E

Ed Link III

@edlinkiii·16 public vizzes

Loading thumbnail…

Bowl of Fruit - General Update Pattern

An animated bowl of fruit demonstrates D3’s general update pattern, showing five fruits that disappear or change type over time. The SVG visualization uses a single reusable `fruitBowl` function, which binds data arrays to `g` groups via `selection.data()`, then handles enter, update, and exit selections with `enter().append()`, `merge()`, and `exit().remove()`. Fruit appearance relies on `scaleOrdinal` color and radius scales, while timed `setTimeout` calls trigger data mutations—removing apples or replacing one with a lemon—to illustrate smooth transitions in the rendered circles and labels.

Loading thumbnail…

Bowl of Fruit - General Update Pattern

Five pieces of fruit sit in a drawn bowl, rendered as SVG circles with labels. The example demonstrates the D3 general update pattern: entering, updating, and exiting selections manage changes to the fruit array over time, as apples are eaten and one apple is replaced by a lemon. The code uses `selection.enter()`, `selection.merge()`, and `selection.exit()` to handle dynamic data, with `d3.scaleOrdinal` mapping fruit types to colors and radii. Updates occur via `setTimeout` calls that modify the fruit data and re-render.

Loading thumbnail…

Bowl of Fruit - General Update Pattern

This example demonstrates the D3 general update pattern by animating a bowl of fruit rendered as SVG circles. Apples and lemons appear as red and yellow circles with different radii, with enter, update, and exit transitions over one-second durations. The code uses `selection.data` with a key function, `merge`, `transition`, and `attr` to handle dynamic changes. D3’s `scaleOrdinal` maps fruit types to colors and sizes, while `setTimeout` schedules fruit removal and type replacement events.

Loading thumbnail…

Let's make a face with D3.js!

This example builds a smiling face out of basic SVG shapes, with animated eyebrows that move up and down. Using D3.js v5, the code selects the SVG element, appends and positions circles for the eyes, rectangles for the brows, and an arc-generated path for the mouth. A transition chain on the eyebrows group animates the transform attribute over two two-second intervals, creating a subtle, looping expression change.

Loading thumbnail…

World Population Area Chart

This area chart shows the growth of world population over time, with time on the x-axis and population count on the y-axis. The visualization uses d3.csv to load data, then d3.scaleTime and d3.scaleLinear to map data to SVG coordinates, with d3.area and curveBasis generating the filled path. Axis labels and a title are added via d3.axisBottom and d3.axisLeft, with tick formatting replacing 'G' with 'B' for billions.

Loading thumbnail…

World Population Area Chart

This area chart shows the growth of world population over time, with time on the x-axis and population count on the y-axis. The visualization uses d3.csv to load data, then d3.scaleTime and d3.scaleLinear to map data to SVG coordinates, with d3.area and curveBasis generating the filled path. Axis labels and a title are added via d3.axisBottom and d3.axisLeft, with tick formatting replacing 'G' with 'B' for billions.

Loading thumbnail…

World Population Area Chart

This area chart visualizes world population growth from 1960 to 2015, with a maroon filled region under a smoothed curve. The x-axis shows time, while the y-axis displays population counts formatted in billions. Built with D3 v5, the visualization uses `d3.area()` with `curveBasis` for the smooth path, `d3.scaleTime` and `d3.scaleLinear` for scales, and `d3.axisBottom` and `d3.axisLeft` with gridlines. Data is loaded from a CSV file via `d3.csv`, and the SVG rendering includes custom axis labels and a title.

Loading thumbnail…

Temperature in San Fransico Area Chart

This area chart visualizes a week of temperature data in San Francisco, plotting time on the x-axis against temperature on the y-axis. The chart uses a smoothed area fill, rendered via D3’s `area` and `curveBasis` functions, to show temperature fluctuations. Data is loaded from a CSV file, with timestamps parsed as dates and temperatures as numbers. The SVG output includes labeled axes, gridlines, and a title, styled through CSS. The example employs D3 v5 APIs like `scaleTime`, `scaleLinear`, `axisBottom`, and `axisLeft`.

