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


In this chapter, we learned how to create all of the standard types of charts supported by Chart.js: bar, horizontal bar, line, area, pie, doughnut, polar area, radar, scatter, and bubble charts.

Different charts are more suited for certain types of datasets and purposes than others. We explored the same examples with different charts and saw how each type communicates different aspects of the data, revealing correlations, proportions, trends, and hidden patterns.

Each chart was introduced with a simple example, but we also created some real world visualizations using public CSV and JSON data, which needed to be downscaled, combined, filtered, and mapped to data formats expected by Chart.js.

We also experimented with several configuration properties, for graphical elements, datasets, and charts, allowing a high degree of customization. Many of these will be explored in greater detail in the next chapters.