Graphics with the mosaic and lattice packages

This vignette is simply a suite of plots that exist primarily as part of our quality control for the package. But since the examples might be useful to others as well, we’ve added this as a vignette in the package.

This way of doing this is largely superceded by our ggformula package which provides a formula interface to ggplot2. You might also like to see the vignette that compares using lattice to using ggformula.

lattice extras

The mosaic package resets the default panel function for histograms. This changes the default for bin selection and provides some additional arguments to histogram.

histogram(~ rbinom( 500, 20, .3), width=1, fit="normal", v=c(6,10), h=0.1 )

ladd()

ladd() provides a relatively easy way to add additional things to a lattice graphic.

xyplot( rnorm(100) ~ rnorm(100) )
ladd( grid.text("Here is some text", x=0, y=0, default.units="native") )
ladd( panel.abline( a=0, b=1, col="red", lwd=3, alpha=.4 ) )
ladd( panel.rect(x=-1, y=-1, width=1, height=1, col="gray80", fill="lightsalmon"))
ladd( panel.rect(x=0, y=0, width=2, height=2, col="gray80", fill="lightskyblue"), 
      under=TRUE)

mplot()

In addition to the interactive uses of mplot(), it can be used in place of plot() in several settings.

require(gridExtra)
mod <- lm(width ~ length * sex, data = KidsFeet)
mplot(mod, which = 1:7, multiplot = TRUE, ncol = 2)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

mplot(mod, which=1:7, system="ggplot", ncol=2)

## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

mplot(mod, which=7)

mplot(mod, which=7, rows=-1)

mplot(mod, which=7, rows=c("sexG", "length", "length:sexG"), 
      title="Custom titles are supported")

mod <- lm(age ~ substance, data=HELPrct)
mplot(TukeyHSD(mod))

mplot(TukeyHSD(mod), system="ggplot")

plotFun() and makeFun()

mod <- lm(width ~ length* sex, data = KidsFeet)
L <- makeFun(mod)
L( length=15, sex="B")

##        1 
## 7.041437

L( length=15, sex="G")

##        1 
## 6.654868

xyplot(width ~ length, groups = sex, data = KidsFeet, auto.key=TRUE)
plotFun( L(length, sex="B") ~ length, add=TRUE, col=1 )

## converting numerical color value into a color using lattice settings

plotFun( L(length, sex="G") ~ length, add=TRUE, col=2 )

## converting numerical color value into a color using lattice settings
## converting numerical color value into a color using lattice settings

For logistic regression, makeFun() handles the conversion back to probabilities by default.

mod <- glm( SmokeNow =="Yes" ~ Age + Race3, data=NHANES, family=binomial())
SmokerProb <- makeFun(mod)
xyplot( SmokeNow=="Yes" ~ Age, groups=Race3, data=NHANES, alpha=.01, xlim=c(20,90) )
plotFun(SmokerProb(Age, Race3="Black") ~ Age, col="black", add=TRUE)
plotFun(SmokerProb(Age, Race3="White") ~ Age, col="red", add=TRUE) 
ladd(grid.text("Black", x=25, y=SmokerProb(25, Race="Black"),hjust = 0, vjust=-0.2,
               gp=gpar(col="black"),
               default.units="native"))
ladd(grid.text("White", x=25, y=SmokerProb(25, Race="White"),hjust = 0, vjust=-0.2,
               gp=gpar(col="red"),
               default.units="native"))

f <- makeFun(sin(x) ~ x)
plotFun( f(x) ~ x, xlim = c( -2 * pi, 2 * pi) )

plotFun( x * sin(1/x) ~ x, xlim=c(-1,1) )

plotFun( x * sin(1/x) ~ x, xlim=c(-1,1), npts=10000 )

Visualizing distributions

plotDist("chisq", df=3)

plotDist("chisq", df=3, kind="cdf")

xpnorm(80, mean=100, sd=15)

##

## If X ~ N(100, 15), then

##  P(X <= 80) = P(Z <= -1.333) = 0.09121

##  P(X >  80) = P(Z >  -1.333) = 0.9088

##

## [1] 0.09121122

xpnorm(c(80,120), mean=100, sd=15)

##

## If X ~ N(100, 15), then

##  P(X <=  80) = P(Z <= -1.333) = 0.09121  P(X <= 120) = P(Z <=  1.333) = 0.90879

##  P(X >   80) = P(Z >  -1.333) = 0.90879  P(X >  120) = P(Z >   1.333) = 0.09121

##

## [1] 0.09121122 0.90878878

pdist("chisq", 4, df=3)

## [1] 0.7385359

pdist("f", 3, df1=5, df2=20)

## [1] 0.9647987

qdist("t", c(.025, .975) , df=5)

## [1] -2.570582  2.570582

histogram( ~ rbinom(1000, 20, .4), width=1, v=20 * .4 )

SD <- sqrt(20 * .4 * .6)
plotDist("norm", mean=.4*20, sd=SD, add=TRUE, alpha=.7)

plotDist("norm", col="blue", mean=2, xlim=c(-4,8))
plotDist("norm", mean=5, col="green", kind='histogram', add=TRUE)  # add, overtop
plotDist("norm", mean=0, col="red", kind='histogram', under=TRUE)  # add, but underneath!

Maps

The mosaic package now provides facilities for producing choropleth maps. The API is still under developement and may change in future releases.

mUSMap(USArrests %>% mutate(state = row.names(.)), key="state", fill = "UrbanPop")

## Mapping API still under development and may change in future releases.

Looks like it is safer to live in the North:

mUSMap(USArrests %>% mutate(state = row.names(.)), key="state", fill = "Murder")

## Mapping API still under development and may change in future releases.

Here is a sillier example

Countries %>% mutate(nletters = nchar(gapminder)) %>%
  mWorldMap(key="gapminder", fill="nletters")

## Mapping API still under development and may change in future releases.