Polished Scatter Plot with Menus

Polished Scatter Plot with Menus

This interactive scatter plot visualizes the Iris dataset, allowing users to dynamically select which flower measurements appear on the X and Y axes via two dropdown menus. The visualization is built with React and D3.js, using d3.scaleLinear for axes and D3’s csv loader to fetch data. SVG circles represent the flowers, with hover tooltips provided by native SVG title elements. Axes are rendered as custom React components, and the interface includes axis labels and a polished Poppins font styling.

Ccshm407

Polished Scatter Plot with Menus

This interactive scatter plot displays the Iris dataset, allowing users to select which flower measurements appear on the x- and y-axes via dropdown menus. Built with React and D3 v7, the visualization uses `d3.csv` to load data, `scaleLinear` for axis scaling, and SVG rendering with custom React components for axes and marks. Tooltips reveal precise values on hover, while axis labels dynamically update based on menu selections.

Jjbruzzo

Polished Scatter Plot with Menus

This interactive scatter plot visualizes the Iris Dataset, letting users switch which flower measurements appear on the x and y axes via dropdown menus. The visualization is built with React and D3, using d3.scaleLinear for axes and d3.extent to set scale domains. It renders SVG circles for each iris specimen, with tooltips showing formatted x-axis values, and resizes the plot area to accommodate a fixed menu height above the chart.

Hhuashe418

Polished Scatter Plot with Menus

This interactive scatter plot displays the Iris dataset, allowing users to explore relationships between flower measurements by selecting different attributes for the x and y axes through dropdown menus. Built with React and rendered as SVG, the visualization uses D3.js functions such as `scaleLinear`, `extent`, and `format` to map data ranges and format tick labels. Each point is a circle with a native SVG `<title>` element for hover tooltips, positioned using computed coordinates from the D3 scales. The plot features labeled axes, styled tick marks, and a dark teal color scheme.

Jj-PGi

Polished Scatter Plot with Menus

This interactive scatter plot visualizes the Iris flower dataset, allowing users to select which numeric attributes are plotted on the x- and y-axes via dropdown menus. The visualization uses React to manage component state and render SVG elements, with D3’s `scaleLinear`, `extent`, and `max` functions handling axis scaling and data mapping. Axis ticks and labels are generated through custom React components, while circles representing data points include tooltips using SVG `<title>` elements. The graph updates dynamically whenever the dropdown selections change.

AAudrey

Polished Scatter Plot with Menus

This interactive scatter plot visualizes the Iris Dataset, allowing users to select which flower measurements appear on the x- and y-axes via two dropdown menus built with the React Dropdown library. The chart renders circles as SVG marks using React components, with axes generated by custom AxisBottom and AxisLeft components that leverage d3.scaleLinear and d3.extent. Polished styling includes Poppins fonts, tick formatting via d3.format, and tooltips using SVG title elements. Data loading relies on d3.csv with type coercion for numerical columns.

TThomas Cormican

Polished Scatter Plot with Menus

This interactive scatter plot displays the classic Iris dataset, allowing users to explore relationships between different floral measurements. Two dropdown menus control which attribute is plotted on the x and y axes. The visualization is built using React and D3 v5, rendering axes and circle marks as SVG elements. Data loading relies on d3.csv, while scales are computed with d3.scaleLinear, utilizing d3.extent and d3.nice for domain and range adjustments.

AAnimesh Nautiyal

Polished Scatter plot with Menus

This example visualizes the Iris dataset as an interactive scatter plot, letting users choose which flower measurements appear on the x- and y-axes through dropdown menus built with React and react-dropdown. The plot renders points as SVG circles, with axes generated using custom React components that call d3.scaleLinear for scaling and d3.extent for domain computation. Data loading is handled by d3.csv, which parses numeric columns for the four measurement attributes. The visualization supports tooltips on hover and uses D3’s format function to display compact axis tick labels like “1.5B”.

Jjiro2017