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anuj1450

@anuj1450·24 public vizzes

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Scatter Plot with Menus

This interactive scatter plot visualizes automotive performance data, allowing users to compare any two numeric columns—such as weight, horsepower, or acceleration—via dropdown menus positioned above the axes. Selecting a new column triggers a smooth 1-second animation where circles transition from the center to their new positions using D3’s `transition().duration()`. The visualization uses SVG for rendering, with axis ticks, grid lines, and labels styled extensively via CSS. Data loads from a remote CSV file, and both x- and y-axis selections are dynamically redrawn using `scaleLinear`, `axisBottom`, and `axisLeft` from the d3 module.

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Choropleth Map with Interactive Filtering

This choropleth map shows world countries colored by income group, with interactive filtering controlled by a clickable legend. Selecting a legend category highlights matching countries while dimming others, and clicking again clears the filter. The map uses a natural Earth projection with an SVG rendering, drawing country paths via `d3.geoPath`. The color legend, built with `d3.scaleOrdinal` and a spectral scheme, triggers the filtering through an `onClick` handler. Country data comes from the world-atlas TopoJSON and TSV files.

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Choropleth Map

This choropleth map visualizes global income groups by coloring each country according to its income_grp property. The map uses a natural Earth projection and fills countries with colors from a spectral ordinal scale, with a background legend showing the color categories. Built with D3 v5, the code loads world geometry from TopoJSON and income data from TSV files, then renders the map as SVG paths with zoom support via d3.zoom. The color legend is implemented as a reusable component, and hovering highlights individual countries.

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Color and Size Legends

This example demonstrates two types of legends built with D3 v5: a color legend for an ordinal scale and a size legend for a square-root scale. The color legend maps fruit names to distinct colors, while the size legend uses `sizeScale.ticks` to generate five circle radii. Both legends are rendered as SVG groups, with circles and text positioned using `spacing` and `textOffset` parameters from custom `colorLegend` and `sizeLegend` modules.

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World Countries Tree

This visualization shows the hierarchical structure of world countries, organized by continent and subregion, as a horizontal tree diagram. It uses D3 v5’s `tree()` layout and `hierarchy()` to compute node positions, with `linkHorizontal()` drawing curved paths between parent and child nodes. Each country name is rendered as SVG text, with font size decreasing by depth. The view supports zooming and panning via D3’s `zoom()` behavior, applied to a containing `<g>` element. Data is loaded from a local `data.json` file, and styling uses the Playfair Display font.

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Blank Canvass.js!

A blank SVG canvas fills the browser window, sized to the full viewport via `document.body.clientWidth` and `document.body.clientHeight`. A single rounded rectangle with a 40-pixel corner radius is drawn across the entire space using D3 v6’s `select` and `attr` methods. The example demonstrates minimal SVG setup with D3, rendering one static shape in a fixed, overflow-hidden layout.

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Cheap Tricks for Interaction

This example shows an interactive world map rendered with SVG, where each country is colored light green and the sphere background is dark blue. A D3 zoom behavior allows users to pan and scale the map by applying transform attributes to the map group. The code uses `d3.geoPath` with a natural Earth projection and TopoJSON data from the world-atlas package to draw country boundaries. A TSV file provides country names, which are displayed as tooltips via SVG `title` elements, and hovering changes the fill color to orange.

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Let's make a map with D3.js!

This example uses a natural Earth projection to render a world map from TopoJSON data, with countries drawn as filled paths and a dark blue sphere as the base. It builds the visualization by loading the world-atlas dataset from unpkg and using `d3.geoNaturalEarth1` for the projection, `d3.geoPath` to generate SVG paths, and the `topojson.feature` method to convert TopoJSON to GeoJSON. The result is a simple, static SVG map with light green countries and translucent black strokes.

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Bowl of Fruit - Hover to Select

This example shows a bowl of fruit rendered as SVG circles, where hovering over a fruit selects it with a black outline and clicking away deselects it. The visualization uses a D3 v5 data join with enter, update, and exit transitions to animate fruit appearing and disappearing over time. Apples and lemons are distinguished through a d3.scaleOrdinal color and radius scale, while mouseover and mouseout events trigger the selection state.

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Bowl of Fruit - Click ti Select

Clicking a fruit in this SVG bowl selects it, highlighting the chosen item with a black outline while all others remain unhighlighted. The visualization demonstrates animated enter, update, and exit transitions using D3 v5, with circles smoothly resizing and repositioning as apples are eaten or replaced by lemons. Fruit types map to distinct colors and radii via ordinal scales, and selection is handled through click events that re-render the scene. The example uses D3 selections, transitions, and scaleOrdinal to manage the dynamic dataset.

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Bowl of Fruit - General Update Pattern

A bowl of fruit demonstrates D3’s general update pattern for entering, updating, and exiting SVG elements. Apples and lemons are drawn as colored circles with labels, and the bowl is a rounded rectangle. The code uses `d3.range` and `select` for data generation, `scaleOrdinal` for color and radius, and `merge` with `exit().remove()` to handle dynamic changes. Over time, fruits are removed or replaced via `setTimeout`, visually updating the bowl with smooth transitions in position and appearance. The example renders static SVG with no animation tweens.

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Bowl of Fruit - General Update Pattern

Five fruits—apples and a lemon—sit in a row, animating in and out of a bowl. The example demonstrates the general update pattern: entering circles scale up from radius zero, exiting circles shrink to zero, and the one changed apple transitions into a lemon with a new color and radius. Built with d3.v6 and the `select`, `scaleOrdinal`, and `.transition().duration()` API, the SVG circles are driven by a `fruitBowl` function that handles enter, merge, and exit states.

