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Fork of Tutorial 3.2 : Scatter plot

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Aajaykumarseelam@my.unt.edu
Last edited Apr 5, 2024
Created on Apr 5, 2024

This scatter plot visualizes the Iris dataset, plotting sepal length against petal length with each point’s shape encoding the species. The visualization uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and joins to render axes and a shape legend. Data is loaded via `csvParse` from a GitHub-hosted CSV file, with numerical columns parsed and passed to the scatter plot for dynamic rendering.

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

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Fork of Fork of Tutorial 3.2 : Scatter plot

A scatter plot displays sepal length against petal length for the Iris dataset, with points colored and shaped by species. The visualization loads data from a remote CSV file and uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and `symbol` to map data values to positions and shapes. The plot includes labeled axes created with `axisLeft` and `axisBottom`, plus a shape legend generated by a custom module. The entire chart renders as an SVG, sized to the full browser window.

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This scatter plot visualizes the Iris dataset, mapping sepal length against petal length and distinguishing iris species by shape using d3.scaleOrdinal and d3.symbolsFill. The code imports modular D3 functions like axisLeft, axisBottom, and csvParse from d3.v7, and builds the chart with a custom scatterPlot module that renders symbols via the symbol generator. A separate shapeLegend module displays the species categories, while axes labels clarify the measurements. Data loads asynchronously via fetch from a GitHub-hosted CSV file.

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This interactive scatter plot displays sepal length against petal length for the Iris dataset, with species differentiated by symbol shape and a corresponding legend. It loads data from a remote CSV and uses D3 v7 methods including `scaleLinear`, `scaleOrdinal`, `symbol`, and `axisLeft`/`axisBottom`. The visualization is built with ES6 modules, including custom axis and legend components, and dynamically fetches and parses the dataset using `csvParse` before rendering.

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