Fundamental Visualizations

Charts, Charts, Charts

This visualization displays four distinct chart types side by side: a doughnut chart, bar chart, radar chart, and pie chart. Each chart presents a different dataset, such as airline passenger numbers, player skill ratings, and EU population figures. The code uses Chart.js 2.8.0 for rendering all charts onto canvas elements, while D3.js v7 handles loading CSV data from a GitHub gist. The bar chart and pie chart use hardcoded arrays, but the doughnut chart demonstrates fetching and parsing external CSV data.

Ddevvansh1997

78% match

Graphical Perception

This example displays a grid of eight static charts—bar, pie, line, scatter, choropleth, and others—derived from the 1984 Cleveland and McGill paper on graphical perception. It uses the Chiasm layout engine to arrange the images in a responsive grid, with each chart rendered via a custom `Image` plugin that sets an `<img>` tag’s `src` attribute to a corresponding PNG file. The code leverages D3.js for DOM manipulation and the Chiasm component API for reactive data flow. The visualization demonstrates how classic chart types encode data, without interactive data handling.

Chiasm Foundation

This example showcases the Chiasm foundation by composing three interactive visualization components within a nested layout. Using SVG, a dummy visualization component renders colored rectangles with draggable X patterns that dynamically adjust stroke width via mouse interaction. An HTML5 Canvas-based wave simulation responds to clicks, while the layout plugin arranges all components in a flexible vertical/horizontal nested structure. A links plugin connects the line width of component B to component C, demonstrating reactive dataflow between components. The page is set against a black background with the visualization container filling the viewport, and all components are coordinated through Chiasm's functional reactive model.

D3 Chart

This example displays six D3.js charts—horizontal and vertical bar charts, a line chart, an area chart, a pie chart, and a donut chart—all rendered as SVG elements. Each visualization uses the d3.csv function to load population data by country from a local CSV file. The code applies d3.scaleLinear and d3.scaleBand for axes, d3.line and d3.area with curveBasis for path generation, and d3.scaleOrdinal with schemeSet2 for categorical colors, while d3.axisLeft and d3.axisBottom create the tick marks.

Sshadreza

Chiasm Charts Example

The Chiasm Charts example presents a grid of multiple coordinated visualizations—bar chart, scatter plots, box plot, heatmaps, and a line chart—arranged in a vertical layout. Built with the Chiasm framework, it links data loaders and chart components via declarative JSON state. Using d3-scale for axes and d3-time for temporal binning, it renders interactive SVG animations from CSV datasets like the Iris flower measurements and a time-series of temperatures.

Bootstrap Chiasm Example

This example demonstrates how to embed multiple interactive visualizations—a bar chart, line chart, scatter plot, and pie chart—into a webpage whose layout is managed by Bootstrap’s grid system. Each chart is wrapped in its own Chiasm component, instantiated from the `barChart.js`, `lineChart.js`, and other visualization modules, and injected into the corresponding DIVs defined in `index.html`. The page uses D3.js for rendering and animation, with Chiasm managing reactive data flow so that updates to shared datasets propagate automatically to all charts. The Bootstrap grid provides a responsive layout: three visualizations sit side-by-side in one row, with a full-width line chart below. The example demonstrates how to integrate multiple Chiasm instances with an external CSS framework, combining the strengths of Bootstrap’s layout with D3’s data-driven rendering.

A4 - Introduction to D3

This example introduces D3.js through a collection of four interactive visualizations: a bar chart, a scatter plot, a boxplot, and a strip plot with a logarithmic scale. Each chart uses D3’s core modules—`d3.scaleLinear`, `d3.scaleLog`, `d3.axisBottom`, and `d3.axisLeft`—to map data to pixel coordinates and render SVG elements like `rect`, `circle`, and `line`. The visualizations draw from datasets embedded in the accompanying script files, with the boxplot summarizing distributions via quartile calculations. The code is structured as separate VizHub projects linked from a single HTML page, demonstrating basic chart construction without external data files.

DDuc Pham

76% match

Visualization Editor

This animated demo of Chiasm, a visualization runtime engine, cycles through configurations that combine a bar chart and a line chart in a split view, alongside a live configuration editor. The interactive editor lets you modify chart properties—such as data bindings, axes, colors, and titles—and see the updates render instantly. Built with reusable D3-based plugins, the example demonstrates how different chart types and data sources can be composed dynamically, with the bar chart plotting letter frequencies and the line chart showing temperature over time.