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geethanjalikotaru

@geethanjalikotaru·19 public vizzes

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Social Network Graph

This social network graph visualizes the connections among eight people, with nodes linked by lines representing friendships. The network uses a force-directed layout via `d3.forceSimulation`, which combines link, many-body, collision, and centering forces to arrange nodes dynamically. Nodes are rendered as green circles with labels, and edges include arrow markers to show direction. The graph is interactive, allowing nodes to be dragged via `d3.drag`, and hovering highlights a node with a green stroke. Data comes from a local JSON file containing node and link definitions.

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Social Network Graph

This social network graph visualizes the relationships between individuals, with nodes representing people and links representing their connections. The network layout is computed using D3.js force-directed algorithms, specifically `d3.forceSimulation` with `d3.forceLink`, `d3.forceManyBody`, and `d3.forceCollide` to position nodes based on their interactions. Users can drag nodes to explore the graph, and hovering highlights a node with a green stroke. The visualization is rendered as an SVG using React, with data loaded from a JSON file.

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Untitled

A family tree displays six names across four generations as colored circles connected by green paths. The visualization uses d3.stratify() to structure hierarchical data and d3.tree() to compute node positions in a 600-by-300 layout. Each generation receives a distinct color via a switch function, with nodes rendered as SVG circles and text labels. Paths animate from source to target using transition().duration(1500), while circles grow into place over 700 milliseconds. The SVG scales to the full window size, and all elements are positioned within a translated group.

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(Topojson)

This visualization displays an interactive map of Argentina, with each department and province rendered as a distinct geographic region. Users can hover over individual areas to see their names via a tooltip, and the map supports zooming and panning. It uses `d3.geoPath` with a `d3.geoTransverseMercator` projection to draw the shapes from TopoJSON data, alongside the `topojson` library's `feature` function for parsing. The zoom interaction is implemented using `d3.zoom` and `d3.event`, while the data is loaded from a remote JSON file containing department boundaries.

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ICE 8 world map

This example renders a world map using the geoNaturalEarth1 projection from D3, displaying countries as green SVG paths with black outlines. The code fetches country boundaries from the world-atlas dataset via the `json` function, extracts features using the `topojson` library's `feature` method, and projects them with `d3.geoPath`. The visualization employs simple CSS styling for the country shapes against a light blue background.

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Area Chart of customer

This area chart visualizes customer sales over time, using a smooth curve to show yearly trends between 2000 and 2010. The chart features a filled region beneath the line, with both line and area rendered as SVG paths using d3.line() and d3.area() with curveBasis smoothing. Axes are generated with d3.axisBottom and d3.axisLeft, while data is loaded from a local CSV file via d3.csv. The visualization applies scales from d3.scaleTime for the year axis and d3.scaleLinear for the customer counts, with custom styling for ticks, labels, and the chart’s color scheme.

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Line Chart

This line chart visualizes customer counts per year from a small CSV dataset, with a red path connecting data points across a band scale for years and a linear scale for customer values. The D3 v7 code loads data via `d3.csv`, renders axes with `d3.axisBottom` and `d3.axisLeft`, and draws the line using `d3.line()` within an SVG container. Labels for both axes are added as SVG text elements.

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Pie Chart

This example displays a pie chart of Netflix basic costs across countries, with each slice sized proportionally to the cost and colored via a categorical scale. The visualization also includes a legend with country names and dollar-formatted values. Built with d3.pie, d3.arc, and d3.scaleOrdinal, it loads CSV data from a GitHub gist and renders SVG paths and legends in a React project.

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Scatter Plot with Symbols

This scatter plot displays the Iris Dataset, mapping petal width and sepal length on the x- and y-axes. Each data point is rendered as a distinct SVG symbol according to the flower species, using the d3 symbols API with a size of 100. The implementation builds a reusable scatter plot module with d3.scaleLinear, d3.scaleOrdinal, d3.axisLeft, and d3.axisBottom. Data is loaded from a CSV file via d3.csv, and the plot fills the browser window by setting width and height to window dimensions.

