M

Morgan Worrell

@morgnism·21 public vizzes

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Scatter Plot with Color Legend

This scatter plot visualizes the iris dataset, mapping petal length on the x-axis and sepal width on the y-axis. Each point represents a flower specimen, colored by species using an ordinal color scale, with a legend showing the species categories. The visualization is built with React and D3 v5, using `scaleLinear` and `scaleOrdinal` for scales, `extent` for domain calculation, and custom SVG components for axes, marks, and the color legend. Data is loaded asynchronously from a remote CSV using D3’s `csv` method.

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Scatter Plot with Color

This example visualizes the Iris dataset, encoding petal length on the x-axis and sepal width on the y-axis. Each flower is a circle colored by its species using a categorical color scale. The visualization is built with React and D3 v5, using `scaleLinear` for axes, `scaleOrdinal` for colors, and `format` for tick labels. SVG primitives render the chart, while the data is loaded asynchronously via the `csv` utility.

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Polished Scatter Plot with Menus

This example renders the classic Iris dataset as an interactive scatter plot, allowing users to explore relationships between sepal and petal measurements by selecting which attributes appear on the X and Y axes via dropdown menus. Built with React components and rendered as SVG, the visualization uses D3's `scaleLinear` and `extent` functions to compute scales from the data, with axes generated by custom `AxisBottom` and `AxisLeft` components. The plot reads data from a CSV file using D3's `csv` utility, and each point displays a tooltip showing its precise value.

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Scatter Plot with Menus

This interactive scatter plot visualizes the Iris dataset, allowing users to explore relationships between sepal and petal measurements. Two dropdown menus let viewers independently select which attribute maps to the x-axis and y-axis, with the chart updating dynamically. The visualization is built using React for component rendering and D3 v5 utilities for data loading, linear scaling, and axis generation, producing an SVG output. Data is fetched from a CSV file using D3’s `csv` function, and circles represent individual flowers with tooltips showing values.

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Menus with React

This example shows a pet-selection menu built with React, displaying a dropdown of six animal options and a label that updates based on the user’s choice. The code uses React’s `useState` hook to manage the selected value and a custom `Dropdown` component that renders a native `<select>` element with `onChange` event handling. It integrates React 16.9 with the `ReactDOM.render` API to mount the app into the DOM, while styling is handled through inline CSS in the `index.html` file.

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World Map

This example renders a world map using the Natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath`. It displays land masses in teal, country borders as interior lines, graticules, and a light-gray sphere background. The SVG is built with React, while D3’s `geoGraticule` generates the latitude/longitude grid and TopoJSON’s `feature` and `mesh` methods extract geometries from the world-atlas dataset.

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Stylized Line Chart

This visualization displays weekly temperature measurements from San Francisco using a stylized line chart rendered as an SVG. The chart features a natural curve connecting data points across time, with labeled axes for temperature and time. Built with React and D3 v5, the code uses `scaleTime` and `scaleLinear` for scaling, `line()` with `curveNatural` for the path, and `csv` to load the dataset. The Poppins font and custom styling give the chart a modern, polished appearance.

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Stylized Scatter Plot

This stylized scatter plot visualizes the Iris dataset, plotting petal length against sepal width. Each circle represents a sample, with a tooltip displaying the x-axis value. The visualization uses React to render SVG elements, while D3 v5 utilities handle data loading, scales, and formatting. CSV data is fetched asynchronously and parsed via `d3.csv`. The chart features custom axes with styled ticks and labels, uses the Poppins font, and applies a distinct color scheme for the marks.

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Stylized Bar Chart

A stylized horizontal bar chart displays the populations of the top 10 countries from the UN Population 2019 dataset, using a teal color scheme and the Poppins font. React renders SVG elements, while D3 v5 utilities handle data loading via `csv`, scaling with `scaleBand` and `scaleLinear`, and axis generation. The chart includes formatted tick labels (e.g., "1.4B") and native SVG tooltips through `<title>` elements.

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Refactored Bar Chart

This refactored bar chart displays the population of ten countries from a 2019 UN dataset, fetched via D3’s `csv()` utility and rendered as SVG rectangles. The code organizes visualization logic into separate React components—`AxisBottom`, `AxisLeft`, and `Marks`—using D3 scale functions like `scaleBand` and `scaleLinear` to map data to coordinates, with axes and bars drawn declaratively.

