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Fork of Fork of reusable D3 Scatter Plot (animated 2)

This animated scatter plot displays the Iris flower dataset, cycling through four measured attributes (petal width, sepal width, petal length, and sepal length) on the x-axis every six seconds. Built with React and D3 v7, it uses a reusable `scatterPlot` module with `scaleLinear`, `extent`, `axisLeft`, and `axisBottom`. Circles transition smoothly via `transition().duration(1000)` with staggered delays, while axes animate concurrently. Data loads from a CSV via `d3.csv` and renders as SVG with D3’s data join and enter/update/exit pattern.

PPeiyang Li

92% match

Fork of D3 Scatter Plot

This is an interactive scatter plot built with React and D3, rendering as an SVG. It visualizes the Iris dataset, plotting petal width on the x-axis against sepal length on the y-axis. The example uses D3's scaleLinear, extent, axisLeft, and axisBottom to map data points and draw the chart axes. It employs a chainable setter pattern in the scatterPlot function to configure its properties. The code also demonstrates how to load CSV data using D3’s csv function and how to dynamically size the visualization to the viewport.

92% match

Fork of reusable D3 Scatter Plot (animated)

This animated scatter plot visualizes the Iris dataset, cycling through four measured attributes (petal and sepal length and width) as the x-axis variable every six seconds. The D3.js implementation uses a reusable scatter plot module with smooth transitions between states, animating circle positions and axis updates. It leverages scaleLinear, extent, axisLeft, axisBottom, and transition APIs from D3 v7. The visualization renders as an SVG in a React environment, loading CSV data asynchronously and mapping each flower variety as a point.

PPeiyang Li

90% match

Fork of D3 Scatter Plot2

This scatter plot visualizes the Iris dataset, mapping petal length to the x-axis and sepal length to the y-axis with circular SVG marks. The code uses D3 v7’s `csv` loader to fetch and parse the data, then applies `scaleLinear` and `extent` to compute domains, with `axisLeft` and `axisBottom` rendering the axes. Data points are drawn as circles via the `join` pattern, sized at a fixed radius, within a full-window SVG canvas.

90% match

Fork of D3 Scatter Plot2

This visualization shows the classic Iris dataset as a scatter plot, mapping petal length on the x-axis against sepal length on the y-axis. Each of the 150 flowers appears as a small circle positioned using D3’s `scaleLinear`, with axes generated by `axisLeft` and `axisBottom`. The code loads the CSV data via `d3.csv`, parses numeric fields, and renders the marks as SVG circles, with the chart sized to fill the browser window. The example uses D3 v7 within a React-independent HTML page.

UUzoemeka Okechukwu Innocent

Fork of Dynamic D3 React Starter

This example shows an interactive scatter plot of the Iris dataset, rendered as SVG shapes whose positions map petal length and sepal length. It uses D3’s `scaleLinear`, `scaleOrdinal`, `axisLeft`, `axisBottom`, and `symbol` APIs to draw symbols with a specified radius. The code loads CSV data from a GitHub gist via `d3.csv`, parses numeric columns, and creates a reusable scatter plot function that joins data to path elements. Styling includes large tick labels and a full-viewport layout.

Animated Reusable D3 Scatter Plot

This example shows an animated scatter plot of the Iris dataset, cycling through different flower measurements every two seconds. Built with React and D3 v7, it uses `scaleLinear` and `extent` for axes, with circles rendered via SVG. A reusable `scatterPlot` function updates positions using `transition` with staggered delays, while `axisLeft` and `axisBottom` refresh axes. Data loads from a CSV via `csv` and `parseRow`, and `setInterval` triggers re-renders with new x-values.

Fork of Reusable D3 Scatter Plot

This example presents an animated scatter plot of the Iris dataset, cycling through different flower measurements (petal width, sepal width, petal length, and sepal length) on the x-axis every two seconds. The visualization is created with D3 and follows a reusable chart pattern, using `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` from the `d3` library. It renders as an SVG, with circles positioned by the current x and y value scales, and axes that update on each interval.

JJyothicherukuri