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gayathrikavya

@gayathrikavya·27 public vizzes

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Social Network graph ICE-8

This interactive social network graph visualizes connections between nine individuals, with nodes grouped by community and linked by weighted edges. The layout uses a force-directed algorithm (d3.layout.force with gravity, distance, and charge parameters) that positions nodes dynamically within an 900x600 SVG canvas. Node styling comes from CSS classes for circles (blue stroke, lightpink fill) and labels (black text with green stroke), while edge thickness maps to link weight. The graph is rendered in SVG, with data loaded from the graph.json file, and nodes are draggable via the force.drag interaction. The visualization uses D3.js v2/v4 APIs for rendering and force simulation.

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Europe Map

This example visualizes the countries of Europe as a static SVG map using a Mercator projection centered on the continent. The code loads GeoJSON feature data via `d3.json` and renders each region as a `path` element using `d3.geoPath` with the projection. React and D3 v5 are used together to manage the document, while TopoJSON is loaded but not directly applied in the visible logic. Country boundaries appear as grey strokes with white fills, set through SVG attributes.

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MY Tree ICE-8

This interactive tree diagram displays a family genealogy with animated expansion and collapse of branches. Built with React and D3.js v3, it uses `d3.layout.tree` to compute node positions and `d3.svg.diagonal` for curved links, rendered as SVG. Clicking a node toggles its children, with transitions animating nodes and links via `transition().duration()`. The data is a hardcoded JavaScript array of family members, styled with cyan circles, green links, and a light pink background.

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World_Map_ICE_8

This example renders a world map using the Natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath` in an SVG. It displays land masses in light blue, graticules in blue, and country borders as black interior lines, all over a white sphere background. The map is generated from TopoJSON data loaded from the world-atlas package, with land features and country meshes extracted using `topojson.feature` and `topojson.mesh`. The visualization is built with React, where the data is fetched asynchronously and passed to a Marks component for rendering.

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3D Scatter plot using D3.js

This example shows a 3D scatter plot of two separate datasets from an ICE Bonus CSV file, rendered on a single Plotly chart. One trace maps ItemType, TotalRevenue, and Country while the other maps OrderDate, SalesChannel, and TotalProfit, with colored circular markers in 3D space. The code uses D3.js to load and parse the CSV via `d3.csv`, then passes the data through custom `unpack` functions to extract columns. Plotly’s `scatter3d` trace type and `Plotly.newPlot` render the interactive visualization in the `myDiv` element on an HTML page.

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3D Scatter plot using D3.js

This example presents an interactive 3D scatter plot built with Plotly.js and D3.js, visualizing agricultural crop data. It renders two marker traces from a CSV file: one mapping crop type, production, and district, the other correlating crop year, season, and area. The code uses `d3.csv` to load the dataset and `Plotly.newPlot` to draw the scatter3d traces within a DIV element. The visualization employs 3D markers with custom opacity and line styling to distinguish the two datasets, with the layout margins set to zero.

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3D Bar Chart using D3.js

This example visualizes regional company performance with a 3D bar chart, comparing TotalCost, TotalRevenue, and TotalProfit for each region. The visualization uses the `d3-3d` and `d3-x3d` libraries alongside D3.js v5, rendering interactive 3D bars within an X3DOM scene. Data is loaded asynchronously from a CSV file using `d3.csv`, with each row parsed into multi-series values. The chart is created using `d3.x3d.chart.barChartMultiSeries()` and attached to the DOM via `chartHolder.datum(data).call(myChart)`, replacing initially generated random dataset.

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3D Bar Chart using D3.js

This example renders a 3D bar chart of car manufacturer data for the first six months of the year, showing regional units sold across multiple categories. The visualization uses the D3.js library with the d3-3d plugin to create the three-dimensional bars, while the d3-x3d bridge and x3dom library handle the X3D scene rendering. Data is loaded asynchronously from a CSV file using d3.csv, with each row parsed into a hierarchical structure containing region and monthly values. The chart is embedded in an HTML page using React-inspired template syntax, attaching the data to a chart holder element and calling the barChartMultiSeries chart component.

