Home > Article > Backend Development > Several of the most commonly used JavaScript chart libraries
Currently, data visualization has become a very important part of the field of data science. The data generated in different network systems needs to be properly visualized in order to be better presented to users for reading and analysis.
For any organization, if it can fully obtain data, visualize data and analyze data, it can greatly help understand the deep-seated reasons for the generation of data so that correct decisions can be made accordingly. .
For front-end developers, it is a great skill to be able to master data visualization techniques in interactive web pages. Of course, front-end data visualization will also become easier through some JavaScript chart libraries. Using these libraries, developers can easily transform data into easy-to-understand charts without having to consider the programming challenges of different syntaxes.
The following are the 9 selected JavaScript chart libraries:
Chart.js Chartist FlexChart Echarts NVD3 C3.js TauCharts ReCharts Flot
Chart.js
Chart.js is a simple, user-friendly charting library and HTML5-based JavaScript library for creating animated, interactive and customizable charts and graphs.
With Chart.js, users can easily and intuitively view mixed chart types. Responsive web pages can also be created using Chart.js by default.
The Chart.js library allows users to quickly create visual data. Chart.js is easy to set up and very beginner-friendly. With Chart.js you don’t have to worry about browser compatibility issues because Chart.js supports older browsers.
Use npm to install Chart.js:
npm install chart.js --save
Chart.js code example for drawing radar chart:
const ctx = document.getElementById("myChart"); const options = { scale: { // Hides the scale } }; const data = { labels: ['Running', 'Swimming', 'Eating', 'Cycling'], datasets: [ { data: [-10, -5, -3, -15], label: "two", borderColor: '#ffc63b' }, { data: [10, 5, 3, 10], label: "three", borderColor: '#1d9a58' }, { data: [18, 10, 4, 2], label: "one", borderColor: '#d8463c' }, ] } const myRadarChart = new Chart(ctx, { data: data, type: 'radar', options: options }); Chartist
The Chartist library is great for creating beautiful, responsive, and read-friendly charts. Chartist uses SVG to render charts.
Chartist also provides the ability to customize charts using CSS media queries and creative animations. Users use Chartist to realize all their creativity in chart design.
Chartist is easy to configure and customize using Sass. However, it does not support older browsers.
Using Chartist, you can beautify your SVG through CSS styles. Users can completely realize all the chart styles they want.
Install Chartist using npm:
npm install chartist --save
Chartist Code example for creating a pie chart with custom labels:
var data = { labels: ['Bananas', 'Apples', 'Grapes'], series: [20, 15, 40] }; var options = { labelInterpolationFnc: function(value) { return value[0] } }; var responsiveOptions = [ ['screen and (min-width: 640px)', { chartPadding: 30, labelOffset: 130, labelDirection: 'explode', labelInterpolationFnc: function(value) { return value; } }], ['screen and (min-width: 1024px)', { labelOffset: 80, chartPadding: 20 }] ]; new Chartist.Pie('.ct-chart', data, options, responsiveOptions); FlexChart
FlexChart is a high-performance charting tool. Using FlexChart, you can easily visualize tabular data into business charts. FlexChart not only supports common chart types, such as line charts, pie charts, area charts, etc., but also supports advanced chart types such as bubble charts, K-line charts, bar charts, and funnel charts.
FlexChart is also very simple to use. FlexChart charts delegate all data-related tasks to the CollectionView class. You only need to operate the CollectionView class to implement functions such as filtering, sorting, and grouping data.
FlexChart also contains comprehensive chart elements, such as chart legends, chart titles, chart footers, number axes, chart series and labels, etc. Users can also add custom elements to charts, such as average lines and trends. Line etc.
FlexChart is essentially an interactive chart. Any changes to the data will be automatically reflected on the chart, such as chart curves zooming in and out, filtering, drilling, animation, etc. along with the data.
View FlexChart’s Chinese study guide and sunburst chart demo.
FlexChart code example for drawing histogram:
onload = function() { // wrap data in a CollectionView so the grid and chart // get notifications var data = new wijmo.collections.CollectionView(getData()); // create the chart var theChart = new wijmo.chart.FlexChart('#theChart', { itemsSource: data, bindingX: 'country', series: [ { binding: 'sales', name: 'Sales' }, { binding: 'expenses', name: 'Expenses' }, { binding: 'downloads', name: 'Downloads' } ] }) // create a grid to show the data var theGrid = new wijmo.grid.FlexGrid('#theGrid', { itemsSource: data }) // create some random data function getData() { var countries = 'US,Germany,UK,Japan,Italy,Greece'.split(','), data = []; for (var i = 0; i < countries.length; i++) { data.push({ country: countries[i], sales: Math.random() * 10000, expenses: Math.random() * 5000, downloads: Math.round(Math.random() * 20000), }); } return data; } } Echarts
Echarts is a very useful library for data visualization on web pages. Using Echarts, developers can create intuitive, customizable interactive charts that make data presentation and analysis easy.
