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

Preprocessing data

In this chapter, you will learn about preprocessing and that it is an important step when you deal with real-world data. If we want to design a robust visualization that can handle different remote data sources, we need to make sure we process the data beforehand. Datasets can have outliers, can be noisy, can have different representations, can have nested objects, can be flat, and so on. You can see that this list is very long.

Thus, whenever we deal with real data, we need to process it before using it in a visualization; this step is called preprocessing.

Filtering data to remove outliers

As a first preprocessing step, we will look at filters. Filters are array operators that return an array containing a subset of the original elements. In the following figure, we see an example of such an operation:

Filtering a dataset

Let's try an example. We load the following array from a remote data source:

var data = [
  {x: 1, y: 6},
  {x: 2, y: undefined},
  {x: 3, y: 4},
  {x: 4...