Data Analyst
Published
Edited
May 5, 2019
md`# Drop viz`
drawRain()
height = 500
width = 500
svg = d3.select(DOM.svg(width, height))
d3 = require("d3@5")
data = d3.csv('https://gist.githubusercontent.com/daranzolin/5f80cdd35c12409c1ed9b475c3f4342f/raw/8ae6c82a6d8b4d39545f50d6eb835cf996ac0fc8/d3rain.csv')
function drawRain() {
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis)
.selectAll("text")
.attr("x", width / 2)
.attr('transform', 'translate(0, 10)')
.style("font", "18px times");
let circles = svg.selectAll('circle')
.data(data)
.enter().append('circle')
.attr('cx', (d, i) => x(i))
.attr('cy', margin.top)
.attr('r', 5)
.attr('fill', 'firebrick')
/*
for (let i = 0; i <= levels.length; i++) {
circles.transition()
.filter(d => d.group == levels[i])
.duration(d => d.group === 'l3' ? 5000 : 1000) */
/*
sleep(2000).then(() => {
setTimeout(function (){
circles.filter(d => d.group == levels[i])
.transition()
.duration(1000)
.ease(d3.easeBounce)
.attr('cy', d => y(d.group) + y.bandwidth() / 2)
}, 1500)
})
} */
circles.transition()
.delay((d, i) => 100 * i)
.duration(1000)
.ease(d3.easeBounce)
.attr('cy', d => y(d.group) + y.bandwidth() / 2)
return svg.node();
}
x = d3.scaleLinear()
.domain([0, data.length])
.range([margin.left, width - margin.right])
xAxis = g => g
.call(d3.axisTop(x))
.call(g => g.select(".domain").remove())
y = d3.scaleBand()
.domain(['l1', 'l2', 'l3'])
.range([margin.top, height - margin.bottom])
yAxis = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y))
.call(g => g.selectAll(".tick line").clone()
.attr("stroke-opacity", 0.2)
.attr("x2", width - margin.right))
.attr('transform', 'translate(0, 10)')
.call(g => g.select(".domain").remove())
margin = ({top: 20, right: 10, bottom: 20, left: 25});
[y('l0'), y('l1'), y('l2'), y("l3")]
millisecondsToWait = 2000
setTimeout(function() {
}, millisecondsToWait);
levels = ['l1', 'l2', 'l3']
levels
duration_waits = [1000, 2000, 3000, 4000, 5000, 6000]
sleep = (milliseconds) => {
return new Promise(resolve => setTimeout(resolve, milliseconds))
}
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.