Task-8-Tutorial-3.2

Jjyothirmaijyothi2017@gmail.com

Task8_Tutorial3.2

This scatter plot displays the Iris dataset, plotting sepal length against petal length and using different point shapes to distinguish between the three iris species. The visualization uses D3 v7 to create the plot, with a categorical shape scale assigning distinct symbols (from `symbolsFill`) to each species. Axes are rendered using D3's `axisLeft` and `axisBottom` functions, while a custom legend wizard communicates the shape-to-species mapping. Data is fetched asynchronously from a remote CSV file and parsed with `csvParse` before being passed to the chart.

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

LLakshmiPrasannaValdas@my.unt.edu

Task 8_tutorial3.2

This scatter plot visualizes the Iris dataset, comparing sepal length against petal length for different iris species. The chart encodes each data point as a distinct shape, using the `d3.symbol` API with a `symbolsFill` scale to differentiate species, and includes a shape legend generated via `shapeLegend`. The visualization uses `d3.scaleLinear` for axes and `d3.scaleOrdinal` for species mapping, with custom axis and legend utilities built on D3's selection join pattern. Data is fetched as CSV from a GitHub repository and parsed with `d3.csvParse`.

Task 8 tutorial 3.2

This scatter plot displays the relationship between sepal length and petal length for iris flowers, with species encoded through different symbol shapes. It uses D3 v7's `scaleLinear`, `scaleOrdinal`, and `symbolsFill` to map data to positions and shapes. The visualization fetches the iris dataset from a GitHub-hosted CSV file, parses it with `csvParse`, and renders axes via `axisLeft` and `axisBottom`, alongside a custom legend built with `symbol()`.

Vvchittimadha@gmail.com

Task 8 Tutorial 3_2

This visualization displays the Iris dataset as a scatter plot of sepal length against petal length, with three iris species distinguished by different point shapes and a shape legend. The plot uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `symbolsFill`, `symbol`, and `axisLeft`/`axisBottom` to render axes and markers. Data is loaded asynchronously from a CSV file using `csvParse` and fetched from a GitHub repository. The visualization adapts to the full browser window size.

YYeshwanth963@gmail.com

Sshraehithareddy@gmail.com

Task8_Tutorial3.2

This scatter plot displays the relationship between sepal length and petal length for the Iris dataset, with points colored and shaped by species. It uses D3 v7’s `scaleLinear` and `scaleOrdinal` for axes and shape encoding, while `symbol` and `symbolsFill` generate distinct point markers. Data is loaded from a remote CSV via `csvParse`.

Task8_Tutorial3.2

This visualization displays an interactive scatter plot of the Iris dataset, mapping sepal length against petal length. Each of the three iris species is represented by a distinct symbol shape, with a custom legend identifying the species categories. The implementation uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, and `symbol` to generate the marks, while `axisLeft` and `axisBottom` render the labeled axes. Data is loaded asynchronously from a CSV file via `csvParse` and rendered within a full-window SVG canvas.

KKeerthi

Vvarun.mahankali39@gmail.com

Activity6_Task3.2

This example shows a scatter plot of the Iris dataset, mapping sepal length on the x-axis and petal length on the y-axis, with points colored and shaped by species. A custom shape legend displays the species categories. The visualization uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, and `symbol` to render the marks, with axis and legend components built as reusable functions. The dataset is loaded as a CSV from a GitHub URL, parsed with `csvParse`, and numeric columns are converted before rendering. The plot updates responsively to the window size.