Cars Scatter Plot

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Cars Scatter Plot

✓ Published1🌍 Public

Last edited Sep 29, 2020

Created on Sep 29, 2020

This scatter plot visualizes the relationship between horsepower and weight for 392 cars from the UCI Machine Learning Repository’s Auto MPG dataset. Red semi-transparent circles are positioned using d3.scaleLinear for both axes, with d3.axisBottom and d3.axisLeft providing labeled ticks. The code loads CSV data via d3.csv, converts values to numbers, and renders the chart as SVG with d3-v5. Axis labels and a title are appended as text elements within a margin-defined group.

AI-generated description

This scatter plot shows data about cars, from UCI Machine Learning Repository: Auto MPG Data Set.

MIT Licensed

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Cars Scatter Plot

This scatter plot visualizes the relationship between horsepower and weight for 392 cars from the UCI Machine Learning Repository’s Auto MPG dataset. Red circles mark each vehicle, with axes labeled and a title displayed. The code uses D3 v5’s `csv`, `scaleLinear`, `axisLeft`, and `axisBottom` to load data, map values, and render SVG axes. Tick marks extend across the chart, while circle opacity is set in CSS for readability.

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This scatter plot visualizes the relationship between horsepower and weight for 392 cars from the UCI Machine Learning Repository’s Auto MPG data set. Red semi-transparent circles represent individual car models, with each point positioned along linear scales created with `scaleLinear`. The chart includes labeled axes, gridlines via tick marks, and a title, all rendered as SVG using d3 v5. Data is loaded asynchronously with `csv` and parsed into numbers before being passed to a render function that appends axes and circles using d3 selections.

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This scatter plot visualizes the relationship between horsepower and weight for 392 cars from the UCI Machine Learning Repository’s Auto MPG dataset. Using D3 v5, the code loads the CSV data, parses numeric fields, and renders red, semi-transparent circles via SVG. It employs `scaleLinear` with `.nice()` for axis scaling, `axisLeft` and `axisBottom` for ticks and grid lines, and appends axis labels and a title. The plot highlights the positive correlation between the two variables.

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