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question 3.2

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Ttsumanth619@gmail.com
Last edited Apr 4, 2024
Created on Apr 4, 2024
Forked from Hello VizHub

This example displays a scatter plot of iris flower measurements, encoding sepal length on the x-axis, petal length on the y-axis, and species as different symbols. It loads CSV data from a remote GitHub source via `fetch` and `csvParse`, then renders the chart using D3 v7 scales, axes, and shape legends. The visualization implements a modular architecture with separate ES6 modules for axes, scatter plot rendering, and legend, demonstrating reusable chart components.

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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Activity6_Question3

This scatter plot visualizes the Iris flower dataset, mapping sepal length on the x-axis and petal length on the y-axis, with three species distinguished by distinct symbol shapes. It loads data from a remote CSV, parses it with `d3.csvParse`, and renders marks using `d3.scaleLinear`, `d3.scaleOrdinal`, and the `symbol` generator. The chart includes labeled axes via custom D3 axes modules and a shape legend, all built with D3 v7 selections and joins.

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This scatter plot visualizes the relationship between sepal length and petal length in the Iris dataset, using different marker shapes to distinguish species. It demonstrates d3's shape encoding via `d3.symbol` and `symbolsFill`, with a custom shape legend generated by `shapeLegend.js`. The visualization loads data from a remote CSV, parses it with `d3.csvParse`, and renders axes using `d3.axisLeft` and `d3.axisBottom` from imported D3 modules. The chart updates reactively through state management and a render function that re-calls the plotting logic.

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This example demonstrates a scatter plot of iris flower measurements, using sepal and petal length to compare species with distinct symbols and a legend. It loads CSV data from a GitHub repository and parses it with D3’s `csvParse`. The visualization builds scales with `scaleLinear` and `scaleOrdinal`, renders marks via `symbol` generators, and adds axes and labels using `axisLeft`, `axisBottom`, and custom D3 selection joins.

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Task 8 Question 3

This example visualizes a scatter plot of iris flower measurements, mapping sepal length and petal length while encoding species through different symbol shapes. It loads CSV data from a GitHub repository and displays it using an SVG chart with labeled axes and a custom shape legend. The code uses d3.csvParse to load data, d3.scaleLinear for positioning, d3.scaleOrdinal with d3.symbolsFill for shape encoding, and d3.axisLeft and d3.axisBottom for axes. A modular structure separates the scatter plot, axes, and legend into local ES6 modules, with rendering driven by a simple state management pattern.

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activity 6 Task 3 question

This example displays a scatter plot of iris flower measurements, plotting sepal length against petal length while encoding species through distinct symbol shapes. The visualization loads CSV data from a GitHub gist and parses it with `d3.csvParse`. It uses `d3.scaleLinear` for both axes, `d3.scaleOrdinal` with `d3.symbolsFill` for shape encoding, and `d3.axisLeft` and `d3.axisBottom` for axes. A custom legend, built with `d3.symbol`, shows the mapping between species and symbol types.

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task 3.2

This example shows a scatter plot of the Iris dataset, with sepal length mapped to the x-axis, petal length to the y-axis, and species distinguished by different symbol shapes. A custom shape legend labels each species group. The visualization loads CSV data via `d3.csvParse` and uses D3 v7’s `scaleLinear`, `scaleOrdinal`, `symbol`, `symbolsFill`, and axis functions (`axisLeft`, `axisBottom`) to render marks and axes. A local ES6 module structure organizes the code into separate files for the chart, axes, and legend.

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Question-3.2

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