Because Echarts is written in ordinary JavaScript, Echarts does not have the problem of seamless migration that other chart libraries have.
At the same time, Echarts also provides many official documents for users to view.
Using npm can easily complete the installation of Echarts:
npm install echarts --save
Echarts scatter plot code example:
var dom = document.getElementById("container"); var myChart = echarts.init(dom); var app = {}; option = null; option = { title: { text: 'Large-scale scatterplot' }, tooltip : { trigger: 'axis', showDelay : 0, axisPointer:{ show: true, type : 'cross', lineStyle: { type : 'dashed', width : 1 } }, zlevel: 1 }, legend: { data:['sin','cos'] }, toolbox: { show : true, feature : { mark : {show: true}, dataZoom : {show: true}, dataView : {show: true, readOnly: false}, restore : {show: true}, saveAsImage : {show: true} } }, xAxis : [ { type : 'value', scale:true } ], yAxis : [ { type : 'value', scale:true } ], series : [ { name:'sin', type:'scatter', large: true, symbolSize: 3, data: (function () { var d = []; var len = 10000; var x = 0; while (len--) { x = (Math.random() * 10).toFixed(3) - 0; d.push([ x, //Math.random() * 10 (Math.sin(x) - x * (len % 2 ? 0.1 : -0.1) * Math.random()).toFixed(3) - 0 ]); } //console.log(d) return d; })() }, { name:'cos', type:'scatter', large: true, symbolSize: 2, data: (function () { var d = []; var len = 20000; var x = 0; while (len--) { x = (Math.random() * 10).toFixed(3) - 0; d.push([ x, //Math.random() * 10 (Math.cos(x) - x * (len % 2 ? 0.1 : -0.1) * Math.random()).toFixed(3) - 0 ]); } //console.log(d) return d; })() } ] }; ; if (option && typeof option === "object") { myChart.setOption(option, true); }
NVD3
NVD3 is a D3-based JavaScript library written by Mike Bostock. NVD3 allows users to create beautiful, reusable diagrams in web applications.
NVD3 has very powerful charting functions and can easily create box and whisker charts, sunburst and candlestick charts, etc. If the user wants to use a lot of capabilities in the JavaScript charting library, it is recommended to try NVD3
The speed of the NVD3 charting library may sometimes be an issue, and it will be faster when used with the Fastdom installation.
NVD3 code example for drawing a simple line chart:
/*These lines are all chart setup. Pick and choose which chart features you want to utilize. */nv.addGraph(function() { var chart = nv.models.lineChart() .margin({left: 100}) //Adjust chart margins to give the x-axis some breathing room. .useInteractiveGuideline(true) //We want nice looking tooltips and a guideline! .transitionDuration(350) //how fast do you want the lines to transition? .showLegend(true) //Show the legend, allowing users to turn on/off line series. .showYAxis(true) //Show the y-axis .showXAxis(true) //Show the x-axis ; chart.xAxis //Chart x-axis settings .axisLabel('Time (ms)') .tickFormat(d3.format(',r')); chart.yAxis //Chart y-axis settings .axisLabel('Voltage (v)') .tickFormat(d3.format('.02f')); /* Done setting the chart up? Time to render it!*/ var myData = sinAndCos(); //You need data... d3.select('#chart svg') //Select the <svg> element you want to render the chart in. .datum(myData) //Populate the <svg> element with chart data... .call(chart); //Finally, render the chart! //Update the chart when window resizes. nv.utils.windowResize(function() { chart.update() }); return chart;});/************************************** * Simple test data generator */function sinAndCos() { var sin = [],sin2 = [], cos = []; //Data is represented as an array of {x,y} pairs. for (var i = 0; i < 100; i++) { sin.push({x: i, y: Math.sin(i/10)}); sin2.push({x: i, y: Math.sin(i/10) *0.25 + 0.5}); cos.push({x: i, y: .5 * Math.cos(i/10)}); } //Line chart data should be sent as an array of series objects. return [ { values: sin, //values - represents the array of {x,y} data points key: 'Sine Wave', //key - the name of the series. color: '#ff7f0e' //color - optional: choose your own line color. }, { values: cos, key: 'Cosine Wave', color: '#2ca02c' }, { values: sin2, key: 'Another sine wave', color: '#7777ff', area: true //area - set to true if you want this line to turn into a filled area chart. } ];} C3.js
Same as TauCharts, C3.js is also a very effective chart visualization library based on D3. In addition, C3.js allows users to create customizable classes with a personal touch.
