M

Fork of D3 Tree with Multiple Parents

This visualization shows a hierarchical tree structure in which multiple parent nodes can share the same child, as demonstrated by two top-level categories both linking to common descendants like “Supply and demand” and “Laborer.” It uses D3 v5’s `stratify` and `tree` layouts to convert flat JSON data into a dendrogram, rendering nodes as text labels and edges as horizontally curved `linkHorizontal` paths. The SVG canvas supports zooming via D3’s `zoom` behavior, and text styling relies on the Playfair Display font with a white shadow for readability.

Fork of D3 Tree with Multiple Parents

This visualization shows a tree diagram with multiple parent nodes, where the U.S. Government and Stock Market both branch into shared child categories like Supply and demand, Foreign worker, Government, and Laborer. The code uses D3 v5’s `d3.stratify` to convert a flat CSV dataset into a hierarchical structure, then applies `d3.tree` layout and `d3.linkHorizontal` to draw the curved links between nodes. Text labels display each node’s name, with font size decreasing by depth, rendered as SVG inside a zoomable `<g>` group controlled by `d3.zoom`. The data is loaded asynchronously from a remote CSV file.

Fork of ICE - 8 Family Tree Maanas Katta

This interactive family tree visualization displays hierarchical relationships as a horizontal dendrogram, with nodes labeled by individual names and links showing parent-child connections. The tree layout uses D3’s `tree()` and `stratify()` functions to convert flat CSV data into a nested structure, while `linkHorizontal()` generates the curved connecting paths. Text elements are positioned via the `hierarchy()` API, with font size scaling by depth. Users can pan and zoom through the SVG canvas using the `zoom()` event handler, which updates the transform of the zoom group. The size of each node’s label reflects its generation level.

Fork of World Countries Tree

A collapsible tree visualization displays the hierarchical structure of world countries, organized by continent and subregion. The code uses D3 v5’s `stratify` to convert a flat CSV dataset into a hierarchy, then applies the `tree` layout and `linkHorizontal` to render curved links. Nodes are labeled with country names, with font size decreasing by depth. The SVG output supports panning and zooming via `d3.zoom`, and links and text are styled with custom CSS including a serif font and text shadows.

Fork of World Countries Tree

A hierarchical tree layout displays the countries of the world organized by continent and subregion, with each country as a leaf node. The visualization uses `d3.tree` and `d3.hierarchy` to compute the layout from a nested JSON structure, rendering curved links with `d3.linkHorizontal`. Text labels position nodes with font sizes inversely scaled by depth inside an SVG, while `d3.zoom` enables panning and zooming the entire tree.

Fork of World Countries Tree

This visualization displays a hierarchical tree of world countries, organized by continent and subregion. It uses a tidy tree layout to show the parent-child relationships, with circular nodes and curved links connecting each level. The visualization is rendered as a static SVG, built with the D3.js library, and uses a custom CSV data source to construct the hierarchy. The code uses a D3 tree layout API to position the nodes and d3.linkHorizontal to draw the connecting paths.

Fork of ICE - 8: Visualizing Tree

This visualization displays a hierarchical tree structure, showing nodes and their relationships as branching links. The data originates from a CSV file, which is processed using `d3.stratify` and `d3.hierarchy`. The tree layout is computed with `d3.tree`, positioning nodes horizontally. Links are drawn as curved paths via `d3.linkHorizontal`, while node labels are rendered as text elements with font sizes scaling by depth. Pan and zoom interactions are enabled through D3's `zoom` behavior, applied to the SVG container.

Fork of World Countries Tree

This fork of the World Countries Tree shows the hierarchical structure of global regions and their member states from CSV data. The treemap-style layout uses a horizontal dendrogram that arranges continents, subregions, and countries as nodes, with leaf labels gradually decreasing in font size by depth. The visualization relies on D3 v5's `tree`, `hierarchy`, `linkHorizontal`, and `stratify` APIs to compute the layout and draw SVG paths and text. Interaction is limited to zooming via the `zoom` event handler.

Fork of ICE-8

A force-directed network graph visualizes the "weights of nodes" in an animal network. The visualization uses a CSV of animal pairs to define the links and nodes, with the simulation driven by `d3.forceSimulation` applying link, many-body, collision, and center forces. The graph is rendered as an SVG, with pink circular nodes and red strokes; node labels are purple. The `d3.drag` API enables interactive node dragging, while a green page background and a custom marker definition for arrowheads are also applied.