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Line Chart

This line chart displays one week of temperature data in San Francisco, sourced from Data Canvas - Sense Your City. The visualization uses D3 version 5 to map timestamps and temperature values to a time-scaled x-axis and a linear y-axis, with a basis-curved line path representing the temperature trend. Axis labels and a title are rendered as SVG text, while grid lines are generated via `axisBottom` and `axisLeft` ticks. The chart is styled with CSS for the line's stroke, width, and title appearance.

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ICE-6 Scatter Plot

This scatter plot visualizes the Iris dataset, mapping petal length on the x-axis and petal width on the y-axis, with each point colored by species (setosa, versicolor, or virginica). The visualization uses D3 v6’s `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to construct axes, while circles are bound to the data via `selectAll` and `join`. Data is loaded asynchronously from a CSV URL using D3’s `csv` function, with numeric parsing applied to all measurements. The chart fills the browser window, displaying axis labels for petal length and sepal length.

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Line Chart Final

This line chart displays one week of temperature data in San Francisco, sourced from Data Canvas - Sense Your City, with time on the x-axis and degrees Celsius on the y-axis. The visualization renders an SVG using D3 v5, employing `scaleTime` and `scaleLinear` for axes, `line` with `curveBasis` for the smoothed path, and `axisBottom`/`axisLeft` for grid lines and labels. Data is loaded via `csv` and parsed into `Date` objects for temporal scaling.

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Area chart

This area chart displays one week of temperature in degrees Celsius for San Francisco, using data from the Data Canvas - Sense Your City project. The visualization fills the space between a baseline and the temperature line with a light pink area, making fluctuations easy to spot. Built with D3 v5, it uses `scaleTime` for the x-axis, `scaleLinear` for the y-axis, and `area` with `curveBasis` to generate the smooth filled path. Axis labels and a title are added via SVG text elements, and the timestamp and temperature values are parsed from a CSV file.

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GDP Serbia

This visualization displays Serbia’s GDP over time as a smooth, natural-curved line chart rendered as SVG in a React app. It uses d3.scaleTime and d3.scaleLinear to map years and GDP values, with d3.line and curveNatural for the path. The data comes from a CSV file filtered to Serbia. Axes are custom-built components, and tooltips appear via SVG titles on circles, though circles are currently commented out.

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Line Chart

This example visualizes the GDP of a dataset over time as a smooth line chart. The chart maps yearly data points using a line path with natural curve interpolation, while axes are labeled for year and GDP. Built with React and D3 v5, it uses `scaleTime` for the x-axis and `scaleLinear` for the y-axis, rendering to SVG. The data is loaded from a CSV file via the `csv` function from D3, and the visualization omits individual data point circles, focusing solely on the trend line.

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Pie Chart

This pie chart visualizes the top 10 provinces in South Korea by confirmed COVID-19 case counts, sorted in descending order with each slice colored via `d3.scaleOrdinal` and `d3.schemeCategory10`. The data is loaded from a CSV file using `d3.csv` and rendered as SVG paths with the `d3.pie` and `d3.arc` generators. A legend, positioned to the right, pairs each province with its confirmed count using text and rectangles. Mouseover events log the selected province’s value to the console.

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Reusable_D3_Scatter_Plot.

This interactive scatter plot visualizes the Iris dataset, cycling through four feature pairs on the x-axis while keeping sepal length on the y-axis. The visualization uses D3.js with React to render animated SVG circles, colored by species, and includes a legend and axis labels. The code leverages D3 utilities like `scaleLinear`, `axisLeft`, and `axisBottom`, with a custom reusable `scatterPlot` component and `setInterval` to rotate the plotted columns.

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Iris dataset scatter plot

This interactive scatter plot visualizes the Iris dataset, mapping measurements like petal width and sepal length across three flower species. Using D3.js with an SVG rendering and animation, the chart automatically cycles through different x-axis variables every 15 seconds, updating the visualization via a reusable scatterPlot function. The code employs d3.scaleLinear for axes, d3.scaleOrdinal for symbol types, and d3.symbol to render distinct species markers, with data loaded asynchronously from a CSV source.

