交互组件
ECharts 提供了很多交互组件：例组件 legend、标题组件 title、视觉映射组件 visualMap、数据区域缩放组件 dataZoom、时间线组件 timeline。
接下来的内容我们将介绍如何使用数据区域缩放组件 dataZoom。
dataZoom
dataZoom 组件可以实现通过鼠标滚轮滚动，放大缩小图表的功能。
默认情况下 dataZoom 控制 x 轴，即对 x 轴进行数据窗口缩放和数据窗口平移操作。
option = {
xAxis: {
type: 'value'
},
yAxis: {
type: 'value'
},
dataZoom: [
{ // 这个dataZoom组件，默认控制x轴。
type: 'slider', // 这个 dataZoom 组件是 slider 型 dataZoom 组件
start: 10, // 左边在 10% 的位置。
end: 60 // 右边在 60% 的位置。
}
],
series: [
{
type: 'scatter', // 这是个『散点图』
itemStyle: {
opacity: 0.8
},
symbolSize: function (val) {
return val[2] * 40;
},
data: [["14.616","7.241","0.896"],["3.958","5.701","0.955"],["2.768","8.971","0.669"],["9.051","9.710","0.171"],["14.046","4.182","0.536"],["12.295","1.429","0.962"],["4.417","8.167","0.113"],["0.492","4.771","0.785"],["7.632","2.605","0.645"],["14.242","5.042","0.368"]]
}
]
}上面的实例只能拖动 dataZoom 组件来缩小或放大图表。如果想在坐标系内进行拖动，以及用鼠标滚轮（或移动触屏上的两指滑动）进行缩放，那么需要 再再加上一个 inside 型的 dataZoom 组件。
在以上实例基础上我们再增加 type: 'inside' 的配置信息：
option = {
...,
dataZoom: [
{ // 这个dataZoom组件，默认控制x轴。
type: 'slider', // 这个 dataZoom 组件是 slider 型 dataZoom 组件
start: 10, // 左边在 10% 的位置。
end: 60 // 右边在 60% 的位置。
},
{ // 这个dataZoom组件，也控制x轴。
type: 'inside', // 这个 dataZoom 组件是 inside 型 dataZoom 组件
start: 10, // 左边在 10% 的位置。
end: 60 // 右边在 60% 的位置。
}
],
...
}当然我们可以通过 dataZoom.xAxisIndex 或 dataZoom.yAxisIndex 来指定 dataZoom 控制哪个或哪些数轴。
var data1 = [];
var data2 = [];
var data3 = [];
var random = function (max) {
return (Math.random() * max).toFixed(3);
};
for (var i = 0; i < 500; i++) {
data1.push([random(15), random(10), random(1)]);
data2.push([random(10), random(10), random(1)]);
data3.push([random(15), random(10), random(1)]);
}
option = {
animation: false,
legend: {
data: ['scatter', 'scatter2', 'scatter3']
},
tooltip: {
},
xAxis: {
type: 'value',
min: 'dataMin',
max: 'dataMax',
splitLine: {
show: true
}
},
yAxis: {
type: 'value',
min: 'dataMin',
max: 'dataMax',
splitLine: {
show: true
}
},
dataZoom: [
{
type: 'slider',
show: true,
xAxisIndex: [0],
start: 1,
end: 35
},
{
type: 'slider',
show: true,
yAxisIndex: [0],
left: '93%',
start: 29,
end: 36
},
{
type: 'inside',
xAxisIndex: [0],
start: 1,
end: 35
},
{
type: 'inside',
yAxisIndex: [0],
start: 29,
end: 36
}
],
series: [
{
name: 'scatter',
type: 'scatter',
itemStyle: {
normal: {
opacity: 0.8
}
},
symbolSize: function (val) {
return val[2] * 40;
},
data: data1
},
{
name: 'scatter2',
type: 'scatter',
itemStyle: {
normal: {
opacity: 0.8
}
},
symbolSize: function (val) {
return val[2] * 40;
},
data: data2
},
{
name: 'scatter3',
type: 'scatter',
itemStyle: {
normal: {
opacity: 0.8,
}
},
symbolSize: function (val) {
return val[2] * 40;
},
data: data3
}
]
}数据的视觉映射
