Fork of D3 Scatter Plot

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Fork of D3 Scatter Plot

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Last edited Jun 22, 2023

Created on Jun 22, 2023

This scatter plot visualizes the Iris Dataset, plotting petal length against sepal length for individual flowers. The visualization uses D3 v6 to load the CSV data, scale the x and y axes with `d3.scaleLinear`, and render circles as SVG marks. Axes are added using `d3.axisLeft` and `d3.axisBottom`, with the plot sized to the full browser window. The code demonstrates a clean mapping of data fields to spatial positions.

AI-generated description

A vanilla HTML implementation of a D3 scatter plot. Shows the Iris Dataset.

MIT Licensed

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Fork of D3 Scatter Plot

This example visualizes the Iris Dataset as a scatter plot, mapping petal length on the x-axis and sepal length on the y-axis. It uses D3 v6 to load CSV data, apply linear scales via `d3.scaleLinear`, and render circles with SVG. The axes are generated using `d3.axisLeft` and `d3.axisBottom`, with margins adjusting the plot area to fill the viewport.

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This scatter plot visualizes the Iris Dataset, plotting petal length against sepal length for individual flowers. The visualization is built with D3 version 6, using `scaleLinear` for both axes, `extent` to compute data domains, and `axisLeft`/`axisBottom` for tick marks. The code retrieves data from a CSV file via `d3.csv`, then renders circles with a fixed radius of 5 pixels. The SVG canvas fills the entire browser window, with axes labeled at 24-pixel font size.

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This example visualizes the Iris dataset as a scatter plot, mapping petal length and sepal length onto the horizontal and vertical axes. Each flower is drawn as a circle with a fixed radius. The visualization uses the D3.js v6 API, including `d3.csv` to load the data, `d3.scaleLinear` for axes, and `d3.axisLeft` and `d3.axisBottom` to render tick labels. The SVG canvas fills the browser window, with circles positioned via data-driven coordinates.

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This scatter plot visualizes the Iris Dataset, plotting petal length on the x-axis against sepal length on the y-axis. Each flower is drawn as a fixed-radius circle in SVG. The code uses d3.csv to load and parse data, d3.scaleLinear with d3.extent for scales, and d3.axisLeft plus d3.axisBottom to render axes with large tick labels. The visualization fills the browser window, with margins applied, and data points are appended via selection joins.

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This scatter plot visualizes the Iris Dataset, plotting petal length against sepal length for individual flower samples. Each circle represents a data point, with axes labeled using D3's scaleLinear, axisLeft, and axisBottom APIs, and tick text styled at 24px. The visualization fills the browser window by setting SVG dimensions to window.innerWidth and innerHeight, reading the dataset from data.csv via d3.csv. Circle positions are computed by mapping data values through linear scales with defined margins and a fixed radius of 5 pixels.

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This fork of a D3 scatter plot visualizes the Iris dataset, plotting petal length against sepal length. The visualization uses D3 v6 with SVG rendering, where each flower observation appears as a circle. The code loads the CSV data via `d3.csv`, scales the axes with `d3.scaleLinear`, and renders the plot using `d3.axisLeft` and `d3.axisBottom`. Circle positions are mapped through the linear scales, with margins and a fixed radius defining the layout across the full browser window.

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This scatter plot visualizes the Iris Dataset, mapping petal length to the x-axis and sepal length to the y-axis. D3 code uses `csv` to load data, `scaleLinear` and `extent` to set scales, and `axisLeft` and `axisBottom` for axes. Each flower is rendered as an SVG circle with a fixed radius. The visualization fills the browser window using `window.innerWidth` and `window.innerHeight`.

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This visualization displays the classic Iris Dataset as a scatter plot, mapping petal length on the x-axis against sepal length on the y-axis. Circles represent individual flower samples, with axes rendered using D3’s `scaleLinear`, `axisLeft`, and `axisBottom`. The code loads data via `d3.csv`, parses numeric columns, and appends SVG circles sized with a fixed radius. It leverages D3 v6, fetching the dataset from a GitHub Gist and responsively filling the browser window with full viewport dimensions.

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