my-workflow-d3

This line chart is constructed from a TSV file storing the daily average temperatures of New York, San Francisco and Austin over the last year. The chart employs conventional margins and a number of D3 features:

d3.tsv - load and parse data
d3.time.format - parse dates
d3.time.scale - x-position encoding
d3.scale.linear - y-position encoding
d3.scale.category10, a d3.scale.ordinal - color encoding
d3.extent, d3.min and d3.max - compute domains
d3.keys - compute column names
d3.svg.axis - display axes
d3.svg.line - display line shape

forked from mbostock's block: Multi-Series Line Chart

forked from EmbraceLife's block: 30. Multi-Series Line Chart

forked from EmbraceLife's block: 30. stock return chart

forked from EmbraceLife's block: 30. 3 stock return chart

forked from EmbraceLife's block: 30. 3-stock-return-missing-data chart

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

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

.line {
  fill: none;
  stroke: steelblue;
  stroke-width: 1.5px;
}

</style>

var svg = d3.select("svg");
  
var  
    margin = {top: 20, right: 80, bottom: 30, left: 50},
    width = svg.attr("width") - margin.left - margin.right,
    height = svg.attr("height") - margin.top - margin.bottom;
  
var  
    g = svg.append("g")
				.attr("transform", "translate(" + margin.left + "," + margin.top + ")");

// ---------- step 1 above: create svg and g  --------------------------
  
  
var parseTime = d3.timeParse("%Y%m%d");

var x = d3.scaleTime().range([0, width]),
    y = d3.scaleLinear().range([height, 0]),
    z = d3.scaleOrdinal(d3.schemeCategory10);

var line = d3.line() 
    .curve(d3.curveBasis)
		.defined(function(d){return d})
    .x(function(d) { if(d !== null) return x(d.date); })
    .y(function(d) { if(d !== null) return y(d.return); });

// ------------ step 3.a above: define scale functions with their ranges ----------
  
  
  
  
function type(d, _, columns) {
  d.date = parseTime(+d.date);
//   i = 1 to skip date, c = columns[i] for +d[c]
  for (var i = 1, n = columns.length, c; i < n; ++i) d[c = columns[i]] = +d[c];
  return d;  
} 
    
  
d3.csv("data_filled.csv", type,  function(error, data) {
  if (error) throw error;  
  
//   console.log(data.columns);
//   console.log(data); 
//   console.log(data[0].date);  
  
  var returns = data.columns.slice(1).map(function(id) {
    return {
      id: id,
      values: data.map(function(d) {
        if(d[id]+"" === "NaN") {
          return null;
        } else {
        return {date: d.date, return: d[id]};
        }
      }) 
    }; 
  }); 
//   console.log(returns);   
//   console.log(returns[0].values);

// ---------------- step 2 above: get row-data and column-data ready -------------
  
  x.domain(d3.extent(returns[2].values, function(d) { return d.date; }));
// console.log(x.domain());

y.domain(
    [d3.min(returns, function(r){return d3.min(r.values, function(p){ if(p !== null) return p.return;});}), 
    d3.max(returns, function(r){return d3.max(r.values, function(p){if(p !== null) return p.return;});})] 
  );
//   console.log(y.domain());

z.domain(returns.map(function(c) { return c.id; }));

// ------------------ step 3.b above: set scale-func domain ----------------------
  
  
  g.append("g")
      .attr("class", "axis axis--x")
      .attr("transform", "translate(0," + height + ")")
      .call(d3.axisBottom(x));

g.append("g")
      .attr("class", "axis axis--y")
      .call(d3.axisLeft(y))
    .append("text")
			.attr("x",20)
      .attr("y", 6)
      .attr("dy", "0.71em")
  		.attr("text-anchor", "start")
      .attr("fill", "#000")
      .text("return, 1-base");

var rr = g.selectAll(".return")
    .data(returns)
    .enter().append("g")
      .attr("class", ".return");

</script>

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