char0496

Fork of HIV/AIDS Choropleth Map

This choropleth map visualizes the share of the population infected with HIV/AIDS in 2017 across world countries, using a yellow-orange-red sequential color scale to encode prevalence rates. Countries missing data appear gray. The visualization uses React for component structure, D3 v5 for map projection and data loading, and SVG rendering with a Natural Earth projection and graticules. Data comes from an IHME CSV dataset, joined to country geometries from World Atlas TopoJSON via ISO country codes.

Apr 18, 2022

Fork of Migrant Deaths Linked Views with Fast Brushing

This interactive visualization combines a world map of migrant deaths with a linked time-series histogram, enabling rapid filtering through brushing. The map uses a natural Earth projection with `d3.geoPath` to render country boundaries and graticules, while circles sized by `d3.scaleSqrt` represent the total dead and missing per location. The histogram aggregates monthly totals using `d3.bin` and `d3.timeMonths`, with a `d3.brushX` selection that instantly filters the map’s displayed data. Built with React hooks and D3 v5, it renders in SVG from a subset of the Missing Migrants Global dataset.

Fork of Multiple Views with Brushing

This visualization combines a world map and a time-series histogram to explore migration incidents. Brushing on the histogram filters the map’s bubbles, which scale by total dead and missing per location. The map uses d3.geoNaturalEarth1 and d3.geoPath for projections, while the histogram is built with d3.bin and d3.brushX. React manages state, and data comes from the Missing Migrants Project and World Atlas TopoJSON.

Fork of Multiple Views Cleanup

This example presents a global view of missing migrant incidents, combining a geographic bubble map with a time-based histogram. The map uses a natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath` to render landmasses, graticules, and spheres, with circle radii scaled by the square root of death counts (`d3.scaleSqrt`). The histogram bins incident dates into monthly intervals using `d3.histogram` and `d3.timeMonths`, summing fatalities per bin with `d3.sum`. Both views render as SVG within a React framework, sourcing city data from a CSV and world boundaries from TopoJSON.

Fork of Missing Migrants Multiple Views

This visualization combines a bubble map and a date histogram to show missing migrant incidents. The map uses a natural Earth projection with graticules to display circles scaled by the total dead and missing, while the histogram aggregates deaths by month. Both views use React and D3 v5, with the map relying on `geoNaturalEarth1`, `geoPath`, and `scaleSqrt`, and the histogram using `bin` and `scaleTime`. Data comes from a CSV of migrant incidents, with country boundaries from World Atlas TopoJSON.

Fork of Multiple Views Cleanup

This visualization combines a bubble map and a date histogram to show migrant deaths and disappearances. The map uses `geoNaturalEarth1` and `geoPath` to render world boundaries with circles scaled by `scaleSqrt`, while the histogram bins reported dates with `d3.histogram` and sums death counts per month. Data comes from the Missing Migrants Project CSV, loaded via `d3.csv`. Both views are rendered as SVG within a React app using `d3.scaleTime` and `d3.scaleLinear`.

Fork of Missing Migrants on a Map

This interactive React application visualizes the Missing Migrants Dataset as a world map, where each circle represents an event and its radius reflects the total number of dead and missing individuals. Built with D3.js v5, the visualization employs a Natural Earth projection with graticules and country boundaries sourced from the World Atlas TopoJSON. The code leverages `geoNaturalEarth1`, `geoPath`, and `geoGraticule` from D3, along with `scaleSqrt` to map the death toll to circle sizes, rendering the SVG output through React components.

Fork of Missing Migrants Date Histogram

A date histogram displays the sum total of dead and missing migrants per month, using data from the Missing Migrants Dataset. The visualization uses React with D3 v5, employing `d3.bin` to create monthly bins and `d3.sum` to aggregate the total dead and missing per bin. Bars are rendered as SVG rectangles with hover tooltips showing the sum total. The chart is styled with a Poppins font and includes time-scaled x-axis and linear y-axis.

Fork of Missing Migrants Scatter Plot

This scatter plot visualizes the Missing Migrants Dataset, with each circle representing a single migration-related incident. The x-axis maps the reported date over time, while the y-axis shows the total number of dead and missing individuals. The visualization uses React components to render SVG elements, with D3 v5 scales (scaleTime and scaleLinear) handling the coordinate mappings. Axis and mark components are built as reusable React functions, and the data is loaded asynchronously via D3’s csv parsing.

