Parallel Coordinates plot for Positioning Technologies. Implementation based on http://mbostock.github.io/d3/talk/20111116/iris-parallel.html
- Coburg, Germany
Treemap with internal labels.
Treemap with changing content showing context-sensitive AR paper.
A box-and-whisker plot with axes. Based on Mike Bostock's implementation. Instead of using individual svg elements as in Mike's implementation, here all boxplots are rendered with in one root element. This makes it easy to add axes.
Further differences between the two implementations are:
- visibility of boxplot labels can be switched with the labels variable
- CSV files are supported in which each column is an independent variable and each row contains measurements for all variables (see data.csv)
- transitions are not used here but can be easily added again
This simple bar chart is constructed from a TSV file storing the frequency of letters in the English language. The chart employs conventional margins and a number of D3 features:
- d3.tsv - load and parse data
- d3.format - format percentages
- d3.scale.ordinal - x-position encoding
- d3.scale.linear - y-position encoding
- d3.max - compute domains
- d3.svg.axis - display axes
Based on the example by Mike Bostock.
Kernel density estimation is a method of estimating the probability distribution of a random variable based on a random sample. In contrast to a histogram, kernel density estimation produces a smooth estimate. The smoothness can be tuned via the kernel’s bandwidth parameter. With the correct choice of bandwidth, important features of the distribution can be seen, while an incorrect choice results in undersmoothing or oversmoothing and obscured features.
This example shows a histogram and a kernel density estimation for times between eruptions of Old Faithful Geyser in Yellowstone National Park, taken from R’s
faithful dataset. The data follow a bimodal distribution; short eruptions are followed by a wait time averaging about 55 minutes, and long eruptions by a wa