Fork of Family Tree

This example visualizes a network of animal relationships as a static, force-directed graph. The code loads a CSV dataset from a remote GitHub Gist, maps the `Animal1` and `Animal2` columns into link pairs, and extracts unique animal names as nodes. An SVG container holds the graph, where nodes are drawn as filled circles and links as lines. The layout is computed using the `d3.forceSimulation` API with `d3.forceLink`, `d3.forceManyBody`, and `d3.forceCenter` forces, and nodes are draggable via `d3.drag`. All rendering is done directly as SVG elements within the 800-by-600 viewport.

Fork of World Countries Tree

This example visualizes the hierarchical structure of world countries organized by continent and subregion, with a tree layout that branches from a global root. Built with D3 v5, it reads hierarchical data from a CSV file and renders it as an SVG using `d3.hierarchy`, `d3.tree`, and `d3.linkHorizontal` for smooth curved links. Pan and zoom functionality is enabled via `d3.zoom`, while text labels display country and region names at varying font sizes based on depth. The visualization uses a clean, minimal aesthetic with green connecting paths and serif typography.

ICE7 piechart

This pie chart displays the population of Indian states. It uses the D3 library's `pie` and `arc` functions to compute and render the SVG paths, with colors assigned via `scaleOrdinal` and `schemeCategory10`. A legend, built with `append` and `text`, shows each state’s name and its population in millions. The data is loaded asynchronously from a remote CSV file using the `csv` function.

ICE7 Reusable scatter plot

This interactive scatter plot visualizes automotive data, cycling through four variables—mileage, engine size, car age, and horsepower—on the x-axis every two seconds. Built with D3.js, the chart uses the `scaleLinear` and `extent` APIs to map data to coordinates, while `axisLeft` and `axisBottom` render axes. The SVG rendering is animated via `setInterval`, and the reusable `scatterPlot` component accepts configurable width, height, margins, and radius, demonstrating a modular chart design.

Fork of Reusable D3 Scatter Plot

This fork of a reusable D3 scatter plot visualizes car specifications from a CSV dataset, plotting engine size against horsepower with circle size encoding mileage and color encoding car age using `d3.scaleOrdinal` and `d3.schemeCategory10`. The chart is constructed with `d3.scaleLinear` for x and y axes, `d3.axisBottom` and `d3.axisLeft` for scales, and `d3.csv` for asynchronous data loading. A separate `scatterPlot.js` module defines a reusable chart factory with configurable accessors for dimensions, margins, and radius, following the "Towards Reusable Charts" pattern.

Fork of Reusable D3 Scatter Plot

This scatter plot visualizes vintage car data, mapping engine size against horsepower with D3’s `scaleLinear`, `axisLeft`, and `axisBottom`. The visualization animates by cycling through four variables—car age, mileage, engine size, and horsepower—on the x-axis every two seconds using `setInterval`. Each point is rendered as an SVG circle with a fixed radius, and the chart updates the x-axis domain and circles accordingly. Data is loaded asynchronously from a CSV via `d3.csv` and parsed into numeric values.

Fork of Scatterplot with Iris Data

This interactive scatterplot visualizes the relationship between temperature (°C) and humidity (%) from a CSS color dataset, styling each data point as a circular mark. Built with React and D3 v7, the code uses `scaleLinear`, `extent`, and `format` to map the values, while `csv` loads the data from a remote URL. The SVG rendering includes custom axis components and hover tooltips that display temperature values. The visualization uses a Poppins font and muted teal marks on a clean background, with labels for both axes.

Fork of Scatterplot with Iris Data

This is a scatterplot visualization of the Iris dataset, displaying measurements of sepal and petal dimensions for different iris species. The chart uses SVG circles to represent individual data points, with position determined by the selected attributes on the x and y axes. The visualization is built using React components that leverage D3.js v6 for data loading and scaling, with custom axis elements rendered as SVG groups. The data is fetched from a remote CSV file and parsed with D3's `csv()` function, then mapped to circle positions using linear scales for both axes.

