Skip to main content
SimilarExport ZIP
100%

SANKEY Experiment

✓ Published0🌍 Public
ZZeroKurosakii
Last edited Nov 15, 2023
Created on Nov 15, 2023
Forked from Hello VizHub

This Sankey diagram visualizes the flow of categorical values across six attributes—buying, maintenance, doors, persons, luggage boot, and safety—from the car.data dataset. Nodes are colored by attribute using the `d3.scaleOrdinal` scheme, with labels and values displayed. The graph is constructed using a custom `d3.sankey()` implementation, with node dragging enabled via `d3.drag()`. The layout is computed through iterative breadth and depth algorithms, rendering SVG paths for links.

AI-generated description

An example showing the capabilities of VizHub:

  • Loads D3 via UNPKG.
  • Demonstrates use of import from "d3".
  • Demonstrates use of import from local ES6 modules.
MIT Licensed

Similar vizzes

Loading thumbnail…

Sankey diagram

This Sankey diagram visualizes the flow of car attributes from the UCI Auto MPG dataset, showing how combinations of buying price, maintenance cost, doors, persons, luggage boot, and safety lead to overall acceptability ratings. Users can reorder the attribute sequence using a dropdown menu, which rearranges the flow columns accordingly. The implementation uses the `d3-sankey` extension library on top of D3 v6, rendering nodes as rectangles and links as horizontal paths within an SVG canvas. Node and link colors are assigned via `d3.scaleOrdinal`, and nodes are draggable through D3's drag behavior.

Hhi4u2qzy@gmail.com
89% match
Loading thumbnail…

SANKEY

This visualization uses a Sankey diagram to show the flow of categorical attributes from a car evaluation dataset, with nodes representing distinct values for features like buying price, maintenance, doors, people, luggage boot, and safety. The diagram is rendered using D3 v4's SVG API and a custom `d3.sankey` layout to position nodes and links. Users can drag node rectangles to rearrange the layout, which calls `sankey.relayout`, and the interactive buttons highlight the specified data columns. The dataset is loaded from a static `sankey.data` file using `d3.text` and parsed with `d3.csvParseRows`.

Sstanley.shen2003@gmail.com
89% match
Loading thumbnail…

109550187_HW8

This visualization uses a Sankey diagram to show the flow of car attribute combinations from the UCI Car Evaluation dataset, sourced via a remote CSV file. It displays how categorical values for buying price, maintenance cost, doors, persons, luggage boot, and safety connect sequentially. The D3.js v4 code employs `d3.sankey()` for layout, `d3.csvParse` for data ingestion, and SVG rendering with `d3.drag()` for interactive node repositioning. Link stroke width represents frequency, while color encodes distinct source nodes.

Ppatricknicholas27@gmail.com
86% match
Loading thumbnail…

109705001_hw8

This interactive sankey diagram visualizes the car evaluation dataset (cars.data), showing flows between six categorical features—buy, maint, door, person, lug, and safety—and the final class outcome. Users can drag column headers horizontally to reorder features and drag individual nodes vertically to explore the hierarchy. Hovering over links displays the count of records passing between each pair of categories. Built with D3 v5, it uses `d3.sankey` for layout and `d3.drag` for interactions, rendering SVG paths with stroke-opacity for flow emphasis.

Yyihsuan
86% match
Loading thumbnail…

Fork of Data Visualization Homework 8: Sankey Diagram

This Sankey diagram visualizes car evaluation results from the car.data dataset, showing how attributes like buying price, maintenance cost, doors, persons, luggage boot, and safety flow into final acceptability ratings. The diagram uses d3.sankey() to compute node positions and link paths, with nodes colored by attribute category and rendered as SVG rectangles. Links are grouped by source-target pairs and their thickness represents the count of cars with that combination. Interactive dragging of nodes is enabled through d3.drag(), allowing users to rearrange the visualization dynamically.

Jjerry70938@gmail.com
85% match
Loading thumbnail…

Data Visualization Homework 8: Sankey Diagram

This visualization is a Sankey diagram that depicts the flow of car attributes—buying price, maintenance cost, number of doors, person capacity, luggage boot size, and safety—toward a final evaluation of acceptability. The diagram uses nodes for each attribute-value combination and weighted links to show the sequential relationships between them. The code builds the graph by parsing a CSV file and then uses a custom `d3.sankey()` layout algorithm to position nodes and links, rendering the final output as SVG. Interactive dropdown menus allow the user to select which columns to compare.

Dderrick373519@gmail.com
85% match
Loading thumbnail…

Fork of 312553048_HW3

This visualization is an interactive Sankey diagram built with D3.js v4 that maps the flow of categorical car attributes (buying price, maintenance cost, doors, persons, luggage boot, safety) from the Car Evaluation dataset. It shows how records transition sequentially through these attribute columns, with node widths representing counts and colored by attribute category. The implementation uses custom D3 Sankey layout functions, the `d3.csvParse` and `d3.text` APIs for data ingestion, and `d3.drag()` for interactive node repositioning. The SVG-based rendering displays attribute titles above each column and uses color scales to differentiate categories.

Jjerry70938@gmail.com
85% match
Loading thumbnail…

HW8_311553050

This interactive Sankey diagram visualizes the flow of car attribute combinations from the Car Evaluation dataset. Seven sequential attribute stages—buying, maintenance, doors, persons, luggage boot, safety, and overall acceptance level—are displayed as linked nodes, with link thickness representing the frequency of each attribute pair. Built with React and D3.js (version 3.5.16), the visualization uses the custom `d3.sankey` module for layout and SVG paths for links. Nodes are draggable, and hovering reveals exact counts. The underlying data is parsed from a raw CSV string, with each row contributing to the aggregated combination counts.

YYunLing
84% match