The Ext JS D3 Heat Map Adapter provides you with incredible built-in data visualization components such as heatmap, treemap, sunburst, and more, as well as complete flexibility to create your own visualizations. You can pick any visualization from the D3.js gallery and create an Ext JS component based on it.
In this article, I’ll introduce a few concepts, components, and utility classes, as well as walk you through the steps of creating your own data visualization UI components such as heat maps.
Table of Contents
Sencha App Camps – Register Today
To learn more, please register for our Data Visualization App Camps taking place in London on October 4th and Munich on October 12th.
What are the Steps to Create a Custom Visualization?
- Choose the base visualization
- Choose an engine: SVG or Canvas
- Define data requirements
- Define axes
- Define legend requirements
- Define tooltip requirements
Now, I’ll go through each step in more detail. I’ll use the following example which is a visualization showing average income in different states.
Is there an Example Visualization?
Step 1: Base Visualization
First, I suggest you find a d3.js visualization that comes close to meeting your requirements. You could use one of the samples from the d3.js gallery or any other resource. This visualization is the base for my example:
Step 2: Visualization Engine – SVG or Canvas
Scalable Vector Graphics (SVG) is an XML-based vector image format for two-dimensional graphics with support for interactivity and animation.
The Canvas element is part of HTML5 and allows for dynamic, scriptable rendering of 2D shapes and bitmap images.
The Ext JS D3 Adapter supports both engines, so you’re free to choose. Here are some things to consider when making your choice.
All major modern web browsers – including Mozilla Firefox, Internet Explorer, Google Chrome, Opera, Safari, and Microsoft Edge – have at least some degree of SVG rendering support.
- Event handling: every item is a separate DOM element. The SVG enables event handlers to be associated with objects, so a rectangle responds to an onClick event.
- Easy modification: every item can be modified independently.
Memory usage: every item is a separate DOM element, so if you have a lot of elements in your visualization, it could be a bottleneck and impact performance.
The Canvas element is supported by the current versions of Mozilla Firefox, Google Chrome, Internet Explorer, Safari, Konqueror and Opera.
- Memory usage: Canvas is a single DOM object, so the page DOM model size remains stable when you add new elements to the Canvas.
- Event handling: To get event handling with Canvas, you have to manually match the coordinates of the mouse click with the coordinates of the drawn rectangle to determine whether it was clicked.
- Any modification requires a full redraw of the Canvas.
For my visualization component, I’ve chosen an SVG engine for the following reasons:
- The item count isn’t very big: I have only 50 states
- Easy event handling
- Easy modification
Step 3: Data Definition
To build my visualization, I need flat data that includes average income value for every state. I can use Ext.data.Store class because my model only has to include two fields:
- The “state” field will store the US state
- The “value” field will store the average income value
Step 4: Axes Definition
For my visualization, I use two axes:
- The mapAxis uses the “state” field to point to the state on the map
- The colorAxis uses the “value” field to illustrate the average income with the specific color
Step 5: Legend Definition
My legend includes:
- Color ticks
- Associated values with formatting
Step 6: Tooltip Definition
The tooltip template looks like this:
[“state” field’s value]: $[”value” field’s value]
What are the Visualization Base Classes?
Every concept that I discussed in the steps above – Graphical Engine, Axis, Legend, and Tooltip – has a separate class in Ext JS that you can easily use when building your own custom visualization.
What are the Component Base Classes?
The base class of every SVG D3 Component can also be used standalone. I won’t use the base class for the US states visualization.
The class has two important events:
- scenesetup – fires once after the scene has been created
- sceneresize – fires after scene size has changed
Both events could be used to draw the visualization.
The base class of every Canvas D3 Component can also be used standalone. I will not use this class in my visualization component.
Store Base Classes
- The Ext.data.Store is used to store flat data that will be displayed in the visualization. This is the base class for the store that I’ll use in my visualization component.
- The Ext.data.TreeStore is used to store hierarchical data that will be displayed in the visualization. I won’t be using this class in my visualization component.
Axis Base Classes
The Ext.d3.axis.Axis class is used to display reference lines for D3 scales. The Ext.d3.axis.Axis component wraps it with an added ability to display an axis title in the user-specified position.
Ext.d3.axis.Data is a sub-class of Ext.d3.axis.Axis that holds extra information needed for use with stores. This will be the base class for my mapAxis.
The Ext.d3.axis.Color maps data values to colors. This will be the base class for my colorAxis.
Legend Base Class
Tooltip Base Class
The ToolTip mixin is used to add Ext.tip.ToolTip support to various D3 components. This will be the base class for my tooltip.
Ready to get started with the next steps?
To build a custom visualization component, I need to extend the base visualization class by following these steps: bind the store with the data to visualize, implement handlers for scenesetup and sceneresize events that will draw and resize the d3.js visualization, and add instances of Axis, Legend, and Tooltip classes to draw axes, legend, and tooltips.
To learn the step-by-step process of combining these classes together to build a great looking custom data visualization, please register for our Data Visualization App Camps taking place in London on October 4th and Munich on October 12th. At the App Camp, I’ll also cover advanced customization of built-in d3.js visualization components and data aggregation with Pivot Grids.