A better way to build confidence bands around mean/median of an observed sample using ggplot2

Question

So I have a three column data frame that has Trials, Ind. Variable, Observation. Something like: `df1<- data.frame(Trial=rep(1:10,5), Variable=rep(1:5, each=10), Observation=rnorm(1:50))` I am trying to plot a 95% conf. Interval around the mean for each trial using a rather inefficient method as follows: ``` b<-NULL b$mean<- aggregate(Observation~Variable, data=df1,mean)[,2] b$sd <- aggregate(Observation~Variable, data=df1,sd)[,2] b$Variable<- df1$Variable b$Observation <- df1$Observation b$ucl <- rep(qnorm(.975, mean=b$mean, sd=b$sd), each=10) b$lcl <- rep(qnorm(.025, mean=b$mean, sd=b$sd), each=10) b<- as.data.frame(b) c <- ggplot(b, aes(Variable, Observation)) c + geom_point(color="red") + geom_smooth(aes(ymin = lcl, ymax = ucl), data=b, stat="summary", fun.y="mean") ``` This is inefficient since it duplicates values for ymin, ymax. I've seen the geom_ribbon methods but I would still need to duplicate. However, if I was using any kind of smoothing like glm, the code is much simpler…

Accepted Answer

With this method, I get the same output as with your method. This was inspired by the docs for ggplot. Again, this will be meaningful so long as each `x` value has multiple points.

```
set.seed(1)
df1 <- data.frame(Trial=rep(1:10,5), Variable=rep(1:5, each=10), Observation=rnorm(1:50))    my_ci <- function(x) data.frame(y=mean(x), ymin=mean(x)-2*sd(x), ymax=mean(x)+2*sd(x))

my_ci <- function(x) data.frame(
  y=mean(x), 
  ymin=mean(x) - 2 * sd(x), 
  ymax=mean(x) + 2 * sd(x)
)
ggplot(df1, aes(Variable, Observation)) + geom_point(color="red") +
  stat_summary(fun.data="my_ci", geom="smooth")
```

A better way to build confidence bands around mean/median of an observed sample using ggplot2

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