D3 data transformation

100%

D3 data transformation

✓ Published0🌍 Public

Last edited Jan 22, 2024

Created on Jan 22, 2024

This example demonstrates a simple data-loading and transformation workflow using D3. It fetches a CSV file containing the classic Iris dataset and uses `d3.csvParse` to convert the raw text into an array of objects, subsequently coercing the numeric measurement columns to numbers. The resulting parsed data is displayed as formatted JSON inside a `<pre>` element. The visualization uses a minimal state management pattern, with a `setState` callback triggering re-renders, and leverages D3’s `select` and `join` for DOM updates.

AI-generated description

MIT Licensed

Similar vizzes

Loading thumbnail…

D3 data loading

This example demonstrates asynchronous data loading in D3 by fetching a CSV file of iris flower measurements (`data.csv`) and displaying the parsed content as a JSON string inside a `<pre>` element. While the request is pending, the text shows “LOADING” before replacing it with the data. Using `d3.csvParse` alongside the `fetch` API, the code converts text into an array of objects, then coerces numeric fields such as `sepal_length` and `petal_width` into numbers with the unary plus operator. The visualization relies on D3 v7 selections with `.join()` to update the DOM, while `index.js` manages a simple state pattern.

SSubhendu Karmakar

89% match

Loading thumbnail…

Loading Data

This example demonstrates how to load and parse a CSV file in D3.js, displaying the raw parsed data as a stringified JSON object in a `<pre>` element. The visualization uses a state management pattern with a `setState` function to handle asynchronous data loading, initially showing a "LOADING" placeholder. It fetches `data.csv`, parses it with `d3.csvParse`, and converts numeric columns to numbers before updating the display. The Iris flower dataset is used as the data source, with the rendering approach being a simple text-based output rather than a graphical chart.

IIshaChid76

87% match

Loading thumbnail…

Fork of Data transformation

This example demonstrates the loading and parsing of the Iris Dataset from a CSV file, displaying the raw tabular data as formatted JSON text. The visualization initially shows a "LOADING" state before fetching the CSV, then uses `d3.csvParse` to convert the text into an array of objects, explicitly coercing the four measurement columns to numbers. The rendering is handled by a simple D3 data join that updates a `<pre>` element with the parsed data, driven by a state management pattern where `setState` triggers re-rendering. The Iris Dataset contains 150 samples of sepal and petal measurements across three iris species.

KKhatera Alizada

87% match

Loading thumbnail…

D3 data transformation

This example demonstrates several D3.js data transformation techniques on the classic Iris flower dataset. The visualization displays the raw data from a CSV file in a styled preformatted block, while the console logs the results of operations including `d3.min`, `d3.max`, `d3.extent`, `d3.group`, `d3.rollup`, and `d3.bin`. Using D3 v7 APIs like `flatGroup` and `flatRollup`, it shows how to aggregate observations by species and compute averages, culminating in a histogram of sepal lengths generated with `d3.bin`. The code loads data asynchronously via `d3.csvParse` after a fetch request.

SSubhendu Karmakar

86% match

Loading thumbnail…

Loading Data

This example demonstrates how to load and parse a CSV file in D3.js, displaying the Iris dataset as formatted JSON text in a `<pre>` element. The visualization initially shows a "LOADING" state, then fetches `data.csv` via the Fetch API and parses it using `d3.csvParse`. The code explicitly converts string measurements into numbers for all four numeric columns. State management is handled through a simple `setState` pattern, where the `viz` function checks whether data is loaded, triggers the asynchronous fetch, and re-renders once the parsed array of objects is available.

CCurran Kelleher

86% 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 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

86% match

Loading thumbnail…

Transforming Data

This example loads the classic Iris flower dataset from a local CSV file and uses D3.js version 7. The visualization demonstrates data transformation by grouping measurements by species and calculating the average sepal length for each group using `d3.flatRollup` and `d3.sum`. The result is rendered as a formatted JSON object inside a `<pre>` element, showing the computed summary statistics. The code also illustrates asynchronous data loading via `fetch()` and uses `d3.csvParse` to convert the CSV text into structured data, while showcasing D3's `select` API for DOM manipulation.

Ssrikkanth05

86% 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

86% match