Published unlisted
Edited
Aug 26, 2020
d3 = require("d3@5")
chart = {
const svg = d3.create("svg")
.attr("viewBox", `0 0 ${width} ${height}`)
.attr("class", "svg-content")
// Drawing axes
svg.append("g")
.attr("class", "axis")
.attr('transform', `translate(0, ${height - margin.bottom})`)
.call(xAxis);
svg.append("g")
.attr("class", "axis")
.attr('transform', `translate(${margin.left}, 0)`)
.call(yAxis)
// adds a label to the Y axis
.append("text")
.attr("transform", "rotate(-90)")
.attr("x", - margin.top)
.attr("y", 16)
.style("text-anchor", "end")
.text("Closing Price");
// Drawing lines
const lines = svg.selectAll("lines")
.data(slices)
.enter()
.append("g")
lines.append("path")
.attr("class", d => `line-${d.num}`)
.attr("fill", "none")
.attr("stroke-width", 2)
.attr("d", d => line(d.values));
// Adding series label at the end of each line
lines.append("text")
.attr("class","series-label")
// datum bypasses the data-join process altogether (enter/update/exit)
// https://ils.unc.edu/~gotz/D3joins/ // data-join visualization
.datum(d => {
return {
id: d.id,
value: d.values[d.values.length - 1]};
})
.attr("transform", d => {
return `translate( ${(xScale(d.value.date))}, ${(yScale(d.value.price))})`;})
.attr("x", 20)
.attr("y", 5)
.text(d => d.id);
// Adding a color square to each series-label
lines.append("rect")
.datum(d => {
return {value: d.values[d.values.length - 1]};
})
.attr("transform", d => {
return `translate( ${xScale(d.value.date)}, ${yScale(d.value.price)})`;})
.attr("x", 5)
.attr("y", -5)
.attr("width", 10)
.attr("height", 10)
return svg.node();
}
dataset = (d3.csvParse(await FileAttachment("stock_data_08-2020@1.csv").text()))
timeConv = d3.timeParse("%Y-%m-%d")
slices = dataset.columns.slice(1).map((id,idx) => {
return {
id: id,
num: idx,
values: dataset.map(d => {
return {
date: timeConv(d.date),
price: +d[id]
};
})
};
})
xScaleMinMax = d3.extent(dataset, d => timeConv(d.date))
yScaleMax = d3.max(slices, column => {
return d3.max(column.values, d => {
return d.price
});
})
xScale = d3.scaleTime()
.domain(xScaleMinMax)
.range([margin.left + 20, width - margin.right])
yScale = d3.scaleLinear()
.domain([0, yScaleMax + 20])
.range([height - margin.bottom, margin.top])
xAxis = d3.axisBottom()
// calculates the ticks to a unit of time aligned with the calendar
.ticks(d3.timeMonth.every(1)) // change .every(2) to display every other month
.tickFormat(d3.timeFormat('%b %Y')) // formats the date
.scale(xScale)
yAxis = d3.axisLeft()
.ticks(10) // spread the ticks by 10 units
.tickFormat(d3.format('$'))
.scale(yScale)
line = d3.line()
.x(d => xScale(d.date))
.y(d => yScale(d.price))
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.