I apologize that this is not precisely about R, but it does fall into
the "what would an R user do?" category of questions.
My colleague came to me and said "I have data on 20 countries, 4
elections in each one. I have measures for each election on a number of
things, and I want to know if the change from one election to the next
follows a pattern. For example, is the measure always rising, or
falling, or rising and then falling, or what?"
If we had a specific hypothesis about which countries should "go
together", I think I could push this into a cross-sectional timeseries
framework and design a test. But, there is no apriori hypothesis of that
sort, we don't know ahead of time whether there will be 20 different
patterns or 1 pattern in common across all countries.
I think the best start is to get plots of these to see if there is
anything obvious, but after that, what would you suggest? I was
thinking that we could take the data from the 4 elections, and then
calculate the differences, so for each country we have a vector
(x1,x2,x3). We could then plot these in a 3d scatter, and maybe
something would jump out at us. Also we can calculate the differences
between these vectors, and use them as inputs into one of the clustering
programs available in the R community.
I hoped that somebody here had seen something like this and could give
me a pointer.