Graphics with the mosaic and lattice packages

In mosaic: Project MOSAIC Statistics and Mathematics Teaching Utilities

library(mosaic)
library(mosaicData)
library(NHANES)
library(gridExtra)

# Some customization.  You can alter or delete as desired (if you know what you are doing).

# This changes the default colors in lattice plots.
trellis.par.set(theme=theme.mosaic())  

# knitr settings to control how R chunks work.
require(knitr)
set.seed(1)
opts_chunk$set(
  tidy=FALSE,     # display code as typed
  size="small",   # slightly smaller font for code
  fig.width=5,
  fig.height=3
)

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This way of doing this is largely superceded by our ggformula package which provides a formula interface to ggplot2.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)

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

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")
L( length=15, sex="G")
xyplot(width ~ length, groups = sex, data = KidsFeet, auto.key=TRUE)
plotFun( L(length, sex="B") ~ length, add=TRUE, col=1 )
plotFun( L(length, sex="G") ~ length, add=TRUE, col=2 )

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

pdist("chisq", 4, df=3)
pdist("f", 3, df1=5, df2=20)
qdist("t", c(.025, .975) , df=5)

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") 

Looks like it is safer to live in the North:

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

Here is a sillier example

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

mosaic documentation built on Nov. 10, 2023, 1:11 a.m.