Fork of Sized Points on a Map

This example displays a world map with cities over 50,000 inhabitants, where each city is represented by a semi-transparent circle sized proportionally to its population using a square-root scale. The map renders in SVG via React, using `d3.geoNaturalEarth1` for projection and `d3.geoPath` to draw landmasses, graticules, and country boundaries from TopoJSON. City coordinates and population values come from a CSV dataset, with circle radii mapped from population via `scaleSqrt`.

Fork of Points on a Map

This example renders a world map using a Natural Earth projection, showing cities with populations over 50,000 as semi-transparent points. The visualization loads geographic boundaries from World Atlas TopoJSON and city data from Simplemaps, then draws them with SVG paths and circles via `d3.geoNaturalEarth1`, `d3.geoPath`, and `d3.geoGraticule`. The React components manage data fetching through custom hooks, while D3’s `csv` and `json` methods load the datasets.

Fork of Interactive Color Legend

This interactive scatter plot visualizes the Iris dataset, displaying petal length against sepal width with points colored by species. The visualization uses react and d3.scaleOrdinal for coloring, d3.scaleLinear for positioning, and SVG for rendering. Hovering over a species in the color legend highlights only those data points, while non-matching points fade to 20% opacity, implemented through React's useState hook for hover state management.

Fork of Scatter Plot with Color Legend

This scatter plot visualizes the Iris Dataset, showing petal length against sepal width with points colored by species. A color legend on the right maps each species to a distinct color, while axis labels and tick marks provide context. Built with React and D3 v5, it uses `scaleLinear` for positioning, `scaleOrdinal` for colors, and custom SVG components for axes, marks, and legend. Data is loaded via `d3.csv` and rendered as SVG circles with hover tooltips.

Fork of Scatter Plot with Color

This scatter plot visualizes the Iris Dataset, showing petal length against sepal width. Circles are colored by species, using an ordinal color scale with three distinct hues. The visualization is built with React and D3 v5, using `scaleLinear` for axes and `scaleOrdinal` for color mapping. SVG rendering handles the marks and axes, with data loaded from a CSV file and formatted using D3's `format` function.

Fork of Polished Scatter Plot with Menus

This interactive scatter plot visualizes the Iris dataset, allowing users to choose which numerical attributes to plot on the x- and y-axes via dropdown menus powered by react-dropdown. The visualization uses React with hooks (useState, useEffect) to manage state and data loading, while D3 version 5 (scaleLinear, extent, csv) handles scaling, axis generation, and data fetching from a GitHub gist. SVG circles represent individual iris samples, and hover tooltips display formatted x-values using d3.format.

Fork of Scatter Plot with Menus

This interactive scatter plot visualizes the Iris dataset, allowing users to dynamically select which flower measurements appear on the x- and y-axes via dropdown menus. The visualization is built with React and rendered as SVG, using D3 v5 scale functions like `scaleLinear` and `extent` for scaling and axis generation. Data is loaded asynchronously with `d3.csv` from a GitHub-hosted CSV file. Each point represents an iris specimen, with native SVG `<title>` elements providing hover tooltips that display precise values.

Fork of Menus with React

This example shows a React-based interactive dropdown menu for selecting a pet, with the current choice logged to the console. It uses the `useState` hook to manage the selected value and a reusable `Dropdown` component that renders a native HTML `<select>` element. The dropdown options are defined as an array of value-label pairs, and the initial selection is set to "hamster". The code imports React and ReactDOM from UMD builds and renders the app into the root div, with custom CSS styling for large fonts.

Fork of Polished World Map

This world map visualizes country boundaries and landmasses using World Atlas TopoJSON data, showing graticules, country borders, and a styled sphere. It uses React with D3 v5’s `geoNaturalEarth1` projection and `geoPath` to render SVG paths for land, interiors, and graticules. The map is split into land and interior meshes via `topojson.feature` and `topojson.mesh`, with a teal color scheme applied through CSS.

Fork of Stylized Line Chart

This fork of a stylized line chart visualizes weekly temperature readings from the Data Canvas Sense Your City dataset. The React app uses d3.csv to load data, d3.scaleTime and d3.scaleLinear for scales, and a d3.line generator with curveNatural to draw a smooth path through the temperature points over time. SVG renders the chart, with Poppins font styling, muted gray axes, and a teal stroke. The code includes components for axes and marks, though circle markers are commented out, leaving only the curved line.

