Skip to main content
SimilarExport ZIP
100%

Fork of Reusable D3 Scatter Plot

✓ Published1🌍 Public
Hharoldle
Last edited Mar 12, 2023
Created on Mar 10, 2023

This example shows a reusable scatter plot of the Iris dataset, cycling through four different axes (petal width, sepal width, petal length, and sepal length) every two seconds to reveal different relationships between measurements. Data points use D3’s `scaleOrdinal` with `symbols` and `schemeCategory10` to give each iris species a distinct shape and color. The chart is built with a custom `scatterPlot` function that leverages `d3.scaleLinear`, `d3.axisLeft`, `d3.axisBottom`, and `selection.join`, and is animated via `setInterval` updating the x-value accessor. The SVG renders full-window, and the dataset is loaded from a CSV using `d3.csv`.

AI-generated description

A reusable scatter plot inspired by Towards Reusable Charts.

Shows the Iris Dataset.

MIT Licensed

Similar vizzes

Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This example demonstrates a reusable scatter plot that visualizes the Iris dataset, cycling through four numerical attributes (petal width, sepal width, petal length, and sepal length) on the x-axis every two seconds while keeping sepal length on the y-axis. It uses D3.js v6 with `scaleLinear`, `axisLeft`, and `axisBottom` to render axes, and updates the visualization by re-calling the chart function. The plot renders as SVG circles with animated transitions, and the code dynamically binds new data values to update the x-scale domain and circle positions, showcasing a modular chart pattern for interactive data exploration.

Sshailajat1998@gmail.com
95% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This example presents an animated scatter plot of the Iris Dataset, cycling through the four measured attributes (petal width, sepal width, petal length, and sepal length) on the x-axis while sepal length remains on the y-axis. It demonstrates a reusable chart pattern using D3 v6, where a `scatterPlot` function returns a configurable closure that sets scales, joins circle elements to data, and renders axes with `axisLeft` and `axisBottom`. The visualization rotates through columns every two seconds via `setInterval`, updating the x-value accessor and re-invoking the chart. It loads data asynchronously with `csv` from a GitHub Gist and renders in SVG within the full browser viewport.

Ssudhamshu24
95% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This example shows an interactive scatter plot of the Iris dataset, where the x-axis cycles through four numeric columns (petal width, sepal width, petal length, sepal length) every 1.5 seconds, while the y-axis stays fixed to sepal length. Each point is colored by species using a d3.scaleOrdinal with a viridis-like palette, and a static legend identifies the three species. Built with D3 v6, the visualization uses d3.csv to load data, d3.scaleLinear and axisLeft/axisBottom for axes, and a reusable scatterPlot function that updates circle positions and axis labels on each interval.

RRavi Teja Bonam
95% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

Animated scatter plot of the Iris dataset built with D3.js, cycling through different attribute pairs (petal width, sepal width, petal length, sepal length) every two seconds. The visualization uses SVG rendering with `scaleLinear`, `axisLeft`, and `axisBottom` to map data to coordinates and axes. A reusable `scatterPlot` function encapsulates the chart, driven by `setInterval` to update the X-axis mapping. Data loads from a CSV via `d3.csv`, with numeric parsing for all four measurements.

AArefa Patwary
95% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This example demonstrates a reusable scatter plot of the Iris Dataset, cycling through the four measured features (sepal length/width and petal length/width) on the x-axis every two seconds while keeping sepal length on the y-axis. It shows how a single chart function can be reconfigured with different accessor functions to update the visualization. The implementation uses D3’s `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` within a custom `scatterPlot` module, driven by `setInterval` to trigger re-renders. The SVG is appended to the body, with data loaded via `d3.csv` and parsed numerically.

BBALAJI MANDAVA
95% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

An animated scatter plot cycles through four Iris dataset measurements, re-mapping the x-axis every two seconds to compare petal width, sepal width, petal length, and sepal length against a fixed y-axis of sepal length. Using the reusable chart pattern, it leverages `d3.scaleLinear`, `d3.extent`, `d3.axisLeft`, and `d3.axisBottom` within a custom `scatterPlot` function called via `d3.select`. Data loads from a remote CSV with `d3.csv`, and circles update positions through `selection.join`, with SVG rendering and a `setInterval` driving the animation.

Eeinsko
94% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This example presents a reusable scatter plot visualizing the Iris Dataset, cycling through four flower measurements—petal width, sepal width, petal length, and sepal length—as the x-axis every two seconds while the y-axis remains fixed on sepal length. Built with D3 v6, the code uses `scaleLinear`, `extent`, `axisLeft`, `axisBottom`, and `scatterPlot()` to render SVG circles with animated transitions. Data is loaded via `d3.csv` from a GitHub Gist, and the plot updates in place by re-calling the chart function with new `xValue` accessors.

Tttummala17
94% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This example presents a reusable scatter plot visualizing the Iris Dataset, cycling through four flower measurements—petal width, sepal width, petal length, and sepal length—as the x-axis every two seconds while the y-axis remains fixed on sepal length. Built with D3 v6, the code uses `scaleLinear`, `extent`, `axisLeft`, `axisBottom`, and `scatterPlot()` to render SVG circles with animated transitions. Data is loaded via `d3.csv` from a GitHub Gist, and the plot updates in place by re-calling the chart function with new `xValue` accessors.

Tttummala17
94% match