How do you choose between ECharts, D3.js, and Chart.js?

ECharts is best for standard charts (line, bar, pie, heatmap) — easy to configure, comprehensive Chinese documentation, works out of the box. D3.js is best for highly customized charts (Sankey, chord, custom visualizations) — maximum flexibility but steep learning curve, requiring 3-5x more code than ECharts. Chart.js sits in between — lightweight, simple, suitable for basic charts. Recommendation: 80% of scenarios → ECharts, 10% special scenarios → D3.js, 10% simple scenarios → Chart.js.

What if charts are slow with large datasets?

Three solutions: ① Data sampling — aggregate raw data (e.g., average by hour) to reduce data points; ② Use Canvas rendering (ECharts uses Canvas by default, D3.js uses SVG — Canvas performs better with large data); ③ Virtualized rendering — only render visible data points, load more on scroll.

How do you choose chart colors?

Follow three principles: ① Accessibility — colors must meet WCAG contrast standards, colorblind-friendly (avoid red-green contrast); ② Consistency — same data uses the same colors across different charts; ③ Semantic meaning — use colors to convey meaning (e.g., red for warnings, green for normal). Recommended: use AntV or ECharts default palettes — they already consider accessibility.

What should you watch for on mobile?

Mobile screens are small and interaction is touch-based. Tips: ① Chart dimensions should be responsive (use percentages or vw/vh); ② Use touch events instead of hover for data tooltips; ③ Reduce legend items — legends take up space on mobile; ④ Keep data labels concise, collapsible if needed.

Data Visualization: Chart Library Comparison and Design Principles

Data visualization is not about "picking a good-looking chart library" — it is about choosing the right chart type, the right interaction pattern, and letting the data speak for itself. This article covers chart type selection, mainstream library comparison (ECharts vs D3.js vs Chart.js), design principles, and performance optimization — for developers integrating data visualization into frontend projects.

The Bottom Line: Choose the Right Chart Type First, Then the Library

The first step in data visualization is not “which library” — it is “what story does your data tell?” The wrong chart type cannot be saved by even the most beautiful library.

1. Chart Type Selection

What You Want to Express	Recommended Chart
Trend over time	Line chart
Category comparison	Bar chart
Part-to-whole ratio	Pie / Donut chart
Relationship between two variables	Scatter plot
Data distribution	Box plot / Histogram
Hierarchy	Tree / Sunburst
Network relationship	Force-directed graph
Geographic location	Map / Heatmap
Flow / Funnel	Funnel / Sankey

2. Library Comparison

Dimension	ECharts	D3.js	Chart.js
Learning curve	Low	High	Low
Customizability	Medium	High	Low
Chart types	60+	Unlimited	8 basic
Large data	Good (Canvas)	Depends	Fair
Mobile	Good	Manual	Good
Bundle size	~300KB	~250KB	~60KB
Community	High	High	High

3. Design Principles

3.1 Data-Ink Ratio

Remove all “ink” that does not convey data — background colors, unnecessary grid lines, 3D effects, decorative elements.

const option = {
  grid: { left: 50, right: 20, top: 20, bottom: 30 },
  xAxis: { axisLine: { show: false }, axisTick: { show: false } },
  yAxis: { splitLine: { lineStyle: { type: 'dashed', opacity: 0.3 } } },
  series: [{ type: 'line', smooth: true, showSymbol: false }],
};

3.2 Color Principles

const COLORS = [
  '#5B8FF9', '#5AD8A6', '#F6BD16', '#E86452',
  '#6DC8EC', '#FF99C3', '#9270CA', '#FF9845',
];

3.3 Interaction Principles

Tooltips for data details
Zoom and pan (for large datasets)
Click to drill down
Consistent interaction patterns

4. Performance Optimization

function sampleData(data: Point[], maxPoints: number): Point[] {
  if (data.length <= maxPoints) return data;
  const step = Math.ceil(data.length / maxPoints);
  return data.filter((_, i) => i % step === 0);
}

Summary

Principle	Note
Chart first, library second	Chart type determines communication effectiveness
80% ECharts	Standard charts out of the box
10% D3.js	Custom charts with maximum flexibility
Data-Ink ratio	Remove all decorative elements
Colorblind-friendly	Avoid red-green, use professional palettes

The core of data visualization is not “does the chart look good” — it is “can the reader understand what the data is saying at a glance.” A simple, clear chart is a hundred times better than a flashy, confusing one.

Need data visualization or frontend development services? Contact us — tell us about your data display needs and scale, feasibility within 24 hours.