Fork of Working with Time

This example visualizes daily temperature variations in San Francisco using a scatter plot. The chart plots timestamp data on the x-axis against temperature on the y-axis, with each data point rendered as a circle. Built with React and D3 v5, the visualization uses the `scaleTime` and `scaleLinear` functions to map the data domain to pixel coordinates within an SVG. The `csv` function loads the dataset from a remote URL, and custom React components (`AxisBottom`, `AxisLeft`, `Marks`) render the axes and data points, with tooltips displaying the formatted date on hover.

Fork of Stylized Scatter Plot

This stylized scatter plot visualizes the Iris Dataset, mapping petal length on the x-axis against sepal width on the y-axis. Rendered as an SVG using React and D3 v5, the example loads data via `d3.csv` and uses `d3.scaleLinear` with `d3.extent` to compute scales. Custom Axis components generate tick marks and labels, while circular marks represent individual data points, each with a tooltip showing its x-value.

Fork of Stylized Bar Chart

This example visualizes the ten most populous countries as a horizontal bar chart using React and D3 v5. Population values from the UN World Population Prospects 2019 dataset are mapped to bar lengths via `scaleLinear`, while country names position bars with `scaleBand`. The SVG rendering uses reusable AxisBottom, AxisLeft, and Marks components. Stylized with the Poppins font, gridlines and labels are styled through CSS classes, and hover tooltips display formatted population values using D3's `format`.

Fork of Refactored Bar Chart

A bar chart displays the top 10 most populous countries for 2020, sourced from the United Nations World Population Prospects 2019 via a CSV file. The visualization uses React components with D3 v5 scales and shapes, rendering bars as SVG rectangles. The code constructs a band scale for countries and a linear scale for population, with custom React components for axes and marks, loading data through a `useData` hook that fetches and parses the CSV.

Fork of Axes with React & D3

This visualization presents the top 10 most populous countries from the UN World Population Prospects 2019 dataset as horizontal bars. React manages the application state and renders the SVG, while D3.js computes the scales and axes. The code uses `d3.csv`, `scaleBand` for the categorical y-axis, `scaleLinear` for the quantitative x-axis, and draws tick lines and country labels with explicit margins.

Fork of Pathetic Bar Chart

This example displays the ten most populous countries as a set of horizontal bars, with no axes or labels, using population data from the United Nations World Population Prospects 2019. The bars extend from the left edge of the SVG, with lengths proportional to each country’s 2020 projected population. The visualization is built with React and D3 v5, using `scaleBand` for the vertical country positions and `scaleLinear` for the bar widths. Data is loaded asynchronously via `d3.csv` and then sliced to the first ten entries.

Fork of Rendering Data with React & D3

A radial burst visualization displays all CSS named colors as equal-sized pie slices, with each slice filled using its corresponding hex value. The example loads color data from a CSV file using the `d3.csv` method, then processes it through D3's `pie` and `arc` generators. React functional components with hooks manage the asynchronous data loading and render the SVG paths, combining React's declarative UI with D3's layout calculations. Live-updating visuals demonstrate how the two libraries can be integrated for data-driven graphics.

Fork of Vega-Lite API Template

This visualization displays an interactive scatter plot of the Auto MPG dataset, plotting miles per gallon against horsepower while encoding vehicle origin by color and weight by point size. Built with the Vega-Lite API, it combines vega, vega-lite, vega-tooltip, and d3.csv for data loading. The code configures axis styling, applies a responsive fit to the window, and uses interactive tooltips showing vehicle names. Data comes from the UCI Machine Learning Repository via a GitHub gist.

Fork of Loading Data with React and D3

This example displays a summary of the CSS named colors dataset, showing its size in kilobytes, row count, and column count. It fetches the data from a remote CSV URL using D3’s `csv` utility and manages loading state with React’s `useState` and `useEffect` hooks. Once the data arrives, the `message` function formats the statistics for display, using `d3.csvFormat` to compute the byte size. The visualization is a simple text output rendered via ReactDOM, with a "Loading..." placeholder during the fetch.