数据可视化简单来讲就是将数据用图表的形式来展示，专业的表达方式就是数据到视觉元素的映射过程。
ECharts 的每种图表本身就内置了这种映射过程，我们之前学习到的柱形图就是将数据映射到长度。
此外，ECharts 还提供了 visualMap 组件 来提供通用的视觉映射。visualMap 组件中可以使用的视觉元素有：
- 图形类别（symbol）
- 图形大小（symbolSize）
- 颜色（color）
- 透明度（opacity）
- 颜色透明度（colorAlpha）
- 颜色明暗度（colorLightness）
- 颜色饱和度（colorSaturation）
- 色调（colorHue）
一、数据和维度
ECharts 中的数据，一般存放于 series.data 中。
不同的图表类型，数据格式有所不一样，但是他们的共同特点就都是数据项（dataItem） 的集合。每个数据项含有 数据值（value） 和其他信息（可选）。每个数据值，可以是单一的数值（一维）或者一个数组（多维）。
series.data 最常见的形式 是线性表，即一个普通数组：
series: {
data: [
{ // 这里每一个项就是数据项（dataItem）
value: 2323, // 这是数据项的数据值（value）
itemStyle: {...}
},
1212, // 也可以直接是 dataItem 的 value，这更常见。
2323, // 每个 value 都是『一维』的。
4343,
3434
]
}
series: {
data: [
{ // 这里每一个项就是数据项（dataItem）
value: [3434, 129, '圣马力诺'], // 这是数据项的数据值（value）
itemStyle: {...}
},
[1212, 5454, '梵蒂冈'], // 也可以直接是 dataItem 的 value，这更常见。
[2323, 3223, '瑙鲁'], // 每个 value 都是『三维』的，每列是一个维度。
[4343, 23, '图瓦卢'] // 假如是『气泡图』，常见第一维度映射到x轴，
// 第二维度映射到y轴，
// 第三维度映射到气泡半径（symbolSize）
]
}在图表中，往往默认把 value 的前一两个维度进行映射，比如取第一个维度映射到x轴，取第二个维度映射到y轴。如果想要把更多的维度展现出来，可以借助 visualMap 。
二、visualMap 组件
visualMap 组件定义了把数据的指定维度映射到对应的视觉元素上。
visualMap 组件可以定义多个，从而可以同时对数据中的多个维度进行视觉映射。
visualMap 组件可以定义为 分段型（visualMapPiecewise） 或 连续型（visualMapContinuous），通过 type 来区分。例如：
option = {
visualMap: [
{ // 第一个 visualMap 组件
type: 'continuous', // 定义为连续型 visualMap
...
},
{ // 第二个 visualMap 组件
type: 'piecewise', // 定义为分段型 visualMap
...
}
],
...
};分段型视觉映射组件，有三种模式：
- 连续型数据平均分段: 依据 visualMap-piecewise.splitNumber 来自动平均分割成若干块。
- 连续型数据自定义分段: 依据 visualMap-piecewise.pieces 来定义每块范围。
- 离散数据根据类别分段: 类别定义在 visualMap-piecewise.categories 中。
分段型视觉映射组件，展现形式如下图：
实例
<!DOCTYPE html>
<html style="height: 100%">
<head>
<meta charset="utf-8">
</head>
<body style="height: 100%; margin: 0">
<div id="container" style="height: 100%"></div>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/dist/echarts.min.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts-gl/dist/echarts-gl.min.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts-stat/dist/ecStat.min.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/dist/extension/dataTool.min.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/map/js/china.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/map/js/world.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/echarts/dist/extension/bmap.min.js"></script>
<script type="text/javascript">
var dom = document.getElementById("container");
var myChart = echarts.init(dom);
var app = {};
option = null;
var geoCoordMap = {
"海门":[121.15,31.89],
"鄂尔多斯":[109.781327,39.608266],
"招远":[120.38,37.35],
"舟山":[122.207216,29.985295],
"齐齐哈尔":[123.97,47.33],
"盐城":[120.13,33.38],
"赤峰":[118.87,42.28],
"青岛":[120.33,36.07],
