Data Wrangling with JavaScript

Name: Data Wrangling with JavaScript
Brand: Manning
Price: 49.44 EUR
Availability: OnlineOnly

Ashley Davis(Author)

Manning (Publisher)

1st Edition

Published on 2. December 2018

432 pages

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ePUB with Adobe-DRM

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978-1-63835-113-9 (ISBN)

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Description

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Summary

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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 dataInteractive 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.

Table of Contents

Getting started: establishing your data pipeline
Getting started with Node.js
Acquisition, storage, and retrieval
Working with unusual data
Exploratory coding
Clean and prepare
Dealing with huge data files
Working with a mountain of data
Practical data analysis
Browser-based visualization
Server-side visualization
Live data
Advanced visualization with D3
Getting to production

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Content

Intro
Titlepage
Copyright
preface
acknowledgments
about this book
Who should read this book
How this book is organized: a roadmap
About the code
Book forum
Other online resources
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
1.9.1 Setting the stage
1.9.2 The data-wrangling process
1.9.3 Planning
1.9.4 Acquisition, storage, and retrieval
1.9.5 Exploratory coding
1.9.6 Clean and prepare
1.9.7 Analysis
1.9.8 Visualization
1.9.9 Getting to production
Summary
Chapter 2: Getting started with Node.js
2.1 Starting your toolkit
2.2 Building a simple reporting system
2.3 Getting the code and data
2.3.1 Viewing the code
2.3.2 Downloading the code
2.3.3 Installing Node.js
2.3.4 Installing dependencies
2.3.5 Running Node.js code
2.3.6 Running a web application
2.3.7 Getting the data
2.3.8 Getting the code for chapter 2
2.4 Installing Node.js
2.4.1 Checking your Node.js version
2.5 Working with Node.js
2.5.1 Creating a Node.js project
2.5.2 Creating a command-line application
2.5.3 Creating a code library
2.5.4 Creating a simple web server
2.6 Asynchronous coding
2.6.1 Loading a single file
2.6.2 Loading multiple files
2.6.3 Error handling
2.6.4 Asynchronous coding with promises
2.6.5 Wrapping asynchronous operations in promises
2.6.6 Async coding with "async" and "await"
Summary
Chapter 3: Acquisition, storage, and retrieval
3.1 Building out your toolkit
3.2 Getting the code and data
3.3 The core data representation
3.3.1 The earthquakes website
3.3.2 Data formats covered
3.3.3 Power and flexibility
3.4 Importing data
3.4.1 Loading data from text files
3.4.2 Loading data from a REST API
3.4.3 Parsing JSON text data
3.4.4 Parsing CSV text data
3.4.5 Importing data from databases
3.4.6 Importing data from MongoDB
3.4.7 Importing data from MySQL
3.5 Exporting data
3.5.1 You need data to export!
3.5.2 Exporting data to text files
3.5.3 Exporting data to JSON text files
3.5.4 Exporting data to CSV text files
3.5.5 Exporting data to a database
3.5.6 Exporting data to MongoDB
3.5.7 Exporting data to MySQL
3.6 Building complete data conversions
3.7 Expanding the process
Summary
Chapter 4: Working with unusual data
4.1 Getting the code and data
4.2 Importing custom data from text files
4.3 Importing data by scraping web pages
4.3.1 Identifying the data to scrape
4.3.2 Scraping with Cheerio
4.4 Working with binary data
4.4.1 Unpacking a custom binary file
4.4.2 Packing a custom binary file
4.4.3 Replacing JSON with BSON
4.4.4 Converting JSON to BSON
4.4.5 Deserializing a BSON file
Summary
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
5.8.1 Using Nodemon
5.8.2 Exploring your data
5.8.3 Using Data-Forge
5.8.4 Computing the trend column
5.8.5 Outputting a new CSV file
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
6.11.1 Rewriting bad rows
6.11.2 Filtering rows of data
6.11.3 Filtering columns of data
Preparing our data for effective use
6.12.1 Aggregating rows of data
6.12.2 Combining data from different files using globby
6.12.3 Splitting data into separate files
Building a data processing pipeline with Data-Forge
Summary
Chapter 7: Dealing with huge data files
7.1 Expanding our toolkit
7.2 Fixing temperature data
7.3 Getting the code and data
7.4 When conventional data processing breaks down
7.5 The limits of Node.js
7.5.1 Incremental data processing