Fork of Scatterplot with Iris Data

This example visualizes the classic Iris dataset as a scatterplot, mapping sepal length to the x-axis and sepal width to the y-axis, with each point colored by species. The React component uses d3.scaleLinear and d3.extent to set the scales, while d3.csv loads the data from a gist. SVG circles are rendered with embedded tooltips showing the sepal length value on hover. The chart includes labeled axes, a bottom axis with tick formatting via d3.format, and a left axis, all built from custom React components that generate SVG elements.

Fork of D3 scatterplot

This fork of the D3 scatterplot visualizes the relationship between age and income for individual data points. The chart displays circular marks positioned along linear scales for both axes, with gridlines and labeled axis titles. The code uses D3’s `scaleLinear`, `max`, `axisBottom`, and `axisLeft` APIs to construct the scales and axes, while `select` and `append` manage the SVG elements. Data is bound through `selectAll('circle').data(...)`, with each circle’s position set via the `cx` and `cy` attributes. No external data source is shown, as the render function expects an array of objects containing `Age` and `Income` fields.

Fork of D3 scatterplot

This example shows a scatterplot of the Iris dataset, plotting petal length on the x-axis against sepal length on the y-axis. The code loads the data via `d3.csv`, parses numeric values, and uses `d3.scaleLinear` with `d3.extent` to map the data ranges to the full browser window. Each data point is rendered as an SVG circle, and axes are generated with `d3.axisLeft` and `d3.axisBottom`. The visualization uses `d3.v7` and a minimal, full-screen SVG layout with fixed margins.

ICE7 Areachart

This area chart visualizes monthly sales data as a smooth, curved region. The chart uses a basis spline curve to connect sales values across months, with the area beneath the curve filled to emphasize trends. Rendered as SVG, it employs D3 v5 APIs including `scalePoint`, `scaleLinear`, `axisBottom`, `axisLeft`, `area`, and `curveBasis`. The data is loaded from a remote CSV file, with sales values parsed from the "Sales (USD 000)" column. Axis labels and a title are added as text elements.

Fork of Line Chart

This fork of a line chart visualizes monthly sales data for 2022, plotting sales figures against months. The chart uses D3 v5 and renders as an SVG, with a purple line connecting data points. It employs `d3.scalePoint` for the x-axis, `d3.scaleLinear` for the y-axis, and `d3.line` to generate the path. Data is loaded from a CSV file via `d3.csv`, with axis labels and a title styled through CSS. The visualization includes gridlines via the y-axis tick size.

ICE7 linechart

This line chart visualizes monthly sales in 2022, with the x-axis showing months and the y-axis displaying sales figures in thousands of USD. The chart is rendered as an SVG using D3 v5, employing `scalePoint`, `scaleLinear`, `axisBottom`, and `axisLeft` for scales and axes. A purple line path is generated with D3’s `line` API, and the data is loaded from a CSV file via `d3.csv`. Styling for fonts, grid lines, and the line stroke is defined in a separate CSS file.

Fork of Line Chart

This line chart displays monthly retail sales for 2023, with months on the x-axis and sales in USD thousands on the y-axis. The SVG renders a red line path using d3.line, with point and linear scales via d3.scalePoint and d3.scaleLinear. Axes are generated with d3.axisLeft and d3.axisBottom, and the data comes from a CSV file loaded through d3.csv. The chart includes labeled axes and a title, with grid lines created by custom tick sizing.

Fork of Line Chart

This fork of a D3 line chart displays card values by suit, plotting each suit’s value as a single connected line. The visualization uses d3.csv to load data from a remote gist, d3.scalePoint for the categorical x-axis, d3.scaleLinear for the numeric y-axis, and d3.line to generate the SVG path. Axes are rendered with d3.axisLeft and d3.axisBottom, featuring gridlines and labeled axis titles, with styling applied via external CSS. The chart includes a title and uses a minimal, clean design.

Fork of Line Chart

This fork of a line chart displays card values grouped by suit across different face values, with separate colored paths for spades, clubs, diamonds, and hearts. It loads a CSV file using `d3.csv`, groups the data by suit with `d3.group`, and maps the values onto a point scale for the x-axis and a linear scale for the y-axis. The lines are drawn using `d3.line` and appended as SVG path elements, with axes generated by `d3.axisBottom` and `d3.axisLeft`.