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World Population Area Chart

This area chart visualizes global population growth over time, with a filled maroon curve plotted against axes for year and population. The data is loaded from a CSV file and rendered as an SVG using D3 v6. The visualization employs `d3.area` with `curveBasis` for smoothing, `d3.scaleTime` and `d3.scaleLinear` for scales, and `d3.axisBottom` and `d3.axisLeft` to generate gridlines and tick labels. Population values are formatted in billions, and the chart includes axis labels and a title.

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Temperature in San Fransico Area Chart

This area chart displays a week of temperature measurements in San Francisco, with time on the x-axis and temperature on the y-axis. The visualization uses D3 v6’s `scaleTime`, `scaleLinear`, `area`, and `curveBasis` to generate a smooth, filled path in SVG. Data is loaded from an external CSV file via `d3.csv`, with axes rendered using `axisBottom` and `axisLeft`, and gridlines created through tick sizes. A descriptive title and axis labels are included, with the area fill styled in maroon.

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Temperature in San Fransico Line Chart

This line chart visualizes a week of temperature readings in San Francisco, with time on the x-axis and temperature on the y-axis, using a smooth curved path to connect the data points. The chart is rendered as an SVG using D3 v6, with `d3.csv` loading the dataset and `scaleTime`, `scaleLinear`, `axisBottom`, and `axisLeft` constructing the axes. A `d3.line` generator with `curveBasis` creates the smoothed line, while dashed gridlines and labeled axis titles enhance readability. Data comes from a CSV file hosted on VizHub.

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Temperature in San Fransico Scatter Plot

A scatter plot displays temperature readings over a week in San Francisco, with time on the x-axis and temperature on the y-axis. The chart uses D3 v6’s `scaleTime` and `scaleLinear` to map data, while `axisBottom` and `axisLeft` render labeled axes with gridlines. Circles are appended to an SVG group, with the dataset loaded via `csv` from a VizHub URL. The visualization includes a title and axis labels, styled through external CSS.

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Cars Scatter Plot

This scatter plot visualizes the relationship between horsepower and weight for a dataset of cars, sourced from the auto-mpg CSV file. Each red, semi-transparent circle represents a car, with its position determined by the d3.scaleLinear scales mapping weight to the x-axis and horsepower to the y-axis. The code uses d3.csv to load data, d3.axisBottom and d3.axisLeft to render gridlines and tick labels, and SVG circles within a margin-based layout. Axis labels and a title are appended as text elements, with styling defined in CSS.

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Scatter Plot

This scatter plot visualizes the population of the top 10 most populous countries, using data from the 2018 estimate in the United Nations World Population Prospects 2017. It uses D3 version 6, with the SVG rendering approach. The code employs `scaleLinear` for the population x-axis and `scalePoint` for the categorical country y-axis. Grid lines are created with `tickSize`, and axis labels are added using D3's `text` API.

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Customizing Axes

This bar chart displays the populations of the ten most populous countries, based on 2018 estimates from the United Nations World Population Prospects. Using d3.scaleBand for the categorical y-axis and d3.scaleLinear for the numerical x-axis, it customizes both axes by removing domain lines and applying tick formatting with d3.format. The visual is rendered as SVG, with gridlines drawn from x-axis ticks and a clear title and axis label.

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Making a Bar Chart

This example displays the populations of the world’s ten most populous countries as a horizontal bar chart, using 2018 estimates from the United Nations World Population Prospects. The chart is rendered as SVG, with bars encoded by a linear population scale and countries by a band scale. Built with D3 v6, the code uses `csv` to load data, `scaleLinear` and `scaleBand` for scaling, and `axisLeft` and `axisBottom` for axes. Population values are multiplied by 1,000 to reflect actual figures, and bars are styled with a steelblue fill.

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Let's make a face with D3.js!

A D3.js example draws a yellow smiley face with SVG shapes, then animates its eyebrows in a bouncing motion. The face is centered using an SVG group with a translation transform, and its features are built from circles, rectangles, and an arc path generated with the `d3.arc` function. Eyebrows transition vertically via chained `transition.attrTween`-like `transition().duration()` calls, moving down and back up repeatedly. The code uses D3 v6 selections and transitions, with no external data source, relying purely on geometric constants for layout.

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Shapes with SVG and CSS

This example demonstrates basic SVG shapes—circles, rectangles, lines, and a path—styled with CSS and inline attributes. It shows how fill colors, stroke widths, and transforms like scaling and translation affect rendering, with groups applying shared properties such as stroke and fill. The code uses raw SVG elements (circle, rect, line, path) and CSS classes (like `.lines` for stroke styling) rather than a JavaScript visualization library. The HTML embeds the SVG directly, while the stylesheet controls global margins and stroke properties.

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HTML Cars Report

This example shows a simple listing of three used cars with prices, highlighting the 2012 Nissan Leaf in red. An unordered list in the HTML document displays each vehicle entry. The page is styled with a monospace font at a large size, and the highlighted entry is colored red via a CSS class. No JavaScript libraries or frameworks are used; the visualization relies solely on static HTML and CSS.

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Cars Report

This example demonstrates a JavaScript data-processing pipeline that filters a hardcoded JSON array of car objects by price and formats the results into a text report. It shows asynchronous loading with a Promise, array filtering and mapping, and module imports/exports. The code uses `JSON.parse`, `setTimeout` within a Promise, and template literals to construct strings. The rendered output is displayed in an HTML `<pre>` element, with a loading message shown initially before the report appears.