Task-3.2

task 3.2

This example displays a scatter plot of the Iris dataset, plotting sepal length against petal length. Points are colored and shaped according to species, with a custom legend identifying each species by its unique symbol. The visualization uses D3.js (v7) to fetch and parse the CSV data from a GitHub repository, then renders the plot within an SVG. It constructs axis scales with `scaleLinear`, applies shape encodings through `scaleOrdinal` and `symbolsFill`, and generates marks via `symbolGenerator`, with axes and legends created by custom functions.

Ssatvikreddy79@gmail.com

Aajaykumarakula00@gmail.com

TASK-3

This example demonstrates a scatter plot of the Iris dataset, showing sepal length against petal length with points shaped by species. The visualization loads the CSV data from a GitHub repository and uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `symbol`, and `axisLeft`/`axisBottom` to render axes and points. A custom legend displays the three species using different symbol shapes, while the code exercises local ES6 module imports alongside D3’s bundled version. The chart rescales to fit the viewport dimensions on load.

Task3.2

This scatter plot visualizes the Iris dataset, showing sepal length against petal length. Points are colored and shaped by species, with a legend mapping shapes to the three iris species. The visualization is built using D3 v7, employing force-directed layout principles through `d3.scaleLinear` and `d3.scaleOrdinal` for scales. The chart fetches data from a remote CSV, parses it with `d3.csvParse`, and renders marks via `d3.symbol` with axes generated by `d3.axisLeft` and `d3.axisBottom`.

IIkram Mohammed

Rrakesh.kavuru@outlook.com

Task_3.2

This visualization shows a scatter plot of the Iris dataset, mapping sepal length on the x-axis against petal length on the y-axis, with points styled by species using different D3 symbols. The code loads data from a CSV file via the Fetch API and parses it with d3.csvParse, then renders the plot using d3.scaleLinear for axes, d3.scaleOrdinal with d3.symbolsFill for shape encoding, and d3.axisLeft/axisBottom via the custom axes module. A custom shape legend module displays the species categories using d3.symbol, while D3’s join pattern handles dynamic element updates.

Task4

This example shows a scatter plot of the iris dataset, displaying sepal length against petal length with species encoded by the shape of each data point. A legend maps each species to its corresponding symbol. The visualization loads the dataset from a remote CSV file using `d3.csvParse` and renders the plot using D3 v7 scales, axes, and symbol generators.

Ssaipaidipalli

3task_activity_6

This example displays an interactive scatter plot of the Iris dataset, mapping sepal length to the x-axis and petal length to the y-axis, with species distinguished by different point shapes. It loads data asynchronously from a CSV file hosted on GitHub, parsing it with `csvParse` and converting numeric fields. The visualization uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and `axisLeft`/`axisBottom` to render scales, axes, and a custom shape legend. The code demonstrates modular ES6 imports, separating scatter plot, axis, and legend logic into distinct local modules.

Aazeemrm630@gmail.com

Llikhitha.sarahah01@gmail.com

Activity6_Task3.2

This example demonstrates a scatter plot of the Iris dataset, mapping sepal length against petal length and distinguishing species by shape. It loads data from a CSV file and uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and `csvParse`. The visualization includes custom axes and a shape legend, built with reusable local ES6 modules.

Rrakshithareddy217@gmail.com

Activity6_Task3.2

This example demonstrates a scatter plot of the classic Iris dataset, showing sepal length against petal length, with each point’s shape indicating its species. It uses D3 v7 with the `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and `symbol` APIs to create the plot and a custom shape legend. The visualization loads the CSV data via a remote URL, parses it with `csvParse`, and renders the chart using an SVG canvas. The code organizes chart components into local ES6 modules for axes, scatter plot, and legend, imported through `import` statements.