Transforming Data

100%

Transforming Data

✓ Published0🌍 Public

Last edited Jun 14, 2023

Created on Jun 14, 2023

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.

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 data transformation by loading the classic Iris dataset from a CSV file and computing the average sepal length for each species. It uses D3 v7 modules such as `csvParse`, `flatRollup`, and `sum` to parse the data, aggregate it by species, and display the resulting summary as JSON in a `<pre>` element. The visualization also shows asynchronous state management with `setState` and a loading indicator.

Aaniteshterala2000

96% 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

96% 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

95% match

Loading thumbnail…

Transforming Data

This example loads the classic Iris dataset from a CSV file and computes the average sepal length for each species. The visualization displays this calculated result as formatted JSON text in a `<pre>` element, demonstrating a simple data-transformation pipeline from raw tabular data to a summary table. The code uses D3 v7 functions including `csvParse` for data loading, `flatRollup` and `sum` for aggregation, and `select` for DOM updates. It also showcases ES6 module imports and a state-management pattern for asynchronous data fetching.

HHemanthh81

95% match

Loading thumbnail…

Transforming Data

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.

Rrushithareddyy

94% match

Loading thumbnail…

Transforming Data

This example demonstrates data transformation by loading the Iris flower dataset from a CSV file and computing the average sepal length for each species. The code uses D3 v7 APIs including `csvParse`, `flatRollup`, and `sum` to process the data, then displays the resulting summary as formatted JSON text in a `<pre>` element. It showcases D3’s data-loading and aggregation capabilities, along with ES6 module imports for modular code organization. The visualization updates dynamically when data is fetched asynchronously.

MManideeprenikindi

94% match

Loading thumbnail…

Transforming Data

This example demonstrates how to load, transform, and summarize the classic Iris dataset. It fetches a CSV file, parses it with `d3.csvParse`, converts numeric fields, and then uses `d3.flatRollup` and `d3.sum` to compute the average sepal length for each species. The resulting summary is displayed as a JSON string in a `<pre>` element. The code relies on D3 v7 for data parsing and aggregation, and uses a simple state-management pattern with `setState` to handle asynchronous loading.

Mmn0658

94% match

Loading thumbnail…

Transforming Data

This example demonstrates data transformation by loading the classic Iris dataset from a CSV file, converting string values to numbers, and computing the average sepal length for each Iris species using the `flatRollup` and `sum` functions from D3 v7. The resulting summary is rendered as a JSON string inside a `pre` element. The visualization also showcases ES6 module imports and asynchronous state management through the `setState` callback pattern.

Ppjdhungana@gmail.com

94% match