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fbudwill

@fbudwill·29 public vizzes

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Selecting a Year on a Line Chart

This line chart shows UN population estimates and projections from 1950 to 2100 for world regions, with a vertical black line marking the currently selected year. Users can hover anywhere on the chart to update the selected year, which is highlighted by the line. The visualization uses D3 v5 with d3.nest() to group data by region, d3.scaleTime() and d3.scaleLinear() for axes, and SVG line paths with a curveBasis interpolation. A color legend and axis labels are also rendered, with data loaded from two CSV files.

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Melting Data for Multiple Lines

This visualization displays population trends over time for various world regions using multiple lines. The chart combines historical estimates from a 2017 UN dataset with medium-variant projections through 2100, showing each region’s population trajectory in a distinct color. The code uses d3.csv to load data, d3.nest to group by region, d3.line with curveBasis for smooth paths, and d3.scaleTime and d3.scaleLinear for axes. A custom colorLegend component renders the categorical scale, while d3.axisLeft and d3.axisBottom create labeled tick marks.

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Line Chart with Multiple Lines

This line chart displays one week of temperature data in degrees Celsius from multiple cities, sourced from Data Canvas - Sense Your City. A colored line represents each city, with a color legend on the right identifying them. The chart uses D3's `scaleTime` and `scaleLinear` for axes, `line` with `curveBasis` for smooth paths, and `nest` to group data by city. Axes feature gridlines and labels, rendered as SVG, with a title and legend included.

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Circles on a Map

This visualization displays global population data for 2018, using a natural Earth projection with circles sized by population at each country’s centroid. Countries with missing data appear in light red, while mapped regions are gray and populating circles use a teal fill. The map supports zooming via d3.zoom, and the code employs d3.geoPath for rendering country boundaries, d3.scaleSqrt for circle sizing, and d3.scaleOrdinal with a spectral scheme for the color legend. Data comes from a CSV file joined to TopoJSON world geometries, with both size and color legends implemented as reusable D3 modules.

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Scatterplot with Menus

This interactive scatterplot lets users explore relationships between variables in the auto-mpg dataset by selecting which columns to plot on the x- and y-axes via dropdown menus. The menus trigger re-renders that update the axes, labels, and animated circle positions. The visualization uses D3 v5 with the `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` APIs, and the dropdown menu component is a custom reusable module. SVG circles transition smoothly over 2 seconds when data changes.

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Chloropleth Map with Interactive Filtering

A choropleth map colors countries by economic classification using an ordinal color scale, with a linked legend for interactive filtering. Clicking a legend category dims non-matching countries and highlights selected ones, while clicking again clears the filter. The visualization uses d3.geoPath and d3.geoNaturalEarth1 for map projection, and d3.zoom for pan and zoom. Data comes from world-atlas TopoJSON and TSV files; colors are applied via d3.scaleOrdinal with the GnBu scheme.

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Chloropleth Map

This choropleth map visualizes global economic classifications by coloring each country according to its economy type, with a color legend to the side. Built with D3 v5, it uses `d3.geoPath` with the Natural Earth projection to render country boundaries from TopoJSON data loaded from the world-atlas package. An ordinal color scale maps economic categories to the spectral scheme, while zoom interaction and hover highlighting are implemented via `d3.zoom` and CSS. The legend is generated as a reusable SVG component through custom functions.

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Color and Size Legends

This example demonstrates two reusable legend components rendered side by side in an SVG: a color legend using an ordinal scale mapping fruit names to distinct colors, and a size legend built from a square-root scale with circular marks sized proportionally to tick values. Both legends use D3 v5’s selection joins to update enter and exit states. The color legend calls `scaleOrdinal`. The size legend uses `scaleSqrt.ticks()` for tick generation.

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World Countries Tree

This example presents a hierarchical tree of world countries, organized by continent and subregion, using a horizontal layout where each node is a country name. The visualization uses D3 v5’s `tree` layout and `linkHorizontal` path generator to draw the branching connections, with labels sized inversely to their depth. The data comes from a local `data.json` file, and the entire SVG rendering supports pan and zoom through D3’s `zoom` behavior, with text styled via the Playfair Display font.

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Blank Canvas

The visualization displays a blank, rounded rectangle that fills the entire browser viewport, with no data or marks drawn. It uses D3 v5 to select the SVG element and set its width and height based on the document body’s client dimensions, then appends a single `<rect>` with a 40-pixel corner radius. The code logs the viewport size to the console, and the CSS fixes the body to prevent scrolling, leaving the canvas as a pure, empty surface.