"乳山":[121.52,36.89],
"金昌":[102.188043,38.520089],
"泉州":[118.58,24.93],
"莱西":[120.53,36.86],
"日照":[119.46,35.42],
"胶南":[119.97,35.88],
"南通":[121.05,32.08],
"拉萨":[91.11,29.97],
"云浮":[112.02,22.93],
"梅州":[116.1,24.55],
"文登":[122.05,37.2],
"上海":[121.48,31.22],
"攀枝花":[101.718637,26.582347],
"威海":[122.1,37.5],
"承德":[117.93,40.97],
"厦门":[118.1,24.46],
"汕尾":[115.375279,22.786211],
"潮州":[116.63,23.68],
"丹东":[124.37,40.13],
"太仓":[121.1,31.45],
"曲靖":[103.79,25.51],
"烟台":[121.39,37.52],
"福州":[119.3,26.08],
"瓦房店":[121.979603,39.627114],
"即墨":[120.45,36.38],
"抚顺":[123.97,41.97],
"玉溪":[102.52,24.35],
"张家口":[114.87,40.82],
"阳泉":[113.57,37.85],
"莱州":[119.942327,37.177017],
"湖州":[120.1,30.86],
"汕头":[116.69,23.39],
"昆山":[120.95,31.39],
"宁波":[121.56,29.86],
"湛江":[110.359377,21.270708],
"揭阳":[116.35,23.55],
"荣成":[122.41,37.16],
"连云港":[119.16,34.59],
"葫芦岛":[120.836932,40.711052],
"常熟":[120.74,31.64],
"东莞":[113.75,23.04],
"河源":[114.68,23.73],
"淮安":[119.15,33.5],
"泰州":[119.9,32.49],
"南宁":[108.33,22.84],
"营口":[122.18,40.65],
"惠州":[114.4,23.09],
"江阴":[120.26,31.91],
"蓬莱":[120.75,37.8],
"韶关":[113.62,24.84],
"嘉峪关":[98.289152,39.77313],
"广州":[113.23,23.16],
"延安":[109.47,36.6],
"太原":[112.53,37.87],
"清远":[113.01,23.7],
"中山":[113.38,22.52],
"昆明":[102.73,25.04],
"寿光":[118.73,36.86],
"盘锦":[122.070714,41.119997],
"长治":[113.08,36.18],
"深圳":[114.07,22.62],
"珠海":[113.52,22.3],
"宿迁":[118.3,33.96],
"咸阳":[108.72,34.36],
"铜川":[109.11,35.09],
"平度":[119.97,36.77],
"佛山":[113.11,23.05],
"海口":[110.35,20.02],
"江门":[113.06,22.61],
"章丘":[117.53,36.72],
"肇庆":[112.44,23.05],
"大连":[121.62,38.92],
"临汾":[111.5,36.08],
"吴江":[120.63,31.16],
"石嘴山":[106.39,39.04],
"沈阳":[123.38,41.8],
"苏州":[120.62,31.32],
"茂名":[110.88,21.68],
"嘉兴":[120.76,30.77],
"长春":[125.35,43.88],
"胶州":[120.03336,36.264622],
"银川":[106.27,38.47],
"张家港":[120.555821,31.875428],
"三门峡":[111.19,34.76],
"锦州":[121.15,41.13],
"南昌":[115.89,28.68],
"柳州":[109.4,24.33],
"三亚":[109.511909,18.252847],
"自贡":[104.778442,29.33903],
"吉林":[126.57,43.87],
"阳江":[111.95,21.85],
"泸州":[105.39,28.91],
"西宁":[101.74,36.56],
"宜宾":[104.56,29.77],
"呼和浩特":[111.65,40.82],
"成都":[104.06,30.67],
"大同":[113.3,40.12],
"镇江":[119.44,32.2],
"桂林":[110.28,25.29],
"张家界":[110.479191,29.117096],
"宜兴":[119.82,31.36],
"北海":[109.12,21.49],
"西安":[108.95,34.27],
"金坛":[119.56,31.74],
"东营":[118.49,37.46],
"牡丹江":[129.58,44.6],
"遵义":[106.9,27.7],
"绍兴":[120.58,30.01],
"扬州":[119.42,32.39],
"常州":[119.95,31.79],
"潍坊":[119.1,36.62],
"重庆":[106.54,29.59],
"台州":[121.420757,28.656386],
"南京":[118.78,32.04],
"滨州":[118.03,37.36],
"贵阳":[106.71,26.57],
"无锡":[120.29,31.59],
"本溪":[123.73,41.3],
"克拉玛依":[84.77,45.59],
"渭南":[109.5,34.52],
"马鞍山":[118.48,31.56],
"宝鸡":[107.15,34.38],
