{"url":"https://api.github.com/gists/1305337","forks_url":"https://api.github.com/gists/1305337/forks","commits_url":"https://api.github.com/gists/1305337/commits","id":"1305337","node_id":"MDQ6R2lzdDEzMDUzMzc=","git_pull_url":"https://gist.github.com/1305337.git","git_push_url":"https://gist.github.com/1305337.git","html_url":"https://gist.github.com/mbostock/1305337","files":{".block":{"filename":".block","type":"text/plain","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/1305337/raw/703d310b399098a243a76a50bc209167e924cfd2/.block","size":17,"truncated":false,"content":"license: gpl-3.0\n","encoding":"utf-8"},"README.md":{"filename":"README.md","type":"text/markdown","language":"Markdown","raw_url":"https://gist.githubusercontent.com/mbostock/1305337/raw/39f11464888437ea36ef0faec3a44268c5baea91/README.md","size":244,"truncated":false,"content":"An example of multiple pie (donut) charts created with [D3](http://mbostock.github.com/d3/). The data is represented as a tabular array of objects; each row in the table is mapped to an arc, and rows are grouped into pie charts using `d3.nest`.","encoding":"utf-8"},"flights.csv":{"filename":"flights.csv","type":"text/csv","language":"CSV","raw_url":"https://gist.githubusercontent.com/mbostock/1305337/raw/aca00d8a2eb119093fbfb7b2671239399b3630a7/flights.csv","size":577,"truncated":false,"content":"origin,carrier,count\nATL,9E,5\nATL,AA,22\nATL,CO,11\nATL,DL,360\nATL,EV,245\nATL,F9,3\nATL,FL,241\nATL,MQ,2\nATL,NW,14\nATL,OH,12\nATL,OO,11\nATL,UA,8\nATL,US,9\nATL,XE,3\nATL,YV,7\nDFW,9E,2\nDFW,AA,448\nDFW,AS,3\nDFW,CO,10\nDFW,DL,10\nDFW,F9,4\nDFW,FL,7\nDFW,MQ,254\nDFW,NW,5\nDFW,OH,3\nDFW,OO,5\nDFW,UA,13\nDFW,US,18\nDFW,XE,4\nORD,AA,203\nORD,AS,4\nORD,B6,7\nORD,CO,14\nORD,DL,10\nORD,MQ,253\nORD,NW,17\nORD,OH,9\nORD,OO,125\nORD,UA,228\nORD,US,19\nORD,XE,3\nORD,YV,60\nSFO,AA,34\nSFO,AS,14\nSFO,B6,5\nSFO,CO,14\nSFO,DL,12\nSFO,F9,4\nSFO,FL,2\nSFO,HA,1\nSFO,MQ,5\nSFO,NW,10\nSFO,OO,110\nSFO,UA,125\nSFO,US,20\nSFO,WN,24\nSFO,XE,5\n","encoding":"utf-8"},"index.html":{"filename":"index.html","type":"text/html","language":"HTML","raw_url":"https://gist.githubusercontent.com/mbostock/1305337/raw/01ab7bffb50ebc55679cd83f5949d5f9be09c3ab/index.html","size":3134,"truncated":false,"content":"<!DOCTYPE html>\n<meta charset=\"utf-8\">\n<style>\n\nbody {\n  text-align: center;\n}\n\nsvg {\n  font: 10px sans-serif;\n}\n\n</style>\n<body>\n<script src=\"//d3js.org/d3.v3.min.js\"></script>\n<script>\n\n// Define the margin, radius, and color scale. Colors are assigned lazily, so\n// if you want deterministic behavior, define a domain for the color scale.\nvar m = 10,\n    r = 100,\n    z = d3.scale.category20c();\n\n// Define a pie layout: the pie angle encodes the count of flights. Since our\n// data is stored in CSV, the counts are strings which we coerce to numbers.\nvar pie = d3.layout.pie()\n    .value(function(d) { return +d.count; })\n    .sort(function(a, b) { return b.count - a.count; });\n\n// Define an arc generator. Note the radius is specified here, not the layout.\nvar arc = d3.svg.arc()\n    .innerRadius(r / 2)\n    .outerRadius(r);\n\n// Load the flight data asynchronously.\nd3.csv(\"flights.csv\", function(error, flights) {\n  if (error) throw error;\n\n  // Nest the flight data by originating airport. Our data has the counts per\n  // airport and carrier, but we want to group counts by aiport.\n  var airports = d3.nest()\n      .key(function(d) { return d.origin; })\n      .entries(flights);\n\n  // Insert an svg element (with margin) for each airport in our dataset. A\n  // child g element translates the origin to the pie center.\n  var svg = d3.select(\"body\").selectAll(\"div\")\n      .data(airports)\n    .enter().append(\"div\") // http://code.google.com/p/chromium/issues/detail?id=98951\n      .style(\"display\", \"inline-block\")\n      .style(\"width\", (r + m) * 2 + \"px\")\n      .style(\"height\", (r + m) * 2 + \"px\")\n    .append(\"svg\")\n      .attr(\"width\", (r + m) * 2)\n      .attr(\"height\", (r + m) * 2)\n    .append(\"g\")\n      .attr(\"transform\", \"translate(\" + (r + m) + \",\" + (r + m) + \")\");\n\n  // Add a label for the airport. The `key` comes from the nest operator.\n  svg.append(\"text\")\n      .attr(\"dy\", \".35em\")\n      .attr(\"text-anchor\", \"middle\")\n      .text(function(d) { return d.key; });\n\n  // Pass the nested per-airport values to the pie layout. The layout computes\n  // the angles for each arc. Another g element will hold the arc and its label.\n  var g = svg.selectAll(\"g\")\n      .data(function(d) { return pie(d.values); })\n    .enter().append(\"g\");\n\n  // Add a colored arc path, with a mouseover title showing the count.\n  g.append(\"path\")\n      .attr(\"d\", arc)\n      .style(\"fill\", function(d) { return z(d.data.carrier); })\n    .append(\"title\")\n      .text(function(d) { return d.data.carrier + \": \" + d.data.count; });\n\n  // Add a label to the larger arcs, translated to the arc centroid and rotated.\n  g.filter(function(d) { return d.endAngle - d.startAngle > .2; }).append(\"text\")\n      .attr(\"dy\", \".35em\")\n      .attr(\"text-anchor\", \"middle\")\n      .attr(\"transform\", function(d) { return \"translate(\" + arc.centroid(d) + \")rotate(\" + angle(d) + \")\"; })\n      .text(function(d) { return d.data.carrier; });\n\n  // Computes the label angle of an arc, converting from radians to degrees.\n  function angle(d) {\n    var a = (d.startAngle + d.endAngle) * 90 / Math.PI - 90;\n    return a > 90 ? a - 180 : a;\n  }\n});\n\n</script>\n","encoding":"utf-8"},"thumbnail.png":{"filename":"thumbnail.png","type":"image/png","language":null,"raw_url":"https://gist.githubusercontent.com/mbostock/1305337/raw/c9923ef66faaf6cabc97c85b76093097ecf9f2ab/thumbnail.png","size":12289,"truncated":false,"content":"iVBORw0KGgoAAAANSUhEUgAAAOYAAAB4CAYAAADmBo6IAAAAGXRFWHRTb2Z0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\nQG8HxDN4b7RaXDOdf7ccOXL4DnicIQT81ij9fE7jx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