Fork of Scatter Plot Simple

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Fork of Scatter Plot Simple

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Last edited Mar 10, 2023

Created on Mar 10, 2023

This example visualizes the classic Iris Dataset as a scatter plot, showing petal length versus sepal length for 150 flowers across three species. The visualization uses D3 v7 to load the CSV data via `csvParse`, and the `scatterPlot` function constructs linear scales with `scaleLinear` and `extent` to map data values to screen positions. Each data point is rendered as a circle using D3’s data join pattern, though the plot omits axes, focusing purely on the spatial distribution of the points. The chart fills the entire browser window dynamically.

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A scatter plot of the Iris Dataset with no axes.

MIT Licensed

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This scatter plot displays the classic Iris dataset, mapping sepal length along the x-axis and petal length along the y-axis. Each flower specimen is rendered as a circle, with all 150 data points plotted without axes or gridlines. The visualization uses D3 v7, loading data via a CSV fetch and parsing it with `csvParse`. The `scatterPlot` module constructs linear scales using `scaleLinear` and `extent`, then positions circles using the `join` API based on the selected measurement columns.

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This example visualizes the Iris Dataset as a scatter plot, mapping sepal length to the x-axis and petal length to the y-axis, with each flower represented by a fixed-radius circle. The plot has no axes, so it focuses purely on the distribution and clustering of the three species. The visualization uses D3 v7, loading data with `csvParse`, mapping values to pixel coordinates via `scaleLinear` and `extent`, and rendering circles through `selection.selectAll` with a `join`. The chart spans the full browser window, with data fetched asynchronously on initial render.

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This visualization displays the classic Iris Dataset as a scatter plot, mapping sepal length on the x-axis against petal length on the y-axis, with each of the 150 flowers represented as a circle. The code uses D3 v7’s `scaleLinear`, `extent`, and `axisLeft`/`axisBottom` functions to position points and render labeled axes. Data is loaded from an external CSV file and parsed with `csvParse`, then rendered using D3’s data join pattern with circles.

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This example plots sepal length against petal length for the Iris Dataset, with each flower represented as a circle. The visualization demonstrates a reusable scatter plot module composed with custom axis and labeling functions. It uses D3 v7 APIs including `scaleLinear`, `extent`, `axisLeft`, `axisBottom`, and data joins via `selectAll` and `join`. The code loads the CSV data with `fetch` and `csvParse`, then renders the plot across the full browser window, coloring points by species.

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A scatter plot of the Iris Dataset uses sepal length on the x-axis and petal length on the y-axis, with circles colored and positioned by species. No axes are rendered. The visualization loads data from a CSV file using the Fetch API and `d3.csvParse`, then applies `d3.scaleLinear` and `d3.extent` to map data values to pixel coordinates. Each data point is rendered as a circle with a fixed radius via `d3.selectAll` and `join`.

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This visualization displays the Iris Dataset as a scatter plot, mapping petal length on the x-axis and sepal length on the y-axis. Each flower is represented by a small circle, with axes generated using `d3.axisLeft` and `d3.axisBottom`. The code loads CSV data via `d3.csv`, parses numeric columns, and applies `d3.scaleLinear` with `d3.extent` to set the scales. The SVG chart resizes to the full browser window, and circles are rendered using `d3.selectAll` with a `.join()` pattern.

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A scatter plot visualizes the classic Iris Dataset, showing sepal length against petal length for 150 flowers across three species. Circles are plotted without color or legend, with fixed 10-pixel radii. The visualization uses d3-fetch’s `csvParse` to load and parse the data, d3-scale’s `scaleLinear` for axes, and d3-axis’s `axisLeft` and `axisBottom` for labeled axes. The implementation is modular, with separate files for axes, scatter plot, and main visualization logic.

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