Below is a code for producing a boxplot using ggplot2 I'm trying to modify in order to suit my problem:
library(ggplot2)
set.seed(1)
# create fictitious data
a <- rnorm(10)
b <- rnorm(12)
c <- rnorm(7)
d <- rnorm(15)
# data groups
group <- factor(rep(1:4, c(10, 12, 7, 15)))
# dataframe
mydata <- data.frame(c(a,b,c,d), group)
names(mydata) <- c("value", "group")
# function for computing mean, DS, max and min values
min.mean.sd.max <- function(x) {
r <- c(min(x), mean(x) - sd(x), mean(x), mean(x) + sd(x), max(x))
names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
r
}
# ggplot code
p1 <- ggplot(aes(y = value, x = factor(group)), data = mydata)
p1 <- p1 + stat_summary(fun.data = min.mean.sd.max, geom = "boxplot") + ggtitle("Boxplot con media, 95%CI, valore min. e max.") + xlab("Gruppi") + ylab("Valori")
In my case I do not have the actual data points but rather only their mean and standard deviation (the data are normally distributed). So for this example it will be:
mydata.mine = data.frame(mean = c(mean(a),mean(b),mean(c),mean(d)),sd = c(sd(a),sd(b),sd(c),sd(d)),group = c(1,2,3,4))
However I would still like to produce a boxplot. I thought of defining:
ymin = mean - 3*sd
lower = mean - sd
mean = mean
upper = mean + sd
ymax = mean + 3*sd
but I don't know how to define a function that will access mean and sd of mydata.mine from fun.data in stat_summary. Alternatively, I can just use rnorm to draw points from a normal parameterized by the mean and sd I have, but the first option seems to me a bit more elegant and simple.
