Untitled

Untitled

This scatterplot uses the D3.js library to visualize the relationship between horsepower and weight for a collection of cars from the UCI Machine Learning Repository’s Auto MPG data set. Each red, semi-transparent circle represents a single car model. The D3 code loads the CSV data, applies linear scales to both axes, and renders the points within an SVG, while also adding labeled axes and a title to the chart.

Jjhcliu19

84% match

Cars Scatter Plot

This scatter plot visualizes automobile data from the UCI Machine Learning Repository’s Auto MPG dataset, displaying relationships between vehicle attributes. Built with D3 v5, the SVG-based visualization uses `d3.scaleLinear` for axes and `d3.scaleOrdinal` for color encoding. The code renders grouped bar charts from CSV data rather than traditional scatter points, with `d3.scaleBand` handling categorical month groupings and `d3.axisLeft` and `d3.axisBottom` constructing the chart’s axes.

84% match

Cars Scatter Plot

This scatter plot displays automobile data from the UCI Machine Learning Repository's Auto MPG data set. The visualization uses D3 version 5 to render SVG circles, with a red, semi-transparent fill, within a fixed 960×500 pixel canvas. The code defines linear scales for both axes, though the current implementation only maps population data from a separate CSV file, which is not the car data mentioned in the title. Axis labels and ticks are styled via CSS, and a title is included, though the visualization appears incomplete.

84% match

Cars Scatter Plot

This scatter plot visualizes car data from the UCI Machine Learning Repository's Auto MPG Data Set, showing the relationship between vehicle attributes. The visualization uses D3.js version 5 with an SVG rendering approach, employing `d3.scaleLinear`, `d3.scaleBand`, and `d3.scaleOrdinal` to map the data. The code reads data from a CSV file and creates grouped bar charts for each month, with colors distinguishing different telescope measurements. Axes are rendered using `axisLeft` and `axisBottom` functions, with styling applied through a linked CSS file.

Cars Scatter Plot

This scatter plot visualizes automobile data from the UCI Machine Learning Repository’s Auto MPG dataset, displaying relationships between vehicle attributes. Red, semi-transparent circles represent individual cars, with overlapping points revealing density patterns. The visualization uses D3 v5’s `csv` loader, `scaleLinear`, `extent`, and axis functions (`axisLeft`, `axisBottom`) to map data values to SVG coordinates. Styled axes and labels with large, muted typography enhance readability, while the `format` utility handles numeric tick labels. The chart is rendered entirely in SVG within a fixed 960×500 viewport.

Untitled

This animated scatter plot explores the relationships between various attributes of cars from the UCI Auto MPG dataset. Dropdown menus let users select different columns for the x- and y-axes, with circles animating from the center to their new positions over 2 seconds. Built with D3.js, it uses `scaleLinear` for axes, `axisBottom` and `axisLeft` for ticks, and `transition().duration().delay()` for the animation effect. The visualization is rendered as an HTML page with Plotly’s `newPlot` for an initial static chart.

Jjesramyep1263

Reuablescatterplot

This interactive scatterplot visualizes automobile data from the UCI Machine Learning Repository’s Auto MPG dataset. Users select which numerical attributes to compare via two dropdown menus, which re-render the chart. Built with D3 v5, the visualization uses `scaleLinear`, `axisLeft`, `axisBottom`, and animated circle transitions. SVG rendering displays data points for attributes like horsepower, weight, and acceleration, with labeled axes and gridlines. The code structures reusable dropdown and scatterplot components, enabling dynamic exploration of relationships between any two selected data columns.

SSwapna1915

Cars Scatter Plot

This scatter plot visualizes car data from the UCI Machine Learning Repository’s Auto MPG dataset, displayed as red semi-transparent circles on an SVG canvas. The chart maps quantitative attributes to the x- and y-axes using D3 v5’s `scaleLinear`, `extent`, and `axisBottom`/`axisLeft` functions, with labeled tick marks and an axis label. Data is loaded via `d3.csv` and rendered with `select` and `svg` elements, styled through CSS for opacity and typography. The code sets a fixed 960×500 pixel SVG and uses only the imported D3 modules for scaling and axes.