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Fork of Reusable scatter plot with different symbols

This example visualizes the Iris dataset as a scatter plot, mapping petal width and sepal length to the x and y axes. Each of the three iris species is represented by a distinct symbol from D3’s `symbol` and `symbols` APIs, with positions scaled using `scaleLinear` and axes drawn via `axisLeft` and `axisBottom`. The chart is built with a reusable `scatterPlot` function in D3 v6, rendering SVG shapes whose paths are generated by `symbolGenerator`, and loaded asynchronously from a CSV URL.

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Fork of Reusable D3 Scatter Plot

This fork of a reusable D3 scatter plot animates through the Iris Dataset, cycling the x-axis between petal width, sepal width, petal length, and sepal length every two seconds. The visualization uses d3.scaleLinear with extent to map data to positions, d3.axisLeft and d3.axisBottom for axes, and d3.selectAll with a join to update circle elements. The reusable chart pattern from Bostock’s “Towards Reusable Charts” exposes width, height, data, margin, and radius setters. Iris measurements are loaded via d3.csv and parsed as numbers.

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Penguin Dataset Geethanjali kotaru

This scatter plot visualizes the relationship between flipper length and body mass for three penguin species, using color to distinguish between Adelie, Chinstrap, and orange-coded default species. The D3 v5 code loads CSV data via `d3.csv`, maps it with `scaleLinear`, and renders circles with SVG in a React framework. It includes axes, a rotated y-axis label, a centered title, and a legend-like color mapping.

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ICE-6

This bar chart displays the ages of the first fifteen employees from a company dataset, with each row labeled by full name and bar length proportional to age. Built with React and D3 v5, it uses `d3.csv` to load remote CSV data, `d3.scaleBand` and `d3.scaleLinear` for axes, and renders an SVG with `rect` elements for bars. Grid lines and tick labels are generated via `d3.ticks`, while the layout follows a margin convention in a `VizContainer` component.

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Fork of ICE-6: Rendering Marks and Channels with React & D3

A horizontal bar chart compares the basic monthly cost of Netflix in seven countries, with country names on the y-axis and cost values on the x-axis. The visualization uses React state and effects to load CSV data via `d3.csv`, then renders SVG marks with `d3.scaleBand` and `d3.scaleLinear` for positioning. Axes are built manually from scales, drawing grid lines and tick labels with React elements. The code highlights integration of React’s `useState` and `useEffect` with D3 utilities for data-driven SVG rendering.

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Fork of Hello HTML

A bare HTML page with an empty body, rendered as a minimal web document. The example demonstrates the fundamental structure of an HTML file, including the `<!DOCTYPE html>` declaration, `head` section with a title, and `body` element. It uses no external libraries or JavaScript APIs, serving as a straightforward starting point for web-based visualization projects. The rendering approach is static, relying solely on the browser’s default HTML parsing and display, with no data sources or dynamic behavior implemented.

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ICE%

This visualization displays metadata about a credit card dataset, including its size in kilobytes, row count, and number of columns, presented as plain text on a styled page. Using D3.js v7, it loads a remote CSV file via `d3.csv`, formats the data with `d3.csvFormat`, and updates the DOM text content through `document.getElementById`. The page’s background is an SVG image applied via CSS, while the script calculates and displays the dataset’s summary information.

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Fork of Fork of Let's make a face with D3.js!

This example shows a static smiley face built entirely with SVG primitives, including a yellow head, eyes, eyebrows, ears, nose, mouth, beard, and mustache. The code constructs the face using D3.js `select` and `append` methods to create and position circles, rectangles, paths, and lines. It uses `d3.arc` to generate the curved shapes for the nose, mouth, and beard. The face is centered within the canvas using a `transform` attribute, demonstrating fundamental DOM manipulation in D3.

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Fork of Smiley Face Part III

This example shows a smiley face rendered as vector graphics with D3 and React. It uses React components to define shapes like a yellow head, eyes, pupils, a red nose, and a tongue, while D3’s `arc` function generates the mouth as an arc between specified angles. The SVG output is positioned relative to a central transform, and the code demonstrates composing D3 calculations with React’s declarative structure.

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Fork of Fork of Smiley Face Part II

The example uses React and the react-dom library to render an SVG smiley face. It combines separate React components, `Eyes` and `Smile`, which contain complex SVG paths and shapes, with CSS classes for detailed styling. The main `App` component layers these on a yellow circle base, using SVG `transform` attributes to position, rotate, and scale the facial features. The code demonstrates how to compose and manipulate vector graphics programmatically within a React application.