Transforming Data

100%

Transforming Data

✓ Published0🌍 Public

Last edited Jun 14, 2023

Created on Jun 14, 2023

This example demonstrates how D3 can transform raw CSV data into a meaningful summary using the `d3.flatRollup` and `d3.sum` APIs to aggregate the iris dataset by species and calculate the average sepal length. The visualization renders the resulting summary as formatted JSON text inside a `<pre>` element, showcasing D3's data loading, parsing, and transformation workflows. It uses `d3.csvParse` to load and type-convert the dataset, with state management handled through a custom `setState` function. The example highlights dynamic data fetching, transformation, and reactive rendering in a single-page D3 application.

AI-generated description

An example showing the capabilities of VizHub:

Loads D3 via UNPKG.
Demonstrates use of import from "d3".
Demonstrates use of import from local ES6 modules.

MIT Licensed

Similar vizzes

Loading thumbnail…

Transforming Data

This example demonstrates how to transform the classic Iris dataset by computing the average sepal length for each species using `d3.flatRollup` and `d3.sum`, then displaying the aggregated results as a JSON summary in a `<pre>` element. The visualization loads CSV data asynchronously via `fetch`, parses it with `d3.csvParse`, and converts numeric fields before processing. Built with D3 v7 and ES6 modules, it showcases lazy state management where data is fetched and transformed only once on initial render.

SSharankumar02

94% match

Loading thumbnail…

Transforming Data

This example demonstrates data transformation by loading the classic Iris dataset from a CSV file and computing the average sepal length for each species. It uses D3's `csvParse` to parse the data, `flatRollup` and `sum` to aggregate values, then displays the resulting summary as JSON text in a `<pre>` element. The visualization highlights D3 v7’s modular import system and local ES6 modules, with a simple state management pattern that asynchronously fetches and processes the data before rendering.

Hharipriyaa07

93% match

Loading thumbnail…

Transforming Data

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.

Ppardha1411

93% match

Loading thumbnail…

Transforming Data

This example demonstrates the power of D3’s data transformation APIs by loading the classic Iris dataset from a local CSV. Using `d3.flatRollup` and `d3.sum`, it groups the records by species to compute the average sepal length, then renders the aggregated results as JSON text in a `<pre>` element. The visualization also highlights asynchronous data loading with `fetch` and `d3.csvParse`, alongside ES6 module imports. The output is a clear, tabular summary of the transformed data, showcasing D3 v7’s streamlining of grouping and aggregation workflows.

SSwapna1915

93% match

Loading thumbnail…

Transforming Data

This example shows the Iris dataset transformed into a summary of average sepal length per species, displayed as formatted JSON text in a `<pre>` element. The visualization fetches and parses `data.csv` using `d3.csvParse`, then applies `d3.flatRollup` and `d3.sum` to compute the species averages. The code also demonstrates a state management pattern with `setState` for handling loading states, rendering the result with D3’s `select` and `join` methods.

Ssreekarthanda@gmail.com

93% match

Loading thumbnail…

Transforming Data

This example demonstrates how to transform and summarize the Iris dataset using D3’s `flatRollup` and `sum` APIs. It first loads a CSV file asynchronously, parses it with `csvParse`, and converts numeric fields from strings to numbers. The visualization then computes the average sepal length per species and displays the resulting summary as formatted JSON in a `<pre>` element. The code also illustrates local ES6 module imports and state management through a simple `setState` pattern.

UUmarani Vemula

93% match

Loading thumbnail…

Transforming Data

This example demonstrates a data transformation pipeline using D3.js v7. It loads the classic Iris dataset from a CSV file, parses it with D3's `csvParse` function, then computes the average sepal length grouped by species using `flatRollup` and `sum`. The resulting summary is displayed as formatted JSON text in a `<pre>` element. It showcases how D3's functional API can be used for data aggregation and transformation, with the visualization updating asynchronously via a state management pattern and local ES6 module imports.

Ssaikrishnabussa

93% match

Loading thumbnail…

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.

Iindushashi.g@gmail.com

92% match