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Transforming Data

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Lleftymadi
Last edited Jun 14, 2023
Created on Jun 14, 2023
Forked from Hello VizHub

This example demonstrates a data transformation pipeline using the classic Iris dataset. It loads the CSV file containing sepal and petal measurements for three flower species, then uses `d3.csvParse` to parse the data and converts the numeric columns to numbers. The visualization calculates the average sepal length per species using `d3.flatRollup` and `d3.sum`, and displays the resulting summary as JSON text in a `<pre>` element. The code uses D3 v7's selection methods (`select`, `selectAll`, `join`) to manage the output.

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This example demonstrates data transformation with the Iris flower dataset, computing the average sepal length for each species and displaying the result as formatted JSON text. The visualization begins by fetching data.csv and parsing it with `d3.csvParse`, converting numeric fields from strings to numbers. It then uses `d3.flatRollup` combined with `d3.sum` to group records by species and calculate mean sepal lengths, with the final output rendered as a `<pre>` element via D3's `select` and `join` methods.

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