-based architecture
- Code restructure for event handling
- Live data processing
- Live visualization
- Summary
- Chapter 13: Advanced visualization with D3
- Chapter 14: Getting to production
- appendix a: JavaScript cheat sheet
- appendix b: Data-Forge cheat sheet
- appendix c: Getting started with Vagrant
- Index
- List of Figures
- List of Tables
- List of Listings
Product information
- Title: Data Wrangling with JavaScript
- Author(s):
- Release date: December 2018
- Publisher(s): Manning Publications
- ISBN: 9781617294846
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