M

mla341

@mla341·25 public vizzes

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Tricks for Interaction

This visualization displays a zoomable, natural Earth projection map with countries colored light green. Users can pan and zoom the map using the d3.zoom behavior, which applies a transform to the SVG group containing all geographic paths. The map data comes from the world-atlas TopoJSON files, with country names joined from TSV data. Hovering over any country turns it red, implemented through CSS :hover rules. The visualization uses d3.geoPath, d3.geoNaturalEarth1, and the topojson.feature API to render the geographic features.

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Temperature in San Francisco

This visualization displays how temperature changes over time in the San Francisco area through a smooth area chart. The time-series data is loaded from a CSV file and rendered as an SVG path using D3’s `area` generator with a `curveBasis` curve. Axes are created with `scaleTime`, `scaleLinear`, `axisBottom`, and `axisLeft`, while grid lines are styled via tick marks. The chart emphasizes the overall temperature trend, with the area filled in maroon and labeled axes for time and temperature.

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Choropleth Map

This choropleth map colors world countries by income group using a categorical color scale with a spectral scheme. The visualization supports zooming and panning through d3.zoom, with country names and income groups revealed via tooltips on hover. Built with d3.geoPath and geoNames from the world-atlas TopoJSON dataset, it renders as SVG paths over a blue sphere. A custom color legend component displays the ordinal scale’s domain, while hover interactions highlight individual countries in red.

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Interactivity in Choropleth Map

This interactive choropleth map visualizes global economic data, coloring each country on a natural Earth projection according to its economy category. Users can click a country to highlight it, dimming all others, or click a color legend to filter countries by economic classification, which also outlines matching countries in red. The example uses d3.geoPath for map rendering, d3.scaleOrdinal with a spectral color scheme, and d3.zoom for pan and zoom interactions. Country data is loaded from a TopoJSON file combined with a TSV file containing ISO country codes and economic classifications.

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Color and Size Legends

This example demonstrates two distinct legend types for data visualization: a color legend mapping categorical fruit names to distinct hues, and a size legend using a square-root scale for numeric values. Animated circles fade in with a two-second transition for the color legend, while the size legend renders static, semi-transparent circles. Both legends use D3 v5 selection methods, including enter/merge/exit patterns, and are built with React. The visualizations are rendered in SVG, with text labels positioned alongside each legend item.

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Link Tree Viz of World Countries

This visualization displays a hierarchical tree of world countries, organized by continent and subregion. It uses a D3.js v5 tree layout with horizontal link paths to show the branching structure. The SVG rendering applies a zoom feature that allows users to pan and scale the visualization. Text labels are styled with the Sriracha font, while data is loaded from a JSON file containing the country hierarchy. The code uses D3's `tree`, `hierarchy`, `linkHorizontal`, and `zoom` components to generate the chart.

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Blank Canvas

This visualization displays a blank, rounded rectangle that fills the entire browser window. The SVG canvas is sized to match the viewport dimensions, and a single rectangle with a 40-pixel corner radius is appended to it. The code uses D3 v5’s `select` and `arc` imports, though only `select` is applied, while the styling relies on a fixed-position body with no margins or overflow. No data is loaded or rendered, leaving the canvas intentionally empty.

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Let's make a map with D3.js

This example displays a world map using a natural Earth projection, with countries rendered as light green SVG paths with subtle black strokes. The map is drawn from TopoJSON data loaded from the visionscarto-world-atlas package, using D3 v5’s `geoPath` and `geoNaturalEarth1` projection. A blue sphere path outlines the globe’s boundary. The code specifically imports `feature` from TopoJSON to convert topologies into GeoJSON features, then binds the country data to SVG path elements. The map appears on a blank background with no additional interactive features or transitions.

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Hover to select

Five fruit circles initially render as apples in a yellow-to-red ordinal scale. Hovering a circle selects it with a black outline, managed through React state and mouseover/mouseout events. Updates demonstrate data joins: a fruit is removed, one apple mutates to a lemon, and another is filtered out. The `fruitbowl` function uses D3 v5 transitions with `selection.merge()` and `exit().remove()` to animate radius and position changes over 1 to 1.5 seconds.

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Click to select

A click-to-select interaction lets users highlight fruit in an SVG bowl, with selected items receiving a black stroke. The visualization uses D3 v5’s `scaleOrdinal` for color and radius, and `transition` to animate circle entry, exit, and property changes. Data updates via `setTimeout` modify the array, triggering re-renders in a React-styled functional component that merges enter and update selections.

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General Update Pattern-With Transitions

Five fruit circles appear in a row, then animate through removal and type changes over time. The visualization demonstrates the general update pattern with smooth transitions for entering, updating, and exiting SVG elements. It uses d3.scaleOrdinal for color and radius mapping, and d3-transition's `.transition().duration()` to animate circle radius, position, and removal. The code is built with React, d3 v5, and renders to SVG.

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Nested Logic

This example demonstrates a dynamic fruit bowl visualization where circles and labels are bound to an array of fruit objects. It shows how updates to the data—removing items or changing a fruit’s type from apple to lemon—trigger animated transitions in position, radius, and color. The code uses d3’s `scaleOrdinal` for color and radius mappings, and the enter-merge-exit pattern to manage SVG groups. The visualization is rendered with React and D3 v5, with timed `setTimeout` calls driving the data mutations.

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General Update Pattern

This example demonstrates the D3 general update pattern for adding, updating, and removing SVG circles over time. The code uses `selection.data()` with a key function, `merge()`, and `exit().remove()` within a `fruitbowl` module. It employs `scaleOrdinal` for color and radius mappings, while setTimeout triggers three sequential data mutations—removing, modifying, and filtering fruits—to show corresponding enter, update, and exit transitions.

