（一）色阶图
1. 前言
色阶图适合二维的数据，而且横轴跟纵轴的标签都比较多。本期的数据：
Example data shows concurrent user sessions over time, taken from a development environment.
翻译过来大意就是：展示的是随着时间的推移用户会话并发的个数
数据结构：
| 星期数 | 时间点 | 会话数 |
| day | hour | value |
| 1 | 1 | 16 |
图形：
2. 色阶图
1）本地链接：本地色阶图demo展示
2）知识点： 1. 怎么画色阶图
2. 读取csv格式数据画图，并且解决中文乱码问题
3. 图形的转变效果: d3.transition().duration()
4. x轴文字竖放以及标签突出重点
3）图形效果：
自己应用的场景：某品牌商想重点关注自己的产品型号在重点电商店铺的销量情况，这里涉及的型号和店铺都很多，导致excel数据表非常的庞大而稀疏，所以用色阶图会比较直观的展示出top-N的店铺里哪种型号卖的好，某些特定的型号会在哪些店铺买的好。颜色的深浅就代表销量的多少，越深越多，越浅越少。
4）完整的网页代码（内含详细解释）：
1 <!DOCTYPE html> 2 <meta charset="utf-8"> 3 <html> 4 <head> 5 <style> //css样式区 6 rect.bordered {stroke: #E6E6E6;stroke-width:2px;} 7 text.mono {font-size: 9pt;font-family: Consolas, courier;fill: #aaa;} 8 text.axis-workweek {fill: #000;} 9 text.axis-worktime {fill: #000;} 10 </style> 11 <script src="http://d3js.org/d3.v3.js"></script> 12 </head> 13 <body> 14 <div id="chart"></div> 15 <div id="dataset-picker"> 16 </div> 17 <script type="text/javascript"> 18 //1. 先定义一些全局变量 19 var margin = { top: 80, right: 0, bottom: 100, left: 150 }, 20 width = 1000 - margin.left - margin.right, 21 height = 800 - margin.top - margin.bottom, 22 gridSize = Math.floor(width / 73),//格子的大小 23 legendElementWidth = gridSize*5, //图例的宽度 24 buckets = 9, 25 colors = ["#ffffd9","#edf8b1","#c7e9b4","#7fcdbb","#41b6c4","#1d91c0","#225ea8","#253494","#081d58"], // alternatively colorbrewer.YlGnBu[9] 26 times=[['E408','0'],['E488','0'],['E518','0'],['E568','0'],['G1800','1'],['G2800','1'],['G3800','1'],['IP110','0'],['IP1188','0'],['IP2780','1'],['IP2880S','1'],['IP7280','1'],['IP8780','0'],['IX6580','0'],['IX6780','0'],['IX6880','0'],['LBP151dw','0'],['LBP2900+','0'],['LBP5960(A3)','0'],['LBP6018L','1'],['LBP6018W','0'],['LBP6230dn','1'],['LBP6230dw','0'],['LBP6300dn','0'],['LBP7010C','1'],['LBP7100Cn','0'],['LBP7200Cd','0'],['LBP8100n(A3)','0'],['LBP9100Cdn(A3)','0'],['MF211','0'],['MF212w','1'],['MF215','0'],['MF216n','0'],['MF226dn','0'],['MF229dw','0'],['MF3010','0'],['MF4712','1'],['MF4752','1'],['MF6140dn','0'],['MF621Cn','0'],['MF623Cn','0'],['MF628Cw','0'],['MF725Cdn','0'],['MF727Cdw','0'],['MF810Cdn','0'],['MG2400','0'],['MG2580S','1'],['MG2980','0'],['MG3680','1'],['MG5780','0'],['MG6880','0'],['MG7780','1'],['MP236','0'],['MP288','1'],['MX498','0'],['MX538','0'],['MX728','0'],['MX928','0'],['PRO-1','0'],['PRO-10','0'],['PRO-100','0'],['PRO-500','0']] 27 days = ['店铺1','店铺2','店铺3','店铺4','店铺5','店铺6','店铺7','店铺8','店铺9','店铺10','店铺11','店铺12','店铺13','店铺14','店铺15','店铺16','店铺17','店铺18','店铺19','店铺20','店铺21','店铺22','店铺23','店铺24','店铺25','店铺26','店铺27','店铺28','店铺29','店铺30','店铺31','店铺32','店铺33','店铺34','店铺35','店铺36','店铺37','店铺38','店铺39','店铺40','店铺41','店铺42','店铺43','店铺44','店铺45','店铺46','店铺47','店铺48','店铺49','店铺50','店铺51','店铺52','店铺53','店铺54','店铺55','店铺56','店铺57','店铺58','店铺59','店铺60','店铺61','店铺62','店铺63','店铺64','店铺65','店铺66','店铺67','店铺68','店铺69','店铺70','店铺71','店铺72','店铺73','店铺74','店铺75','店铺76','店铺77','店铺78','店铺79','店铺80','店铺81','店铺82','店铺83','店铺84','店铺85','店铺86','店铺87','店铺88','店铺89','店铺90','店铺91','店铺92','店铺93','店铺94','店铺95','店铺96','店铺97','店铺98','店铺99','店铺100','店铺101','店铺102','店铺103','店铺104','店铺105','店铺106','店铺107','店铺108','店铺109','店铺110','店铺111','店铺112','店铺113','店铺114','店铺115','店铺116','店铺117','店铺118','店铺119','店铺120'] 28 datasets = ["data.csv", "data2.csv"]; //数据文件变量 29 //2. 画布 30 var svg = d3.select("#chart").append("svg") 31 .attr("width", width + margin.left + margin.right) 32 .attr("height", height + margin.top + margin.bottom) 33 .append("g") 34 .