Scatterplot

LLikhitaMudagereShivaraj@my.unt.edu

Scatter Plot

This scatter plot visualizes the classic Iris dataset, displaying sepal length against sepal width for three species of iris flowers. Each point represents a sample, color-coded by species (setosa, versicolor, virginica) using blue, green, and orange, and includes interactive tooltips that reveal exact measurements on hover. The visualization is built with D3.js v7, using `d3.csv` to load the local CSV data, `d3.scaleOrdinal` for color mapping, and `d3.scaleLinear` to map data to pixel coordinates. The SVG rendering includes labeled axes, a legend, and smooth tooltip transitions for enhanced readability.

93% match

Scatter plot

This scatter plot visualizes the Iris dataset, comparing sepal length and sepal width across 150 individual flowers. Each circle represents one sample, with its position determined by these two measured attributes while a green, semi-transparent fill makes overlapping points visible. The visualization is built using D3 v6, loaded via UNPKG, and rendered as SVG. Code imports functions like `scaleLinear`, `axisLeft`, and `axisBottom` from the D3 module to construct the axes and scales. Data is loaded from a local CSV file using D3’s `csv` method.

Wwei

93% match

Iris scatterPlot

This scatter plot visualizes the classic Iris dataset, plotting sepal width against sepal length for three species of iris flowers. Each flower is represented as a small circle, color-coded by species (setosa, versicolor, virginica) using a categorical color scale, with a legend in the upper right. The visualization uses D3 v3 to render an SVG graphic, employing `d3.scale.linear()` for the x and y axes, `d3.svg.axis()` for the axes, and `d3.scale.category10()` for coloring the dots. The data is loaded asynchronously from a tab-separated values file via `d3.tsv()`.

93% match

Iris_Scatter_Plot

This scatter plot visualizes the classic Iris dataset, showing the relationship between sepal length and sepal width for three iris species, with each species represented by a distinct color. Data is loaded from a tab-separated values file using `d3.tsv`, and the visualization is rendered as SVG circles using D3 v3. Linear scales (`d3.scale.linear`) map the quantitative axes, while `d3.svg.axis` generates the bottom and left axes with labels. A color legend identifies the species categories, and all points are drawn with a fixed radius of 3.5 pixels.

Iris_Scatter_Plot

Iris Scatter Plot

This scatter plot visualizes the Iris dataset, showing sepal length against sepal width for three species of iris flowers. The data comes from a CSV file containing measurements of 150 samples, with each species represented by a different color using D3’s scaleOrdinal and schemeSet1 palette. The visualization uses D3 v5 to load the data via csv(), create linear scales with scaleLinear, and render SVG circles with axisBottom and axisLeft for labeled axes. The chart includes gridlines and a title, with opacity applied to circles to reveal overlapping points.

Xxiplus

Iris scatterPlot

This example visualizes the classic Iris flower dataset, plotting sepal width against sepal length to reveal clusters by species. Each flower is a small circle, colored by species using `d3.scale.category10()`, with axis labels for the measurement units. The visualization loads tab-separated data from `iris.tsv`, uses `d3.scale.linear()` for x and y scales, and renders axes via `d3.svg.axis()`. A legend identifies the three species, while the SVG circles are positioned with `x()` and `y()` scales and filled based on the species value.

D3 Scatter Plot

This D3 scatter plot visualizes the classic Iris dataset, plotting sepal length against petal length for 150 flower samples. Each circle represents an individual flower, with semi-transparent fill opacity to reveal overlapping points. The visualization is rendered as SVG, using d3.scaleLinear and d3.extent to map data values to screen coordinates, with axes generated via d3.axisLeft and d3.axisBottom. Data is loaded from a local CSV file using d3.csv, and the species of each sample is visible in the data but not encoded visually in this example.

CCurran Kelleher