Fork of Line Chart

This line chart displays the area in square kilometers for each U.S. state, using a categorical point scale for states and a linear scale for area values. The visualization loads CSV data from a GitHub gist and renders an SVG with axes, gridlines, and a red line path generated by `d3.line`. It employs `d3.scalePoint`, `d3.scaleLinear`, `d3.axisBottom`, `d3.axisLeft`, and `d3.csv` from D3 v5, with custom tick styling and rotated axis labels.

Fork of Pie Chart

This fork of a pie chart displays the basic cost of Netflix, but the accompanying code renders a table of Indian state data—including population, area, and capital—loaded from a CSV file via `d3.csv`. The SVG-based visualization uses D3 v5 to bind data to table rows and columns, while the page layout relies on CSS for styling and text formatting. Despite the title, the implementation focuses on tabular data presentation rather than pie-chart geometry.

Fork of Pie Chart

This fork of a pie chart visualizes Democrat state population data as a labeled pie chart, with the title centered above the SVG rendering. It uses d3.csv to load state demographics from a GitHub-hosted CSV file and d3’s pie and arc generators to compute and draw the slices. The view includes white text labels on each wedge, while legend entries and values are styled separately via CSS. The page also displays a tabular breakdown of the same dataset, rendered through D3’s data-join methods.

ICE6-Bargraph

This bar chart displays population figures for individual U.S. states, with each bar’s height encoding the population value and its hue varying by state. The visualization first presents the data through categorical x-axis (state names) and linear y-axis (population) scales, then explains its construction: React manages the component state while D3’s `scaleBand`, `scaleLinear`, and `max` functions compute the scales, and `axisBottom`/`axisLeft` generate the axes. Population data is loaded from a CSV file, and the SVG rendering uses colored rectangles for each bar.

ICE6-Manogna

This scatter plot visualizes the relationship between population and area for U.S. states, using a circle for each state’s data point. It shows the distribution of states across these two quantitative dimensions, highlighting potential clusters or outliers. The visualization is built with D3 v7, using `d3.csv` to load the data from a remote CSV file and `d3.scaleLinear` to map the values to screen coordinates. Axes are generated with `d3.axisBottom` and `d3.axisLeft`, while circles are appended via SVG elements, with styling applied directly to their fill color.

Fork of Fork of ICE5 - chaturya d3.js parsing

This example shows a dual-panel bar chart comparing BMI values against height and weight measurements for individuals in a BMI dataset. The left panel displays BMI as a single green bar per person, while the right panel splits each category into orange (height) and blue (weight) bars using a grouped bar layout. The visualization uses React with hooks to manage data loading via `d3.csv`, and D3 scales (`scaleBand`, `scaleLinear`) and axes (`axisBottom`, `axisLeft`) to render the SVG marks. Data is parsed from a remote CSV file, with margins and rounded bar corners adding polish to the chart.

ICE - 6 Bar Graph

The visualization displays a bar chart comparing the population of U.S. states, sourced from a CSV file. It uses React with D3 v5 to render SVG bars, where each bar’s height and color vary by population value. The code employs `d3.scaleBand` and `d3.scaleLinear` for axes, with `d3.axisBottom` and `d3.axisLeft` to generate the scales, and formats population values by stripping commas.

Fork of Akhil -vizhub assignment bat-scatter

This example shows a horizontal bar chart of aggregated card values grouped by suit, with bar lengths encoding totals and colors distinguishing suits. It uses React hooks (`useState`, `useEffect`) to load CSV data via D3’s `csv` function, then aggregates values by suit. The rendering relies on React for SVG elements and D3’s `scaleBand`, `scaleLinear`, `axisBottom`, and `axisLeft` for positioning and axes. Axis labels (“Total Card Value” and “Card Suit”) clarify the channels. The data source is a remote CSV URL, and the visualization is drawn entirely with SVG within a React component.

Fork of Akhil -vizhub assignment bat-scatter

This bar chart visualizes the distribution of playing cards from a CSV dataset, mapping each card’s face and suit to a categorical y-axis and its numeric value to a linear x-axis. The chart uses steelblue rectangles to encode the value, with black strokes for clarity. Built with React and D3 v5, the code employs `scaleBand` for the y-axis, `scaleLinear` for the x-axis, and renders axes via `axisBottom` and `axisLeft`. The data is fetched from a remote CSV, parsed locally, and rendered as SVG, providing a straightforward comparison of card values.

