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ajaysonu

@ajaysonu·50 public vizzes

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Fork of ICE-8

This force-directed network chart visualizes a small graph of seven people grouped into four clusters, with links weighted by connection strength. The visualization uses D3 v4’s `d3.layout.force` to position nodes and render SVG lines for links and circles with labels for each person. Nodes are draggable via the force layout’s drag behavior, and tick updates synchronize link coordinates and node transforms. Data comes from a local JSON file containing node names, group assignments, and link weights, which also drive stroke thickness via `Math.sqrt(d.weight)`. The page uses a red background and white link strokes for contrast.

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Family tree ice8

An interactive family tree for a lineage starting with Sathri, showing descendants like Chandra Shekar and Padmavathi. The visualization uses a collapsible tree layout with D3.js, where clicking a node toggles its children, animating transitions between expanded and collapsed states. The code loads JSON data from `qn_tree.json` and uses `d3.layout.tree()` and `d3.svg.diagonal()` to position nodes and draw curved links. Styling is done via CSS, with nodes as circles and text labels, rendered as animated SVG.

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Fork of Family tree ice8

This interactive family tree visualization displays hierarchical relationships from a JSON dataset, with nodes representing individuals and links showing parent-child connections. Users can click any node to collapse or expand its subtree, with smooth 1100ms transitions animating nodes and links to their new positions. The code uses D3.js v3’s `d3.layout.tree` for layout and `d3.svg.diagonal` for curved link paths, rendering as SVG. Nodes are styled with green circles and bold text, while collapsed nodes appear lightsteelblue. The animation is driven by D3 transitions, updating node transforms and link path geometry dynamically.

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Family tree ice8

An interactive family tree for a lineage starting with Sathri, showing descendants like Chandra Shekar and Padmavathi. The visualization uses a collapsible tree layout with D3.js, where clicking a node toggles its children, animating transitions between expanded and collapsed states. The code loads JSON data from `qn_tree.json` and uses `d3.layout.tree()` and `d3.svg.diagonal()` to position nodes and draw curved links. Styling is done via CSS, with nodes as circles and text labels, rendered as animated SVG.

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India map ICE-8

This visualization displays a map of India with district boundaries colored by state and state outlines highlighted. It uses d3.geoPath and geoMercator projection to render GeoJSON TopoJSON data from GitHub sources for both state and district features. The map supports zooming via d3.zoom, and district hover effects change fill color to black. State capitals are not plotted but district tooltips reveal district and state names using SVG title elements.

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India map ICE-8

This visualization displays a map of India with district boundaries colored by state and state outlines highlighted. It uses d3.geoPath and geoMercator projection to render GeoJSON TopoJSON data from GitHub sources for both state and district features. The map supports zooming via d3.zoom, and district hover effects change fill color to black. State capitals are not plotted but district tooltips reveal district and state names using SVG title elements.

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Fork of World_Map_ICE_8

This world map visualization displays global landmasses and country boundaries using the Natural Earth projection. The map renders land areas in purple with red interior borders and blue graticule lines on a white sphere. Data comes from the world-atlas TopoJSON dataset, loaded via d3.json and processed with topojson.feature and topojson.mesh. The visualization uses d3.geoNaturalEarth1, d3.geoPath, and d3.geoGraticule to generate SVG paths, with React components (App, Marks, useData) managing the rendering pipeline.

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Fork of ICE-8(World Map)

This example renders a world map using D3 v5 and TopoJSON, displaying countries as dark green filled polygons against a blue sphere. It uses the `geoEquirectangular` projection with `d3.geoPath` to generate SVG paths for each country, while the sphere and country hover states are styled via CSS classes. The map data comes from the world-atlas countries-110m.json file, and the `feature` method from TopoJSON converts the TopoJSON data into GeoJSON features for rendering.

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Fork of Fork of ice8

This interactive globe visualization displays world country boundaries on a draggable, rotatable orthographic projection. Users click countries to toggle between global orthographic and Mercator projections, with the United States triggering a specialized Albers USA view. The code uses d3.geoPath for rendering, d3.drag for rotation, d3.zoom for scaling, and d3-geo-projection for alternate projections. Country boundaries come from TopoJSON world-atlas data, with names matched via TSV, rendered as SVG paths with hover highlighting and smooth update transitions.