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World Population Area Chart

This area chart visualizes world population growth over time, showing the upward trend across the years with a maroon-filled curve. The chart uses D3 v5 to load CSV data, create time and linear scales, and generate the area shape with the `area()` and `curveBasis` APIs. Grid-pattern axes are drawn with `axisLeft` and `axisBottom`, while custom formatting converts large population numbers into billions. The SVG rendering includes labeled axes, a title, and styled tick marks, with data sourced from a CSV file hosted on VizHub.

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COVID chart

The bar chart displays the rate of coronavirus cases per 100,000 habitants for nine U.S. counties, ordered from highest to lowest, using a green fill for each rectangle. It uses D3 v5’s `scaleLinear` for the x-axis, `scaleBand` for the y-axis, and `axisLeft`/`axisBottom` to render the axes. Data is loaded from `data.csv` via `csv` and appended as SVG rectangles. The source is Elflein (June 2020) via Statista.

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Temperature in San Francisco

This line chart shows temperature readings in San Francisco over time, with time on the x-axis and temperature on the y-axis. The visualization uses D3 v5’s `scaleTime`, `scaleLinear`, `line`, `axisLeft`, and `axisBottom` to render the SVG path and axes. Data is loaded via `csv` from a remote dataset, with timestamps parsed as dates and temperatures as numbers. The chart features a smoothed curve through `curveBasis`, labeled axes, and a title, all styled with custom CSS.

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Temperature in San Francisco

This scatterplot displays temperature readings in San Francisco over a week, with time on the x-axis and temperature on the y-axis. Each orange circle represents a single hourly observation, revealing the daily temperature cycles. The visualization uses D3 v5 with SVG rendering, employing `scaleTime` and `scaleLinear` for the axes, `axisLeft` and `axisBottom` for ticks, and data loaded from a CSV file. Grid lines and styled labels enhance readability.

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Cars Scatterplot

This scatterplot visualizes the relationship between car weight and miles per gallon (MPG) for vehicles from the UCI Auto MPG dataset. Each circle represents a car, with weight plotted on the x-axis and MPG on the y-axis, revealing a negative correlation. The visualization uses D3 v5’s `scaleLinear`, `axisLeft`, and `axisBottom` to construct the axes, while data is loaded via `csv` and rendered as SVG circles with gridlines. Axis labels and a title are added using D3 selections.

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Scatterplot

This scatterplot displays COVID-19 case rates per 100,000 people for ten highly affected U.S. counties, ranked along the y-axis with case counts on the x-axis. The visualization uses d3.csv to load the data, d3.scaleLinear and d3.scalePoint for positioning, and d3.axisLeft and d3.axisBottom with custom tick formatting. Circles are appended via d3.selectAll and data joins, styled with blue fills and black strokes. The chart includes grid lines and a title, with data sourced from Statista via Elflein (June 2020).

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Customizing Axes

This bar chart displays COVID-19 infection rates per 100,000 people for the most affected U.S. counties, with values sourced from Statista (2020). It shows how cases are distributed among ten counties, using a custom-formatted x-axis that abbreviates large numbers with a capital "K". The visualization uses d3.scaleLinear and d3.scaleBand for axes, and d3.axisLeft/d3.axisBottom for custom tick marks and labels. The code removes default axis lines, adds custom tick styling with d3.format, and annotates the chart with a title and axis label. Bars are rendered as SVG rectangles with a blue fill.

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Making a bar chart

This example shows a horizontal bar chart of country populations, with bars sorted by population and labeled on the y-axis. It uses D3 v5 methods such as `scaleBand` for the categorical axis, `scaleLinear` for the numeric population scale, and the `axisLeft` and `axisBottom` functions to generate the axes. The chart is rendered as SVG, with each bar’s height set by `yScale.bandwidth()` and its width proportional to population, as read from a CSV file and scaled by a factor of 1,000.

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Shapes with SVG and CSS

This example shows a collection of primitive shapes—circles, rectangles, lines, and a path—arranged within a single SVG canvas. The code demonstrates how CSS classes and inline attributes control visual properties such as fill colors, stroke widths, and line joins. A scale transformation enlarges the entire group, and a separate translate group shifts a set of blue shapes with black outlines. The rendering relies on the SVG API directly within an HTML document, with the linked stylesheet applying presentation rules like the rounded line join for the path.

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HTML cars report

This example shows a basic HTML page titled "Cars Report" that displays a static list of three used cars, each with its model year, make, and price. The page uses plain HTML with an external CSS stylesheet (`styles.css`) to style the body text with a monospace font and 25-point size, and it highlights the first car entry in red by applying the `.highlighted` class. The example demonstrates simple document structure and CSS class-based styling without any JavaScript or data visualization libraries.

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Cars Report

This report displays a list of cars filtered by a maximum price, formatted as text within a styled HTML page. It loads car data asynchronously via a JavaScript Promise, then applies a price filter and formats each car’s details into a string using custom functions. The page uses standard HTML DOM manipulation to set the text content and CSS with the Ubuntu font for styling.

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Hello World

This example demonstrates the minimal setup required to render a text message using D3.js within a VizHub project. It shows a single styled line of text, “Hello VizHub!”, displayed prominently on a dark blue background with Ubuntu typography. The code imports the `select` function from D3 and a custom ES6 module containing the message string, then uses `select('#message').text(message)` to inject that value into the HTML. Styling is handled entirely through CSS, including an imported Google Font.