Fork of Akhil -vizhub assignment bat-scatter

This bar chart visualizes the relationship between card categories (face and suit) and their associated values, with the horizontal axis representing price and the vertical axis displaying categorical labels. The visualization encodes data through bar length, where longer bars indicate higher sales or price values. The chart uses React with hooks (useState, useEffect) to parse CSV data fetched from a GitHub gist, while D3 v5 provides the scales (scaleBand, scaleLinear) and axes (axisBottom, axisLeft). The code renders SVG rectangles for each bar, with gridlines extending across the plot area. The data is processed by splitting the raw CSV text, which appears to contain card descriptions and numerical values.

Fork of Akhil -vizhub assignment bat-scatter

This visualization shows a horizontal bar chart of car sales prices aggregated by car type, using a bat-scatter style layout. The code loads CSV data from a remote GitHub gist, aggregates the Price field by Type, and renders bars with SVG in a React app. It uses D3 v5 functions like `scaleBand` for the categorical y-axis, `scaleLinear` for the numeric x-axis, and `axisBottom`/`axisLeft` for axes. The bars are colored with an RGB scale, and the chart includes labeled axes for average price and car sales.

Mar 18, 2024

Fork of Akhil -vizhub assignment bat-scatter

This example visualizes the relationship between car type and total price using a bar chart, with horizontal bars showing aggregated price sums per car category. The code uses React state and effects to load CSV data via D3’s `csv` method, then aggregates prices by car type with a reduce operation. D3’s `scaleBand` and `scaleLinear` map the categorical types and continuous price values, while `axisBottom` and `axisLeft` render the axes. The SVG rendering approach positions bars and labels, with title text annotating the chart. Data comes from a cleaned cars dataset hosted in a GitHub gist.

Mar 18, 2024

Fork of ICE-6: Rendering Marks and Channels (Bar Chart)

This bar chart displays the population of three countries in 2022, using horizontal rectangles to encode population size and country names as category labels. The visualization is built with React for component structure and D3 v5 for data binding and scales—specifically `d3.scaleBand` for the y-axis, `d3.scaleLinear` for the x-axis, and `d3.max` to set the domain. Data is loaded asynchronously from a CSV file using `d3.csv`. The chart includes axis ticks and gridlines, with all marks rendered as SVG elements.

Mar 14, 2024

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

This visualization displays the top 10 countries by death cases in 2021 using a horizontal bar chart. The chart maps country names on the y-axis and population values on the x-axis, with blue bars representing each country's population. The React application uses d3.scaleBand and d3.scaleLinear to position bars and axes, while d3.csv loads the data from a GitHub-hosted CSV file. The visualization is rendered as an SVG element, with the code demonstrating how React manages state and D3 handles data processing and scaling.

Mar 14, 2024

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

This example displays the basic cost of Netflix subscriptions in fifteen countries as horizontal bars. The bars encode cost along a linear scale, with country names listed on a band scale. Built with React and D3 v5, it uses the `csv` function to load data from a GitHub gist, then renders SVG `<rect>` elements and custom axes. Scales and ticks are generated with `scaleBand`, `scaleLinear`, and manual positioning rather than D3 axis components.

Mar 14, 2024

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

This example displays a horizontal bar chart of the top 10 most populous states in India, using React components to render SVG marks and D3 scales for layout. The chart encodes population values as bar widths on a linear x-axis and state names as categorical bands on a band scale y-axis. Data loads asynchronously from a CSV file via `d3.csv`, and the code manually constructs axes and tick marks without using D3’s axis generators, relying instead on `scaleLinear`, `scaleBand`, and `max` from D3 v5.

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

A bar chart displays the top 15 most populous Indian states, ranked by basic cost. The visualization uses React to manage state and render SVG elements, while D3.js provides scales, axes, and data loading via `d3.csv`. The horizontal bars are drawn with SVG `rect` elements, with axes generated using `d3.scaleBand` and `d3.scaleLinear`. Data is fetched from a remote CSV file hosted on GitHub, styled with explicit margins and a loading state.

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

This visualization shows the basic monthly cost of Netflix in the top 15 countries, rendered as horizontal bars in an SVG. It uses React for component state and lifecycle, loading the CSV dataset with the `csv` function from D3. The visualization manually constructs x- and y-axes using `scaleLinear` and `scaleBand`, drawing tick marks and labels as SVG elements without the axis helper components, and maps each country to a `rect` bar scaled by its cost.