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Cheap Tricks for Interaction

This interactive map displays countries of the world, with a natural Earth projection and hover highlighting. The visualization uses D3’s `geoPath` and `geoNaturalEarth1` projections to render the sphere and country boundaries, while a `zoom` event enables pan and zoom interactions. The map’s data comes from a world-atlas TopoJSON file, and hover states are styled via CSS on the SVG paths.

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Let's make a map with D3.js!

This example displays an interactive world map using a natural Earth projection, with countries rendered as green SVG paths that turn red on hover. The map supports zooming and panning through a D3 zoom behavior applied to the SVG group. The visualization uses d3.geoNaturalEarth1, d3.geoPath, and topojson.feature to parse geographic data from the world-atlas dataset. A semi-transparent blue sphere outlines the globe, while country names appear as tooltips via appended title elements.

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Bowl of Fruit - Hover to Select

Five fruit circles, colored red for apples and yellow for lemons, sit in a row across an SVG canvas. Hovering over a circle selects it, drawing a black outline via mouseover and mouseout events that call the `setSelectedFruit` callback. The example demonstrates data joins with `selection.data`, `enter`, and `exit`, animating radius changes through `transition().duration(1000)`. Timed updates remove and replace fruits, changing their type and scale using `scaleOrdinal` for both color and radius.

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Bowl of Fruit - Click to Select

Five apples and a lemon render as colored circles, with a black outline marking the currently selected fruit. Clicking any fruit selects it, and items animate in and out of the bowl over time as apples are eaten or replaced. The visualization uses D3 v5’s data join with `selection.enter`, `merge`, and `exit` methods, transitioning circle radii and positions via `transition.attr`. An ordinal color scale maps fruit types to specific hex values, while the SVG canvas and its circle elements update dynamically.

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Bowl of Fruit - General Update Pattern

This example demonstrates the general update pattern for data joins in D3, animating a bowl of fruit as apples are eaten or replaced by lemons. Rectangles form the bowl, while circles and labels inside groups render each fruit. The code uses `d3.scaleOrdinal` for color and radius, and separates enter, update, and exit selections via `selection.enter()`, `.merge()`, and `.exit().remove()`. Timed `setTimeout` calls trigger render functions that modify the data array, showcasing how the SVG updates reactively.

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Bowl of Fruit - General Update Pattern

This example demonstrates the general update pattern in D3, showing how data joins handle entering, updating, and exiting elements. It renders a row of fruit as SVG circles, with apples and lemons distinguished by a `scaleOrdinal` color scale and radius scale. The code uses `selection.data()` with a key function, then applies transitions to animate new circles scaling up from radius zero, existing ones updating position and size, and removed fruits shrinking away before being deleted. The animation is driven by `setTimeout` calls that modify the fruit array and re-render.

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Temperature in San Francisco Area Chart

This area chart visualizes one week of temperature data in San Francisco, sourced from Data Canvas - Sense Your City. The chart displays temperature in degrees Celsius over time, using a smooth curve to fill the area beneath the line. Built with D3 v5, the visualization employs d3.area, d3.curveBasis, d3.scaleTime, and d3.scaleLinear to render the SVG path. It includes axes with grid lines and labels, using d3.axisBottom and d3.axisLeft, plus a title and formatted tick labels.

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Temperature in San Francisco Area Chart

This area chart displays one week of temperature readings in San Francisco, sourced from Data Canvas - Sense Your City. The visualization uses a filled path with a curved basis line to show temperature fluctuations over time. Built with D3 v5, the code employs `scaleTime`, `scaleLinear`, `area`, `curveBasis`, and axis generators to render axes, while the SVG-based chart includes labeled axes and a title.

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Temperature in San Francisco Linechart

This line chart visualizes one week of temperature in degrees Celsius in San Francisco, using data from Data Canvas - Sense Your City. It shows temperature trends over time, with time on the x-axis and temperature on the y-axis, styled with a maroon line and gridlines. The visualization is built with D3 v5, using APIs such as `d3.csv`, `d3.scaleTime`, `d3.scaleLinear`, `d3.axisLeft`, `d3.axisBottom`, and `d3.line` with `curveBasis`. The SVG rendering includes axis labels and a title, with data loaded asynchronously and parsed into date and numeric values.