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Fork of Fork of 3d- chart

This 3D column chart displays average sleeping hours across seven age groups, comparing male and female data with stacked, grouped columns. The Highcharts library enables the three-dimensional rendering through its highcharts-3d module, with chart options configuring alpha, beta, and depth for perspective. Data is supplied via series arrays with explicit coordinates, while category labels on the x-axis range from under 12 to over 60 years. The legend distinguishes gender, and axis titles describe sleeping hours and countries, though country values are not explicitly provided.

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Fork of Fork of Pie chart

This example displays an interactive pie chart showing the distribution of survey participants across different age groups, with each slice labeled with its value and accompanied by a legend listing age groups and member counts. The visualization fetches CSV data from a remote GitHub gist using `d3.csv`, then renders SVG paths with `d3.pie`, `d3.arc`, and `d3.scaleOrdinal` for color mapping. Text labels are positioned using `arc.centroid`, and the legend is built manually with SVG rectangles and text elements. The chart is styled with custom CSS, including hover-free static rendering and a centered title.

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Fork of Fork of Area graph for sleping time and population

This area chart visualizes the relationship between sleeping hours and age group population counts, with the x-axis mapping sleeping time and the y-axis showing age group values. The chart renders a smooth purple area beneath a black line, using d3.area and d3.line with curveBasis interpolation. Data is loaded via d3.csv from a GitHub-hosted CSV file, while d3.scaleTime and d3.scaleLinear handle axis scaling, and d3.axisBottom and d3.axisLeft generate labeled axes.

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Fork of Fork of bonus ice

This 3D scatter plot visualizes the Iris flower dataset, mapping calyx length and width against corolla length and width as separate traces. The visualization uses Plotly.js to render interactive 3D markers with adjustable opacity and size, with data loaded asynchronously via D3.js (v3) CSV parsing from a GitHub-hosted dataset. Two traces distinguish the different corolla measurements, while the layout removes all margins for a full-viewport presentation. The code utilizes Plotly.newPlot to render the chart inside a designated div, combining D3's data-handling capabilities with Plotly's 3D rendering engine.

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Fork of bonus ice

The visualization displays 3D bar charts derived from a CSV file, with cubes representing data values and an animated, interactive rotation controlled by mouse drag. Built with React and D3 v4, it uses the `d3._3d` API combined with SVG paths and `transition` animations for cube rendering and text labels. The underlying data is loaded via `d3.csv` from a GitHub-hosted source, while a Plotly 3D scatter plot module is also present for comparison.

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Fork of Fork of Line chart car Sales

This line chart shows annual car sales from 2001 to 2008, with the x-axis representing years and the y-axis showing sales volume. The visualization uses D3 v5 with SVG rendering, employing `scaleTime` and `scaleLinear` for axes, `d3.line` for the path generator, and `axisBottom`/`axisLeft` for tick marks. Data is loaded from a CSV file hosted on GitHub Gist, with the year parsed as a date and sales converted to numeric values. Gridlines and clear axis labels enhance readability.

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Fork of Fork of Reusable_D3_Scatter_Plot.

This interactive scatter plot visualizes the Iris dataset, cycling through different sepal and petal measurements on the x-axis via a timed interval. The visualization encodes species with distinct colors and uses SVG circles whose elliptical radii map to sepal dimensions. Built with React and D3 v3, the example employs d3.csv for data loading, d3-scaleLinear and d3-axis for scaling and ticks, and d3-selection for dynamic updates. Animated transitions between measurement pairs occur every few seconds, with a legend and axis labels rendered as SVG text elements.

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Fork of Fork of Fork of ice7

This chart displays the opening bid prices (BO) of ICE7 data for each month in 2020, shown as a smoothed line and a semi-transparent red area beneath it. The x-axis is labeled “Months” with abbreviated month names, while the y-axis is labeled “Opening bid price.” Built with d3.v5, the visualization uses `d3.csv` to load data from a GitHub gist, `scaleTime` and `scaleLinear` for axes, and `d3.line` with `curveBasis` plus `d3.area` to render the SVG paths. Axis labels are styled with orange, and tick lines are brown.

