D3 Examples - step3

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<!DOCTYPE html>
<meta charset="utf-8">
<style>
​
.axis--x path {
  display: none;
}
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.line {
  fill: none;
  stroke: steelblue;
  stroke-width: 1.5px;
}
​
</style>
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<svg width="960" height="500"></svg>
​
<script src="//d3js.org/d3.v4.min.js"></script>
<script>
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var svg = d3.select("svg"),
    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,
    g = svg.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")");
​
  
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)
    .x(function(d) { return x(d.date); })
    .y(function(d) { return y(d.temperature); });
​
  
// 1. tranform each row of data as needed  
// 1.1 d => a row of data turn => as object 
// 1.2 columns => column names
// 1.3  _ is index of each row, ignored here
function type(d, _, columns) {
  
// 2. apply data to date format func 
  d.date = parseTime(d.date);
  
  
  
// 3 convert temperature from string type to numeric type
// 3.1 loop every column except the date column
// 3.2 assign each column name to variable c  
// 3.3 extract each city's temperature value of a row using column name
// 3.4 + convert string number to numeric number
  for (var i = 1, n = columns.length, c; i < n; ++i) d[c = columns[i]] = +d[c];
  return d;
}  
  
  
  
// 4. load data file and preprocess data 
// 4.1 d3.tsv("data.tsv").row(type).get(function(error, data){ })
d3.tsv("data.tsv", type, function(error, data) {
//   4.2 the transformed data is stored inside variable data
//   4.3 data is array of objects, each row is an object 
//   4.4 each object has 4 named values: date and 3 cities' temp
console.log(data);
  
  
  
//   ignore it for now
  if (error) throw error; 
​
  
// 5. if var data organises dataset by rows, var cities organises dataset by columns
//   5.1 create an array with 3 city names
  var cities = data.columns.slice(1)
//   5.2 loop each city name
                            .map(function(id) {
//   5.3 for each city name return an object: 2 named values 
    return {
//   5.4 id: city name
      id: id,
//   5.5 values: array of objects created using every row of data
      values: data.map(function(d) {
//   5.6 inside each object: 2 named values: 
        return {date: d.date, temperature: d[id]};
      }) 
    };
  }); 
​
​
// test it 
console.log(cities);
   
  
});
​
​
​
</script>
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