Published
Edited
Jun 5, 2020
chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
svg.append("path")
.datum(pdata_si)
.attr("fill", "none")
.attr("stroke", "steelblue")
.attr("stroke-width", 1.5)
.attr("stroke-linejoin", "round")
.attr("stroke-linecap", "round")
.attr("d", line_si);
svg.append("path")
.datum(pdata_ba)
.attr("fill", "none")
.attr("stroke", "orange")
.attr("stroke-width", 1.5)
.attr("stroke-linejoin", "round")
.attr("stroke-linecap", "round")
.attr("d", line_ba);
return svg.node();
}
// data = Object.assign((d3.csvParse(await FileAttachment("aapl.csv").text(), d3.autoType)).map(({date, close}) => ({date, value: close})), {y: "$ Close"})
sortOrder = ["v6", "v4"];
fieldName = "probesNum"
dd = await FileAttachment("data.csv").text() //.map(d => ({ ...d, date: new Date(d.date)}))
ddd = d3.csvParse(dd).flatMap(r => ([{ probesNum: 100 * Number(r["V6PROBES"]) / Number(r["V4PROBES"]), date: new Date(r['DATE']), name: 'v6' }, { probesNum: Number(r["V4PROBES"]), date: new Date(r['DATE']), name: 'v4'}]) )
data = {
const data = ddd
.filter(d => new Date(d.date) > new Date('2013-10-01'))
.map(d => ({ ...d, date: new Date(d.date)}))
.sort((a,b) => sortOrder.indexOf(a.name) < sortOrder.indexOf(b.name) && -1 || +1 );
const dates = Array.from(d3.group(data, d => +d.date).keys()).sort(d3.ascending);
return {
dates: dates.map(d => new Date(d)),
series: d3.groups(data, d => d.name).map(([name, values]) => {
const value = new Map(values.map(d => [+d.date, d[fieldName]]));
return {name, values: dates.map(d => value.get(d))};
})
};
}
pdata_si = data.series[0].values.map((d,i) => ({ date: data.dates[i], value: d}));
pdata_si[pdata_si.length-1]
pdata_ba = data.series[1].values.map((d,i) => ({ date: data.dates[i], value: d}));
pdata_ba[pdata_ba.length-1]
line_si = d3.line()
.defined(d => !isNaN(d.value))
.x(d => x_si(d.date))
.y(d => y(d.value))
line_ba = d3.line()
.defined(d => !isNaN(d.value))
.x(d => x_ba(d.date))
.y(d => y(d.value))
x_si = d3.scaleUtc()
.domain(d3.extent(pdata_si, d => d.date))
.range([margin.left, width - margin.right])
x_ba = d3.scaleUtc()
.domain(d3.extent(pdata_si, d => d.date))
.range([margin.left, width - margin.right])
y = d3.scaleLinear()
// .domain([0, d3.max(data, d => d.value)]).nice()
.domain([0, 100]).nice()
.range([height - margin.bottom, margin.top])
xAxis = g => g
.attr("transform", `translate(0,${height - margin.bottom})`)
.call(d3.axisBottom(x_si).ticks(width / 80).tickSizeOuter(0))
yAxis = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y))
.call(g => g.select(".domain").remove())
.call(g => g.select(".tick:last-of-type text").clone()
.attr("x", 3)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.text(pdata_si.y))
margin = ({top: 20, right: 30, bottom: 30, left: 40})
height = 500
d3 = require("d3@5", "d3-array@2")
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.