Skip to main content
SimilarExport ZIP
100%

Fork of Hello VizHub

✓ Published0🌍 Public
Vvaishalireddy55555@gmail.com
Last edited Apr 28, 2024
Created on Apr 28, 2024
Forked from Hello VizHub

This scatter plot visualizes the Boston Housing dataset, showing the relationship between the number of rooms and property tax rates. The visualization loads CSV data via D3's `d3.csv`, then scales both axes using `d3.scaleLinear` with `d3.extent` to determine domains. Circles are rendered with `d3.selectAll` and `.enter()`, while axes are generated using `d3.axisBottom` and `d3.axisLeft`. The chart is built with D3 v7, rendering SVG elements within a flex-centered container.

AI-generated description

An example showing the capabilities of VizHub:

  • Loads D3 via UNPKG.
  • Demonstrates use of import from "d3".
  • Demonstrates use of import from local ES6 modules.
MIT Licensed

Similar vizzes

Loading thumbnail…

Fork of Boston Housing Dataset Part I

This visualization displays four scatter plots exploring correlations in the Boston Housing Dataset, with median home value (medv) plotted against lstat, pupil-teacher ratio, age, and industrial character (indus/nox). Each plot uses color and size encodings for additional variables, revealing relationships such as the strong negative correlation between housing price and lower-status population percentage. Built with the vega-lite-api library, the code fetches CSV data via d3.csv and renders interactive SVG charts with custom tooltips, axis styling, and a dark color scheme through the vega-tooltip handler and vega-lite configuration.

Llimenzi
82% match
Loading thumbnail…

Fork of Reusable D3 Scatter Plot

This interactive scatter plot visualizes the Boston housing dataset, comparing median home values against various attributes such as room count, lower status population, and crime rate. Built with D3.js, the visualization cycles through these different x-axis variables every two seconds, dynamically updating the plot and axis labels. The chart uses SVG rendering and employs a custom, reusable scatter plot charting function that leverages D3's linear scales, extent, and axis utilities for data mapping and visual encoding.

Rranjithkmr598@gmail.com
80% match
Loading thumbnail…

Fork of Simple Scatter Plot

This scatter plot displays the relationship between house size in square feet and the number of bedrooms, using data from a Boston Houses dataset loaded as a CSV. It first shows the positive correlation between the two variables, with each point representing a house. The visualization is built with D3 v7, using `d3.csvParse` to parse the data and `d3.scaleLinear` for the x and y axes, while SVG circles are appended to render the points. Axes are drawn with `d3.axisBottom` and `d3.axisLeft`.

Sshailajat1998@gmail.com
79% match
Loading thumbnail…

Boston

A pie chart visualizes the distribution of Boston housing prices, categorized into low, medium, and high ranges from the BostonHousing CSV dataset. The SVG chart uses D3.js v5, with `d3.pie`, `d3.arc`, and `d3.scaleOrdinal` to generate colored slices and labels. Data is fetched via `d3.csv` and mapped from the `medv` column, with counts computed per category before rendering.

Mmailtomeanusha11@gmail.com
78% match
Loading thumbnail…

Interactive Scatter Plot for Boston Housing Dataset

This interactive scatter plot explores correlations among variables in the Boston Housing Dataset, with dropdown menus to select X and Y axes. Points encode Charles River proximity via color and pupil-teacher ratio via size. Hovering over legend items filters and highlights matching points by adjusting opacity (20%, 50%, 80%). Built with React and D3 v5, it uses `scaleLinear` for positioning and size, `scaleOrdinal` for color, and SVG rendering. Data is loaded via `d3.csv` from a GitHub-hosted CSV file.

Yyuzhang21
78% match
Loading thumbnail…

Boston Housing Dataset Part I

This visualization presents four scatter plots exploring relationships in the Boston Housing Dataset, focusing on median home value (medv) against variables like lower-status population percentage, pupil-teacher ratio, building age, and nitrous oxide concentration. The plots reveal correlations, such as the strong negative relationship between medv and lstat, while coloring points by additional attributes like indus or ptratio and sizing by the chas binary variable. Built with the vega-lite-api library and D3, the charts load CSV data from a GitHub Gist and render interactively with tooltips and a custom dark-themed configuration.

Yyuzhang21
77% match
Loading thumbnail…

Boston Housing Dataset Part II

This visualization explores the Boston Housing dataset by splitting it according to the `rad` variable (accessibility to highways), which clusters at values 1–8 or 24. Four scatterplots compare house price (`medv`) against `rad` and lower-status population percentage (`lstat`), with circle size encoding proximity to the Charles River (`chas`) and color indicating average room count (`rm`). Built with the `vega-lite-api` and rendered using Vega-Lite, the plots load CSV data from a Gist and are configured via a custom `config.js`. The author uses `vl.markPoint`, `vl.markSquare`, and `vl.markCircle` to distinguish the visualizations.

Yyuzhang21
77% match
Loading thumbnail…

Scatter Plot with Menus for Boston Housing Dataset

This interactive scatter plot explores correlations among variables in the Boston Housing Dataset, which includes 14 attributes such as crime rate, room count, and median home value. Users select any two variables from dropdown menus to map to the X and Y axes, while point color encodes proximity to the Charles River, size reflects pupil-teacher ratio, and 35% opacity reveals overlapping points. The visualization uses React with d3.scaleLinear and d3.scaleOrdinal for scaling, SVG for rendering, and fetches CSV data via d3.csv, with tooltips showing median home value.

Yyuzhang21
77% match