Plotting average of multiple variables in time-series using ggplot

Question

I have a file which contains time-series data for multiple variables from a to k.

I would like to create a graph that plots the average of the variables a to k over time and above and below that average line adds a smoothed area representing maximum and minimum variation on each day.

So something like confidence intervals but in a smoothed version.

Here's the dataset: https://dl.dropbox.com/u/22681355/co.csv

and here's the code I have so far:

library(ggplot2)
library(reshape2)
meltdf <- melt(df,id="Year")
ggplot(meltdf,aes(x=Year,y=value,colour=variable,group=variable)) + geom_line()

Answer 1

This depicts bootstrapped 95 % confidence intervals:

ggplot(meltdf,aes(x=Year,y=value,colour=variable,group=variable)) +
  stat_summary(fun.data = "mean_cl_boot", geom = "smooth")

This depicts the mean of all values of all variables +-1SD:

ggplot(meltdf,aes(x=Year,y=value)) +
  stat_summary(fun.data ="mean_sdl", mult=1, geom = "smooth")

You might want to calculate the year means before calculating the means and SD over the variables, but I leave that to you.

However, I believe a boostrap confidence interval would be more sensible, since the distribution is clearly not symmetric. It would also be narrower. ;)

And of course you could log-transform your values.