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Axes with React & D3

This example shows a horizontal bar chart of the top 10 most populous countries in 2020, rendered with React components as SVG elements. The code loads the UN population dataset asynchronously using `d3.csv`, then uses `scaleBand` and `scaleLinear` to position bars and generate tick marks for both axes. React manages the loading state and renders the chart only after data arrives. Custom axis lines and labels are created manually from the scales' tick values, avoiding the D3 axis component altogether.

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Rendering Bar Chart with React & D3

This visualization displays the ten most populous countries in 2020 as a horizontal bar chart. The bars are rendered as SVG rectangles, scaled proportionally to each country’s population. The code combines D3’s `csv`, `scaleBand`, `scaleLinear`, and `max` utilities for data loading and scaling, while React manages the component state and conditional rendering of the SVG output.

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Rendering Pie Chart with React & D3

This example visualizes CSS named colors as a pie chart, with each slice representing one color and filled with its corresponding hex value. The chart uses React to manage component state and rendering, while D3 utilities handle data loading and geometry: `d3.csv` fetches the color data, and `d3.pie` and `d3.arc` compute the slice angles and paths. The visualization initially displays a loading message until the data arrives, then renders an SVG with equal-sized slices arranged in a full circle.

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Rendering data with React

This example visualizes CSS named colors as a stacked sequence of horizontal bars, each colored by its RGB hex value. Using React for rendering, it first loads the color data from a remote CSV file via D3’s `csv` utility and stores it in state. Once loaded, it maps over the data, creating a `<div>` for each color with the appropriate `backgroundColor` and fixed dimensions. The implementation combines React’s `useState` and `useEffect` hooks with D3’s data-fetching and parsing API, while also referencing a separate `message` module that computes file size, row, and column counts via D3’s format functions.

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Mouse Follower

A full-screen React application renders an SVG circle that tracks the mouse cursor across the viewport. The component listens to the SVG’s `onMouseMove` event, updating React state with the cursor’s `clientX` and `clientY` coordinates, which are then bound to the circle’s `cx` and `cy` attributes. The circle has a fixed radius of 30 pixels, and the SVG dimensions match the browser window. React uses the `useState` and `useCallback` hooks to manage and optimize the position updates.

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Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using modern JavaScript asynchronous patterns. It fetches a CSS Named Colors dataset from a GitHub gist. The program defines an async function that uses the Fetch API to retrieve the text, then chains a promise to log the contents to the browser console. No visualization or rendering occurs; the focus is purely on data ingestion. It relies on D3.js v5 for the d3.min.js library but primarily showcases native ES2017 async/await syntax with fetch.

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Smiley Face Part V

A grid of eighteen smiley faces, each with randomized eye sizes, renders as a playful array of SVG circles and arcs. The visualization demonstrates modular React component composition, with separate files for the face, eyes, mouth, and background circle, all brought together through ES6 imports. D3’s `arc` generator shapes the mouth paths, while React Fragments group the eyes without extra DOM nodes. Faces are generated from an array produced by `d3.range` and positioned via SVG transforms.

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Smiley Face Part III

A smiley face is drawn using React components and D3's arc generator in an SVG canvas. The face consists of a yellow circle with a black stroke, two black eyes positioned symmetrically, and a curved mouth created with the d3.arc function. The code defines reusable React components and uses D3's arc to compute the mouth path from radius and angle parameters. All shapes are rendered declaratively with JSX inside a single SVG element.

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Smiley Face Part II

A large smiley face fills the browser window, drawn with SVG shapes: a white circular head, two outlined eyes with gray irises, small nostrils, and a curved mouth formed by two arc paths. The visualization is built with React and D3, using the `arc()` generator to define the mouth’s annular sectors, while all positions and radii are computed from the viewport dimensions. The code uses React’s `render()` to mount the SVG and D3’s geometry API for the arc paths, with styling handled via SVG attributes and stroke properties.

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Let's Make a Face Part I

A yellow smiley face rendered with three SVG circles appears against a blank background. React 18 manages the component tree, while SVG attributes define the face’s geometry: a large yellow circle for the head with a black stroke, and two smaller black circles positioned symmetrically for eyes. The code computes coordinates from fixed width, height, and offset constants, using React's `createElement` through JSX to render the SVG elements directly in the browser.

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Smiley Face Part 1

A yellow circle with a black outline and two smaller black circles forms a simple smiley face on an SVG canvas. The example demonstrates basic SVG shape rendering, using circle elements to construct the face. It relies on HTML, CSS, and inline SVG attributes, with the SVG canvas set to 960 by 500 pixels. The code does not include a mouth or any interactive behavior, showing only the static facial features.