Published
Edited
Dec 23, 2019
1 fork
1 star
html`<svg height="300" viewBox="0 0 10 10">
${data.map(x => g(...x))}
</svg>`
viewof t = Scrubber(numbers, {delay: 70})
g = function(x,y,col,t) {
const dStep = [
'',
`M 0 ${y} L ${x} ${y} L ${x} 10`,
`M 0 ${y} L ${x} ${y} L 10 ${x}`,
`M 0 ${y} L ${x} ${x*(x-y)/10 + y} L 10 ${x}`,
`M 0 ${y} L ${x} ${x*(x-y)/10 + y} L 10 ${x}`,
'',
].map((a,i,array) => d3.interpolate(array[i-1],a))
const circStep = [
{cx:x, cy:y, r: .2, fill: col,},
{cx:x, cy:y, r: .2, fill: col,},
{cx:x, cy:y, r: .2, fill: col,},
{cx:x, cy:x*(x-y)/10 + y, r: .2, fill: col,},
{cx:x, cy:x*(x-y)/10 + y, r: 0, fill: col,},
{},
].map(JSON.stringify) //stringify and then parse feels ugly, but it was the fastest solution when I got here.
.map((a,i,array) => d3.interpolate(array[i-1],a))
return html`<svg viewport="0 0 10 10">
<g>
<circle ${JSON.parse(circStep[Math.floor(t)](t - Math.floor(t)))}/>
<path
d=${dStep[Math.floor(t)](t - Math.floor(t))}
fill="none"
stroke-width="0.15"
stroke-linejoin="round"
stroke="${col}"
/>
</g></svg>`
}
data = [
[6,9,colors[3]],
[1,7,colors[0]],
[6,4,colors[1]],
[3,3,colors[2]],
].map(x => x.concat([t]))
colors = ["#d43536", "#852df1", "#f2b0f6", "#6153a0", "#70a5c1"]
title = ['Scatterplot','Perpendicular coordinates?', 'crooked slope chart', 'almost there...', 'slope chart!!!', 'Nothing'][[Math.floor(t)]]
import {html, svg} from "@observablehq/htl"
import {Scrubber} from "@mbostock/scrubber"
d3 = require("d3-interpolate@1")
interp = (x,y) => d3.interpolate(`M 0 ${y} L ${x} ${y} L ${x} 10`,
`M 0 ${y} L ${x} ${y} L 10 ${x}`, `M 0 ${y} L ${x} ${y} L 0 ${x}`)
interp(4,5)(.3)
md`interpolate through an array of strings`
interpolations = (x,y) => [
'',
`M 0 ${y} L ${x} ${y} L ${x} 10`,
`M 0 ${y} L ${x} ${y} L 10 ${x}`,
`M 0 ${y} L ${x} ${x*(x-y)/10 + y} L 10 ${x}`,
`M 0 ${y} L ${x} ${x*(x-y)/10 + y} L 10 ${x}`,
'',
].map((a,i,array) => d3.interpolate(array[i-1],a))
interpolations(3,4)[3](.3)
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.