Lattice Plotting System in R

Few things to remember about Lattice plotting system in R.

1. It is an implementation of Trellis graphics.
2. Unlike base plotting system, all the plotting and annotations are done by calling a single function.
3. They are great for representing multivariate/conditional data.
4. xyplot() is the main function in this system. However, like plot() function it lacks the flexibility of creating different plot types.
5. While lattice plotting system doesn’t have elegance of ggplot, I must say it’s really easy to learn.
6. Margins and spacing are automatically handled.
7. Panel functions can be used to control each of the panels.

Let’s learn by examples:

Example 1: Exploring European Stock Indices(Germany DAX , Switzerland SMI, France CAC, and UK FTSE) from 1991-1998 in EuStockMarkets  data set. The data are sampled in business time. Let’s visualize it!

xyplot(EuStockMarkets,layout=c(2,2),ylim=c(0,max(EuStockMarkets)))

Note: If you want one single column containing four plots then layout=c(1,4) will do the trick.

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Example 2: Visualizing Fuel Economy vs. Performance from mtcars data set.

myfactors <-factor(mtcars$cyl, levels=c(4,6,8),  labels=c(“Four”,”Six”,”Eight”))
xyplot(mpg ~ hp | myfactors, layout=c(3,1), data=mtcars,
              pch=5,type=c(‘p’,’r’),  ## we are combining points and regression type together
              par.settings=simpleTheme(col=’brown’,col.line=’blue’), 
              main=list(label=’Fuel Economy vs. Performance’,cex=2), 
              xlab=list(label=’Horse Power’,cex=1),
              ylab=list(label=’Fuel Economy’,cex=1,col=’red’),
              scales=list(cex=.75,col=’green’,alternating=1)) ## change alternating to 2,3,4 to observe what it does

Note:  We could have also gone the panel.xyplot(), panel.lmline() route for drawing the regression line.

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Example 3: Exploring number of breaks in year during weaving from warpbreaks data set.

histogram( ~ breaks | tension*wool, data = warpbreaks,
             type = “density”, border = “transparent”, col.line = “red”,
             main= ‘Breaks in Yarn during Weaving’,
             xlab = “Type of Wool”,
             ylab = “Breaks”,scales=list(alternating=3),
             panel = function(x, …) {
             panel.histogram(x,col=’yellow’, …)
             panel.mathdensity(dmath = dnorm,
             args = list(mean=mean(x),sd=sd(x)), …)
} )
Example 4: 3D scatterplot of a mathematical function

x <- seq(-10, 10, 1)
y <- seq(-10, 10, 1)
mytable <- data.frame(expand.grid(x,y))
colnames(mytable) <- c(“x”,”y”)
mytable$z <- (myGrid$x)^2 + (myGrid$y)^2
wireframe(mytable$z ~ mytable$x * mytable$y, xlab=”X”, ylab=”Y”, zlab=”Z”,
                     main=expression(Z = x^2 + y^2),
                     shade=T,aspect = c(.8, .8),
                     light.source = c(-1,-2,3))
cloud(mytable$z ~ mytable$x * mytable$y, xlab=”X”, ylab=”Y”, zlab=”Z”)

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Example 5: Drawing Conditional Scatter Plot using airquality data set.

splom(~airquality[1:4],groups=as.factor(Month),data=airquality,pch=20,type=’p’,cex=1)

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I highly recommend exploring other functions like levelplot, contourplot, dotplot, stripplot etc.