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Tutorial 3.1

✓ Published0🌍 Public
Eeedaraajay02@gmail.com
Last edited Apr 14, 2024
Created on Apr 14, 2024
Forked from Hello VizHub

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.

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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Tutorial 3.2 : Scatter plot

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 example shows an interactive scatter plot of the Iris dataset, comparing sepal length against petal length, with each of the three species distinguished by a unique symbol shape via a custom legend. The visualization is built with D3 v7, using `d3.scaleLinear` for axes, `d3.scaleOrdinal` and `d3.symbolsFill` to map species to symbols, and `d3.csvParse` to load and parse the remote CSV data. Custom modules handle axis rendering and legend construction, with the chart filling the entire viewport.

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This example demonstrates a scatter plot of the Iris dataset, showing sepal length versus petal length, with species encoded by symbol shapes and a corresponding shape legend. The visualization uses D3 v7 APIs including `d3.scaleLinear`, `d3.scaleOrdinal`, `d3.symbol`, `d3.axisLeft`, and `d3.axisBottom`. Data is loaded asynchronously as CSV and parsed with `d3.csvParse`. The code is modularized into separate ES6 modules for the scatter plot, axes, and legend, built as a reusable visualization component.

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Tutorial 3.2

A scatter plot displays Iris flower measurements, plotting sepal length against petal length with a legend differentiating species by symbol shape. The visualization uses D3 v7’s `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and custom axis and legend modules. Data loads from a CSV via `csvParse` and renders with `join` and `symbolGenerator`, while axes are built using `axisLeft` and `axisBottom`.

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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.

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This example demonstrates a D3.js scatter plot of the Iris dataset, showing sepal length against petal length with species encoded by symbol shapes. It uses D3 modules like `csvParse`, `scaleLinear`, `scaleOrdinal`, `schemeCategory10`, and `symbol` to render the marks. It also includes custom axes and a shape legend built with `axisLeft`, `axisBottom`, and `symbolGenerator`. Data is fetched from a CSV file via UNPKG and processed into numeric fields before visualization.

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This scatter plot displays the relationship between sepal length and petal length for iris flowers, with species differentiated by symbol shapes. The visualization uses D3 v7's scaleLinear for axes, scaleOrdinal with symbolsFill for shape encoding, and a custom shape legend to identify species. Data is loaded asynchronously from a CSV file containing the classic iris dataset. The axes and legend components are modularized into separate functions, with the chart updating responsively to the browser window size.

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