Book Image

Learning Responsive Data Visualization

By : Christoph Körner
Book Image

Learning Responsive Data Visualization

By: Christoph Körner

Overview of this book

Using D3.js and Responsive Design principles, you will not just be able to implement visualizations that look and feel awesome across all devices and screen resolutions, but you will also boost your productivity and reduce development time by making use of Bootstrap—the most popular framework for developing responsive web applications. This book teaches the basics of scalable vector graphics (SVG), D3.js, and Bootstrap while focusing on Responsive Design as well as mobile-first visualizations; the reader will start by discovering Bootstrap and how it can be used for creating responsive applications, and then implement a basic bar chart in D3.js. You will learn about loading, parsing, and filtering data in JavaScript and then dive into creating a responsive visualization by using Media Queries, responsive interactions for Mobile and Desktop devices, and transitions to bring the visualization to life. In the following chapters, we build a fully responsive interactive map to display geographic data using GeoJSON and set up integration testing with Protractor to test the application across real devices using a mobile API gateway such as AWS Device Farm. You will finish the journey by discovering the caveats of mobile-first applications and learn how to master cross-browser complications.
Table of Contents (16 chapters)
Learning Responsive Data Visualization
About the Author
About the Reviewer

Chapter 3. Loading, Filtering, and Grouping Data

In the previous chapter, you learned to draw a simple bar chart. In this chapter, you will learn to load, parse, and display real data from a remote location with this chart. In this chapter, you will learn the following:

  • Why we need to preprocess our data

  • How to filter outliers

  • How to map data to another representation

  • How to aggregate information

  • How to load data using D3

  • How to parse strings using Regular Expressions

  • How to parse date strings

  • How to format numbers

  • How to group flat data

First, we will start with some basic preprocessing steps that are always necessary. We will use the filter function to filter outliers and unexpected values from the dataset; we will also use the map function to access inner properties of array elements.

Then, you will learn about reduce functions and how they can help in aggregating and extracting information out of the data. We will implement our own simple reduce function and also take a look at the ones provided...