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Fork of Line chart car Sales

This visualization displays annual car sales from 2001 to 2008 as a line chart. The line plot uses a red, thick stroke to trace sales values over time, with green gridlines and burgundy tick labels. The chart is rendered as an SVG using D3 v5, with time-scaled x-axis and linear y-axis. Data is loaded from a CSV file via `d3.csv`, processed by converting year strings to `Date` objects, and mapped with `scaleTime` and `scaleLinear`. The line path is generated using `d3.line`, with axes created via `axisBottom` and `axisLeft`.

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Fork of Fork of Fork of ICE 7 Line Chart

This line chart shows car sales in India from 2009 to 2017, with the x-axis encoding the year and the y-axis encoding sales volume. The chart is rendered using D3 v5 with an SVG output, where scales are built with `scaleTime` and `scaleLinear`, axes are generated via `axisLeft` and `axisBottom`, and a smooth path is drawn using the `line` generator. Data is loaded asynchronously from a CSV file using `csv`, and the year values are parsed with `timeParse` before plotting. The grid lines and styled axis labels come from custom CSS, while the line path uses a distinct stroke color and width.

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Fork of ICE-7 Area Chart

This area chart visualizes YouTube annual users from 2006 to 2012, combining a smooth line and filled area to show growth trends. The visualization uses D3 v5 with `scaleLinear`, `axisBottom`, `axisLeft`, and the `area` and `line` generators with `curveBasis` for smoothing. Data is loaded from a CSV via `d3.csv`, with year and user values parsed as numbers. The SVG renders axes with colored gridlines and labels, while the line and area paths are styled separately in CSS.

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Fork of Fork of ICE - 7

This pie chart visualizes the top 10 countries by air pollution deaths in 2020, with each slice sized by death count and labeled in thousands. Built with D3 v5, the SVG chart uses `d3.pie` and `d3.arc` to generate segments, while `d3.scaleOrdinal` assigns colors from a fixed palette. Hoverless text labels and a legend map country names to death values, with data fetched from a CSV via `d3.csv`. The chart is rendered entirely with SVG paths and text elements.

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Fork of Pie Chart

This example visualizes the basic cost of Netflix subscriptions across ten countries using a pie chart, sorted from highest to lowest cost. The chart displays each country’s cost as a filled arc segment, with a legend listing country names and their corresponding dollar values. The code uses D3 v5 APIs including `d3.pie`, `d3.arc`, `d3.scaleOrdinal`, and `d3.csv` to load data from a gist. Interactive mouseover events log the cost to the console, and the rendering is done via SVG within a React-free HTML page.

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Fork of ICE-7 Pie Chart

This pie chart displays the top 10 countries with the most COVID-19 deaths, using a legend to identify each country and its corresponding death count in thousands. The visualization loads CSV data from a GitHub gist via `d3.csv`, then builds the chart with `d3.pie`, `d3.arc`, and `d3.scaleOrdinal` for color encoding. SVG elements are rendered dynamically, with the chart centered on a 960x500 canvas and legends placed to the right. The code uses D3 v5 and imports specific modules from the library.

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Fork of Reusable_D3_Scatter_Plot.

This interactive scatter plot animates through different x-axis variables from the Iris dataset, cycling between petal width, sepal width, petal length, and sepal length every few seconds using `setInterval`. Built with React and D3 v6, it uses `scaleLinear`, `axisLeft`, and `axisBottom` to render SVG circles colored by species (setosa, versicolor, virginica) with a custom legend. The data is loaded via `d3.csv` from a GitHub gist, and the reusable `scatterPlot` function manages the axes and marks.

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Fork of ICE 7 Reuse Scatter Plot

This visualization displays the Iris Dataset as an animated scatter plot, cycling through four different axes every 10 seconds to reveal patterns across sepal and petal measurements. Using d3.v6 with SVG rendering, the chart employs d3.scaleOrdinal to assign unique colors and symbol shapes to each species. The reusable chart pattern from Bostock’s “Towards Reusable Charts” is implemented via a scatterPlot function, with d3.axisLeft and d3.axisBottom updating dynamically as the x-value changes.

