Skip to content

Instantly share code, notes, and snippets.

View jensgrubert's full-sized avatar

Jens Grubert jensgrubert

View GitHub Profile
@jensgrubert
jensgrubert / README.md
Created January 7, 2014 09:24
Positioning Technologies Parallel Coordinates
View README.md
@jensgrubert
jensgrubert / README.md
Last active December 31, 2015 05:49 — forked from billdwhite/demo_treemap_04_wrappinglabels.html
Treemap with labels
View README.md

Treemap with internal labels.

@jensgrubert
jensgrubert / Readme.md
Last active January 14, 2016 00:19 — forked from billdwhite/index.html
Treemap with changing content.
View Readme.md

Treemap with changing content showing context-sensitive AR paper.

@jensgrubert
jensgrubert / README.md
Last active January 7, 2019 19:02 — forked from mbostock/.block
D3.js Boxplot with Axes and Labels
View README.md

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
@jensgrubert
jensgrubert / README.md
Last active December 30, 2015 05:59 — forked from mbostock/.block
Simple Bar Chart
View README.md
@jensgrubert
jensgrubert / README.md
Last active April 18, 2017 18:39 — forked from mbostock/.block
Histogram and Kernel density estimation
View README.md

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