Functions to make ggplot KM survival / cumulative incidence plot from survfit() models ( library(survival) )

ggsurvival.R

#
# Functions to make ggplot KM survivor curves made with survfit() in library(survival)
#
# code written by Ramon Saccilotto
# and included in his ggplot2 tutorial
# 2010-12-08

# define custom function to create a survival data.frame
createSurvivalFrame <- function(f.survfit){
  # initialise frame variable
	f.frame <- NULL

	# check if more then one strata
	if(length(names(f.survfit$strata)) == 0){
		# create data.frame with data from survfit
		f.frame <- data.frame(time=f.survfit$time, n.risk=f.survfit$n.risk, n.event=f.survfit$n.event, 
                       n.censor = f.survfit$n.censor, surv=f.survfit$surv, upper=f.survfit$upper, 
                       lower=f.survfit$lower)
		# create first two rows (start at 1)
  		f.start <- data.frame(time=c(0, f.frame$time[1]), n.risk=c(f.survfit$n, f.survfit$n), n.event=c(0,0), 
                       n.censor=c(0,0), surv=c(1,1), upper=c(1,1), lower=c(1,1))
	 	# add first row to dataset
		f.frame <- rbind(f.start, f.frame)
		# remove temporary data
		rm(f.start)
	} 
else {
	 	# create vector for strata identification
		f.strata <- NULL
		for(f.i in 1:length(f.survfit$strata)){
	 	 	 # add vector for one strata according to number of rows of strata
		f.strata <- c(f.strata, rep(names(f.survfit$strata)[f.i], f.survfit$strata[f.i]))
	 	}
	 	# create data.frame with data from survfit (create column for strata)
		f.frame <- data.frame(time=f.survfit$time, n.risk=f.survfit$n.risk, n.event=f.survfit$n.event, 
                       n.censor = f.survfit$n.censor, surv=f.survfit$surv, upper=f.survfit$upper, 
                       lower=f.survfit$lower, strata=factor(f.strata))
	 	 # remove temporary data
		rm(f.strata)
		# create first two rows (start at 1) for each strata
		for(f.i in 1:length(f.survfit$strata)){
	 	 	# take only subset for this strata from data
			f.subset <- subset(f.frame, strata==names(f.survfit$strata)[f.i])
			# create first two rows (time: 0, time of first event)
			f.start <- data.frame(time=c(0, f.subset$time[1]), n.risk=rep(f.survfit[f.i]$n, 2), 
				n.event=c(0,0), n.censor=c(0,0), surv=c(1,1), upper=c(1,1), lower=c(1,1), 
				strata=rep(names(f.survfit$strata)[f.i],2))
	 	 	# add first two rows to dataset
			f.frame <- rbind(f.start, f.frame)
			# remove temporary data
		rm(f.start, f.subset)
	 	}
	 	# reorder data
		f.frame <- f.frame[order(f.frame$strata, f.frame$time), ]
		# rename row.names
	 	rownames(f.frame) <- NULL
	}
	# return frame
	return(f.frame)
}

# define custom function to draw kaplan-meier curve with ggplot
qplot_survival <- function(f.frame, f.CI="default", f.shape=3){
	# use different plotting commands dependig whether or not strata's are given
	if("strata" %in% names(f.frame) == FALSE){
		# confidence intervals are drawn if not specified otherwise
		if(f.CI=="default" | f.CI==TRUE ){
	 	 	# create plot with 4 layers (first 3 layers only events, last layer only censored)
			# hint: censoring data for multiple censoring events at timepoint are overplotted
			# (unlike in plot.survfit in survival package)
			ggplot(data=f.frame) + geom_step(aes(x=time, y=surv), direction="hv") + geom_step(aes(x=time, 
				y=upper), directions="hv", linetype=2) + geom_step(aes(x=time,y=lower), 
                        direction="hv", linetype=2) + geom_point(data=subset(f.frame, n.censor==1), 
                        aes(x=time, y=surv), shape=f.shape)
	 	 }
	 	 else {
	 	 	 # create plot without confidence intervalls
			ggplot(data=f.frame) + geom_step(aes(x=time, y=surv), direction="hv") + 
       			geom_point(data=subset(f.frame, n.censor==1), aes(x=time, y=surv), shape=f.shape)
	 	 	}
		}
		else {
	 		if(f.CI=="default" | f.CI==FALSE){
	 	 		# without CI 
				ggplot(data=f.frame, aes(group=strata, colour=strata)) + geom_step(aes(x=time, y=surv), 
					direction="hv") + geom_point(data=subset(f.frame, n.censor==1), 
					aes(x=time, y=surv), shape=f.shape)
	 	 	}
	 	 	else {
	 	 		# with CI (hint: use alpha for CI)

				ggplot(data=f.frame, aes(colour=strata, group=strata)) + geom_step(aes(x=time, y=surv), 
					direction="hv") + geom_step(aes(x=time, y=upper), directions="hv", linetype=2, 
					alpha=0.5) + geom_step(aes(x=time,y=lower), direction="hv", linetype=2, alpha=0.5) + 
					geom_point(data=subset(f.frame, n.censor==1), aes(x=time, y=surv), shape=f.shape)
	 	 	}
		}
}



################################
# examples

# create frame from survival class (survfit)
library(survival)
t.survfit <- survfit(Surv(time, status) ~ 1, data=lung)
t.survframe <- createSurvivalFrame(t.survfit)

# create kaplan-meier-plot with ggplot
qplot_survival(t.survframe)

# drawing survival curves with several strata
t.Surv <- Surv(lung$time, lung$status)
t.survfit <- survfit(t.Surv~sex, data=lung)

# conventional plot method
# plot(t.survfit)

# two strata
t.survframe <- createSurvivalFrame(t.survfit)
qplot_survival(t.survframe)

# with CI
qplot_survival(t.survframe, TRUE)


# add ggplot options, use different shape

 qplot_survival(t.survframe, TRUE, 1) + theme_bw() + 
    scale_colour_manual(values=c("green", "steelblue")) +
    theme(legend.position="none") + 
    xlab("Time (days)") + 
    ylab("Survival")

# multiple strata
t.survfit <- survfit(t.Surv~ph.karno, data=lung)
t.survframe <- createSurvivalFrame(t.survfit)
qplot_survival(t.survframe)
# plot without confidence intervals and with different shape
qplot_survival(t.survframe, FALSE, 20)

Great stuff! But I'd suggest to use n.censor>=1.