"焦作":[113.21,35.24],
"句容":[119.16,31.95],
"北京":[116.46,39.92],
"徐州":[117.2,34.26],
"衡水":[115.72,37.72],
"包头":[110,40.58],
"绵阳":[104.73,31.48],
"乌鲁木齐":[87.68,43.77],
"枣庄":[117.57,34.86],
"杭州":[120.19,30.26],
"淄博":[118.05,36.78],
"鞍山":[122.85,41.12],
"溧阳":[119.48,31.43],
"库尔勒":[86.06,41.68],
"安阳":[114.35,36.1],
"开封":[114.35,34.79],
"济南":[117,36.65],
"德阳":[104.37,31.13],
"温州":[120.65,28.01],
"九江":[115.97,29.71],
"邯郸":[114.47,36.6],
"临安":[119.72,30.23],
"兰州":[103.73,36.03],
"沧州":[116.83,38.33],
"临沂":[118.35,35.05],
"南充":[106.110698,30.837793],
"天津":[117.2,39.13],
"富阳":[119.95,30.07],
"泰安":[117.13,36.18],
"诸暨":[120.23,29.71],
"郑州":[113.65,34.76],
"哈尔滨":[126.63,45.75],
"聊城":[115.97,36.45],
"芜湖":[118.38,31.33],
"唐山":[118.02,39.63],
"平顶山":[113.29,33.75],
"邢台":[114.48,37.05],
"德州":[116.29,37.45],
"济宁":[116.59,35.38],
"荆州":[112.239741,30.335165],
"宜昌":[111.3,30.7],
"义乌":[120.06,29.32],
"丽水":[119.92,28.45],
"洛阳":[112.44,34.7],
"秦皇岛":[119.57,39.95],
"株洲":[113.16,27.83],
"石家庄":[114.48,38.03],
"莱芜":[117.67,36.19],
"常德":[111.69,29.05],
"保定":[115.48,38.85],
"湘潭":[112.91,27.87],
"金华":[119.64,29.12],
"岳阳":[113.09,29.37],
"长沙":[113,28.21],
"衢州":[118.88,28.97],
"廊坊":[116.7,39.53],
"菏泽":[115.480656,35.23375],
"合肥":[117.27,31.86],
"武汉":[114.31,30.52],
"大庆":[125.03,46.58]
};
var convertData = function (data) {
var res = [];
for (var i = 0; i < data.length; i++) {
var geoCoord = geoCoordMap[data[i].name];
if (geoCoord) {
res.push(geoCoord.concat(data[i].value));
}
}
return res;
};
option = {
backgroundColor: '#404a59',
title: {
text: '全国主要城市空气质量',
subtext: 'data from PM25.in',
sublink: 'http://www.pm25.in',
left: 'center',
textStyle: {
color: '#fff'
}
},
tooltip: {
trigger: 'item'
},
legend: {
orient: 'vertical',
top: 'bottom',
left: 'right',
data:['pm2.5'],
textStyle: {
color: '#fff'
}
},
visualMap: {
min: 0,
max: 300,
splitNumber: 5,
color: ['#d94e5d','#eac736','#50a3ba'],
textStyle: {
color: '#fff'
}
},
geo: {
map: 'china',
label: {
emphasis: {
show: false
}
},
itemStyle: {
normal: {
areaColor: '#323c48',
borderColor: '#111'
},
emphasis: {
areaColor: '#2a333d'
}
}
},
series: [
{
name: 'pm2.5',
type: 'scatter',
coordinateSystem: 'geo',
data: convertData([
{name: "海门", value: 9},
{name: "鄂尔多斯", value: 12},
{name: "招远", value: 12},
{name: "舟山", value: 12},
{name: "齐齐哈尔", value: 14},
{name: "盐城", value: 15},
{name: "赤峰", value: 16},
{name: "青岛", value: 18},
{name: "乳山", value: 18},
{name: "金昌", value: 19},
{name: "泉州", value: 21},
{name: "莱西", value: 21},
{name: "日照", value: 21},
{name: "胶南", value: 22},
{name: "南通", value: 23},
{name: "拉萨", value: 24},
{name: "云浮", value: 24},
{name: "梅州", value: 25},
{name: "文登", value: 25},
