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Gist 74232f38b06638bb7acb4205ebf3b216

✓ Published0🌍 Public
FFerjopero
Last edited Jul 27, 2018
Created on Jul 27, 2018

This visualization, from a GitHub gist by Ferjopero, uses the classic Iris dataset to display a scatter plot of sepal length against sepal width. Points are colored by species—setosa, versicolor, and virginica—revealing distinct clusters. The chart is rendered using an interactive plotting library, allowing viewers to explore the relationships between these two measurements across the three species.

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Gist 0bd164d320049d1926cdb6310644fa40

This visualization uses the classic Iris dataset to display the relationships between sepal and petal measurements for three flower species. The code loads the CSV data and renders a scatter plot that maps sepal length and width on the axes, with each point colored by species. The implementation relies on the Plotly.js library's `Scatter` and `Layout` classes to generate the interactive chart, with species values driving both the color and legend entries for the setosa, versicolor, and virginica groups.

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This scatterplot visualizes the Iris flower dataset, plotting sepal width against sepal length, with each point colored by species (setosa, versicolor, or virginica). The chart is built with D3.js, using `d3.scale.linear` for the axes and `d3.svg.axis` for rendering tick marks and labels. Data is loaded from a CSV file via `d3.csv`, and points animate upward from the bottom of the chart using chained transitions with staggered delays. Hovering reveals a tooltip with the species and measurements via appended SVG `title` elements.

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This scatter plot visualizes the classic Iris dataset, displaying sepal width along the x-axis and sepal length along the y-axis. Each data point is a filled circle, with color encoding the flower species (setosa, versicolor, or virginica). The chart is rendered using D3.js, with axes drawn via the `d3.conventions()` helper from the d3-starterkit. A tooltip, enabled through `d3.attachTooltip`, appears on hover to show additional details for each point.

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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.

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This interactive visualization uses the Iris dataset to explore relationships between sepal and petal measurements. Two scatter plots are shown side by side, each displaying a different pair of attributes (sepal length vs. sepal width, and petal length vs. petal width). Points are colored by species. A distinctive feature is that brushing (clicking and dragging to select a region) in one plot automatically zooms the other plot to the corresponding points, enabling linked exploration of the dataset. The visualization is built with D3 v3, renders in SVG, and loads the data from a CSV file with metadata describing variable types and labels.

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This example visualizes the classic Iris dataset, plotting sepal length against sepal width for three iris species. Built with D3 v4, the scatterplot uses SVG shapes to encode species: circles for setosa, red squares for versicolor, and black squares for virginica. A square-root scale controls point size, and hover tooltips display species names. Animated transitions and a categorical color legend complete the chart, which reads directly from iris.csv.

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1

This interactive scatter plot visualizes the classic Iris dataset, displaying sepal length on the x-axis and petal length on the y-axis. Each point represents an individual flower, colored by its species (setosa, versicolor, or virginica) according to a categorical color legend. The visualization is built with D3.js, using `scaleLinear` for axes, `scaleOrdinal` with `schemeCategory10` for colors, and modular components for drawing axes and the color legend. The code leverages D3's selection joins for efficient SVG element updates.

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