d3-viz
Create interactive data visualizations using D3.js. Use this when creating charts, graphs, network diagrams, geographic visualizations, or custom SVG-based data visualization.
When & Why to Use This Skill
This Claude skill enables the creation of highly customizable and interactive data visualizations using the D3.js library. It empowers users to transform complex datasets into engaging visual narratives, ranging from standard charts to intricate network diagrams and geographic maps. By leveraging SVG-based rendering, it ensures high-quality, scalable graphics suitable for professional web-based reporting and deep data exploration.
Use Cases
- Building interactive business dashboards that support advanced user features like zooming, panning, and data brushing for exploratory analysis.
- Visualizing complex relational data through force-directed network graphs to identify clusters and connections in social or technical systems.
- Creating custom geographic visualizations and choropleth maps using specific projections for regional or global spatial data analysis.
- Generating publication-quality, animated SVG charts that provide smooth transitions and dynamic updates for data-driven storytelling.
- Developing bespoke visualization types that are not available in standard charting libraries, tailored to specific industry requirements.
| name | d3-viz |
|---|---|
| description | Create interactive data visualizations using D3.js. Use this when creating charts, graphs, network diagrams, geographic visualizations, or custom SVG-based data visualization. |
| allowed-tools | Read, Glob, Grep, Edit, Write |
| license | MIT |
| author | chrisvoncsefalvay |
| version | "1.0" |
D3.js Visualization
D3.js를 사용한 인터랙티브 데이터 시각화 가이드입니다.
When to Use D3.js
적합한 경우:
- 커스텀 차트 (표준 라이브러리에 없는)
- 인터랙티브 탐색 (pan, zoom, brush)
- 네트워크/그래프 시각화
- 지리 시각화 (커스텀 projection)
- 애니메이션 transition
- 출판 품질 그래픽
대안 고려:
- 3D 시각화 → Three.js
- 간단한 차트 → Chart.js, Recharts
Setup
// npm
import * as d3 from 'd3';
// CDN
<script src="https://d3js.org/d3.v7.min.js"></script>
Core Workflow
function drawVisualization(data) {
if (!data || data.length === 0) return;
const svg = d3.select('#chart');
svg.selectAll("*").remove(); // Clear previous
// 1. Dimensions
const width = 800;
const height = 400;
const margin = { top: 20, right: 30, bottom: 40, left: 50 };
const innerWidth = width - margin.left - margin.right;
const innerHeight = height - margin.top - margin.bottom;
// 2. Main group with margins
const g = svg.append("g")
.attr("transform", `translate(${margin.left},${margin.top})`);
// 3. Scales
const xScale = d3.scaleLinear()
.domain([0, d3.max(data, d => d.x)])
.range([0, innerWidth]);
const yScale = d3.scaleLinear()
.domain([0, d3.max(data, d => d.y)])
.range([innerHeight, 0]);
// 4. Axes
g.append("g")
.attr("transform", `translate(0,${innerHeight})`)
.call(d3.axisBottom(xScale));
g.append("g")
.call(d3.axisLeft(yScale));
// 5. Data binding
g.selectAll("circle")
.data(data)
.join("circle")
.attr("cx", d => xScale(d.x))
.attr("cy", d => yScale(d.y))
.attr("r", 5)
.attr("fill", "steelblue");
}
Common Patterns
Bar Chart
const xScale = d3.scaleBand()
.domain(data.map(d => d.category))
.range([0, innerWidth])
.padding(0.1);
g.selectAll("rect")
.data(data)
.join("rect")
.attr("x", d => xScale(d.category))
.attr("y", d => yScale(d.value))
.attr("width", xScale.bandwidth())
.attr("height", d => innerHeight - yScale(d.value))
.attr("fill", "steelblue");
Line Chart
const line = d3.line()
.x(d => xScale(d.date))
.y(d => yScale(d.value))
.curve(d3.curveMonotoneX);
g.append("path")
.datum(data)
.attr("fill", "none")
.attr("stroke", "steelblue")
.attr("stroke-width", 2)
.attr("d", line);
Pie Chart
const pie = d3.pie().value(d => d.value);
const arc = d3.arc()
.innerRadius(0)
.outerRadius(Math.min(width, height) / 2 - 20);
g.selectAll("path")
.data(pie(data))
.join("path")
.attr("d", arc)
.attr("fill", (d, i) => d3.schemeCategory10[i]);
Interactivity
Tooltips
const tooltip = d3.select("body").append("div")
.attr("class", "tooltip")
.style("visibility", "hidden");
circles
.on("mouseover", (event, d) => {
tooltip.style("visibility", "visible")
.html(`Value: ${d.value}`);
})
.on("mousemove", (event) => {
tooltip
.style("top", (event.pageY - 10) + "px")
.style("left", (event.pageX + 10) + "px");
})
.on("mouseout", () => {
tooltip.style("visibility", "hidden");
});
Zoom & Pan
const zoom = d3.zoom()
.scaleExtent([0.5, 10])
.on("zoom", (event) => {
g.attr("transform", event.transform);
});
svg.call(zoom);
Transitions
// Basic
circles.transition()
.duration(750)
.attr("r", 10);
// Staggered
circles.transition()
.delay((d, i) => i * 50)
.duration(500)
.attr("cy", d => yScale(d.value));
Scales Reference
| Scale Type | Use Case |
|---|---|
scaleLinear |
연속 수치 |
scaleLog |
지수 데이터 |
scaleTime |
시간/날짜 |
scaleBand |
Bar chart 카테고리 |
scaleOrdinal |
색상 매핑 |
scaleSequential |
연속 색상 |
Color Schemes
// Categorical
d3.schemeCategory10 // 10 colors
d3.schemeTableau10 // Tableau 10
// Sequential
d3.interpolateBlues
d3.interpolateYlOrRd
// Diverging
d3.interpolateRdBu
Best Practices
- Data Validation: null/NaN 체크
- Responsive: ResizeObserver 사용
- Performance: 1000+ 요소 시 Canvas 고려
- Accessibility: ARIA labels 추가
- Clean Up: 이전 렌더링 제거