Multi-Line Voronoi w commuter data

This multi-line chart uses an invisible Voronoi tessellation to handle mouseover; the closest point to the mouse on any line is highlighted. Click the checkbox in the top-right to toggle the visibility of the Voronoi overlay.

forked from mbostock's block: Multi-Line Voronoi

<!DOCTYPE html>
<meta charset="utf-8">
<style>

.axis--y path {
  display: none;
}

.sites {
  fill: none;
  stroke: #aaa;
  stroke-linejoin: round;
  stroke-linecap: round;
  stroke-width: 1.5px;
}

.site--hover {
  stroke: #000;
}

.focus text {
  text-anchor: middle;
  text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
}

.voronoi path {
  fill: none;
  pointer-events: all;
}

.voronoi--show path {
  stroke: red;
  stroke-opacity: 0.2;
}

#form {
  position: absolute;
  top: 20px;
  right: 30px;
}

</style>
<svg width="960" height="500"></svg>
<label id="form" for="show-voronoi">
  Show Voronoi
  <input type="checkbox" id="show-voronoi" disabled>
</label>
<script src="https://d3js.org/d3.v4.min.js"></script>
<script>

//var months,
   // monthKeys,
   // monthParse = d3.timeParse("%Y-%m");
var steps,
    stepKeys;

var svg = d3.select("svg"),
    margin = {top: 20, right: 30, bottom: 30, left: 40},
    width = svg.attr("width") - margin.left - margin.right,
    height = svg.attr("height") - margin.top - margin.bottom,
    g = svg.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")");

var xScale = d3.scaleBand()
    .rangeRound([0, width])
    .padding(0.9);
  
var yScale = d3.scaleLinear()
    .range([height, 0]);

var voronoi = d3.voronoi()
    .x(function(d) { return xScale(d.step); })
    .y(function(d) { return yScale(d.value); })
    .extent([[-margin.left, -margin.top], [width + margin.right, height + margin.bottom]]);

var line = d3.line()
    .x(function(d) { return xScale(d.step); })
    .y(function(d) { return yScale(d.value); });

d3.csv("datanorth2_testdataset.csv", type, function(error, data) {
  if (error) throw error;

//xScale.domain(d3.extent(months));
  xScale.domain(data.columns.slice(1));
  yScale.domain([0, 6000]);
  //yScale.domain([0, d3.max(data, function(c) { return d3.max(c.values, function(d) { //return d.value; }); })]).nice();

g.append("g")
      .attr("class", "axis axis--x")
      .attr("transform", "translate(0," + height + ")")
      .call(d3.axisBottom(xScale));

g.append("g")
      .attr("class", "axis axis--y")
      .call(d3.axisLeft(yScale).ticks(10))
    .append("text")
      .attr("x", 4)
      .attr("y", 0.5)
      .attr("dy", "0.32em")
      .style("text-anchor", "start")
      .style("fill", "#000")
      .style("font-weight", "bold")
      .text("Commuters");

g.append("g")
      .attr("class", "sites")
    .selectAll("path")
    .data(data)
    .enter().append("path")
      .attr("d", function(d) { d.line = this; return line(d.values); });

var focus = g.append("g")
      .attr("transform", "translate(-100,-100)")
      .attr("class", "focus");

focus.append("circle")
      .attr("r", 3.5);

focus.append("text")
      .attr("y", -10);

var voronoiGroup = g.append("g")
      .attr("class", "voronoi");

voronoiGroup.selectAll("path")
    .data(voronoi.polygons(d3.merge(data.map(function(d) { return d.values; }))))
    .enter().append("path")
      .attr("d", function(d) { return d ? "M" + d.join("L") + "Z" : null; })
      .on("mouseover", mouseover)
      .on("mouseout", mouseout);

d3.select("#show-voronoi")
      .property("disabled", false)
      .on("change", function() { voronoiGroup.classed("voronoi--show", this.checked); });

function mouseover(d) {
    d3.select(d.data.site.line).classed("site--hover", true);
    d.data.site.line.parentNode.appendChild(d.data.site.line);
    focus.attr("transform", "translate(" + xScale(d.data.step) + "," + yScale(d.data.value) + ")");
   // focus.select("text").text(d.data.site.origin);
    focus.select("text").text(d.data.site.origin + ': ' + d.data.value);
  }

function mouseout(d) {
    d3.select(d.data.site.line).classed("site--hover", false);
    focus.attr("transform", "translate(-100,-100)");
  }
});

//function type(d, i, columns) {
  //if (!months) monthKeys = columns.slice(1), months = monthKeys.map(monthParse);
 // var c = {name: d.name.replace(/ (msa|necta div|met necta|met div)$/i, ""), values: //null};
 // c.values = monthKeys.map(function(k, i) { return {city: c, date: months[i], value: d[k] /// 100}; });
//  return c;
//}
function type(d, i, columns) {
  stepKeys = columns.slice(1); // steps = stepKeys; //.map();
  var c = {origin: d.origin, values: null};
  c.values = stepKeys.map(function(k, i) { return {site: c, step: stepKeys[i], value: d[k]}; });
  return c;
}

</script>

https://d3js.org/d3.v4.min.js