fresh block

100%

✓ Published0🌍 Public

Last edited May 24, 2019

Created on May 24, 2019

A stacked area chart displays the changing market share of multiple diabetes drugs over time, with each colored layer representing a drug and its thickness showing proportional share. The visualization uses a time-series x-axis and percentage-scaled y-axis to compare trends across three months of data from a CSV file. Built with D3 v4, the code employs `d3.stack()` to compute layer offsets, `d3.area()` for path generation, and `d3.scaleOrdinal` with `schemeCategory10` for colors. Drug labels appear only for layers exceeding a one-percent threshold at the final date.

AI-generated description

Built with blockbuilder.org

forked from <a href='http://bl.ocks.org/GerardoFurtado/'>GerardoFurtado</a>'s block: <a href='http://bl.ocks.org/GerardoFurtado/6091af6a1e44068b8f9d26d641fcb5d1'>fresh block</a>

mit Licensed

Similar vizzes

Loading thumbnail…

fresh block

This interactive stream graph visualizes the monthly market share of various diabetes drugs over several months. The stacked area chart displays each drug’s proportional contribution over time, using a time-series layout with a filled area for each category. The code leverages D3.js v4 APIs including `d3.stack()`, `d3.area()`, and `d3.scaleTime()`, processing data from a CSV file. It renders as SVG, with axes and labels, and uses a categorical color scheme to distinguish drugs.

GGerardoFurtado

94% match

Loading thumbnail…

fresh block

This visualization displays the daily market share of web browsers over a three-month period using a stacked area chart. The chart encodes each browser as a colored layer, with the y-axis showing percentage of total usage and the x-axis showing time. Built with D3 v4, it uses `d3.stack()` to stack the data series, `d3.area()` to construct the path geometries, and `d3.scaleTime()` and `d3.scaleLinear()` for the axes. The data is loaded from a TSV file, and category labels are positioned at the right edge of the chart for the larger share layers.

AAsilve00

79% match

Loading thumbnail…

fresh block

A stacked area chart displays how four unnamed data categories change over time, with each layer labeled at the right edge and a y-axis formatted as a percentage. The code loads tab-separated data via `d3.tsv`, parses dates with `d3.timeParse`, and builds the visualization using `d3.stack`, `d3.area`, `d3.scaleTime`, `d3.scaleLinear`, and `d3.scaleOrdinal` with `d3.schemeCategory10`. SVG rendering and axes are created with `d3.axisBottom` and `d3.axisLeft`.

AAsilve00

78% match

Loading thumbnail…

fresh block

This visualization gallery entry presents a suite of D3.js charts, each embedded via external scripts and configured through data attributes. It shows multiple interactive views of 2018 data, including regional comparisons, partner breakdowns, and beneficiary analyses, with toggles for sorting and average lines. The code leverages D3.js for rendering, with responsive layouts and lazy loading, while the source data is fetched from linked gist-hosted scripts.

GGerardoFurtado

74% match

Loading thumbnail…

fresh block

The visualization shows a time-series area chart of stock prices, with the area colored red for values above 150 and blue for values below 150, highlighting threshold crossings. It uses D3.js (v3) with `d3.svg.area()`, `d3.time.scale()`, and `d3.svg.axis()` to render the SVG. Data is loaded from an external TSV file and parsed with `d3.time.format`. The code splits the dataset into contiguous segments based on the price threshold, then draws each segment as a separate colored area path. Axes are added for time and price, with the y-axis labeled “Price ($)”.

AAaronWitter

73% match

Loading thumbnail…

fresh block

A monthly time-series line chart displays a dataset of twelve alphabetical categories (A–L) with associated values. The visualization uses an SVG line and red circular markers to trace value changes over time. Built with D3.js v3, it employs ordinal and linear scales, a line generator, and custom mouseover/mouseout interactions that highlight data points with blue fills, dashed vertical guide lines, and enlarged value labels. The chart reads from an external JSON file, rendering axis labels as text elements and supporting smooth transitions during hover effects.

BBrunoDumas

72% match

Loading thumbnail…

fresh block

A stacked bar chart displays the causes of mortality—wounds, other, and disease—among a historical population over several months in 1855–1856. The visualization uses a horizontal orientation, with each bar segmented by cause and colored via a categorical scale. Built with D3 v4, it employs `d3.stack()` to compute layer positions, `d3.scaleBand` for the time-based y-axis, and `d3.axisBottom` and `d3.axisLeft` for axes. Data is parsed with `d3.timeParse` and rendered as SVG rectangles.

KKacieW

72% match

Loading thumbnail…

Untitled Document

This example visualizes the distribution of drug categories and scores from a CSV dataset as an animated, interactive bar chart. Using D3 v4 with SVG rendering, the chart displays grouped bars for each drug category, with the bars transitioning smoothly when toggling between views. The data, loaded from drug1.csv, maps the s_category values on the x-axis and drug_cat values on the y-axis, with the animation providing a dynamic comparison of the categories. The visualization is built with D3.js and rendered as scalable vector graphics, allowing for crisp, responsive bars that update with user interaction.

GGerardoFurtado

72% match