Jdlv00

@Jdlv00·9 public vizzes

fresh block

A force-directed bubble chart visualizes task priorities and assignees from a CSV dataset. Node radii encode a priority scale (WISH, COULD, SHOULD, MUST), while colors distinguish priorities and labels show team members. The D3 v3 layout applies gravity and collision detection via a quadtree, and a fixed root node follows the mouse to repel bubbles interactively.

Oct 31, 2017

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Collision Detection

This example demonstrates collision detection among 200 circles, where moving the mouse repels the nodes in real time. The visualization loads task data from a CSV file, though the circles themselves are generated with random radii using `d3.range`. It uses D3 v3’s `d3.layout.force` for physics simulation, a quadtree (`d3.geom.quadtree`) to efficiently detect overlaps, and SVG circles for rendering. The mouse position controls a root node, which pushes other circles away as the force layout updates on each tick.

Oct 31, 2017

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Collision Detection

This example demonstrates a collision detection simulation where 200 nodes bounce off one another while a fixed root node is repelled by the mouse. The visualization uses D3 v3 with an SVG canvas, rendering circles with a color scale. The simulation relies on `d3.layout.force` for the force-directed layout, `d3.geom.quadtree` for spatial indexing, and the `force.on("tick")` event to update node positions. The mouse interaction uses `svg.on("mousemove")` to reposition the root node and resume the simulation.

Oct 31, 2017

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Collision Detection

A force-directed layout places 200 circles on an SVG canvas, with a fixed invisible root node at the center of a charge-based repulsion field. Mouse movement repositions this root, causing the surrounding circles to scatter and collide. Using the d3.layout.force simulation, a quadtree indexes nodes for efficient pairwise collision resolution, adjusting circle positions on each tick. The circles, whose radii vary randomly, are colored via d3.scale.category10 and loaded from an external CSV file, while the root’s position tracks the cursor to repel the swarm.

Oct 31, 2017

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D3 treemap tutorial

This example demonstrates a D3 treemap that visualizes task data from a CSV file, aggregated by priority (e.g., WISH, COULD) and then by status (DONE, TODO). Each leaf rectangle’s size encodes the total time spent on tasks in that group, while its color indicates the priority category, with a legend for reference. The visualization uses `d3.nest()` for hierarchical aggregation, `d3.hierarchy()` to build the tree, and `d3.treemap()` to compute tile layouts. Rendering is done with SVG, showing rectangles, text labels, and hover titles.

Oct 31, 2017

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D3 aggregation tutorial

This example demonstrates how to aggregate and visualize task data by priority using D3.js. The bar chart shows the total time spent on tasks for each priority category, with bars colored via a categorical color scale. The code loads CSV data, groups records using `d3.nest()` with a `rollup` function that computes both task count and summed time, and renders the resulting bars as SVG rects. Axes are generated with `d3.axisBottom` and `d3.axisLeft`, while scales include `d3.scaleBand`, `d3.scaleLinear`, and `d3.scaleOrdinal`.

Oct 31, 2017

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Normalized Stacked Bar Chart

This example shows survey responses across different frequencies of playing, with each bar normalized to represent the proportion of respondents in each occupational category. The chart uses a stacked bar layout where the total height of each bar equals 100%, making it easy to compare category distributions across the frequency groups. The visualization is built with D3 v4, using `d3.stack()` with `d3.stackOffsetExpand` to normalize the data, and renders the bars as SVG rectangles. The data comes from a CSV file, and the chart includes a legend positioned along the rightmost bar to identify each occupational group.

Oct 24, 2017

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Stacked Bar Chart

This stacked bar chart visualizes the frequency of game-playing habits across different age groups, with each bar segment representing a specific frequency category. The data is loaded from a CSV file using d3-dsv, then processed with d3-array to compute totals and sorted in descending order. The chart uses d3-scale for positioning and color encoding, d3-shape to compute stacked positions, and d3-axis for the axes, with y-axis labels formatted using SI prefixes via d3-format. The visualization is rendered as SVG, displaying the full distribution of responses across all age cohorts.

Oct 24, 2017

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Stacked Bar Chart

A stacked bar chart displays the frequency of responses to a survey question about playing habits, broken down by occupational categories. Population totals across all categories are shown per response frequency, with each bar segment color-coded by profession. The chart is built using D3 v4 APIs including d3.csv for loading data, d3.stack for computing stacked positions, d3.scaleBand and d3.scaleLinear for positioning, and d3.scaleOrdinal for colors. Data is parsed from a CSV file and rendered as SVG with axes generated via d3.axisBottom and d3.axisLeft.

Oct 24, 2017