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); 35 //3. 轴Y 36 var dayLabels = svg.selectAll(".dayLabel") 37 .data(days) 38 .enter().append("text") 39 .text(function (d) { return d; }) 40 .attr("x", 0) 41 .attr("y", function (d, i) { return i * gridSize; }) 42 .style("text-anchor", "end") 43 .attr("transform", "translate(-6," + gridSize / 1.5 + ")") 44 .attr("class", function (d, i) { return ((i >= 0 && i <= 29) ? "dayLabel mono axis axis-workweek" : "dayLabel mono axis"); }) ;//轴标签是否明显显示 45 //4. 轴X 46 var timeLabels = svg.selectAll(".timeLabel") 47 .data(times) 48 .enter().append("text") 49 .text(function(d) { return d[0]; }) 50 .attr("x",gridSize) 51 .attr("y", 0) 52 .style("text-anchor", "start") 53 .attr("transform",function(d,i) { return "translate(" + gridSize*(i+1) + ", 8)rotate(" + (- 90) + ")"}) //x轴文字竖放 54 .attr("class", function(d, i) {console.log(d);return ((d[1]==1) ? "timeLabel mono axis axis-worktime" : "timeLabel mono axis"); })//轴标签是否明显显示 55 56 //5. 定义heatmapChart函数，输入文件路径即可画图 57 var heatmapChart = function(tsvFile) { 58 var csv = d3.dsv(",", "text/csv;charset=gb2312"); //解决中文转码 59 csv(tsvFile,function(d) { return {day: +d.day,hour: +d.hour,value: +d.value};}, 60 61 function(error, data) { 62 var colorScale = d3.scale.quantile() //比例尺：与quantize类似，但输入值域是独立的值，适合已经对数据分类的情形。 63 .domain([0, buckets - 1, d3.max(data, function (d) { return d.value; })]) 64 .range(colors); 65 66 var cards = svg.selectAll(".hour") 67 .data(data, function(d) {return d.day+':'+d.hour;}); 68 69 cards.append("title"); 70 71 cards.enter().append("rect") 72 .attr("x", function(d) { return (d.hour - 1) * gridSize; }) 73 .attr("y", function(d) { return (d.day - 1) * gridSize; }) 74 .attr("rx", 4) 75 .attr("ry", 4) 76 .attr("class", "hour bordered") 77 .attr("width", gridSize) 78 .attr("height", gridSize) 79 .style("fill", colors[0]); 80 81 //颜色渐变效果 82 cards.transition().duration(1000) 83 .style("fill", function(d) { return colorScale(d.value); }); 84 85 cards.select("title").text(function(d) { return d.value; }); 86 87 cards.exit().remove(); 88 89 //添加图例 90 var legend = svg.selectAll(".legend") 91 .data([0].concat(colorScale.quantiles()), function(d) { return d; }); 92 93 legend.enter().append("g") 94 .attr("class", "legend"); 95 96 legend.append("rect") 97 .attr("x", width-150) 98 .attr("y",function(d, i) { return legendElementWidth * i; }) 99 .attr("width", legendElementWidth) 100 .attr("height", gridSize / 2) 101 .style("fill", function(d, i) { return colors[i]; }); 102 103 legend.append("text") 104 .attr("class", "mono") 105 .text(function(d) { return "≥ " + Math.round(d); }) 106 .attr("x", width-150+gridSize*2) 107 .attr("y",function(d, i) { return legendElementWidth * i-gridSize; }) 108 .style("fill", "black"); 109 legend.exit().remove(); 110 111 }); 112 }; 113 114 //6. 调用前面的heatmapChart函数，输入数据文件名称 115 heatmapChart(datasets[0]); 116 117 //7. 按钮 118 var datasetpicker = d3.select("#dataset-picker").selectAll(".dataset-button") 119 .data(datasets); 120 121 datasetpicker.enter() 122 .append("input") 123 .attr("value", function(d){ return "Dataset " + d }) 124 .attr("type", "button") 125 .attr("class", "dataset-button") 126 .on("click", function(d) { 127 heatmapChart(d); 128 }); 129 </script> 130 </body> 131 </html>
posted on 2016-10-17 00:09 datastory 阅读(4936) 评论(0) 编辑 收藏 举报
