## Will you buy a Christmas tree?

Not everyone buys a Christmas tree. š Draw a donut chart of people's thoughts.

## How it works āļø

This donut chart build was short and sweet. D3 has all the ingredients we need, Chroma's got the colors, d3-svg-legend has nice legend stuff. Oh and we used it as an excuse to update my d3blackbox library so it actually exports the hooks version.

Thought it did, had it in the docs, published version didn't have it. 20 day old issue report on GitHub. Oops š

You can see data loading in the Codesandbox above. Here's the fun stuff

## React and D3 pie chart tutorial with React hooks

Pie charts and donut charts are the same. If there's a hole in the middle it's a donut, otherwise it's a pie. You should always make donuts because donuts are delicious and easier to read due to intricacies around area size perception.

Our code fits in a functional React component

`const TreeDonut = ({ data, x, y, r }) => {}`

Takes `data`, `x,y` coordinates for positioning, and `r` for the total radius.

We begin with a bunch of D3 objects. Scales, pie generators, things like that.

`const pie = d3.pie().value((d) => d.percentage)const arc = d3.arc().innerRadius(90).outerRadius(r).padAngle(0.01)const color = chroma.brewer.set1const colorScale = d3  .scaleOrdinal()  .domain(data.map((d) => d.answer))  .range(color)`

Here's what they do:

1. The `d3.pie()` generator takes data and returns everything you need to create a pie chart. Start and end angles of each slice and a few extras.
2. The `d3.arc()` generator creates path definitions for pie slices. We define inner and outer radiuses and add some padding.
3. We take the `color` list from one of Chroma's pre-defined colors.
4. We'll use `colorScale` for the legend. Maps answers from our dataset to their colors

Next thing we need is some state for the overlay effect. It says which slice is currently selected.

`const [selected, setSelected] = useState(null)`

Hooks make this way too easy. š We'll use `setSelected` to set the value and store it in `selected`.

Then we render it all with a loop.

`return (  <g transform={`translate(\${x}, \${y})`}>    {pie(data).map((d) => (      <path        d={arc          .outerRadius(selected === d.index ? r + 10 : r)          .innerRadius(selected === d.index ? 85 : 90)(d)}        fill={color[d.index]}        onMouseOver={() => setSelected(d.index)}        onMouseOut={() => setSelected(null)}      />    ))}    <Legend x={r} y={r} colorScale={colorScale} />  </g>)`

A grouping element positions our piechart from the center out.

Inside that group, we iterate over the output of our `pie()` generator and render a `<path>` for each entry. Its shape comes from the `arc` generator.

We update inner and outer radius on the fly depending on whether the current slice is highlighted. This creates the become-bigger-on-mouse-over effect. We drive it with mouse event callbacks and the `setSelected` method.

`setSelected` stores the current selected index in `selected`. This triggers a re-render. The selected slice shows as bigger.

Perfect š

## PS: The legend component with hooks is a piece of cake

`d3-svg-legend` does it all for us. We use `useD3` from my d3blackbox to make it work.

```const Legend = function ({ x, y, colorScale }) {  const ref = useD3((anchor) => {    d3.select(anchor).call(d3legend.legendColor().scale(colorScale))  }).css-13aqjzy{display:inline-block;}
return <g transform={`translate(\${x}, \${y})`} ref={ref} />}```

Lets us render any D3 code into an anchor element and wrap it in a React component. Behind the scenes `useD3` is a combination of `useRef` and `useEffect`.

Enjoy āļø

Hi, Iām Swizec Teller. I help coders become software engineers.

Story time š

React+D3 started as a bet in April 2015. A friend wanted to learn React and challenged me to publish a book. A month later React+D3 launched with 79 pages of hard earned knowledge.

In April 2016 it became React+D3 ES6. 117 pages and growing beyond a single big project it was a huge success. I kept going, started live streaming, and publishing videos on YouTube.

In 2017, after 10 months of work, React + D3v4 became the best book I'd ever written. At 249 pages, many examples, and code to play with it was designed like a step-by-step course. But I felt something was missing.

So in late 2018 I rebuilt the entire thing as React for Data Visualization ā a proper video course. Designed for busy people with real lives like you. Over 8 hours of video material, split into chunks no longer than 5 minutes, a bunch of new chapters, and techniques I discovered along the way.

React for Data Visualization is the best way to learn how to build scalable dataviz components your whole team can understand.

Some of my work has been featured in š

Created bySwizecwith ā¤ļø