React for Data Visualization
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How React makes D3 easier

Our visualizations are going to use SVG – an XML-based image format that lets us describe images in terms of mathematical shapes. For example, the source code of an 800x600 SVG image with a rectangle looks like this:

<svg width="800" height="600">
<rect width="100" height="200" x="50" y="20" />

These four lines create an SVG image with a black rectangle at coordinates (50, 20) that is 100x200 pixels large. Black fill with no borders is default for SVG shapes.

SVG is perfect for data visualization on the web because it works in all browsers, renders without blurring or artifacts on all screens, and supports animation and user interaction. You can see examples of interaction and animation later in this book.

But SVG can get slow when you have many thousands of elements on screen. We're going to solve that problem by rendering bitmap images with canvas. More on that later.

Another nice feature of SVG is that it's just a dialect of XML - nested elements describe structure, attributes describe the details. Same principles that HTML uses.

That makes React's rendering engine particularly suited for SVG. Our 100x200 rectangle from before looks like this as a React component:

const Rectangle = () => <rect width="100" height="200" x="50" y="20" />

To use this rectangle component in a picture, you'd use a component like this:

const Picture = () => (
<svg width="800" height="600">
<Rectangle />

Sure looks like tons of work for a static rectangle. But look closely! Even if you know nothing about React and JSX, you can look at that code and see that it's a Picture of a Rectangle.

Compare that to a pure D3 approach:"svg")
.attr("width", 800)
.attr("height", 600)
.attr("width", 100)
.attr("height", 200)
.attr("x", 50)
.attr("y", 20)

It's elegant, it's declarative, and it looks like function call soup. It doesn't scream "Rectangle in an SVG" to as much as the React version does.

You have to take your time and read the code carefully: first, we select the svg element, then we add attributes for width and height. After that, we append a rect element and set its attributes for width, height, x, and y.

Those 8 lines of code create HTML that looks like this:

<svg width="800" height="600">
<rect width="100" height="200" x="50" y="20" />

Would've been easier to just write the HTML, right? Yes, for static images, you're better off using Photoshop or Sketch then exporting to SVG.

Dealing with the DOM is not D3's strong suit. There's a lot of typing, code that's hard to read, it's slow when you have thousands of elements, and it's often hard to keep track of which elements you're changing. D3's enter-update-exit cycle is great in theory, but most people struggle trying to wrap their head around it.

If you don't understand what I just said, don't worry. We'll cover the enter-update-exit cycle in the animations example.

Don't worry about D3 either. It's hard! I've written two books about D3, and I still spend as much time reading the docs as writing the code. The library is huge and there's much to learn. I'll explain everything as we go along.

D3's strong suit is its ability to do everything except the DOM. There are statistical functions, great support for data manipulation, a bunch of built-in data visualizations, magic around transitions and animation ... D3 can calculate anything for you. All you have to do is draw it out.

That's why our general approach sounds like this in a nutshell:

  • React owns the DOM
  • D3 calculates properties

We leverage React for SVG structure and rendering optimizations; D3 for its mathematical and visualization functions.

Now let's look at three different ways of using React and D3 to build data visualization:

  • using a library
  • quick blackbox components
  • full feature integration
Recap (1:49)
What about existing libraries? (6:19)
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