Activity_6_Task3

Activity6_Task3.1

This scatter plot visualizes the Iris dataset, showing sepal length versus petal length for three flower species. The visualization uses d3.v7 with scaleLinear for axes, scaleOrdinal with symbolsFill to encode species as different point shapes, and a custom legend. Data loads asynchronously from a GitHub-hosted CSV file, parsed with csvParse, with axes and shape legends created via reusable D3 modules. The plot updates automatically when the data arrives.

SSowjanya Devupalli

Vvandanakosuri@my.unt.edu

Activity-6(Task3(1))

This scatter plot visualizes the Iris dataset, displaying sepal length on the x-axis and petal length on the y-axis, with each point’s shape representing the species of iris. The implementation uses D3 v7’s `scaleLinear`, `scaleOrdinal`, and `symbolsFill` to map data values to positions and shapes, while `axisLeft` and `axisBottom` render the axes. A custom legend, built with `symbol().type()`, identifies each species by its corresponding shape. Data is loaded asynchronously from a CSV file and parsed using `csvParse`.

Kkowshiksamudrala08@gmail.com

Activity6_Task3-2

This scatter plot visualizes the Iris dataset, mapping sepal length on the x-axis and petal length on the y-axis, with species encoded by different symbol shapes. The visualization loads the data from a remote CSV file using `d3.csvParse` and renders it using D3 v7. It employs `scaleLinear` for axes, `scaleOrdinal` for shape mapping, and `axisLeft`/`axisBottom` for tick marks. A custom shape legend, built with `d3.symbol`, displays the species labels.

Activity-6 Task3.1

This scatter plot visualizes the Iris dataset, showing sepal length against petal length for three flower species. Each species is distinguished by a distinct symbol shape, with a legend explaining the mapping. The visualization uses D3 v7's `scaleLinear`, `scaleOrdinal`, and `symbol` APIs to position points and generate shapes, with axes created via `axisLeft` and `axisBottom`. Data is loaded asynchronously from a CSV file and parsed with `csvParse`.

Hharshaps15@gmail.com

Activity6_Task3.2

This scatter plot visualizes the Iris dataset, mapping sepal length to the x-axis and petal length to the y-axis, with species indicated by different filled symbols. The chart includes a shape legend and labeled axes, rendered using D3 v7 with a custom scatterplot module that relies on `scaleLinear` for the axes and `scaleOrdinal` for the symbol mapping. Data is loaded asynchronously from a CSV file, and the visualization updates dynamically as the data state transitions from loading to parsed.

Ssindhuja27b@gmail.com

Activity6_Task3.2

This scatter plot visualizes the Iris dataset, mapping sepal length against petal length for individual specimens. Data points are rendered as distinct shapes, with a legend denoting the species of each flower. The visualization employs the D3.js library, utilizing its `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and `axisLeft`/`axisBottom` components. It reads the dataset from a remote CSV file, parses it with `csvParse`, and constructs the chart using a custom React framework, with axes created via the `axes` module.

JJhansi Balla

Aakitisaitejareddy@gmail.com

Activity-6_task3.2

This scatter plot visualizes the Iris dataset, showing sepal length against petal length, with each of the three species represented by a distinct symbol shape and a corresponding legend. The visualization uses d3.scaleLinear for the x and y axes, d3.scaleOrdinal with d3.symbolsFill for species-based shapes, and d3.axisLeft and d3.axisBottom for the axes. Data is loaded asynchronously via fetch and parsed with d3.csvParse.

Activity6_Task3.2

This scatter plot visualizes the Iris dataset, showing sepal length against petal length, with each of the three species represented by a distinct symbol. The visualization uses D3 version 7 to render SVG elements, employing `scaleLinear` for axes and `scaleOrdinal` with `symbolsFill` to map species to different point shapes. Data is loaded asynchronously through a CSV file fetch, then parsed with `csvParse` before being displayed with custom axis and shape legend components.

GGunna,Praneeth Reddy