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ICE-6 Bar Graph

This example displays a horizontal bar chart of total revenue for twelve countries, loaded from a CSV file. Built with React and D3 v5, it uses `scaleBand` for the categorical y-axis and `scaleLinear` for the numeric x-axis. SVG rectangles are rendered for each bar, with gridlines and axis tick labels generated via `xScale.ticks()` and custom text elements. The data is fetched asynchronously using `d3.csv` and processed with a row function to convert revenue values to numbers.

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ICE 5

This example renders a full-screen astronomical image as an SVG pattern using D3 v5, with a black background and the image overlaid across the canvas. The visualization is a static data-art piece that utilizes React for the framework and SVG for rendering, with the image data embedded directly in the HTML file. The code defines patterns and paths to display the image, demonstrating how D3 can be used for declarative graphic composition.

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ICE 5

This example displays a detailed photographic image of ice within an SVG container, set against a light gray background. The visualization uses D3.js to load and position the image as a pattern fill, with the pattern applied to a rectangle that covers the full viewBox. The code defines the SVG structure manually, references the image data as a base64-encoded JPEG, and uses D3’s pattern and image elements to render the visual.

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ICE4_Gayathri_Katukojwala

This example generates 501 procedurally placed ellipses and circles in an SVG scene, where each shape’s position and color derive from random values scaled by an index loop. A React app maps over an array of numbers, rendering rotated ellipse pairs with stroke-width 2, colored by interpolated RGB values, alongside small circles and a dark green-to-black gradient background. The visualization also overlays a static, complex path element representing a map-like or vector shape, combining algorithmic generative art with structured vector geometry.

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Fork of Fork of Ellipse flower - image

A generative flower-like visualization fills an SVG canvas with hundreds of randomly positioned ellipses and circles, each colored by a gradient that shifts from green to red. The code maps over an array of 501 indices, using each index to compute pseudo-random x/y coordinates and RGB color values. React components `Ellipse` and `Circles` render nested ellipse pairs and small circles. A large embedded SVG path depicts a hand-drawn image, overlaid on a linear-gradient background defined in the HTML style block.

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Fork of Fork of Buddha Mask

The visualization renders a mask-like pattern built from thousands of randomly generated lines, clipped to a Buddha silhouette path using an SVG `clipPath`. Each line’s position and color derive from procedural loops over pixel offsets and array indices, with a radial gradient fill applied via the `style` attribute. The React components (`Circles.js`, `index.js`) manage the SVG output, while D3’s `line` generator is defined but unused. The scene sits on a dark green-to-black CSS gradient background.

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Buddha viz

A generative Buddhist-inspired composition uses layered ellipses and circles to build a dense, repeating pattern across the canvas. Each element is positioned randomly and scaled proportionally along a number line, with colors derived from the same progression. The artwork is rendered as SVG through React components, with the `Ellipse` component drawing two intersecting ellipses per instance and the main `App` component mapping over arrays to generate the full field of shapes.

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Lets make a face with d3.js

A smiling yellow face is drawn with SVG shapes: a large circle for the head, black circles for eyes and nose, and a curved path for a mouth. Animated rectangular eyebrows move up and down using D3 v7's `transition` API. The visualization uses D3's `select` and `arc` functions to construct the SVG elements within a 960 by 500 canvas, with the face centered and repositioned via a coordinate transform.

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Fork of Fork of Smiley Face Part IV

A yellow smiley face is drawn as a static SVG graphic, centered on a 960 by 500 canvas. The code uses React components to structure the face, with a `BackgroundCircle` for the head and separate circle elements for the eyes, while an `arc` generator from D3 (d3.arc) defines the mouth’s path. D3 v5 and React 16.9 are loaded via CDN, and the full rendering happens through ReactDOM, with the SVG elements created declaratively in JSX.

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Fork of Smiley Face Part IV

This example shows a smiley face built from a yellow circle, black outline, two eyes, and a mouth, all rendered as SVG elements. The React component defines the face’s structure, while D3’s `arc` generator creates the mouth path with specified inner and outer radii and start and end angles. The background circle uses a fixed radius, stroke width, and fill color. The code imports `arc` from D3 and uses ReactDOM to render the SVG into the page.