K

kosakase

@539-sks·20 public vizzes

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SVG Fundamentals

This example illustrates fundamental SVG shapes and rendering using raw SVG markup in an HTML page. It shows primitive geometric elements including circles, a rectangle, and a path, with varied presentations like colored fills, strokes, and stroke widths. The code uses no external libraries or frameworks, relying purely on SVG’s native elements (`<circle>`, `<rect>`, `<path>`) and CSS attributes. The fixed 960x500 viewport and hardcoded coordinates in the markup demonstrate a static, hand-coded approach to constructing vector graphics without data binding or interactivity.

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SVG Fundamentals

This example demonstrates the fundamental building blocks of SVG graphics using React. It renders a bare HTML page that is set up to incorporate CSS and JavaScript for interactive visualization. The code utilizes the React framework to structure the component, though the current implementation is minimal, showcasing an empty body element. It serves as a foundational template for understanding how SVG elements can be integrated and manipulated within a React application, with references to a separate React Starter project for more advanced usage.

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Axes with React & D3

This example uses React with D3 to display a horizontal bar chart of 2019 UN population data for the top ten countries. It loads the CSV data with the `csv` utility, scales the population values with `scaleLinear` for the x-axis and country names with `scaleBand` for the y-axis, and renders SVG rectangles for the bars. The chart’s axes are drawn using D3’s scale methods, with tick labels positioned via SVG text elements. Rendering is handled through React state and the `ReactDOM.render` method.

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Rendering data with React & D3

This example visualizes all 147 CSS named colors as a circular pie chart, with each slice filled with its corresponding RGB hex value. The code uses React hooks (useState and useEffect) to fetch and store the data from a CSV file, then renders SVG paths via React elements. D3’s csv, pie, and arc functions compute the slice angles and path geometry, while React manages the component lifecycle and DOM updates. The chart loads asynchronously, showing a loading message until the data arrives.

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Refactored Bar Chart

This visualization presents a horizontal bar chart ranking the ten most populous countries based on 2020 UN population estimates. The chart uses a linear scale for population values and a band scale for country names, with axes and gridlines rendered as SVG elements. React manages the component state and rendering, while D3 utilities handle data loading via `csv()` and scale construction through `scaleLinear` and `scaleBand`. The data is fetched from a CSV file hosted in a GitHub gist, and the interface displays a loading message until the data arrives.

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The Margin Convention

A horizontal bar chart displays the top ten countries by 2020 population, parsed from a CSV of UN population data. React state loads the data via `d3.csv`, while `d3.scaleLinear` and `d3.scaleBand` map population values and country names to SVG rectangles within a standard margin layout.

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UN Population Prospects 2019

A horizontal bar chart displays the ten most populous countries in 2020 based on UN Population Prospects 2019 data. The visualization loads a CSV file via `d3.csv`, parses the population column, and maps country names to a `scaleBand` y-axis while populations drive a `scaleLinear` x-axis. React state manages the asynchronous data loading, and SVG rectangles render the final bars. The chart uses D3 v5 for scales and data parsing, with React components handling the rendering.

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Rendering Data with React & D3

This example renders CSS named colors as a radial burst of pie slices, with each slice filled by its corresponding RGB hex value from a CSV dataset loaded via D3’s `csv` utility. React state manages the asynchronous data loading, while D3’s `pie` layout and `arc` generator compute slice geometries for SVG `<path>` elements. The visualization demonstrates a hybrid approach where React controls rendering and lifecycle, and D3 handles data parsing and shape math.

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Rendering Data with React

The example displays CSS named color data as full-width horizontal bars stacked vertically, using React components to render each color’s hex value as the background. It loads the CSV via D3’s `csv` utility into React state with `useState` and `useEffect`, mapping the parsed rows to `<div>` elements. The SVG output is a commented-out placeholder; the visible rendering relies on React’s virtual DOM and inline styles, producing a 960×500 pixel spectrum. No interaction or transitions are present in the code.

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Loading Data with React & D3

This example demonstrates loading a CSS named colors dataset and displaying its summary statistics as text. The app uses React state to manage the data, which is fetched asynchronously via D3’s `csv` utility from a GitHub gist URL. Once loaded, the `message` function calculates and presents the formatted size in kilobytes, row count, and column count using D3’s `csvFormat` and array properties. The visualization is rendered as SVG within a React component, showing the raw table dimensions rather than any graphical encoding.

