summarize function - RDocumentation (2025)

if (FALSE) {s <- summarize(ap>1, llist(size=cut2(sz, g=4), bone), mean, stat.name='Proportion')dotplot(Proportion ~ size | bone, data=s7)}set.seed(1)temperature <- rnorm(300, 70, 10)month <- sample(1:12, 300, TRUE)year <- sample(2000:2001, 300, TRUE)g <- function(x)c(Mean=mean(x,na.rm=TRUE),Median=median(x,na.rm=TRUE))summarize(temperature, month, g)mApply(temperature, month, g)mApply(temperature, month, mean, na.rm=TRUE)w <- summarize(temperature, month, mean, na.rm=TRUE)library(lattice)xyplot(temperature ~ month, data=w) # plot mean temperature by monthw <- summarize(temperature, llist(year,month),  quantile, probs=c(.5,.25,.75), na.rm=TRUE, type='matrix')xYplot(Cbind(temperature[,1],temperature[,-1]) ~ month | year, data=w)mApply(temperature, llist(year,month), quantile, probs=c(.5,.25,.75), na.rm=TRUE)# Compute the median and outer quartiles. The outer quartiles are# displayed using "error bars"set.seed(111)dfr <- expand.grid(month=1:12, year=c(1997,1998), reps=1:100)attach(dfr)y <- abs(month-6.5) + 2*runif(length(month)) + year-1997s <- summarize(y, llist(month,year), smedian.hilow, conf.int=.5)smApply(y, llist(month,year), smedian.hilow, conf.int=.5)xYplot(Cbind(y,Lower,Upper) ~ month, groups=year, data=s,  keys='lines', method='alt')# Can also do:s <- summarize(y, llist(month,year), quantile, probs=c(.5,.25,.75), stat.name=c('y','Q1','Q3'))xYplot(Cbind(y, Q1, Q3) ~ month, groups=year, data=s, keys='lines')# To display means and bootstrapped nonparametric confidence intervals# use for example:s <- summarize(y, llist(month,year), smean.cl.boot)xYplot(Cbind(y, Lower, Upper) ~ month | year, data=s)# For each subject use the trapezoidal rule to compute the area under# the (time,response) curve using the Hmisc trap.rule functionx <- cbind(time=c(1,2,4,7, 1,3,5,10),response=c(1,3,2,4, 1,3,2,4))subject <- c(rep(1,4),rep(2,4))trap.rule(x[1:4,1],x[1:4,2])summarize(x, subject, function(y) trap.rule(y[,1],y[,2]))if (FALSE) {# Another approach would be to properly re-shape the mm array below# This assumes no missing cells. There are many other approaches.# mApply will do this well while allowing for missing cells.m <- tapply(y, list(year,month), quantile, probs=c(.25,.5,.75))mm <- array(unlist(m), dim=c(3,2,12),  dimnames=list(c('lower','median','upper'),c('1997','1998'), as.character(1:12)))# aggregate will help but it only allows you to compute one quantile# at a time; see also the Hmisc mApply functiondframe <- aggregate(y, list(Year=year,Month=month), quantile, probs=.5)# Compute expected life length by race assuming an exponential# distribution - can also use summarizeg <- function(y) { # computations for one race group futime <- y[,1]; event <- y[,2] sum(futime)/sum(event) # assume event=1 for death, 0=alive}mApply(cbind(followup.time, death), race, g)# To run mApply on a data frame:xn <- asNumericMatrix(x)m <- mApply(xn, race, h)# Here assume h is a function that returns a matrix similar to xmatrix2dataFrame(m)# Get stratified weighted meansg <- function(y) wtd.mean(y[,1],y[,2])summarize(cbind(y, wts), llist(sex,race), g, stat.name='y')mApply(cbind(y,wts), llist(sex,race), g)# Compare speed of mApply vs. by for computing d <- data.frame(sex=sample(c('female','male'),100000,TRUE), country=sample(letters,100000,TRUE), y1=runif(100000), y2=runif(100000))g <- function(x) { y <- c(median(x[,'y1']-x[,'y2']), med.sum =median(x[,'y1']+x[,'y2'])) names(y) <- c('med.diff','med.sum') y}system.time(by(d, llist(sex=d$sex,country=d$country), g))system.time({ x <- asNumericMatrix(d) a <- subsAttr(d) m <- mApply(x, llist(sex=d$sex,country=d$country), g) })system.time({ x <- asNumericMatrix(d) summarize(x, llist(sex=d$sex, country=d$country), g) })# An example where each subject has one record per diagnosis but sex of# subject is duplicated for all the rows a subject has. Get the cross-# classified frequencies of diagnosis (dx) by sex and plot the results# with a dot plotcount <- rep(1,length(dx))d <- summarize(count, llist(dx,sex), sum)Dotplot(dx ~ count | sex, data=d)}d <- list(x=1:10, a=factor(rep(c('a','b'), 5)), b=structure(letters[1:10], label='label for b'), d=c(rep(TRUE,9), FALSE), f=pi*(1 : 10))x <- asNumericMatrix(d)attr(x, 'origAttributes')matrix2dataFrame(x)detach('dfr')# Run summarize on a matrix to get column meansx <- c(1:19,NA)y <- 101:120z <- cbind(x, y)g <- c(rep(1, 10), rep(2, 10))summarize(z, g, colMeans, na.rm=TRUE, stat.name='x')# Also works on an all numeric data framesummarize(as.data.frame(z), g, colMeans, na.rm=TRUE, stat.name='x')