Dynamic D3 Scatter Plot visualization

Dynamic sPlot with symbols

This dynamic scatter plot visualizes the Iris Dataset, mapping petal width against sepal length while encoding the three iris species with distinct D3 symbol shapes. The visualization uses `d3.scaleLinear` and `d3.scaleOrdinal` to position points and assign symbols, with `d3.axisLeft` and `d3.axisBottom` providing labeled axes. Each data point renders as an SVG path generated by the `d3.symbol` generator, sized at 100 pixels squared, and the chart updates transparently when the dataset or encoding functions change through its reusable `scatterPlot` module.

DDonald Bagwell

91% match

D3 scatter plot

This D3 scatter plot visualizes the Iris flower dataset, plotting petal length against sepal length for individual samples. The SVG rendering uses d3.scaleLinear to map data values to pixel coordinates, with d3.axisLeft and d3.axisBottom providing labeled scales. d3.csv loads the external data source, and d3.select creates circles for each data point. The chart fills the browser window, with margins separating the plot area from its axes.

Reuseable D3 Scatter Plot

This reusable D3 scatter plot visualizes the Iris dataset, mapping petal length on the x-axis and sepal length on the y-axis. It employs a functional, configurable design using d3’s `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to automatically compute scales and render axes. The code loads CSV data via d3’s `csv` and draws SVG circles with a fixed radius, adapting to the browser window’s dimensions. The implementation showcases a modular chart-building pattern with settable properties for dimensions, data, and margins.

Dynamic D3 Scatterplot

The dynamic D3 scatterplot visualizes the Iris dataset, cycling through petal width, sepal width, petal length, and sepal length on the x-axis every two seconds while sepal length remains on the y-axis. It uses SVG rendering with D3 scales, axes, ordinal symbols for species, and a reusable chart function following Mike Bostock's convention. The chart updates via `setInterval`, calling the chart with new accessor functions, while axes and labels adjust to data ranges extended by ten percent.

MMatthew Bruffey

D3 Scatter Plot

A D3 scatter plot visualizes the Iris dataset from a CSV file by encoding petal length on the x-axis and sepal length on the y-axis. The code loads data with `d3.csv`, scales the values using `d3.scaleLinear`, and renders circles as SVG elements with axes added via `d3.axisLeft` and `d3.axisBottom`. The visualization fills the entire browser window and uses the classic three-species flower measurements to reveal clustering patterns in flower dimensions.

The Dynamic Reusable D3 Scatter Plot

This dynamic scatter plot visualizes the Iris dataset, with points representing individual flowers. It uses a reusable D3 module with `scaleLinear`, `axisLeft`, and `axisBottom` to render axes in SVG. Every two seconds, the x-axis attribute cycles through petal width, sepal width, petal length, and sepal length, automatically updating the scales and circles. Data is loaded via `d3.csv` from a GitHub gist, and the chart animates through these four feature comparisons.

AAndre J Denais

Reusable D3 scatter plot

This reusable D3 scatter plot visualizes the Iris dataset, showing the relationship between petal length and sepal length for individual flowers. Built with D3 v7, it renders circles as SVG marks positioned by d3.scaleLinear scales derived from the data extents. The code uses d3.csv to load the data, d3.axisLeft and d3.axisBottom to generate axes, and d3.selectAll with data joins to bind circles. The visualization fills the entire browser window and includes a 24-pixel tick font size, making it suitable for large displays.

D3 scatter plot

This D3 scatter plot visualizes the classic Iris dataset by positioning each flower as a circle, with sepal length plotted on the y-axis and petal length on the x-axis. The circles are rendered as SVG elements using data-driven joins, with scales created via `d3.scaleLinear` and axes generated through `d3.axisLeft` and `d3.axisBottom`. The code loads the CSV data using `d3.csv`, parses numeric fields, and fills the entire viewport. Tick text is enlarged to 24 pixels for improved readability.