{name: "上海", value: 25},
{name: "攀枝花", value: 25},
{name: "威海", value: 25},
{name: "承德", value: 25},
{name: "厦门", value: 26},
{name: "汕尾", value: 26},
{name: "潮州", value: 26},
{name: "丹东", value: 27},
{name: "太仓", value: 27},
{name: "曲靖", value: 27},
{name: "烟台", value: 28},
{name: "福州", value: 29},
{name: "瓦房店", value: 30},
{name: "即墨", value: 30},
{name: "抚顺", value: 31},
{name: "玉溪", value: 31},
{name: "张家口", value: 31},
{name: "阳泉", value: 31},
{name: "莱州", value: 32},
{name: "湖州", value: 32},
{name: "汕头", value: 32},
{name: "昆山", value: 33},
{name: "宁波", value: 33},
{name: "湛江", value: 33},
{name: "揭阳", value: 34},
{name: "荣成", value: 34},
{name: "连云港", value: 35},
{name: "葫芦岛", value: 35},
{name: "常熟", value: 36},
{name: "东莞", value: 36},
{name: "河源", value: 36},
{name: "淮安", value: 36},
{name: "泰州", value: 36},
{name: "南宁", value: 37},
{name: "营口", value: 37},
{name: "惠州", value: 37},
{name: "江阴", value: 37},
{name: "蓬莱", value: 37},
{name: "韶关", value: 38},
{name: "嘉峪关", value: 38},
{name: "广州", value: 38},
{name: "延安", value: 38},
{name: "太原", value: 39},
{name: "清远", value: 39},
{name: "中山", value: 39},
{name: "昆明", value: 39},
{name: "寿光", value: 40},
{name: "盘锦", value: 40},
{name: "长治", value: 41},
{name: "深圳", value: 41},
{name: "珠海", value: 42},
{name: "宿迁", value: 43},
{name: "咸阳", value: 43},
{name: "铜川", value: 44},
{name: "平度", value: 44},
{name: "佛山", value: 44},
{name: "海口", value: 44},
{name: "江门", value: 45},
{name: "章丘", value: 45},
{name: "肇庆", value: 46},
{name: "大连", value: 47},
{name: "临汾", value: 47},
{name: "吴江", value: 47},
{name: "石嘴山", value: 49},
{name: "沈阳", value: 50},
{name: "苏州", value: 50},
{name: "茂名", value: 50},
{name: "嘉兴", value: 51},
{name: "长春", value: 51},
{name: "胶州", value: 52},
{name: "银川", value: 52},
{name: "张家港", value: 52},
{name: "三门峡", value: 53},
{name: "锦州", value: 54},
{name: "南昌", value: 54},
{name: "柳州", value: 54},
{name: "三亚", value: 54},
{name: "自贡", value: 56},
{name: "吉林", value: 56},
{name: "阳江", value: 57},
{name: "泸州", value: 57},
{name: "西宁", value: 57},
{name: "宜宾", value: 58},
{name: "呼和浩特", value: 58},
{name: "成都", value: 58},
{name: "大同", value: 58},
{name: "镇江", value: 59},
{name: "桂林", value: 59},
{name: "张家界", value: 59},
{name: "宜兴", value: 59},
{name: "北海", value: 60},
{name: "西安", value: 61},
{name: "金坛", value: 62},
{name: "东营", value: 62},
{name: "牡丹江", value: 63},
{name: "遵义", value: 63},
{name: "绍兴", value: 63},
{name: "扬州", value: 64},
{name: "常州", value: 64},
{name: "潍坊", value: 65},
{name: "重庆", value: 66},
{name: "台州", value: 67},
{name: "南京", value: 67},
{name: "滨州", value: 70},
{name: "贵阳", value: 71},
{name: "无锡", value: 71},