C3.js seems to be a difficult library, but once you master the C3.js skills, you can use it easily.
有了 C3.js 图表库,即使在第一次渲染之后,用户也可以通过创建回调来更新图表。C3.js 也允许用户为自己的 Web 应用程序创建可复用的图表,从而减少工作量。
使用 npm 安装 C3.js 图表库:
npm install c3
C3.js 绘制组合图的代码示例:
var chart = c3.generate({ data: { columns: [ ['data1', 30, 20, 50, 40, 60, 50], ['data2', 200, 130, 90, 240, 130, 220], ['data3', 300, 200, 160, 400, 250, 250], ['data4', 200, 130, 90, 240, 130, 220], ['data5', 130, 120, 150, 140, 160, 150], ['data6', 90, 70, 20, 50, 60, 120], ], type: 'bar', types: { data3: 'spline', data4: 'line', data6: 'area', }, groups: [ ['data1','data2'] ] }}); TauCharts
TauCharts 是最灵活的 JavaScript 图表库之一。它是基于 D3 创建的,是一个以数据为中心的 JavaScript 图表库,可以改进数据可视化的效果。
TauCharts 十分灵活,访问其 API 也十分轻松。TauCharts 为用户提供了无缝映射和可视化的数据,使用 TauCharts 能够设计出十分美观的数据界面。同时,TauCharts 也和易于学习。
通过 npm 安装 TauCharts:
npm install taucharts
TauCharts 绘制水平线的代码示例:
var defData = [ {"team": "d", "cycleTime": 1, "effort": 1, "count": 1, "priority": "low"}, { "team": "d", "cycleTime": 2, "effort": 2, "count": 5, "priority": "low" }, {"team": "d", "cycleTime": 3, "effort": 3, "count": 8, "priority": "medium"}, { "team": "d", "cycleTime": 4, "effort": 4, "count": 3, "priority": "high" }, {"team": "l", "cycleTime": 2, "effort": 1, "count": 1, "priority": "low"}, { "team": "l", "cycleTime": 3, "effort": 2, "count": 5, "priority": "low" }, {"team": "l", "cycleTime": 4, "effort": 3, "count": 8, "priority": "medium"}, { "team": "l", "cycleTime": 5, "effort": 4, "count": 3, "priority": "high" }, {"team": "k", "cycleTime": 2, "effort": 4, "count": 1, "priority": "low"}, { "team": "k", "cycleTime": 3, "effort": 5, "count": 5, "priority": "low" }, {"team": "k", "cycleTime": 4, "effort": 6, "count": 8, "priority": "medium"}, { "team": "k", "cycleTime": 5, "effort": 8, "count": 3, "priority": "high" }];var chart = new tauCharts.Chart({ data: defData, type: 'horizontalBar', x: 'effort', y: 'team', color:'priority' });chart.renderTo('#bar'); Recharts
ReCharts 是一个使用 React 构建的,基于 D3 的图表库。
使用 ReCharts,用户可以在 React Web 应用程序中无缝地编写图表。
Recharts 非常轻巧,并使用 SVG 元素来创建很奇特的图表。
使用 npm 安装 Recharts:
npm install recharts
Recharts 没有冗长的文档,它很直接。当你遇到困难时,使用 Recharts 可以很容易找到解决方案。
Recharts 创建自定义内容树图的代码示例:
const {Treemap} = Recharts; const data = [ { name: 'axis', children: [ { name: 'Axes', size: 1302 }, { name: 'Axis', size: 24593 }, { name: 'AxisGridLine', size: 652 }, { name: 'AxisLabel', size: 636 }, { name: 'CartesianAxes', size: 6703 }, ], }, { name: 'controls', children: [ { name: 'AnchorControl', size: 2138 }, { name: 'ClickControl', size: 3824 }, { name: 'Control', size: 1353 }, { name: 'ControlList', size: 4665 }, { name: 'DragControl', size: 2649 }, { name: 'ExpandControl', size: 2832 }, { name: 'HoverControl', size: 4896 }, { name: 'IControl', size: 763 }, { name: 'PanZoomControl', size: 5222 }, { name: 'SelectionControl', size: 7862 }, { name: 'TooltipControl', size: 8435 }, ], }, { name: 'data', children: [ { name: 'Data', size: 20544 }, { name: 'DataList', size: 19788 }, { name: 