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Fork of Fork of ICE7

This animated scatter plot cycles through four Iris flower measurements—petal width, sepal width, petal length, and sepal length—updating the x-axis every 10 seconds while keeping sepal length on the y-axis. It uses the D3 v6 library with SVG rendering, featuring pink circles and white axis labels on a pink background, and reads the ICE-7 dataset from a GitHub CSV URL via d3.csv. The plot is built with a custom scatterPlot function using d3.scaleLinear, d3.extent, d3.axisLeft, and d3.axisBottom.

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Fork of Reusable D3 Scatter Plot

This is a reusable D3.js scatter plot that visualizes the Iris Dataset, cycling through four flower measurements (petal width, sepal width, petal length, sepal length) on the x-axis every two seconds while keeping sepal length on the y-axis. The code uses D3 v6 modules including `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to compute scales and render SVG axes and circles. The plot is built as a reusable JavaScript function with configurable properties like width, height, margin, and radius, and it re-renders the SVG with animated transitions as the selected column changes.

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Fork of Fork of scatter-plot

This visualization displays a scatter plot of game sales data, mapping North American sales on the x-axis and European sales on the y-axis. Each point’s color represents a different game publisher, and the circles are drawn with variable width and height properties derived from Japanese and global sales figures. The code loads CSV data via `d3.csv`, scales with `d3.scaleLinear`, and renders axes using `d3.axisLeft` and `d3.axisBottom`. The SVG is created with React and D3 v6, using a custom color mapping function for publisher categories.

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Fork of Fork of Fork of Fork of Fork of ICE-6 Scatter Plot

This scatter plot visualizes the Iris dataset, mapping petal length and width as point positions, with sepal length and width stored as additional data properties. Points are colored by species (red, green, pink) using a switch statement. The visualization uses D3 v6’s `csv`, `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` APIs to load data, scale coordinates, and render SVG axes. The code sets fixed radius circles, handles viewport-sized dimensions, and displays axis labels with custom styling.

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Fork of scatter-plot

This scatter plot visualizes global video game sales data, comparing North American and European sales on the x- and y-axes while mapping Japanese and global sales to point radius dimensions. The visualization uses D3 v6’s `csv` loader, `scaleLinear`, `extent`, and `axisLeft`/`axisBottom` functions, rendering circles as SVG elements. Point colors are determined by the game’s publisher, with a custom `marker()` function that assigns distinct colors to specific developers like Ubisoft Montreal and Rockstar North, while defaulting to brown for others. The chart includes axis labels and a title, with styling applied via CSS.

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Fork of Fork of Fork of Fork of ICE-6 Scatter Plot

This scatter plot visualizes the Iris dataset, showing Petal Length and Petal Width for each sample. The SVG circles are colored by species: blue for setosa, yellow for versicolor, and red for virginica. The code uses D3 v6 APIs including `csv`, `select`, `scaleLinear`, `extent`, `axisLeft`, and `axisBottom`. Additionally, it computes separate radius values based on sepal dimensions, though the circles uniformly use a fixed radius of 5 pixels. The chart includes custom axis labels and is rendered responsively to the browser window size.

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Fork of Bar graph-task1

This example shows a horizontal bar chart of the top 12 video games by North American sales, with game names on the y-axis and sales values on the x-axis. It uses React state to load CSV data via d3.csv, then renders SVG rectangles scaled by d3.scaleBand and d3.scaleLinear. Custom gridlines and tick labels are drawn manually using d3.scaleLinear.ticks, while axes are positioned with SVG group transforms. The visualization updates asynchronously when data arrives, with a loading state displayed beforehand.