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Temperature in San Francisco Scatterplot

This scatter plot visualizes one week of temperature data in San Francisco, with time on the x-axis and temperature in degrees Celsius on the y-axis. Using D3 v5, the code employs `scaleTime` and `scaleLinear` for axis scaling, `axisBottom` and `axisLeft` for grid lines, and SVG circles to represent each temperature reading. The data is sourced from Data Canvas - Sense Your City, and the chart includes labeled axes and a title.

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Cars Scatterplot

This scatterplot displays the relationship between horsepower and weight for cars from the UCI Machine Learning Repository’s Auto MPG dataset, with each red circle representing a vehicle. The visualization uses D3 v5’s `scaleLinear`, `axisBottom`, `axisLeft`, and `extent` functions to map the data to SVG coordinates, with axes labeled "Horsepower" and "Weight" and a title above the plot. Circles are rendered with opacity and a fixed radius, while gridlines and tick labels facilitate comparison across the 960-by-500 SVG canvas.

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Population Lollipop Chart

This chart displays the populations of the ten most populous countries using a lollipop-style visualization, where each country is represented by a circle positioned along a linear population scale and connected by a line to a common baseline. The data comes from a CSV file, with populations multiplied by 1,000 for accuracy. Built with D3 v5, it uses `scaleLinear` and `scalePoint` for axes, rendered as SVG with `axisBottom` and `axisLeft`, and formatted tick labels via `format('.3s')` to abbreviate large numbers.

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Scatterplot

This scatterplot displays the population of the top ten most populous countries, with each country positioned along the y-axis and its population along the x-axis. The chart uses D3 v5 with SVG rendering, employing `scaleLinear` and `scalePoint` for axes, `axisBottom` and `axisLeft` for ticks, and `format` to abbreviate population values. Data is loaded from a CSV file via `csv`, with population values multiplied by 1000 before rendering circles representing each country.

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Customizing Axes

This horizontal bar chart displays the population of the ten most populous countries, with values formatted in billions using a custom tick format that replaces 'G' with 'B'. The chart uses d3.scaleBand for the categorical y-axis and d3.scaleLinear for the population x-axis, with d3.axisLeft and d3.axisBottom rendering the axes. SVG rectangles represent the bars, and the y-axis gridlines are removed while the x-axis includes a label. Data comes from a CSV file loaded via d3.csv.

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Making a Bar Chart

This bar chart displays the population of the top 10 most populous countries, using 2018 estimates from the United Nations World Population Prospects. The chart renders as SVG, with a linear scale for population values and a band scale for country names. The code uses d3.csv to load the data, then applies d3.scaleLinear, d3.scaleBand, d3.axisLeft, and d3.axisBottom to construct the visualization. Bars are appended as rect elements, with China and India visibly dominating the population totals.

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Let's make a face with D3.js!

A yellow circle with two eyes, eyebrows, and a mouth forms a simple face in an SVG canvas. The code uses D3 v5 selections to build the shapes, with `d3.arc` generating the mouth’s path. A `transition` applies a two-second upward and downward movement to the eyebrow group, animating the face without any data input. All elements are centered via a translated `<g>` group, and the rendering relies strictly on SVG attributes and D3’s transition system.

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Shapes with SVG and CSS

This example displays a collection of basic geometric shapes—circles, rectangles, a line, and a path—all rendered inside a single SVG canvas. It demonstrates how CSS classes control vector styling, such as setting the stroke color, width, and line-join for the line and path elements, while inline attributes like `fill` and `transform` handle shape-specific properties and scaling. The code uses raw SVG elements and a linked stylesheet, with no external JavaScript libraries.

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HTML Cars Report

This example displays a simple, text-based cars report listing three vehicles with their model years and prices. The data appears as a static HTML list, with the first entry (a 2012 Nissan Leaf) highlighted in red via CSS. The visualization relies on vanilla HTML and CSS, using a monospace font at a large size for readability and a `.highlighted` class to draw attention to that specific car. No JavaScript libraries or interactive features are present.

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Cars Report

This example displays a list of used cars that cost less than $2,000, formatted as text lines in the browser. It begins with a “Loading…” message, then after a two-second delay, it generates and presents the report. The visualization uses React for the interface, with custom JavaScript functions `getCars` and `generateReport` to fetch the data asynchronously and filter the car entries. The data is embedded as a JSON string in the source code.