Fork of Loading and Parsing CSV Data

This example displays basic statistics about a CSV dataset containing CSS named colors, including file size, row count, and column count, rendered as large text in the browser. It demonstrates two alternative approaches for loading and parsing CSV data: one uses `d3.csv` with promises from D3 v5, while the other, commented out, uses the Fetch API with `async` and `await` combined with `d3.csvParse`. The data is fetched from a remote GitHub Gist URL.

Untitled

This example displays a concise summary of a CSV dataset loaded from a remote URL, showing the file size in kilobytes, the number of rows, and the number of columns. Using D3.js v5, it calls `d3.csv` to fetch and parse the data, then uses `d3.csvFormat` and `data.columns` to compute the statistics, displaying them in a large HTML `<pre>` element. The visualization demonstrates asynchronous data loading with promises, highlighting the simplicity of the `d3.csv` API over raw `fetch` and `await` alternatives.

Fork of Mouse Follower

A React application renders an SVG circle that follows the mouse cursor across a 960×500 canvas. Using the `useState` and `useCallback` hooks, it initializes the mouse position to the center of the screen, then updates the circle’s `cx` and `cy` attributes on each `mousemove` event. The `handleMouseMove` callback reads `clientX` and `clientY` from the event object and sets new state, triggering a re-render. The circle has a fixed radius of 30 pixels, and the SVG responds to pointer movement anywhere in the viewport.

Fork of Loading and Parsing CSV Data

This example demonstrates how to load and parse a CSV file containing CSS Named Colors, then displays the file size, row count, and column count as a text summary. The code uses D3 v5’s `d3.csv` method to fetch and parse the remote CSV data, while also providing commented alternatives using the Fetch API with `async`/`await` and `d3.csvParse`. The rendered output is a simple `<pre>` element with large text, showing the parsed dataset’s basic metrics.

Apr 15, 2022

Fork of Smiley Face Part VI

The visualization displays a grid of randomly generated smiley faces, each with slightly varied stroke widths, eye positions, eye radii, and mouth dimensions. Built with React components and D3.js v5, the code uses `d3.arc` to construct the mouth path, while `d3.range` generates the array of face instances. Each face is rendered as an SVG group, with properties like `eyeOffsetX`, `eyeRadius`, and `mouthWidth` randomized via `Math.random()` to create visual diversity across the eighteen faces.

Fork of Smiley Face Part V

A React component splits a yellow SVG smiley face into reusable parts—background circle, eyes, and mouth—rendered within a single SVG group. The face uses d3’s arc generator to draw a crescent-shaped mouth, while eye positions and radii are passed as React props. SVG coordinate transformations center the graphic, and React Fragments group the two eye circles without extra DOM nodes.

Fork of Smiley Face Part IV

This example shows a yellow smiley face drawn with SVG shapes, centered on a 960×500 canvas. It uses React components to render a background circle, two eye circles, and a mouth arc generated with D3’s `arc()` function. The face is positioned using SVG transforms, with stroke width, eye offsets, and mouth radius defined as constants. The code combines React’s declarative JSX with D3’s geometry helpers, loading d3.v5 and React 16.9 from CDNs, and renders everything as static SVG without interaction or animation.

Fork of Smiley Face Part III

This visualization displays a classic yellow smiley face, rendered as a vector graphic. The face is composed of several SVG shapes, including circles for the head and eyes, and a path for the mouth. The code uses React to define the SVG structure and D3 v5's `arc` generator to create the mouth's curve. The entire graphic is centered within an 960x500 canvas, and its proportions are controlled by named variables such as `eyeRadius` and `mouthWidth`.

Fork of Smiley Face Part II

This example shows a yellow smiley face centered on a screen, with two black eyes and a yellow circular head scaled to the window height. The face is rendered as a React component using SVG primitives, with the center and eye positions calculated from `window.innerWidth` and `window.innerHeight`. The code uses React 16.9 and ReactDOM to mount the component into the DOM, and applies a `<g>` transform to translate the drawing to the screen’s center. Eye offsets and radii are defined as constants, while the head radius derives from the viewport height minus stroke width.

Apr 12, 2022

Fork of Let's Make a Face Part I

A React application renders a single yellow circle with a black outline, representing the initial step of a smiley face. The SVG is sized at 960 by 500 pixels, with the circle centered at (480, 250) and a radius of 245. React 16.9.0 and ReactDOM 18.0.0 are used via the `React.createElement` and `ReactDOM.render` APIs. The rendered output is static, showing only the face's base shape without eyes or a mouth.

Apr 12, 2022