Choose for simple interactive SVG charts in the browser when WebGL is unnecessary.
π Interactive JavaScript Charts built on SVG
- 15.2k
- JavaScript
- NOASSERTION
data visualization / geospatial / WebGL rendering
deck.gl is a WebGL2/WebGPU JavaScript framework for high-performance, large-scale data visualization with composable layers and map integration.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose for simple interactive SVG charts in the browser when WebGL is unnecessary.
π Interactive JavaScript Charts built on SVG
Choose for .NET desktop plotting instead of browser-based WebGL.
Interactive plotting library for .NET
Choose for D3-based SVG charts with a simple API when geospatial features are not needed.
π Re-usable, easy interface JavaScript chart library based on D3.js, with SVG and Canvas rendering support
Choose for C++ native visualization in desktop applications.
Matplot++: A C++ Graphics Library for Data Visualization ππΎ
Choose for AI-generated charts from textual specs when you want chart creation by agents.
πͺ Flint is a visualization language that lets AI agents reliably create expressive, good-looking charts from simple, human-editable chart specs.
Choose for native Android charts in Java/Kotlin apps.
AnyChart Android Chart is an amazing data visualization library for easily creating interactive charts in Android apps. It runs on API 19+ (Android 4.4) and features dozens of built-in chart types.
Choose for animated data stories in JavaScript with a high-level chunked API.
Library for animated data visualizations and data stories.
Choose for Python-native charting without a JavaScript frontend.
Ultra-fast and customizable Python charts
Choose for Flutter/Dart charting in cross-platform mobile apps.
A grammar of data visualization and Flutter charting library.
Choose for a feature-rich commercial JavaScript chart library when geospatial and WebGL are not required.
The most advanced amCharts charting library for JavaScript and TypeScript apps.
Choose for declarative grammar-based visualization with a reactive approach, different from imperative layer composition.
A visualization grammar.
Choose for modular, component-based charts across multiple frontend frameworks when you donβt need WebGL layer stacking.
Modular data visualization framework for React, Angular, Svelte, Vue, and vanilla TypeScript or JavaScript
Choose for comprehensive chart types in the browser when maps arenβt required and Canvas/SVG is acceptable.
Apache ECharts is a powerful, interactive charting and data visualization library for browser
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose for tiny static CSS-only charts with no JavaScript logic.
Open source CSS framework for data visualization.
Choose for React Native charts but note itβs unmaintained since 2023; prefer current alternatives.
victory components for react native
These projects were analysed and named deck.gl among their alternatives. The relationship is not symmetric β how deck.gl rates them is a separate judgement, made when deck.gl is analysed in its own right.
calls deck.gl βChoose for WebGL-powered large-scale data visualization when performance on big datasets is critical.β
π― visx | visualization components
calls deck.gl βChoose for high-performance WebGL2 visualization of large geospatial/layer-based data in pure JavaScript.β
Interactive Data Visualization in the browser, from Python
calls deck.gl βWebGL framework for large-scale geospatial/layered visualizations; choose for high-performance custom layers, not general charts.β
Open-source JavaScript charting library behind Plotly and Dash
calls deck.gl βchoose when you need high-performance WebGL rendering of large data sets or maps; not for general charting.β
A visualization grammar.
calls deck.gl βwhen you need custom WebGL2 visualizations (e.g., maps, 3D) rather than an out-of-the-box analytics component.β
A data visualization and analytics component, especially well-suited for large and/or streaming datasets.