My fisrt viz with javascript

Mandelbrot

This interactive visualization computes and renders the Mandelbrot set as a static canvas image. The code iterates through each pixel, applying a mathematical formula to determine whether points in the complex plane diverge or remain bounded, coloring each point based on the iteration count. It uses a gray-scale gradient, with darker values for points that escape quickly and black for those within the set. Rendering is handled through a custom Svelte Canvas component, which registers individual drawing functions for each pixel, using the Canvas 2D API’s `fillRect` method. The computation logic is adapted from The Coding Train’s tutorial on the Mandelbrot set.

KKristin Baumann

Untitled

A scatter plot displays 100 randomly generated points within a fixed coordinate space. Each point is rendered as a red circle on an SVG canvas, with axes labeled from 0 to 500. The visualization uses D3 v4’s `d3.scaleLinear` for scaling, `d3.axisBottom` and `d3.axisLeft` to draw the axes, and appends circles via `selectAll` and `enter`. The data is synthetic, created with `Math.random` in a loop.

Ssaipaidipalli

Untitled

This example shows a simple scatter plot of 100 randomly generated points, each positioned on a 500-by-500 pixel SVG canvas. The visualization uses D3.js v4 scales and axes to map linear domains to screen coordinates, with red circles drawn for each point. The code appends an SVG group to the page, translates it by a 40-pixel margin, and uses `d3.scaleLinear` for both the x- and y-axes, which are rendered via `d3.axisBottom` and `d3.axisLeft`. Data is created in a loop using `Math.random()`.

Ffay Alomar

Large number of points

A scatterplot displays 50,000 generated points, with color and radius encoding each point’s position in the lower, core, or upper quartile range. Built with D3 v4, the visualization uses SVG circles and an animated transition to update the points when the user edits the quantile, color, or radius values. The x-axis maps the point index, while the y-axis shows the generated normal distribution values, with scales recalculated via the `d3.scaleLinear` and `d3.quantile` APIs.

CCJKraenzle

Canvas Scatterplot

Two thousand normally distributed points render as a steelblue canvas scatterplot. The code generates data with d3.randomNormal, binds it to custom elements with a d3 data join, and draws the points using Canvas 2D context arcs. A d3.timer repeatedly calls the draw function for 300 milliseconds, clearing and repainting the canvas to animate the visualization. The example uses d3.v4, with linear scales mapping the domain from 2 to 8 onto a 1400 by 750 pixel canvas.

JJverma

Canvas Scatterplot

This example shows a scatterplot of 20,000 randomly generated data points, each positioned on a two-dimensional Gaussian distribution. The visualization animates by redrawing the canvas for 300 milliseconds, creating a flickering effect as the points appear. The code uses the Canvas API directly with `getContext('2d')` for rendering, while employing a custom D3 selection abstraction to bind data to circle elements with `d3.range`, `d3.randomNormal`, and `d3.scaleLinear`. The points are drawn as steelblue circles via `context.arc` and `context.fill` within a repeated timer loop using `d3.timer`.

JJverma

D3: Scatterplot!

This interactive scatterplot displays 50 random data points on Cartesian axes, with each circle colored blue or red depending on whether its x-coordinate falls in the left or right half of the plot. Clicking the “Randomize” button generates a fresh dataset and smoothly animates the points, axes, and color updates over two seconds. The visualization is built with D3 3.5.5, using the `d3.scale.linear`, `d3.svg.axis`, and `transition().duration(2000)` APIs, and renders entirely as SVG. The code derives from Scott Murray’s examples, forked from earlier gists by anonymous and jalapic.

MMissHanJ

Document

The visualization generates a random sequence of twenty charts, cycling through line charts, pie charts, and bar charts. Each line chart displays four smoothed monotone curves with colored dots, while the pie chart and bar chart render categorical and time-based data. Built with D3 v4, the code uses `d3.line`, `d3.arc`, and `d3.axisBottom`/`d3.axisLeft` to produce SVG output. Data is procedurally generated using `d3.randomUniform` and `d3.range`, with each chart styled via inline attributes and CSS classes.

DDabbar