scagnostics-sw: Calculcate scagnostics for a scatterplot

In hadley/scagnostics: Compute scagnostics.

Description

Scagnostics summarise potentially interesting patterns in 2d scatterplot

Usage

scagnostics(x, ...)

Arguments

x	object to calculate scagnostics on: a vector, a matrix or a data.frame
...	...

Details

Current scagnostics are:

• Outlying

• Skewed

• Clumpy

• Sparse

• Striated

• Convex

• Skinny

• Stringy

• Monotonic

These are described in more detail in: Graph-Theoretic Scagnostics, Leland Wilkinson, Anushka Anand, Robert Grossman. http://www.ncdm.uic.edu/publications/files/proc-094.pdf

You can call the function with two 1d vectors to get a single vector of scagnostics, or with a 2d structure (matrix or data frame) to get scagnostics for every combination of the variables.

Author(s)

Hadley Wickham <[e-mail]>

Examples

scagnostics(1:10, 1:10)
scagnostics(rnorm(100), rnorm(100))
scagnostics(mtcars)
scagnostics(as.matrix(mtcars))

if (require(rggobi)) ggobi(scagnostics(mtcars))

hadley/scagnostics documentation built on May 17, 2019, 12:46 p.m.