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Fork of Fork of Fork of ICE-6 Bar Graph

This D3.js bar chart in React displays the top twelve countries by monthly basic Amazon subscription cost from a CSV file, using a horizontal bar layout with labeled axes and red gridlines. The chart uses `d3.csv` to load data, `d3.scaleBand` for the categorical y-axis, `d3.scaleLinear` for the numeric x-axis, and renders SVG rectangles for each bar. The code uses React’s `useEffect` hook for data fetching and renders the visualization directly into an SVG container.

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Fork of Bar graph-task1

This example shows a horizontal bar chart of North American video game sales for the top twelve titles, ranked by their NA_Sales values from a CSV dataset. The chart uses D3 scales (scaleBand for categories and scaleLinear for values) to position bars and axis ticks, while React manages the component state and data loading. The visualization renders as an SVG with red gridlines and labeled axes, using d3.csv to fetch and parse the data, and d3.max to determine the x-axis domain.

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Fork of ICE-6.js

This bar chart, forked from ICE-6.js, visualizes the total reported COVID-19 deaths for ten countries, with the United States showing the highest count and South Korea the lowest. The visualization uses D3 v7 to map the categorical country names to a band scale on the y-axis and the continuous death counts to a linear scale on the x-axis, rendering the bars as SVG rectangles. The chart’s axes are generated using `d3.axisLeft` and `d3.axisBottom`, while the data is loaded from a local `data.csv` file and styled with cyan fills via CSS.

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Fork of Fork of Fork of ICE-6 Bar Graph

This interactive bar chart visualizes Amazon Basic subscription costs across twelve countries, using SVG rectangles scaled with d3.scaleLinear and d3.scaleBand. The React component loads CSV data via d3.csv, then renders axis ticks and gridlines manually with SVG lines and text. Colors highlight the varying monthly prices, while the chart updates dynamically when new data arrives. The visualization uses D3 v5 selection methods to draw all marks within a translated SVG group, with a fixed width and height that accommodates a horizontal layout with labeled countries.

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Fork of Fork of Fork of ICE-6 Bar Graph

This example displays a horizontal bar chart comparing monthly basic Amazon costs across different countries, using the first twelve rows of a CSV dataset. The visualization renders SVG elements through React components, with D3 v5 handling data loading via `d3.csv`, scale construction using `scaleBand` and `scaleLinear`, and axis generation through `axisBottom` and `axisLeft`. The chart includes red gridlines with tick labels, country names along the y-axis, and colored rectangles representing costs. Data updates occur after the CSV loads, triggering React state changes that re-render the SVG. The visualization appears within a React app, with D3’s `max` function setting the x-axis domain.

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Data visualization

This example visualizes a dataset of match statistics, using a static SVG illustration that represents the data with a decorative, single-line chart and various abstract shapes. The visualization is built with the D3 v7 library and is implemented within a React framework. The code loads the data from a CSV file and uses SVG elements for the static rendering, though the image also utilizes a patterned WebGL-based background to enhance the visual presentation.

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Fork of Fork of Fork of Data Table Summary

This example displays a summary of a diabetes awareness dataset, presenting the total row and column counts alongside a preview of the first ten records in JSON format within a web page. The visualization uses D3 v5’s `select`, `csv`, and `csvFormat` functions to fetch and process the data from a remote CSV file, while also rendering an SVG background image and additional text elements. The page incorporates a large header and a hyperlink to the external data source, with the results displayed in styled `<pre>` tags.

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Untitled

A minimalist SVG illustration depicts a factory or industrial scene with chimneys, pipes, and storage tanks arranged in a stylized layout. The diagram uses simple geometric shapes and muted grays, with a stepped bar-chart-like structure representing a production line or process flow. Rendered as an inline SVG within a React component, the visualization demonstrates how static vector graphics can convey technical or architectural information without interactive elements.

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DATA VISUALIZATION

This example displays a text summary of a population dataset from the 2018 United Nations World Population Prospects, presenting the file size, row count, and column count in a terminal-style black-and-green interface. It loads a CSV file from a GitHub gist using the `d3.csv` method, then computes the data’s size in kilobytes with `d3.csvFormat` and appends the tabular dimensions to a `<pre>` element. The visualization relies on D3 v5 and demonstrates basic data loading, formatting, and DOM manipulation.

