Question-3.2

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Last edited Oct 22, 2024

Created on Oct 19, 2024

This scatter plot displays the Iris dataset, mapping sepal length on the x-axis against petal length on the y-axis. Each point is encoded with a distinct symbol representing its species, with a legend identifying the three categories. The visualization is built using D3’s `scaleLinear` for coordinate mapping and `symbolsFill` for shape encoding. Data is loaded from a remote CSV file and parsed with `csvParse`, while axes and labels are generated through the custom `axes` module using `axisLeft` and `axisBottom`.

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Question3

This visualization is a scatter plot of the Iris flower dataset, mapping sepal length on the x-axis and petal length on the y-axis, with points colored by species. It uses D3 v7's `scaleLinear`, `scaleOrdinal`, and `symbolsFill` to position points and assign shapes, while the `axisLeft` and `axisBottom` functions render labeled axes. A custom shape legend, built with `shapeLegend` and D3's `symbol` generator, identifies each species. The plot is rendered in a full-window SVG, with data loaded via `csvParse` from an external source.

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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 scatter plot visualizes the Iris dataset, mapping sepal length on the x-axis and petal length on the y-axis, with each species represented by a distinct symbol shape. A legend on the right identifies the three species categories. The visualization is built with D3 v7, using d3.scaleLinear for positioning, d3.scaleOrdinal with d3.symbolsFill for shape encoding, and d3.axisLeft and d3.axisBottom for the axes. Data is loaded from a remote CSV file and parsed with d3.csvParse.

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This scatter plot visualizes the relationship between sepal length and petal length for different iris species, using distinct symbols to differentiate each species. The visualization is built with D3 v7, employing `scaleLinear` for the axes, `scaleOrdinal` and `symbolsFill` to assign shapes, and `symbol` to render the data points. A legend, created via `shapeLegend`, maps each species to its corresponding symbol. The data is loaded from a CSV file and rendered using the `scatterPlot` and `axes` functions.

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This scatter plot visualizes the Iris dataset, plotting sepal length against petal length. Each point is rendered as a distinct symbol—circle, cross, or square—to represent the three species of Iris flowers, categorized on a categorical color scale. The visualization is built using D3.js (v7), using the `scaleLinear` and `scaleOrdinal` APIs to map data to positions and symbols. Axes and a shape legend are drawn using custom `axes` and `shapeLegend` modules, which utilize D3’s `axisLeft` and `axisBottom` functions. Data is loaded from a remote CSV file and parsed with `csvParse`.

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This scatter plot visualizes the Iris dataset, showing sepal length against petal length, with each species represented by a distinct symbol shape. The plot encodes the three Iris species through shape, using d3.scaleOrdinal with symbolsFill to assign different geometric marks, while a custom legend explains the species mapping. Axes are rendered via d3.axisLeft and d3.axisBottom, with labels for both dimensions. The data is loaded asynchronously from a GitHub-hosted CSV file using d3.csvParse, and the visualization updates through a state management pattern that handles loading and rendering.

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