Joyplot Update Pattern II

Joyplot Update Pattern

This example demonstrates a joyplot—a series of layered, filled-area charts—that updates its data every 100 milliseconds. It shows how the general update pattern can smoothly transition between datasets while preserving the visual identity of each layer. The code uses d3.area with a Catmull-Rom curve to generate the shapes, and d3.scaleBand and d3.scaleLinear for positioning. A d3.interval triggers redraws, and transitions animate changes to each path’s `d` attribute. The data is randomly generated per letter, producing alternating steelblue and tomato layers.

90% match

Joyplot Update Pattern

This example demonstrates a joyplot update pattern, visualizing the distribution of three chemical response metrics (BmdA, BmdB, BmdC) across 50 chemicals using stacked kernel density estimates and rug plots. Each row in the SVG represents one metric, with a filled density curve drawn using D3’s area generator and a rug plot of individual data points marked as orange lines. The y-axis uses a band scale to separate the three distributions, while the x-axis maps the measured values. The code uses d3.v4 with SVG rendering, and includes an interactive tooltip library via d3-tip, though no explicit interaction is shown.

82% match

Joyplot

This joyplot shows the distribution of sports and leisure activity participation across the day, with each activity rendered as a filled, layered density curve. The x-axis maps time of day in 5-minute increments, while the y-axis encodes the proportion of participants. Each activity—such as Running—is represented by a separate row, with curves offset vertically and partially overlapping to create the classic “joyplot” ridgeline effect. The SVG rendering uses smooth paths to show the peaks and troughs of participation, making it easy to compare daily rhythms like morning and evening workouts, midday hobbies, and late-night outings. The chart is built with D3 v4 and the underlying data comes from the American Time Use Survey.

77% match

Joyplot

This joyplot visualizes the peak time of day for sports and leisure activities, showing the number of participants throughout the day compared to peak popularity. Using D3 v4 with SVG rendering, the chart displays overlapping area charts for each activity, ordered by their peak time, revealing patterns like morning and evening workouts, midday hobbies, and late-night outings. The data comes from the American Time Use Survey, processed with R and loaded from a TSV file. The code uses `d3.area`, `d3.scaleTime`, `d3.scaleBand`, and `d3.nest` to structure and render the overlapping distributions.

TThanaporn-sk

75% match

Interactive donut chart with transitions

This interactive donut chart updates live as the number of data points changes, with each arc smoothly transitioning to a new size and position. Users can type a new count into an input field, triggering random data regeneration and a one-second animated redraw. The visualization uses D3 v3’s `d3.layout.pie`, `d3.svg.arc`, and `transition.attrTween` to interpolate arc geometry, while `setInterval` automatically re-renders every two seconds. SVG paths are colored via `d3.scale.category20`, and the code also handles entering and exiting arcs for dataset size changes.

TThanaporn-sk

D3 scatter plot with react

This React-based scatter plot demonstrates animated data updates with D3.js. The visualization renders seven circles with distinct colors and sizes, positioned by x/y coordinates, with axes labeled X and Y. Data updates trigger a button click, which initiates D3 transitions using `transition().duration(1000).ease(easeLinear)` for enter animations and faster transitions for updates, while exit animations shrink circles to zero radius. The SVG rendering uses `scaleLinear`, `axisBottom`, `axisLeft`, and `selectAll('circle').join()` for binding data to DOM elements.

JJulien Tremblay

Path update

A line chart animates between two datasets, updating its path shape over time. The example uses d3.line to generate the path from an array of numbers, mapping each value to x and y coordinates based on its index and magnitude. D3’s selection and transition methods handle the smooth morphing of the path’s `d` attribute when new data arrives. The SVG rendering is driven by a simple render function called twice with different data arrays, demonstrating a basic update pattern with d3.v5.

MMarian Schubert

KDE Joyplot w/ rugplot x axis

A KDE joyplot displays the smoothed probability distributions of three chemical measurement groups (BmdA, BmdB, BmdC) as stacked density curves, with a rug plot along the x-axis for each group. The chart highlights distribution shape, spread, and differences across the three response variables. The visualization is built with D3 v4 and Underscore.js, using `d3.area` with Catmull-Rom curve interpolation to render each KDE path, `kernelDensityEstimator` and `kernelEpanechnikov` functions to compute densities, and orange tick marks beneath each curve to show individual data points. The SVG output uses a band scale for the y-axis grouping and linear scales for both x and density values.