Why react-charty? When to pick it

The main selling points of react-charty are small API surface, composable components and a focus on interactivity: tooltips, hover states, and pan/zoom where relevant. For many dashboards you don’t need the heavy machinery of a full D3 integration — react-charty covers the 80% use cases cleanly.

Compared to heavier frameworks, react-charty tends to be easier to integrate into existing React apps: components take data and configuration props, and they conform to React rendering lifecycle. That makes them friendly to hooks, memoization and SSR‑aware patterns.

That said, if you need extremely custom visuals or huge datasets with WebGL acceleration, evaluate alternatives (Chart.js with plugins, Victory or custom D3 pipelines). For most business dashboards, react-charty is pragmatic and fast to implement.

Installation and getting started

Installation is standard. From your project root:

npm install react-charty --save
/* or */
yarn add react-charty

Then import components you need. A minimal app skeleton:

import React from "react";
import { LineChart } from "react-charty";

function App(){
  const data = [{x: "2023-01-01", y: 10}, {x: "2023-02-01", y: 15}];
  return <LineChart data={data} xAccessor="x" yAccessor="y" />;
}

If you prefer a step‑by‑step tutorial with screenshots and sandbox examples, check the community guide: react-charty tutorial.

Examples: line, bar and pie charts (practical patterns)

Line charts are the bread-and-butter for time series. Provide a data array and accessors. Keep your data normalized (numbers, ISO dates) and leverage built‑in parsing if available. Example props to look for: xScale, yScale, curve, tooltipFormatter, responsive.

For bar charts, mind the categorical axis: set bandwidth and spacing, and use stacked mode if you have grouped data. Tooltips for bars should include both category and absolute/percent values — show the most relevant metric upfront.

Pie charts are great for high‑level composition, but avoid them for many tiny slices. Use colorScheme or color accessor functions to map categories to colors consistently across charts in a dashboard.

Code pattern: a simple line chart example

Below is a minimal example illustrating data flow and customization hooks. It’s intentionally compact — for clarity and copy‑paste usability.

import React from "react";
import { LineChart, Tooltip } from "react-charty";

const data = [
  { date: "2023-01-01", value: 20 },
  { date: "2023-02-01", value: 35 },
  { date: "2023-03-01", value: 28 }
];

export default function MyLine(){
  return (
    <LineChart
      data={data}
      xAccessor="date"
      yAccessor="value"
      responsive
      tooltip={({d}) => <Tooltip title={d.date} value={d.value} />}
      smooth
    />
  );
}

Key takeaways: keep accessors simple, use responsive prop for automatic resizing, and pass custom tooltip as a render prop if you need HTML/React nodes inside tooltips.

Customization and advanced usage

Customization is usually via props or render props. Typical hooks:

• colorScheme / color accessor — consistent theming across charts;
• customTooltip / tooltipFormatter — for HTML/JSX tooltips;
• axes props (tick formatting, rotation) and legend placement.

For deep customization, compose low‑level components (grid, axes, series). If react-charty exposes primitive renderers, you can replace internals selectively rather than forking the whole lib.

Accessibility: ensure charts have ARIA labels for interactive elements. For keyboard interaction, expose callbacks for focusing series points or using arrow keys to move focus between data points.

Building dashboards with react-charty

Dashboards need consistent color scales, shared tooltips and cross‑filtering. Patterns:

– Centralize color and formatters in a theme/context. Wrap charts in a provider that supplies colorScheme and dateFormat.

– For cross‑filtering, lift state up: charts emit click/hover events; the dashboard coordinator updates query state and passes filtered data back down.

Performance and best practices

Keep datasets pre-aggregated for large time series. Avoid rendering thousands of SVG points; consider downsampling or progressive rendering. Memoize chart components and accessors with React.memo and useCallback.

If you need WebGL level performance, evaluate libraries that offer canvas/WebGL renderers. For moderate datasets, virtual DOM + SVG is fine.

Lazy‑load heavy chart components on routes/accordion expansion to reduce initial bundle size. Code splitting keeps your app snappy.

SEO, voice search and featured snippets

To help featured snippets and voice search:

– Provide concise instructions and plain commands (e.g., “npm install react-charty”) near the top.

– Use short Q&A blocks (like the FAQ below) to target PAA and voice queries directly.