Activity 6 Task 4 Transforming Data

100%

Activity 6 Task 4 Transforming Data

✓ Published0🌍 Public

Last edited Jul 4, 2023

Created on Jun 14, 2023

This visualization shows a JSON summary of average sepal length grouped by species, calculated from a CSV dataset. The code uses D3 v7 functions including `csvParse` to load data from `data.csv`, `flatRollup` and `sum` to aggregate the data, and `select` to render the resulting summary as a `<pre>` element. The visualization initially loads the CSV asynchronously, parses numeric fields, and then displays the computed group averages.

AI-generated description

MIT Licensed

Similar vizzes

Loading thumbnail…

Activity6_4_Transforming the Data

This example demonstrates data transformation by loading the classic Iris flower dataset from a CSV file and computing the average sepal length for each species using D3’s `flatRollup` and `sum` functions. The results are displayed as a formatted JSON summary in a `<pre>` element. The visualization fetches the local CSV with `fetch`, parses it via `csvParse`, and converts numeric fields to numbers. The code also shows state management patterns, including a loading state and conditional rendering, relying on D3 v7’s selection and join methods.

AArun Pratap

87% match

Loading thumbnail…

Activity 6 - 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 visualization displays the results as a formatted JSON summary, using a minimal text-based output. The code leverages D3 v7 functions including `csvParse` to parse the data, `flatRollup` and `aggregate` to group records and calculate sums, and `select` for DOM manipulation. This highlights how D3 can transform raw tabular data into structured summaries.

HHarshithaAmbilpur

86% match

Loading thumbnail…

Activity6_4_Transforming the Data

This example demonstrates how to transform the Iris flower dataset by computing the average sepal length for each species using D3's `flatRollup` and `sum` functions. The resulting summary is displayed as a JSON-formatted text block via `select`, `data`, and `join`. The visualization loads CSV data with `csvParse`, coerces numeric fields, and uses local ES6 modules alongside D3 v7 loaded from UNPKG.

MmNamratha25

84% match

Loading thumbnail…

Transforming Data

This example shows the average sepal length for each iris species from a CSV dataset of flower measurements. It first loads the data with `fetch` and parses it using `d3.csvParse`, then groups and aggregates the values with `d3.flatRollup` and `d3.sum`. The resulting summary is rendered as formatted JSON text inside a `pre` element using D3's `select` and data joins. The visualization demonstrates D3 v7 loading via UNPKG and local ES6 module imports.

LLakshmanRangisetti

84% 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

84% match

Loading thumbnail…

Activity_6-Task_4

This example displays the average sepal length for each iris species in the classic Iris dataset, presenting the computed summary as a formatted JSON list in a `<pre>` element. The visualization first loads and parses `data.csv`, then uses the `flatRollup` API with a custom `sum` aggregation to group records by species and calculate the mean. It relies on D3 v7's `select`, `csvParse`, and `flatRollup` functions to transform the raw measurements into a concise textual summary.

Ssaipaidipalli

83% match

Loading thumbnail…

Transforming Data

This example demonstrates data transformation from a CSV file using D3.js. The visualization loads fruit pricing data and calculates the average sepal length for each species using `flatRollup` and `sum`. The transformed data is then rendered as a JSON string in a `<pre>` element. The code utilizes `csvParse` for data ingestion, `select` for DOM manipulation, and the `flatRollup` aggregation API to summarize the dataset.

Rrushithareddyy

83% match

Loading thumbnail…

Transforming Data

This visualization 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’s `csvParse` to import raw comma-separated values, then parses numeric fields with unary plus operators. It applies `flatRollup` and `sum` from D3 to group the data by species and calculate the mean aggregate. The resulting summary is rendered as a JSON string inside a `<pre>` element, showing the pipeline from raw data to structured analysis. The example fetches the CSV asynchronously using browser `fetch` and the Promise API.

VVinay Vemuri

83% match