(This article was first published on

**Statistik Stuttgart » R**, and kindly contributed to R-bloggers)In this short tutorial I will show how you can add the relative amount over the barplots, such that you have both, the absolute and relative Information in the plot. First, I create some artificial SNPs and TPMT-genotype.

set.seed(123) library(data.table) library(ggplot2) library(reshape) df <- data.frame(snp=factor(sample(3, 100, replace=T, p=c(0.25, 0.4, .35))), tpmt=factor(sample(3, 100, replace=T, p=c(0.40, 0.55, .05)))) levels(df$snp) <- c("G/G", "A/G", "A/A") levels(df$tpmt) <- c("1/1", "1/3A", NA)

Now we calculate the percentage and reshape the data for ggplot.

df <- melt(df, id.vars="tpmt") df <- data.table(df) dt <- df[, table(value), by=tpmt] dt$label <- df[, names(table(value)), by=tpmt]$V1 dt[, V1:=as.numeric(V1)] dt[, tpmt:=as.character(tpmt)] dt$tpmt[which(is.na(dt$tpmt))] <- "NA" setkey(dt, tpmt) dt["1/1", rel:=round(V1 / sum(V1) * 100, 0)] dt["1/3A", rel:=round(V1 / sum(V1) * 100, 0)] dt["NA", rel:=round(V1 / sum(V1) * 100, 0)] dt[, rel:=paste(rel, "%", sep="")]

I used two times geom_bar as a hack for the legend. The text fileds are added by stat_identity. Experiment with it.

ggplot(dt, aes(x=tpmt, y=V1, fill=label)) + geom_bar(position = "dodge", stat = "identity") + geom_bar(position = "dodge", stat = "identity", colour = "white", show_guide = FALSE) + scale_fill_manual(values = c("#E69F00", "#56B4E9", "maroon"), name = "") + theme_classic() + theme(legend.position="top") + xlab("TPMT-genotype") + ylab("Count") + labs(title="Titel") + stat_identity(aes(x=tpmt, label=rel), geom="text", position=dodgewidth, vjust=-1) + ylim(c(0, 40))

**Update 18.04.2013** Here you get a better version.

To

**leave a comment**for the author, please follow the link and comment on his blog:**Statistik Stuttgart » R**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...