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
Contributors
Preface
Index

Creating a bar chart


A bar chart displays a list of categories associated with values represented by the length of the bars. To create a simple bar chart, we need a list of categories, as well as list of values.

Let's create a simple chart to display the volume of water in each ocean. We will need an array of categories, as follows:

const labels = ["Arctic", "North Atlantic", "South Atlantic",
                "Indian", "North Pacific", "South Pacific",
                "Southern"];

In addition, we will also need a corresponding array of values, as follows:

const volumes = [18750,146000,160000,264000,341000,329000,71800]; // 10^3 km3

The data object should contain a labels property, which will refer to the categories array, and a datasets property, which contains an array with at least one dataset object. Each dataset object has a label property, and a data property, which will receive the data for our chart (the volumes array), as follows:

const dataObj = {
     labels: labels,
     datasets: ...