r - Comparing plot variables with average results -

provided following dataframe have been able create following plot:

library(ggplot2)  df = read.csv("http://pastebin.com/raw.php?i=mltkev3z")  ggplot(df,        aes(x = factor(identificación.con.el.barrio),            fill = nombre.barrio) ) +   geom_histogram(position="dodge") +   ggtitle("¿te identificas con tu barrio?") +   labs(x="grado de identificación con el barrio", fill="barrios") 

resulting in following plot:

however, add new column average results per each observation "grado" variable (with no stratification per neighborhood - aka"barrio"), able compare each neighborhood result city's.

could me in how achieve that?

not elegant, works. involves changing original data around make frequency table, adding group averages. necessitates using geom_bar() in ggplot instead of geom_histogram(). result identical.

# make frequency table of data library('plyr') df2 <- ddply(df, .(barrio,grado), summarise, freq=length(grado))  # make table of averages avg <- data.frame(as.data.frame(table(df2$grado)/3,stringsasfactors=f)) names(avg)[1] <- "grado" avg$barrio <- "average"  # combine tables df2 <- rbind(df2, avg) df2$grado <- as.character(df2$grado) df2[is.na(df2$grado),"grado"] <- "n/a"  # plot using barplot instead of histogram ggplot(df2, aes(x=grado,y=freq,fill=barrio)) +   geom_bar(stat="identity",position=position_dodge()) +   scale_x_discrete("grado de identificación con el barrio") +   scale_y_continuous("count") 

note: changed variable names simpler, hence scale labels.

the result this: