{
const svg = d3.select(DOM.svg(width, width))
.attr("viewBox", `${-padding} 0 ${width} ${width}`)
.style("max-width", "100%")
.style("height", "auto");
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
const cell = svg.append("g")
.selectAll("g")
.data(d3.cross(d3.range(columns.length), d3.range(columns.length)))
.join("g")
.attr("transform", ([i, j]) => `translate(${i * size},${j * size})`);
cell.append("rect")
.attr("fill", "none")
.attr("stroke", "#aaa")
.attr("x", padding / 2 + 0.5)
.attr("y", padding / 2 + 0.5)
.attr("width", size - padding)
.attr("height", size - padding);
cell.each(function([i, j]) {
d3.select(this).selectAll("circle")
.data(data)
.join("circle")
.attr("cx", d => x[i](d[columns[i]]))
.attr("cy", d => y[j](d[columns[j]]));
});
const circle = cell.selectAll("circle")
.attr("r", 3.5)
.attr("fill-opacity", 0.7)
.attr("fill", d => z(d.species));
svg.append("g")
.style("font", "bold 10px sans-serif")
.selectAll("text")
.data(columns)
.join("text")
.attr("transform", (d, i) => `translate(${i * size},${i * size})`)
.attr("x", padding)
.attr("y", padding)
.attr("dy", ".71em")
.text(d => d);
return svg.node();
}
x = columns.map(c => d3.scaleLinear()
.domain(d3.extent(data, d => d[c]))
.rangeRound([padding / 2, size - padding / 2]))
z = d3.scaleOrdinal()
.domain(data.map(d => d.species))
.range(d3.schemeCategory10)
xAxis = {
const axis = d3.axisBottom()
.ticks(6)
.tickSize(size * columns.length);
return g => g.selectAll("g").data(x).join("g")
.attr("transform", (d, i) => `translate(${i * size},0)`)
.each(function(d) { return d3.select(this).call(axis.scale(d)); })
.call(g => g.select(".domain").remove())
.call(g => g.selectAll(".tick line").attr("stroke", "#ddd"));
}
yAxis = {
const axis = d3.axisLeft()
.ticks(6)
.tickSize(-size * columns.length);
return g => g.selectAll("g").data(y).join("g")
.attr("transform", (d, i) => `translate(0,${i * size})`)
.each(function(d) { return d3.select(this).call(axis.scale(d)); })
.call(g => g.select(".domain").remove())
.call(g => g.selectAll(".tick line").attr("stroke", "#ddd"));
}
//data = d3.csvParse(await FileAttachment("iris.csv").text(), d3.autoType)
data = {
const url = "https://raw.githubusercontent.com/rasbt/python-machine-learning-book-2nd-edition"
+ "/master/code/ch10/housing.data.txt";
const header = "CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV\n";
return await d3.text(url)
.then(d => d3.csvParse(header + d.trim().replace(/\n /g, "\n").replace(/[ ,]+/g, ','), d3.autoType))
}
//columns = data.columns.filter(d => d !== "species")
columns = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV'];
width = 954
size = (width - (columns.length + 1) * padding) / columns.length + padding
padding = 20
d3 = require("d3@^5.9")
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.