Untitled Document

Untitled Document

This animated bar chart visualizes the distribution of drug categories across survey responses. The visualization loads a CSV dataset containing two columns—s_category and drug_cat—and renders a grouped bar chart using D3 v4 with SVG. The chart animates between two states, likely comparing the frequency of drug categories by s_category groups. The bars are color-coded and transition smoothly to reflect the data, with axes and labels supporting readability. The animation highlights changes in the dataset, making it easy to compare category distributions dynamically.

94% match

Untitled Document

This interactive D3.js visualization displays a grouped bar chart showing the frequency of drug categories (1-6) across different survey categories (2-5), with each group further broken down into colored sub-bars. The chart uses d3.scaleBand for the grouped axes, d3.scaleOrdinal for color encoding, and d3.nest for hierarchical data aggregation. Bars animate from zero to their final heights using randomized delays with SVG transitions. The visualization is built on D3 v4 and Bootstrap, reading data from a local CSV file.

87% match

Untitled Document

This example shows a grouped bar chart of drug counts across five symptom categories (s_category), with each group subdivided into six drug categories (drug_cat) and colored accordingly. The chart animates bar heights from zero to their final values using D3’s `transition.attr` with random delays, and bars darken on mouseover. It loads data from `drug.json` via `d3.json`, nests records by symptom category using `d3.nest`, and builds the SVG with `d3.scaleBand` for both axes and `d3.scaleOrdinal` for color mapping.

84% match

Data Visualization with D3

This example displays a bar chart of categorical data from a CSV file, comparing numeric values across named categories. The visualization uses an SVG canvas with axes, mapping the data's numeric values to horizontal bar lengths and category names to vertical positions. The implementation relies on D3 v5 utilities for data loading via `d3.csv`, linear scaling with `scaleLinear`, and axis generation with `axisLeft` and `axisBottom`, while the chart is rendered as SVG rectangles within a React-based HTML page.

Ssvensonneveld

76% match

Untitled

This example shows a grouped bar chart comparing values across three categories for multiple groups, with each category represented by a colored bar. The visualization uses D3.js v4 to load data from a remote CSV file, which is parsed with `d3.csv`. Bars are rendered as SVG rectangles, with an ordinal color scale mapping categories to red, blue, and green. The X-axis uses `d3.scaleBand` for group positioning, while a nested subgroup scale positions bars within each group, and a linear scale maps values to the Y-axis.

CCo10

D3: Loading data from a CSV file

This example demonstrates how D3.js loads data from a CSV file and renders an interactive bar chart. It shows the “Deliciousness” ratings for different foods, with radio buttons to switch between two market groups (A and B). The visualization uses D3 v4’s `d3.csv` to parse the external `food.csv`, then updates the chart via a `redraw` function that recomputes the band and linear scales (`d3.scaleBand`, `d3.scaleLinear`). SVG rectangles are bound to the data, and axes are generated with `d3.axisBottom` and `d3.axisLeft`. The interaction relies on radio-button change events to filter the dataset and trigger transitions.

Customizing Axes

This example visualizes the count of useful drug names using horizontal bars. It customizes the axes by removing tick lines and domains, applying a custom tick format that replaces 'G' with 'B', and styling tick labels, axis labels, and the title with CSS. D3 v5’s `scaleLinear`, `scaleBand`, `axisLeft`, and `axisBottom` are used, with the chart drawn as SVG. The data comes from a CSV of drug statistics linked in the code.

Aaishwarya8615

Test

A bar chart displays count values from a CSV file across a categorical axis. The chart uses D3 v4 to load the data with `d3.csv`, scales the counts with `d3.scaleLinear` and the category positions with `d3.scaleBand`, and renders axes via `d3.axisBottom` and `d3.axisRight`. The SVG plot is positioned with margins, and the code includes calls to `getBoundingClientRect` to determine the available space. The visualization is built as a static SVG graphic with no interactive features.

GGoomble