{name: "本溪", value: 71},
{name: "克拉玛依", value: 72},
{name: "渭南", value: 72},
{name: "马鞍山", value: 72},
{name: "宝鸡", value: 72},
{name: "焦作", value: 75},
{name: "句容", value: 75},
{name: "北京", value: 79},
{name: "徐州", value: 79},
{name: "衡水", value: 80},
{name: "包头", value: 80},
{name: "绵阳", value: 80},
{name: "乌鲁木齐", value: 84},
{name: "枣庄", value: 84},
{name: "杭州", value: 84},
{name: "淄博", value: 85},
{name: "鞍山", value: 86},
{name: "溧阳", value: 86},
{name: "库尔勒", value: 86},
{name: "安阳", value: 90},
{name: "开封", value: 90},
{name: "济南", value: 92},
{name: "德阳", value: 93},
{name: "温州", value: 95},
{name: "九江", value: 96},
{name: "邯郸", value: 98},
{name: "临安", value: 99},
{name: "兰州", value: 99},
{name: "沧州", value: 100},
{name: "临沂", value: 103},
{name: "南充", value: 104},
{name: "天津", value: 105},
{name: "富阳", value: 106},
{name: "泰安", value: 112},
{name: "诸暨", value: 112},
{name: "郑州", value: 113},
{name: "哈尔滨", value: 114},
{name: "聊城", value: 116},
{name: "芜湖", value: 117},
{name: "唐山", value: 119},
{name: "平顶山", value: 119},
{name: "邢台", value: 119},
{name: "德州", value: 120},
{name: "济宁", value: 120},
{name: "荆州", value: 127},
{name: "宜昌", value: 130},
{name: "义乌", value: 132},
{name: "丽水", value: 133},
{name: "洛阳", value: 134},
{name: "秦皇岛", value: 136},
{name: "株洲", value: 143},
{name: "石家庄", value: 147},
{name: "莱芜", value: 148},
{name: "常德", value: 152},
{name: "保定", value: 153},
{name: "湘潭", value: 154},
{name: "金华", value: 157},
{name: "岳阳", value: 169},
{name: "长沙", value: 175},
{name: "衢州", value: 177},
{name: "廊坊", value: 193},
{name: "菏泽", value: 194},
{name: "合肥", value: 229},
{name: "武汉", value: 273},
{name: "大庆", value: 279}
]),
symbolSize: 12,
label: {
normal: {
show: false
},
emphasis: {
show: false
}
},
itemStyle: {
emphasis: {
borderColor: '#fff',
borderWidth: 1
}
}
}
]
};
if (option && typeof option === "object") {
myChart.setOption(option, true);
}
</script>
</body>
</html>
三、视觉映射方式的配置
visualMap 中可以指定数据的指定维度映射到对应的视觉元素上。
实例 1
option = {
visualMap: [
{
type: 'piecewise'
min: 0,
max: 5000,
dimension: 3, // series.data 的第四个维度（即 value[3]）被映射
seriesIndex: 4, // 对第四个系列进行映射。
inRange: { // 选中范围中的视觉配置
color: ['blue', '#121122', 'red'], // 定义了图形颜色映射的颜色列表，
// 数据最小值映射到'blue'上，
// 最大值映射到'red'上，
// 其余自动线性计算。
symbolSize: [30, 100] // 定义了图形尺寸的映射范围，
// 数据最小值映射到30上，
// 最大值映射到100上，
// 其余自动线性计算。
},
outOfRange: { // 选中范围外的视觉配置
symbolSize: [30, 100]
}
},
...
]
};实例 2
option = {
visualMap: [
{
...,
inRange: { // 选中范围中的视觉配置
colorLightness: [0.2, 1], // 映射到明暗度上。也就是对本来的颜色进行明暗度处理。
// 本来的颜色可能是从全局色板中选取的颜色，visualMap组件并不关心。
symbolSize: [30, 100]
},
...
},
...
]
};以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持阿兔在线工具。
