Book Image

Learn Chart.js

By : Helder da Rocha
Book Image

Learn Chart.js

By: Helder da Rocha

Overview of this book

Chart.js is a free, open-source data visualization library, maintained by an active community of developers in GitHub, where it rates as the second most popular data visualization library. If you want to quickly create responsive Web-based data visualizations for the Web, Chart.js is a great choice. This book guides the reader through dozens of practical examples, complete with code you can run and modify as you wish. It is a practical hands-on introduction to Chart.js. If you have basic knowledge of HTML, CSS and JavaScript you can learn to create beautiful interactive Web Canvas-based visualizations for your data using Chart.js. This book will help you set up Chart.js in a Web page and show how to create each one of the eight Chart.js chart types. You will also learn how to configure most properties that override Chart’s default styles and behaviors. Practical applications of Chart.js are exemplified using real data files obtained from public data portals. You will learn how to load, parse, filter and select the data you wish to display from those files. You will also learn how to create visualizations that reveal patterns in the data. This book is based on Chart.js version 2.7.3 and ES2015 JavaScript. By the end of the book, you will be able to create beautiful, efficient and interactive data visualizations for the Web using Chart.js.
Table of Contents (14 chapters)
Title Page
Copyright and Credits
About Packt

Configuring advanced scales

This section contains a brief overview on some configurations that you will probably not use very often. For more details on these topics, refer to the official documentation.

Multiple Cartesian axes

You only need two axes to plot data in a two-dimensional Cartesian grid, but you can add more if you need to. You may wish to repeat axis titles or tick labels on both sides of a chart for clarity. You may also wish to show two datasets with different scales (although this is usually a bad practice in data visualization).

If you have multiple axes, you can control their positions with the axis.weight and axis.position properties. Unless you connect an axis to a specific dataset using the id property, the first axis in the yAxis array will be used for all datasets. A dataset is linked to an axis using the yAxisID or xAxisID properties that reference the ID of an axis. See Advanced/adv-1-position-evil.html for an example.

The following code fragment configures three axes...