Polished Iris Scatter Chart with Menus

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, 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 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 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 Iris with Menus

A scatter plot of the Iris dataset lets users explore relationships between flower measurements through interactive dropdown menus that select which attribute appears on each axis. The visualization renders 150 circles as SVG elements using React components, with scales created via `d3.scaleLinear` and axes built from `xScale.ticks()`. Users switch between sepal and petal dimensions or species via a React Dropdown component, while D3's `csv` loads the remote dataset and `format` handles tick labels.

EEFezerKW

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 interactive scatter plot displays the Iris dataset, allowing users to select which flower measurements appear on the x- and y-axes via dropdown menus. The visualization renders points as SVG circles using React components and D3 scales, with axes generated through reusable AxisBottom and AxisLeft components. Dropdown selection updates state in the React app, which re-renders the scatter plot with new scale domains and labels. The Iris data is loaded asynchronously from a CSV file using D3's csv function.

CChris Briggs

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