Description The science data frame has 1385 rows and 7 columns. The data are on attitudes to science, from a survey where there were results from 20 classes in private schools and 46 classes in public schools. Usage science Format This data frame contains the following columns: State a factor with levels ACT Australian Capital Territory, NSW New South Wales PrivPub a factor with levels private school, public school school a factor, coded to identify the school class a factor, coded to identify the class sex a factor with levels f, m like a summary score based on two of the questions, on a scale from 1 (dislike) to 12 (like) Class a factor with levels corresponding to each class Source Francine Adams, Rosemary Martin and Murali Nayadu, Australian National University Examples classmeans <- with(science, aggregate(like, by=list(PrivPub, Class), mean)) names(classmeans) <- c("PrivPub","Class","like") dim(classmeans) attach(classmeans) boxplot(split(like, PrivPub), ylab = "Class average of attitude to science score", boxwex = 0.4) rug(like[PrivPub == "private"], side = 2) rug(like[PrivPub == "public"], side = 4) detach(classmeans) if(require(lme4, quietly=TRUE)) { science.lmer <- lmer(like ~ sex + PrivPub + (1 | school) + (1 | school:class), data = science, na.action=na.exclude) summary(science.lmer) science1.lmer <- lmer(like ~ sex + PrivPub + (1 | school:class), data = science, na.action=na.exclude) summary(science1.lmer) ranf <- ranef(obj = science1.lmer, drop=TRUE)[["school:class"]] flist <- science1.lmer@flist[["school:class"]] privpub <- science[match(names(ranf), flist), "PrivPub"] num <- unclass(table(flist)); numlabs <- pretty(num) ## Plot effect estimates vs numbers plot(sqrt(num), ranf, xaxt="n", pch=c(1,3)[as.numeric(privpub)], xlab="# in class (square root scale)", ylab="Estimate of class effect") lines(lowess(sqrt(num[privpub=="private"]), ranf[privpub=="private"], f=1.1), lty=2) lines(lowess(sqrt(num[privpub=="public"]), ranf[privpub=="public"], f=1.1), lty=3) axis(1, at=sqrt(numlabs), labels=paste(numlabs)) }

PROBLEM 2 For the data frame science (DAAG package)

(a) Make an appropriate pie chart

(b) Make an appropriate bar plot