'DataSprite', size: 10349 }, { name: 'EdgeSprite', size: 3301 }, { name: 'NodeSprite', size: 19382 }, { name: 'render', children: [ { name: 'ArrowType', size: 698 }, { name: 'EdgeRenderer', size: 5569 }, { name: 'IRenderer', size: 353 }, { name: 'ShapeRenderer', size: 2247 }, ], }, { name: 'ScaleBinding', size: 11275 }, { name: 'Tree', size: 7147 }, { name: 'TreeBuilder', size: 9930 }, ], }, { name: 'layout', children: [ { name: 'AxisLayout', size: 6725 }, { name: 'BundledEdgeRouter', size: 3727 }, { name: 'CircleLayout', size: 9317 }, { name: 'CirclePackingLayout', size: 12003 }, { name: 'DendrogramLayout', size: 4853 }, { name: 'ForceDirectedLayout', size: 8411 }, { name: 'IcicleTreeLayout', size: 4864 }, { name: 'IndentedTreeLayout', size: 3174 }, { name: 'Layout', size: 7881 }, { name: 'NodeLinkTreeLayout', size: 12870 }, { name: 'PieLayout', size: 2728 }, { name: 'RadialTreeLayout', size: 12348 }, { name: 'RandomLayout', size: 870 }, { name: 'StackedAreaLayout', size: 9121 }, { name: 'TreeMapLayout', size: 9191 }, ], }, { name: 'Operator', size: 2490 }, { name: 'OperatorList', size: 5248 }, { name: 'OperatorSequence', size: 4190 }, { name: 'OperatorSwitch', size: 2581 }, { name: 'SortOperator', size: 2023 }, ], } ]; const COLORS = ['#8889DD', '#9597E4', '#8DC77B', '#A5D297', '#E2CF45', '#F8C12D']; const CustomizedContent = React.createClass({ render() { const { root, depth, x, y, width, height, index, payload, colors, rank, name } = this.props; return ( <g> <rect x={x} y={y} width={width} height={height} style={{ fill: depth < 2 ? colors[Math.floor(index / root.children.length * 6)] : 'none', stroke: '#fff', strokeWidth: 2 / (depth + 1e-10), strokeOpacity: 1 / (depth + 1e-10), }} /> { depth === 1 ? <text x={x + width / 2} y={y + height / 2 + 7} textAnchor="middle" fill="#fff" fontSize={14} > {name} </text> : null } { depth === 1 ? <text x={x + 4} y={y + 18} fill="#fff" fontSize={16} fillOpacity={0.9} > {index + 1} </text> : null } </g> ); } }); const SimpleTreemap = React.createClass({ render () { return ( <Treemap width={400} height={200} data={data} dataKey="size" ratio={4/3} stroke="#fff" fill="#8884d8" content={<CustomizedContent colors={COLORS}/>} /> ); } }) ReactDOM.render( <SimpleTreemap />, document.getElementById('container') );
Flot
目前,jQuery 已经成为 Web 开发人员非常重要的工具。有了 Flot.js,前端设计也变得更加容易。
Flot.js 是 JavaScript 库中较为古老的图表库之一。尽管如此,Flot.js 也不会因为绘制折线图、饼图、条形图、面积图、甚至堆叠图表而降低其性能。
Flot.js 有一个很完善的文档。当用户遇到困难时,可以很容易地找到解决办法。Flot.js 也支持旧版本的浏览器。
可以选择不使用 npm 来安装 Flot.js,而是在 HTML5 中包含 jQuery 和 JavaScript 文件。
Flot.js 的基本用法代码示例:
$(function () { var d1 = []; for (var i = 0; i < 14; i += 0.5) d1.push([i, Math.sin(i)]); var d2 = [[0, 3], [4, 8], [8, 5], [9, 13]]; // a null signifies separate line segments var d3 = [[0, 12], [7, 12], null, [7, 2.5], [12, 2.5]]; $.plot($("#placeholder"), [ d1, d2, d3 ]); });
总结
以上介绍的 JavaScript 库都是高质量的图表库。但是在学习这些库的过程中,可能会因为学习曲线陡峭或是缺乏学习资料而遇到困难,一种很好的方案是将这些库结合起来使用。最后也欢迎大家补充更多的 JavaScript 图表库。
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