Untitled

100%

✓ Published0🌍 Public

Last edited Mar 10, 2022

Created on Mar 10, 2022

This visualization displays a 3D surface plot of mountain elevation data, with latitude and longitude coordinates mapped onto the x- and y-axes and elevation values represented on the z-axis. It uses Plotly’s `newPlot` API to render the interactive surface, while D3’s `csv` method fetches the dataset from a remote CSV file. The React and ReactDOM libraries manage the page structure, though the actual chart is rendered entirely by Plotly. The layout sets explicit pixel dimensions and margins, and the color gradient on the surface encodes elevation variations across the terrain.

AI-generated description

Boilerplate code for a React + D3 visualization with:

CDN links for React, ReactDOM, and D3
Basic React boilerplate
Variables for dimensions and margins
A VizContainer component that:
- Abstracts SVG/group container elements
- Follows the margin convention
- Show the margins, which can be easily removed by deleting the showMargins={true} prop

MIT Licensed

Similar vizzes

Loading thumbnail…

Untitled

This example renders a 3D scatter plot from a CSV dataset using Plotly.js within a D3 v6-loaded HTML page. It visualizes two series of coordinates—x1/y1/z1 and x2/y2/z2—as distinct marker groups with differing colors and opacity. The code loads data via D3’s `d3.csv` and transforms rows using a custom `unpack` function to extract columns for the Plotly traces, which are configured as `scatter3d` markers. The layout margins are set to zero to maximize the plot area.

Ssalimjivani

79% match

Loading thumbnail…

Untitled

This example shows an interactive terrain visualization built with React and D3.js, displaying a 3D surface rendered on an HTML canvas. The code uses `d3.forceSimulation` to position nodes in a network layout, while `d3.geoPath` generates geographic shapes from projected data. The data appears to be a dataset of geographic coordinates, with the visualization applying a custom projection and color encoding to represent elevation or another spatial attribute. The scene is rendered through a canvas element, with the React framework managing state and updates between user interactions and the underlying D3 rendering pipeline.

TTrustPaul

79% match

Loading thumbnail…

Untitled

This example shows a 3D surface plot of a mathematical function, where the height at each point is determined by a cosine wave based on distance from the origin, scaled by a random factor. Users can drag the surface to rotate it in three dimensions, or click the "update" button to generate a new surface with a different random amplitude. The visualization uses d3._3d to project and draw the surface as SVG paths, with d3-scale-chromatic's interpolateSpectral for coloring and d3.drag for interaction. Each update transitions the new surface in over one second, dynamically recalculating the color scale domain based on the new height range.

SShuyu Zheng

76% match

Loading thumbnail…

Untitled

This visualization displays two sets of three-dimensional scatterplot data as grouped point clouds, with one trace rendered in warm red tones and the other in neutral gray. Built using Plotly.js within an HTML page, the code loads a CSV dataset from a GitHub repository via the D3.js `d3.csv` function, then maps columns to x, y, and z coordinates. The markers use a fixed size of 12, partial opacity, and custom line colors, while the plot layout strips all margins for a clean, full-frame view.

SSravan1888

75% match

Loading thumbnail…

Untitled

An interactive 3D surface plot visualizes a mathematical function of two variables, rendered as animated SVG polygons that respond to drag gestures for rotation. The code uses the d3-3d library with d3.v4 to project points into 3D space, while `d3.drag()` handles mouse interaction to update rotation angles. Surface color varies by height using `d3.interpolateSpectral`. A button triggers `d3.randomUniform` to generate new function parameters, transitioning the surface over one second via D3 transitions.

Jjorelchan

74% match

Loading thumbnail…

Untitled

This example shows a 3D scatter plot comparing two sets of data points across three variables: a1/s1/h1 and a2/s2/h2. The visualization uses two distinct marker styles—one with black outlines and one with pink circles and green outlines—to differentiate the datasets. The code loads CSV data from a GitHub gist using the D3.js `d3.csv` function, then constructs the plot with Plotly's `scatter3d` trace type. The layout removes all margins to maximize the plot area, and the chart is rendered into a div using `Plotly.newPlot`.

AAshrith Kudidhi

73% match

Loading thumbnail…

Untitled

This example displays a 3D scatter plot with two sets of markers, each representing a distinct series of data points. It uses Plotly.js to render the visualization, with a D3 CSV loader fetching the dataset from a GitHub Gist. The code defines two traces—one with default styling and another with gray markers—and plots them across x, y, and z coordinates. The layout removes all margins, and the chart is embedded in a designated div element.

SSaahithi49

73% match

Loading thumbnail…

Untitled

This 3D scatter plot visualizes population data from a CSV file loaded via D3.js, displaying two distinct series as clusters of points in three-dimensional space. The first trace uses default Plotly coloring while the second employs gray markers with circular symbols, both featuring semi-transparent points with thin outline strokes. Built with Plotly.js's `scatter3d` type and `Plotly.newPlot`, the visualization renders into a single div element with zero margins, allowing the data to fill the entire viewport. The data is fetched using D3's `d3.csv` method and mapped through a custom `unpack` function to extract x, y, and z coordinates from the dataset.

SSurikanti Swathi

73% match