Loading thumbnail…

Temperature in San Fransico Line Chart

This line chart displays a week of temperature readings in San Francisco, with the smooth curve rising and falling across time. The visualization loads its data from a CSV file and renders it as an SVG using D3 v5, employing `scaleTime` and `scaleLinear` for axes, `line` with `curveBasis` for the path, and `axisLeft` and `axisBottom` for labeled tick marks. Gridlines extend from both axes, and the chart is titled "A Week In San Fransico" with axis labels for Time and Temperature.

Loading thumbnail…

Temperature in San Fransico Scatter Plot

This scatter plot visualizes a week of temperature readings in San Francisco, mapping time on the x-axis and temperature on the y-axis. The chart uses SVG circles to represent each data point, with axis labels and gridlines rendered via D3’s `scaleTime`, `scaleLinear`, `axisBottom`, and `axisLeft`. Data is loaded from a CSV file using D3’s `csv` method. The visualization includes a title, clear axis annotations, and a clean white background with steel-blue points.

Loading thumbnail…

Cars Scatter Plot

This scatter plot visualizes the relationship between horsepower and weight for individual cars from the auto-mpg dataset. Each red, semi-transparent circle represents a vehicle, positioned according to its horsepower and weight values. The visualization uses D3.js version 5.6, specifically leveraging `scaleLinear`, `axisBottom`, and `axisLeft` to construct the axes and plot the data as an SVG. The code parses the remote CSV file, converting numeric fields, then renders the scatter plot with custom margins and axis labels.

Loading thumbnail…

Cars Scatter Plot

This scatter plot visualizes the relationship between horsepower and weight for individual cars from the auto-mpg dataset. Each red, semi-transparent circle represents a vehicle, positioned according to its horsepower and weight values. The visualization uses D3.js version 5.6, specifically leveraging `scaleLinear`, `axisBottom`, and `axisLeft` to construct the axes and plot the data as an SVG. The code parses the remote CSV file, converting numeric fields, then renders the scatter plot with custom margins and axis labels.

Loading thumbnail…

Scatter Plot

This scatter plot displays the top 10 most populous countries in 2018, using data from the United Nations World Population Prospects. Population values are plotted on a linear scale along the x-axis, while country names are positioned on a point scale along the y-axis, with axis tick marks extending across the plot. The visualization is rendered as SVG using D3 v5, specifically leveraging `scaleLinear`, `scalePoint`, `axisBottom`, and `axisLeft`. It also customizes tick formatting with `format` and includes styled gridlines and labels defined in CSS.

Loading thumbnail…

Customizing Axes

This bar chart displays the population of the top 10 most populous countries, using 2018 estimates from the United Nations World Population Prospects. It demonstrates how D3 v5 axes can be customized by removing default domain lines, adjusting tick labels with a custom formatting function that abbreviates numbers and replaces ‘G’ with ‘B’, and adding styled axis and chart titles. The chart uses `d3.csv` to load data, `scaleLinear` and `scaleBand` for positioning, and `axisLeft` and `axisBottom` for rendering axes, with population values multiplied by 1,000.

Loading thumbnail…

Customizing Axes

This bar chart displays the top 10 most populous countries using 2018 estimates from the United Nations World Population Prospects. It demonstrates customized D3 v5 axes, including a formatted x-axis with tick marks that extend across the chart and a simplified y-axis without domain lines. The code uses d3.csv, d3.scaleLinear, d3.scaleBand, d3.axisBottom, d3.axisLeft, and d3.format to render the SVG visualization. Population values are multiplied by 1000, and axis labels and titles are styled via external CSS.

Loading thumbnail…

Responding to Resize

This example demonstrates how a D3.js v5 visualization can respond to browser window resizing by redrawing its content to match the computed CSS size of its container. The SVG element’s width and height are dynamically updated using `chartDiv.clientWidth` and `clientHeight`, and two diagonal lines are drawn to form an X shape as a visual confirmation of the new dimensions. The code uses D3 selections with `selectAll`, `enter`, and `merge` to update the line elements, and a `window` `resize` event listener triggers the redraw function. The container is fixed to fill the viewport via CSS.