Web Datavis researcher by day, Indie Game designer by night
Public
Edited
Jun 8, 2023
chart = SlopeChart(data, {
x: d => d.time,
y: d => d.value,
z: d => d.class,
width,
height: 600,
strokeWidth: 0.05,
mixBlendMode: 'multiply'
})
amount = 4000
before = d3.range(amount).map(c => ({time: 'before', class: c, value: d3.randomNormal(50,5)()}))
after = before.map(d => ({time: 'after', class: d.class, value: d.value - d3.randomNormal(10, 30)()}))
data = [before, after].flat(1)
// Copyright 2021 Observable, Inc.
// Released under the ISC license.
// https://observablehq.com/@d3/slope-chart
function SlopeChart(data, {
x = ([x]) => x, // given d in data, returns a (ordinal) column name
y = ([, y]) => y, // given d in data, returns a (quantitative) value
z = () => 1, // given d in data, returns a (categorical) series name
defined, // for gaps in data
curve = d3.curveLinear, // method of interpolation between points; try d3.curveBumpX
marginTop = 20, // top margin, in pixels
marginRight = 30, // right margin, in pixels
marginBottom = 20, // bottom margin, in pixels
marginLeft = 30, // left margin, in pixels
inset, // additional margins
insetTop = inset === undefined ? 20 : inset, // separation between y-axis and top line
insetBottom = inset === undefined ? 0 : inset, // additional bottom margin
labelPadding = 3, // padding from the start or end of the line to label, in pixels
labelSeparation = 10, // separation in pixels for avoiding label collisions
width = 640, // outer width, in pixels
height = 400, // outer height, in pixels
xDomain, // array of x-values
xRange = [marginLeft, width - marginRight], // [left, right]
xPadding = 0.5, // padding for the x-scale (for first and last column)
yType = d3.scaleLinear, // type of y-scale
yDomain, // [ymin, ymax]
yRange = [height - marginBottom - insetBottom, marginTop + insetTop], // [bottom, top]
yFormat, // a format for the value in the label
zDomain, // array of z-values
color = "currentColor", // alias for stroke
stroke = color, // stroke color of line
strokeLinecap, // stroke line cap of line
strokeLinejoin, // stroke line join of line
strokeWidth, // stroke width of line
strokeOpacity, // stroke opacity of line
mixBlendMode, // blend mode of lines
halo = "#fff", // color of label halo
haloWidth = 4, // padding around the labels
} = {}) {
// Compute values.
const X = d3.map(data, x);
const Y = d3.map(data, y);
const Z = d3.map(data, z);
if (defined === undefined) defined = (d, i) => !isNaN(Y[i]);
const D = d3.map(data, defined);
// Compute default domains, and unique the x- and z-domains.
if (xDomain === undefined) xDomain = X;
if (yDomain === undefined) yDomain = d3.extent(Y);
if (zDomain === undefined) zDomain = Z;
xDomain = new d3.InternSet(xDomain);
zDomain = new d3.InternSet(zDomain);
// Omit any data not present in the x- and z-domain.
const I = d3.range(X.length).filter(i => xDomain.has(X[i]) && zDomain.has(Z[i]));
// Construct scales, axes, and formats.
const xScale = d3.scalePoint(xDomain, xRange).padding(xPadding);
const yScale = yType(yDomain, yRange);
const colorScale = d3.scaleSequential(d3.interpolateRdBu)
.domain([-50,50])
.clamp(true);
const xAxis = d3.axisTop(xScale).tickSizeOuter(0);
yFormat = yScale.tickFormat(100, yFormat);
// Construct a line generator.
const line = d3.line()
.defined(i => D[i])
.curve(curve)
.x(i => xScale(X[i]))
.y(i => yScale(Y[i]));
const svg = d3.create("svg")
.attr("width", width)
.attr("height", height)
.attr("viewBox", [0, 0, width, height])
.attr("style", "max-width: 100%; height: auto; height: intrinsic;")
.attr("font-family", "sans-serif")
.attr("font-size", 10);
svg.append("g")
.attr("transform", `translate(0,${marginTop})`)
.call(xAxis)
.call(g => g.select(".domain").remove());
svg.append("g")
.attr("fill", "none")
.attr("stroke", stroke)
.attr("stroke-linecap", strokeLinecap)
.attr("stroke-linejoin", strokeLinejoin)
.attr("stroke-width", strokeWidth)
.attr("stroke-opacity", strokeOpacity)
.selectAll("path")
.data(d3.group(I, i => Z[i]))
.join("path")
.style("mix-blend-mode", mixBlendMode)
.attr("d", ([, I]) => line(I))
.attr("stroke", ([, I]) => colorScale( Y[I[1]]-Y[I[0]] ))
//.attr("stroke", ([, I]) => Y[I[0]] < Y[I[1]] ? 'steelblue' : Y[I[0]] > Y[I[1]] ? 'brown' : 'black');
/*
const Ix = d3.group(I, i => X[i]);
// Iterates over each column, applying the dodge heuristic on inline labels.
for (const [i, x] of [...xDomain].entries()) {
const text = svg.append("g")
.attr("text-anchor", i === 0 ? "end"
: i === xDomain.size - 1 ? "start"
: "middle")
.selectAll("text")
.data(Ix.get(x))
.join("text")
.attr("x", xScale(x))
.call(dodgeAttr, "y", i => yScale(Y[i]), labelSeparation)
.attr("dy", "0.35em")
.attr("dx", i === 0 ? -1
: i === xDomain.size - 1 ? 1
: 0 * labelPadding)
.text(i === 0 ? i => `${Z[i]} ${yFormat(Y[i])}`
: i === xDomain.size - 1 ? i => `${yFormat(Y[i])} ${Z[i]}`
: i => yFormat(Y[i]))
.call(text => text.clone(true))
.attr("fill", "none")
.attr("stroke", halo)
.attr("stroke-width", haloWidth);
}
// Sets the specified named attribution on the given selection to the given values,
// after applying the dodge heuristic to those values to ensure separation. Note
// that this assumes the selection is not nested (only a single group).
function dodgeAttr(selection, name, value, separation) {
const V = dodge(selection.data().map(value), separation);
selection.attr(name, (_, i) => V[i]);
}
// Given an array of positions V, offsets positions to ensure the given separation.
function dodge(V, separation, maxiter = 10, maxerror = 1e-1) {
const n = V.length;
if (!V.every(isFinite)) throw new Error("invalid position");
if (!(n > 1)) return V;
let I = d3.range(V.length);
for (let iter = 0; iter < maxiter; ++iter) {
I.sort((i, j) => d3.ascending(V[i], V[j]));
let error = 0;
for (let i = 1; i < n; ++i) {
let delta = V[I[i]] - V[I[i - 1]];
if (delta < separation) {
delta = (separation - delta) / 2;
error = Math.max(error, delta);
V[I[i - 1]] -= delta;
V[I[i]] += delta;
}
}
if (error < maxerror) break;
}
return V;
}
*/
return svg.node();
}
import {howto, altplot} from "@d3/example-components"
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
