I have tried several things to make ggplot plot barcharts with means derived from factors in a dataframe, but i wasnt successful. If you consider:
df <- as.data.frame(matrix(rnorm(60*2, mean=3,sd=1), 60, 2)) df$factor <- c(rep(factor(1:3), each=20))
I want to achieve a stacked, relative barchart like this: 
This chart was created with manually calculating group means in a separate dataframe, melting it and using geom_bar(stat="identity", position = "fill) and scale_y_continuous(labels = percent_format()). I havent found a way to use stat_summary with stacked barcharts.
In a second step, i would like to have errorbars attached to the breaks of each column. I have six treatments and three species, so errorbars should be OK.
For anything this complicated, I think it's loads easier to pre-calculate the numbers, then plot them. This is easily done with dplyr/tidyr (even the error bars):
gather(df, 'cat', 'value', 1:2) %>% group_by(factor, cat) %>% summarise(mean=mean(value), se=sd(value)/sqrt(n())) %>% group_by(cat) %>% mutate(perc=mean/sum(mean), ymin=cumsum(perc) -se/sum(mean), ymax=cumsum(perc) + se/sum(mean)) %>% ggplot(aes(x=cat, y=perc, fill=factor(factor))) + geom_bar(stat='identity') + geom_errorbar(aes(ymax=ymax, ymin=ymin))
Of course this looks a bit strange because there are error bars around 100% in the stacked bars. I think you'd be way better off ploting the actual data points, plus means and error bars and using faceting:
gather(df, 'cat', 'value', 1:2) %>% group_by(cat, factor) %>% summarise(mean=mean(value), se=sd(value)/sqrt(n())) %>% ggplot(aes(x=cat, y=mean, colour=factor(factor))) + geom_point(aes(y=value), position=position_jitter(width=.3, height=0), data=gather(df, 'cat', 'value', 1:2) ) + geom_point(shape=5, size = 3) + geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.1) + facet_grid(factor ~ .)
This way anyone can examine the data and see for themselves that they are normally distributed
