Graphics with the mosaic package
Randall Pruim
August, 2014
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.
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)## Loading required package: gridExtramod <- lm( width ~ length * sex, data=KidsFeet )
mplot(mod, which=1:7, multiplot = TRUE, ncol=2)## TableGrob (4 x 2) "arrange": 8 grobs
## z cells name
## 1 1 (1-1,1-1) arrange
## 2 2 (1-1,2-2) arrange
## 3 3 (2-2,1-1) arrange
## 4 4 (2-2,2-2) arrange
## 5 5 (3-3,1-1) arrange
## 6 6 (3-3,2-2) arrange
## 7 7 (4-4,1-1) arrange
## 8 8 (4-4,2-2) arrange
## grob
## 1 lattice[GRID.lattice.39]
## 2 lattice[GRID.lattice.40]
## 3 lattice[GRID.lattice.41]
## 4 lattice[GRID.lattice.42]
## 5 lattice[GRID.lattice.43]
## 6 lattice[GRID.lattice.44]
## 7 lattice[GRID.lattice.45]
## 8 model: lm(formula = width ~ length * sex, data = KidsFeet) \nmplot(mod, which=1:7, system="ggplot", ncol=2)## TableGrob (4 x 2) "arrange": 8 grobs
## z cells name
## 1 1 (1-1,1-1) arrange
## 2 2 (1-1,2-2) arrange
## 3 3 (2-2,1-1) arrange
## 4 4 (2-2,2-2) arrange
## 5 5 (3-3,1-1) arrange
## 6 6 (3-3,2-2) arrange
## 7 7 (4-4,1-1) arrange
## 8 8 (4-4,2-2) arrange
## grob
## 1 gtable[layout]
## 2 gtable[layout]
## 3 gtable[layout]
## 4 gtable[layout]
## 5 gtable[layout]
## 6 gtable[layout]
## 7 gtable[layout]
## 8 model: lm(formula = width ~ length * sex, data = KidsFeet) \nmplot(mod, which=7)## TableGrob (2 x 1) "arrange": 2 grobs
## z cells name
## 1 (1-1,1-1) arrange
## main 2 (2-2,1-1) arrange
## grob
## lattice[GRID.lattice.353]
## main model: lm(formula = width ~ length * sex, data = KidsFeet) \nmplot(mod, which=7, rows=-1)## TableGrob (2 x 1) "arrange": 2 grobs
## z cells name
## 1 (1-1,1-1) arrange
## main 2 (2-2,1-1) arrange
## grob
## lattice[GRID.lattice.354]
## main model: lm(formula = width ~ length * sex, data = KidsFeet) \nmplot(mod, which=7, rows=c("sexG", "length", "length:sexG"),
title="Custom titles are supported")## TableGrob (2 x 1) "arrange": 2 grobs
## z cells name grob
## 1 (1-1,1-1) arrange lattice[GRID.lattice.355]
## main 2 (2-2,1-1) arrange Custom titles are supportedmod <- 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
## 7.041437L( length=15, sex="G")## 1
## 6.654868xyplot(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 settingsplotFun( 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)## Warning in inferTransformation(formula(object)): You may need to specify
## transformation to get the desired results.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) = 0.0912
## P(X > 80) = P(Z > -1.333) = 0.9088
## [1] 0.09121122xpnorm(c(80,120), mean=100, sd=15)##
## If X ~ N(100,15), then
##
## P(X <= 80) = P(Z <= -1.333) = 0.0912
## P(X <= 120) = P(Z <= 1.333) = 0.9088
## P(X > 80) = P(Z > -1.333) = 0.9088
## P(X > 120) = P(Z > 1.333) = 0.0912
## [1] 0.09121122 0.90878878pdist("chisq", 4, df=3)
## [1] 0.7385359pdist("f", 3, df1=5, df2=20)
## [1] 0.9647987qdist("t", c(.025, .975) , df=5)
## [1] -2.570582 2.570582histogram( ~ 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.
# we need to get state names into the data frame and then fix two of them with
# wrong state abbreviations. Then we are ready to make maps
sAnscombe <- car::Anscombe %>%
group_by(state = rownames(car::Anscombe)) %>%
summarise(income = sum(income)) %>%
mutate(state = standardName(state, c(IO = "IA", KA = "KS"), quiet=TRUE))
mUSMap(sAnscombe, key="state", fill="income")## Mapping API still under development and may change in future releases.
mUSMap(sAnscombe, key="state", fill="income", style="real") ## Mapping API still under development and may change in future releases.
# A sillier example
if (require(mapproj)) {
Countries %>% mutate( nletters = nchar(gapminder) ) %>%
mWorldMap( key="gapminder", fill="nletters") + coord_map()
} else {
Countries %>% mutate( nletters = nchar(gapminder) ) %>%
mWorldMap( key="gapminder", fill="nletters")
}## Loading required package: mapproj
## Loading required package: maps
##
## # ATTENTION: maps v3.0 has an updated 'world' map. #
## # Many country borders and names have changed since 1990. #
## # Type '?world' or 'news(package="maps")'. See README_v3. #
##
##
## Mapping API still under development and may change in future releases.## Warning in standardName(x, countryAlternatives, ignore.case =
## ignore.case, : 99 items were not translated