D3.js is the state-of-the-art library for data visualization. Check out the gallery for stunning and beautiful examples. Happily, many visualizations are given along their source code, so that you can easily duplicate them.
For instance, let’s try to replicate the parallel coordinates chart, created by Mike Bostock. This is a cool (and useful) data viz. A parallel coordinates chart allows you to quickly visualize a multi-dimensional (but relatively small) dataset: you can immediately spot out correlations across dimensions and uncover clusters. In this interactive viz, you can explore the data in depth by filtering values on each dimension with a brush tool.
Furthermore, this data viz is given with the generating D3 code and data! Let’s try to replicate it in a web app.
Create a new webapp. Then, import the D3 library. As a reminder, this post explains how to do this.
Finally, you need to import the data. The parallel coordinates chart is illustrated on the famous cars dataset. In your project, create the
cars dataset from this CSV file. To access this data in your web app, open the Settings screen using the button at the right of the editor. In the dataset list, find the
cars dataset and allow your webapp to read it.
Many D3 code samples, given in the gallery or bl.ocks.org, have the same overall structure.
In the parallel coordinates chart example, there is no HTML code written withing the
<body> tags, and thus no HTMl code to copy in the editor.
Here’s the CSS code, defined within the
<style> tags, that you should copy in the CSS panel of your editor.
The trickiest part in adapting a D3 template is always to shape the data in the format required by the data viz. In the parallel coordinates charts, the data in the D3 code is represented as the
cars JSON array.
But generally you do not directly have JSON data. In many D3 templates, the data is given as a csv file, while in DSS you will have to connect to your dataset (which can be stored in a great variety of format and database system).
In the D3 original code, the data is thus read from the
Then the D3 code defined inside the
d3.csv function is applied on the
cars JSON array.
and copy it in the JS panel of the web app editor.
In other words, keep the entire D3 code unchanged expect the call to the
d3.csv function, that is replaced by defining the
parallelCoordinatesChart function, which takes the
cars JSON array as input.
Now, we only need to connect to the
cars dataset through the dataiku JS API, in order to create the corresponding cars JSON array.
Notice that, when you selected the cars datsaset in your web app, the following code was automatically added:
PROJECTNAME actually stands for the name of your project.
You finally need to copy the JS code defined in the
dataiku.fetch below. This code creates the
cars JSON array and calls the
parallelCoordinatesChart function to create the chart.
That’s it, you have a running D3 data viz in your web app!