Fork of Scatter Plot Simple

Fork of Scatter Plot Simple

This scatter plot visualizes the Iris Dataset, showing the relationship between sepal length and petal length across three species. The chart displays colored circles without axes, using D3 v7's `scaleLinear` and `extent` functions to map data values to SVG coordinates. The visualization is made responsive by applying a `viewBox` attribute instead of fixed width and height, while CSS `clamp` controls the SVG's displayed size. Data is loaded from a CSV file and parsed with `csvParse` before rendering.

Zzach b.

Fork of Scatter Plot

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.

Rradhadevarapu

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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.

SSeekerzero

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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`.

Ssnehareddygurram

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 APIs including `csv` for data loading, `scaleLinear` for scaling, and `axisLeft` and `axisBottom` for axes. The SVG circles are rendered via `selectAll` and `join`, with a fixed radius. The plot fills the browser window, using `window.innerWidth` and `window.innerHeight` to size the chart, and includes tick labels with 24px font styling.

JJordi

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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.

JJesper Martinsson

Fork of Scatter Plot Simple

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.

AAsherpranayp

Fork of D3 Scatter Plot

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.

JJyothicherukuri