n <- 100
f <- function(na=FALSE) {
x <- sample(c('N', 'Y'), n, TRUE)
if(na) x[runif(100) < .1] <- NA
x
}
set.seed(1)
d <- data.frame(x1=f(), x2=f(), x3=f(), x4=f(), x5=f(), x6=f(), x7=f(TRUE),
age=rnorm(n, 50, 10),
race=sample(c('Asian', 'Black/AA', 'White'), n, TRUE),
sex=sample(c('Female', 'Male'), n, TRUE),
treat=sample(c('A', 'B'), n, TRUE),
region=sample(c('North America','Europe'), n, TRUE))
d <- upData(d, labels=c(x1='MI', x2='Stroke', x3='AKI', x4='Migraines',
x5='Pregnant', x6='Other event', x7='MD withdrawal',
race='Race', sex='Sex'))
dasna <- subset(d, region=='North America')
with(dasna, table(race, treat))
s <- summaryP(race + sex + ynbind(x1, x2, x3, x4, x5, x6, x7, label='Exclusions') ~
region + treat, data=d)
# add exclude1=FALSE below to include female category
plot(s, groups='treat')
ggplot(s, groups='treat')
plot(s, val ~ freq | region * var, groups='treat', outerlabels=FALSE)
# Much better looking if omit outerlabels=FALSE; see output at
# http://biostat.mc.vanderbilt.edu/HmiscNew#summaryP
# See more examples under bpplotM
# Make a chart where there is a block of variables that
# are only analyzed for males. Keep redundant sex in block for demo.
# Leave extra space for numerators, denominators
sb <- summaryP(race + sex +
pBlock(race, sex, label='Race: Males', subset=sex=='Male') ~
region, data=d)
plot(sb, text.at=1.3)
plot(sb, groups='region', layout=c(1,3), key=list(space='top'),
text.at=1.15)
ggplot(sb, groups='region')
## Not run:
# plot(s, groups='treat')
# # plot(s, groups='treat', outerlabels=FALSE) for standard lattice output
# plot(s, groups='region', key=list(columns=2, space='bottom'))
# colorFacet(ggplot(s))
#
# plot(summaryP(race + sex ~ region, data=d), exclude1=FALSE, col='green')
#
# # Make your own plot using data frame created by summaryP
# useOuterStrips(dotplot(val ~ freq | region * var, groups=treat, data=s,
# xlim=c(0,1), scales=list(y='free', rot=0), xlab='Fraction',
# panel=function(x, y, subscripts, ...) {
# denom <- s$denom[subscripts]
# x <- x / denom
# panel.dotplot(x=x, y=y, subscripts=subscripts, ...) }))
#
# # Show marginal summary for all regions combined
# s <- summaryP(race + sex ~ region, data=addMarginal(d, region))
# plot(s, groups='region', key=list(space='top'), layout=c(1,2))
#
# # Show marginal summaries for both race and sex
# s <- summaryP(ynbind(x1, x2, x3, x4, label='Exclusions', sort=FALSE) ~
# race + sex, data=addMarginal(d, race, sex))
# plot(s, val ~ freq | sex*race)
# ## End(Not run)
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