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For a long time, people have been sending me requests for a suitable smoothing / contouring / interpolation geometry be made available via ggtern, over and above the Kernel Density function. I am very pleased to say, that the recent version 1.0.6 has this feature added. Let me demonstrate how it works.

This geometry & stat required an additional mapping, to ‘value’, in order to conduct the interpolation, which by default is done using multivariate linear regression as the mechanism for the interpolation:

#Start by setting up the environment
library(ggtern)
data(Feldspar)
theme_base <-  function(){
list(
theme_bw(),
theme(legend.position      = c(0,1),
legend.justification = c(0,1)))
}

#Demonstrate the simplest example
plot <- ggtern(Feldspar,aes(x=Or,y=An,z=Ab)) +
geom_interpolate_tern(aes(value=P.Gpa,color=..level..)) +
geom_point() +
theme_base() + labs(color="P/Gpa Model")
plot


There are plenty of options to tailor how the interpolation gets done, lets reduce the spacing between the contours, and add some more vibrant colours:

#Demonstrate the use of some options
plot <- ggtern(Feldspar,aes(x=Or,y=An,z=Ab)) +
geom_interpolate_tern(aes(value= P.Gpa,fill=..level..),
colour   = "white",
formula  = value~poly(x,y,
degree=2,
raw=TRUE),
method   = "lm",
binwidth = 25,
buffer   = 1.5,
n        = 200) +
geom_point() +
theme_base() +
labs(fill="P/GPa Model")
plot


For further information, see the help file, ?stat_interpolate_tern or ?geom_interpotate_tern

The post Ternary Interpolation / Smoothing appeared first on ggtern: ternary diagrams in R.