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

A horizontal bar chart shows the basic monthly cost of Netflix in 15 countries, using blue bars sorted by country name on the y-axis and cost on the x-axis. The visualization is built with React components that render SVG elements, while D3 v5 handles data loading via `d3.csv` and scales with `scaleBand` and `scaleLinear`. Axes are manually drawn using gridlines and text labels rather than axis generators. Data comes from a GitHub-hosted CSV file, and the chart updates reactively through React's `useState` and `useEffect` hooks.

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

This example shows a horizontal bar chart of COVID-19 death cases (or populations) for twenty countries, using green rectangles scaled by numeric values against categorical country labels on the vertical axis. The React component loads CSV data via `d3.csv`, slices the first 20 rows, and renders SVG marks with React’s `useState` and `useEffect` hooks. D3’s `scaleBand` and `scaleLinear` map positions and widths, while manual tick generation draws gridlines and axis labels, avoiding `d3.axis` components.

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

This example shows a horizontal bar chart of COVID-19 cases by country, displaying the top 20 countries based on population. The visualization uses React to manage state and render SVG elements, while D3 v5 handles data loading via `d3.csv` and scales with `d3.scaleBand` for the y-axis and `d3.scaleLinear` for the x-axis. The chart renders tick marks, labels, and rectangular bars directly as JSX components, with data sourced from a CSV file hosted on GitHub.

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

A bar chart displays the top 10 most populous countries, with bars ordered by country name and sized by 2019 population. The visualization is built using React and D3.js (v5), loading data from a CSV file and rendering SVG elements. It uses `scaleBand` for the y-axis, `scaleLinear` for the x-axis, and `csv` to fetch and parse the data, with React managing the component state and rendering.

Fork of Barchart_ICE-6

This example shows an interactive bar chart rendered as SVG via D3 v5 within a React framework. The chart visualizes categorical data with vertical bars, featuring hover interactions and tooltips that display values. The graphic includes a stylized decorative header illustration with abstract curves and shapes, and a complex y-axis with value labels. The visualization uses a clean, minimal design with gray tones and orange highlights for the bars, with axes and labels clearly delineated.

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using D3’s `d3.csv` method, displaying the file size, row count, and column count of a remote dataset. The visualization shows summary statistics in a large monospaced text block, computed by converting the parsed data back to CSV format with `d3.csvFormat` to measure its length. The code also includes commented-out alternatives using `fetch`, `async`/`await`, and `d3.csvParse`, illustrating different asynchronous approaches to the same task. The data source is a remote CSV file containing animal information.

Mar 5, 2024

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data from a remote URL, displaying the file size, row count, and column count as text. It uses the D3.js v5 `d3.csv` method to fetch and parse the data, then formats the output with `d3.csvFormat` to calculate the kilobyte size. The data comes from a GitHub Gist containing animal information. Alternative commented-out approaches show fetching raw text with `fetch`, `async`/`await`, and `d3.csvParse`, as well as a nested promise pyramid, but the active code relies solely on `d3.csv` with its built-in promise handling. The result is rendered into a large-styled `<pre>` element.

Mar 5, 2024

.

This example demonstrates how to load and parse a remote CSV file using D3.js v7, displaying the file’s size, row count, and column count in a styled HTML container. The visualization fetches the CSV data from a GitHub Gist URL via the Fetch API, then parses the raw text with `d3.csvParse` to extract structured data. The resulting summary—including the file’s kilobyte size, number of rows, and number of columns—is rendered inside a `<pre>` element using inline HTML styling. The page’s layout is centered with a clean white card design, and the code relies on D3’s parsing utilities and standard browser `fetch` for data acquisition.

Mar 5, 2024

Fork of Create a face with d3.js!

This example demonstrates how to construct a stylized cartoon face entirely from vector shapes using D3.js v7. It shows a circle for the face, ellipses for eyes, rectangles for eyebrows, and a curved path for the mouth, all positioned within an SVG container. The code uses `d3.select` to access the SVG element and `d3.arc` to generate the mouth’s arc path, with dimensions like eye spacing and offsets defined as constants.

Feb 27, 2024