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Loading and Parsing CSV data

This example demonstrates the basic workflow of loading and parsing a CSV file with D3.js, displaying the data’s size, row count, and column count in a large text element. It uses `d3.csv` to fetch a remote CSV of CSS named colors from a GitHub gist, then computes the formatted string’s kilobyte size with `d3.csvFormat` and `Math.round`. The page also includes commented-out alternatives using `d3.csvParse` with `fetch` and async/await, though only the primary `d3.csv` promise-based method runs.

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Mouse Follower

A circle follows the mouse cursor across an SVG canvas, updating its position in real time to mirror the pointer’s movement. The visualization uses React’s `useState` and `useCallback` hooks to track cursor coordinates (`clientX`, `clientY`) and re-render the circle’s `cx` and `cy` attributes on every mouse move. The SVG element handles the `onMouseMove` event, while the circle’s radius is fixed at 30 pixels. The code imports React, ReactDOM, and D3 v5, though no D3-specific functions are invoked beyond the library’s inclusion.

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d3 playground

This example demonstrates a time-series line chart of daily amounts loaded from a CSV file, using axes with tick labels. It uses d3.csv to parse data, d3.scaleTime and d3.scaleLinear for scales, and d3.axisBottom and d3.axisLeft for the axes. Guide lines are drawn as SVG line elements, followed by circles for each data point, all rendered within an SVG container sized via width and height attributes.

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my d3 canvas

This example displays a large, centered “Hello D3!” message with heavy CSS styling, demonstrating a minimal integration of D3 within a React framework. The page uses plain HTML and CSS for layout and typography, with an empty JavaScript script tag that leaves room for D3 code. The visualization highlights the simplicity of combining D3 v3 with standard web technologies, showing how a basic canvas or SVG scene can be set up within a React project. The data source is absent, focusing instead on the foundational structure for future D3-based graphics.

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Fork of Rendering data with React & D3

This example shows a horizontal bar chart of the top ten countries by population in 2020, using a combination of React and D3. The chart displays population values as proportional bar widths, with axis ticks and country labels generated from the loaded CSV data. React manages the component state and lifecycle through hooks like `useState` and `useEffect`, while D3’s `scaleBand`, `scaleLinear`, and `max` functions handle scaling and data processing. The visualization renders as SVG, with bars and text elements created declaratively in JSX.

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Loading and Parsing CSV Data

This example demonstrates the basic workflow of loading and parsing CSV data using D3.js v6. It displays the file size in kilobytes, the number of rows, and the number of columns from a remote CSV file containing CSS named colors. The code uses `d3.csv()` to fetch and parse the data, then `d3.csvFormat()` to calculate the formatted size, and reads the parsed array’s `length` and `columns` properties. The resulting statistics are rendered as large text inside a `<pre>` element via the DOM API.

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Fork of Vega-Lite API Template

This example visualizes the Auto MPG dataset from the UCI Machine Learning Repository, plotting each car’s fuel efficiency (mpg) against its horsepower. Circles are colored by country of origin and sized by vehicle weight, with tooltips revealing the car model name. The visualization is built using the Vega-Lite API, which wraps d3.csv for data loading and vega-tooltip for interactive hover details. The plot dynamically fits the full viewport width and height, while custom axis and label styling are defined in a separate config file to improve readability.

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Loading with React & D3

This example demonstrates how React handles asynchronous data loading with D3. The App component initializes state with null, then uses useEffect to fetch a CSS named colors CSV from a GitHub gist via d3.csv, updating state once the data arrives. While loading, the screen displays the text "loading"; afterward, it renders a message summarizing the dataset’s size in kilobytes, row count, and column count, computed using d3.csvFormat and the data’s columns property. The visualization uses React’s useState and useEffect hooks with D3’s csv parser, rendering output as SVG-backed React elements.

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Let's Make a Face Part I

This example renders eighteen identical cartoon faces in a grid using React components, each composed of a yellow background circle, two black eyes, and a curved mouth. The faces are generated from an array created with `d3.range`, and each face is drawn as SVG elements within a transformed group. The mouth shape is computed using the `d3.arc` API, with inner and outer radii and angular start and end points defining its curve. React’s component-based structure, including `FaceContainer`, `BackgroundCircle`, `Eyes`, and `Mouth`, demonstrates a modular approach to building and repeating SVG visualizations.

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

A smiley face rendered with SVG animates its eyebrows upward and downward in a looping transition. The example demonstrates fundamental D3.js DOM manipulation by selecting an SVG element, appending groups, circles, rectangles, and a path for the mouth using `d3.select` and `d3.arc()`. Eyebrow movement is driven by chained `transition().duration()` calls that alternate the group’s `transform` attribute. The code uses D3 v6, with dimensions defined by the SVG’s width and height attributes.