7.5.2 Incremental core data representation
7.5.3 Node.js file streams basics primer
7.5.4 Transforming huge CSV files
7.5.5 Transforming huge JSON files
7.5.6 Mix and match
Summary
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
8.4.1 Start small
8.4.2 Go back to small
8.4.3 Use a more efficient representation
8.4.4 Prepare your data offline
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
8.8.1 Optimize your code
8.8.2 Optimize your algorithm
8.8.3 Processing data in parallel
Summary
Chapter 9: Practical data analysis
9.1 Expanding your toolkit
9.2 Analyzing the weather data
9.3 Getting the code and data
9.4 Basic data summarization
9.4.1 Sum
9.4.2 Average
9.4.3 Standard deviation
9.5 Group and summarize
9.6 The frequency distribution of temperatures
9.7 Time series
9.7.1 Yearly average temperature
9.7.2 Rolling average
9.7.3 Rolling standard deviation
9.7.4 Linear regression
9.7.5 Comparing time series
9.7.6 Stacking time series operations
9.8 Understanding relationships
9.8.1 Detecting correlation with a scatter plot
9.8.2 Types of correlation
9.8.3 Determining the strength of the correlation
9.8.4 Computing the correlation coefficient
Summary
Chapter 10: Browser-based visualization
10.1 Expanding your toolkit
10.2 Getting the code and data
10.3 Choosing a chart type
10.4 Line chart for New York City temperature
10.4.1 The most basic C3 line chart
10.4.2 Adding real data
10.4.3 Parsing the static CSV file
10.4.4 Adding years as the X axis
10.4.5 Creating a custom Node.js web server
10.4.6 Adding another series to the chart
10.4.7 Adding a second Y axis to the chart
10.4.8 Rendering a time series chart
10.5 Other chart types with C3
10.5.1 Bar chart
10.5.2 Horizontal bar chart
10.5.3 Pie chart
10.5.4 Stacked bar chart
10.5.5 Scatter plot chart
10.6 Improving the look of our charts
10.7 Moving forward with your own projects
Summary
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
11.5.1 Web scraping
11.5.2 Other uses
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
12.6.1 HTTP POST for infrequent data submission
12.6.2 Sockets for high-frequency data submission
12.7 Refactor for configuration
12.8 Data capture
12.9 An event-based architecture
Code restructure for event handling
12.10.1 Triggering SMS alerts
12.10.2 Automatically generating a daily report
Live data processing
Live visualization
Summary
Chapter 13: Advanced visualization with D3
13.1 Advanced visualization
13.2 Getting the code and data
13.3 Visualizing space junk
13.4 What is D3?
13.5 The D3 data pipeline
13.6 Basic setup
13.7 SVG crash course
13.7.1 SVG circle
13.7.2 Styling
13.7.3 SVG text
13.7.4 SVG group
13.8 Building visualizations with D3
13.8.1 Element state
13.8.2 Selecting elements
13.8.3 Manually adding elements to our visualization
13.8.4 Scaling to fit
13.8.5 Procedural generation the D3 way
13.8.6 Loading a data file
13.8.7 Color-coding the space junk
13.8.8 Adding interactivity
13.8.9 Adding a year-by-year launch animation
Summary
Chapter 14: Getting to production
14.1 Production concerns
14.2 Taking our early warning system to production
14.3 Deployment
14.4 Monitoring
14.5 Reliability
14.5.1 System longevity
14.5.2 Practice defensive programming
14.5.3 Data protection
14.5.4 Testing and automation
14.5.5 Handling unexpected errors
14.5.6 Designing for process restart
14.5.7 Dealing with an ever-growing database
14.6 Security
14.6.1 Authentication and authorization
14.6.2 Privacy and confidentiality
14.6.3 Secret configuration
14.7 Scaling
14.7.1 Measurement before optimization
14.7.2 Vertical scaling
14.7.3 Horizontal scaling
Summary
appendix a: JavaScript cheat sheet
Updates
Logging
Objects
Arrays
Regular expressions
Read and write text files (Node.js, synchronous)
Read and write JSON files (Node.js, synchronous)
Read and write CSV files (Node.js, synchronous)
appendix b: Data-Forge cheat sheet
Updates
Loading data into a DataFrame
Loading CSV files
Loading JSON files
Data transformation
Data filtering
Removing a column
Saving CSV files
Saving JSON files
appendix c: Getting started with Vagrant
Updates
Installing VirtualBox
Installing Vagrant
Creating a virtual machine
Installing software on your virtual machine
Running code on your virtual machine
Turning off your virtual machine
Index
List of Figures
List of Tables
List of Listings

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Data Wrangling with JavaScript

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