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Transforming Data

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CCurran Kelleher
Last edited Jan 24, 2023
Created on Jan 23, 2023
Forked from Loading Data

This example demonstrates data transformation techniques using the classic Iris dataset. It computes the average sepal length for each species using `flatRollup` and `sum`, then displays the aggregated result as JSON text on the page. The visualization loads the dataset with `csvParse`, converts the numeric columns, and illustrates how binned aggregation and grouping work through the D3 data-processing API.

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Examples of various data transformation techniques, specifically binned aggregation.

MIT Licensed

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Transforming Data

The visualization demonstrates how D3 data transformation APIs can reshape the Iris dataset, computing the average sepal length for each species via `flatRollup` and `sum`. After fetching the CSV with `fetch()`, the data is parsed with `csvParse` and converted to numeric types. Results are displayed as formatted JSON in a `<pre>` element, showing a concise summary of the aggregated values. The example highlights the use of `flatRollup` and `sum` from the D3 API to perform binned aggregation and group operations.

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This example demonstrates how to transform the classic Iris dataset by loading a CSV file and calculating the average sepal length for each species. The visualization displays the resulting summary as a JSON-formatted list of objects, showing the species name alongside its computed average. The code uses D3 v7 APIs including `csvParse` to parse the data, `flatRollup` to group and aggregate values, and `sum` to calculate totals. It also showcases ES6 module imports, a fetch-based data-loading pattern, and a state management system for handling asynchronous data retrieval.

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This example demonstrates data transformation using the Iris dataset, loading CSV data and computing the average sepal length for each species. The visualization displays the resulting summary as formatted JSON text. Built with D3 v7, the code uses `csvParse` to parse the data, `flatRollup` and `sum` to aggregate values, and `select` to render the output. The example also illustrates asynchronous data loading with `fetch` and state management through a custom `setState` pattern.

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This example demonstrates data transformation using the Iris dataset. It loads a CSV file and computes the average sepal length for each species, displaying the resulting summary as JSON text in a `<pre>` element. The visualization uses D3 v7 APIs including `csvParse` for data loading, `flatRollup` for grouping and aggregation, and `sum` for calculating totals. The implementation follows a state-based pattern with a `setState` function to manage the asynchronous data fetching and rendering lifecycle.

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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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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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