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DATA VISUALIZATION

This example loads a CSV of country populations from a GitHub gist and displays a plain-text summary of the dataset in the browser. Using `d3.csv`, it fetches the data and then computes the file size in kilobytes, row count, and column count, which are formatted with `d3.csvFormat` and written to a `<pre>` element styled with black background and green text. The data originates from the 2018 United Nations World Population Prospects estimate, though the live code references a different gist URL than the documented file.

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Fork of Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

A React and SVG visualization displays a dense, overlapping Buddha silhouette rendered as a path, with three nested array maps generating dozens of semi-transparent, randomly colored null elements that scatter across a starlit background. The image layers atomic elements over the Buddha’s vector geometry, creating a chaotic, multicolored abstraction. The code uses React 16.10 and ReactDOM, drawing SVG paths and fills with inline computed rgb() values and opacity, set against an external astronomy photo as the webpage background.

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Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

This visualization layers 21 by 21 by 21 rotated, semi-transparent polygons to create an overlapping, colorful Buddha silhouette. Each polygon’s position, fill color, and rotation are computed using JavaScript’s `Math.random()` within nested `map` loops, producing a chaotic, generative composition. The Buddha outline is drawn separately as an SVG `path` with a complex `d` attribute, scaled and translated into place. The code uses React and ReactDOM to render the SVG elements, with the background set via CSS in the HTML file.

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Fork of Fork of Fork of Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

A colorful Buddha outline is stacked and repeated with semi-transparent layers, creating an overlapping, dense pattern against a blue-bordered page. The code uses React and ReactDOM to render an SVG, mapping arrays to generate dozens of random circles with varying RGB fills and opacity. A single path element draws the Buddha shape, which is transformed and scaled, while the background references an external image URL and CSS applies styling to the document body.

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Fork of Fork of Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

The visualization displays a colorful, overlapping Buddha figure composed of hundreds of semi-transparent, randomly positioned and colored squares, rendered inside an SVG canvas using React and ReactDOM. A nested array mapping loop generates the square elements, each with randomized x and y coordinates, RGB fill values, and opacity, creating a layered, chaotic composition. The Buddha path itself is drawn with a vector path data string, scaled and translated via an SVG group transform, while the body background uses a CSS background image.

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Fork of Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

A Buddha silhouette, rendered as a black path, is repeatedly drawn with varying scales and positions to create a colorful, overlapping mandala-like pattern. The visualization uses React to generate a static SVG, mapping over an array to produce multiple instances of the same path with randomized coordinates and RGB fill colors. The code leverages ReactDOM to render the SVG to the DOM, with a background image set via CSS. The path data itself is a static string, and no interaction or animation is present.

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Fork of Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

The visualization displays a dense, colorful field of overlapping Buddha-like figures, constructed from cascading SVG paths that appear to fork and repeat in a recursive, organic pattern against a black background. A matrix of semi-transparent circles, each with randomized coordinates and RGB fill values, overlays the central figure, adding layered depth and texture. Built with React and rendered to SVG, the piece uses JavaScript array mapping to generate the circle grid, while each Buddha outline is defined by detailed path data transformed via SVG `translate` and `scale` operations.

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Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

A colorful, overlapping Buddha path is rendered multiple times across an SVG canvas, with randomized positions and fill colors creating a layered, kaleidoscopic effect. The visualization uses React and ReactDOM to manage the component structure, while the Buddha shape itself is defined by a complex SVG path string. A nested map function generates 30 duplicated instances, each with randomly generated RGB values and opacity, over a floral background image. The rendering approach relies entirely on SVG elements within a 1000x1000 viewport, with no external data source or interaction logic beyond the static generative composition.

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Fork of Fork of Fork of Fork of Fork of Buddha in colors -overlapping Buddha

A generative SVG artwork renders hundreds of semi-transparent colored circles on a black background, with coordinates derived from an array and random values. The visualization layers a repeated Buddha silhouette path, scaled and translated, over these circles. The React application uses ReactDOM to render the SVG, with the path data and circle positions defined in JavaScript. This creates an overlapping, colorful abstract composition where the Buddha form emerges from the chaotic field of dots.