A simple frequency plot

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I’m currently working on a paper that uses Polish survey data (EVS 2008). I am specifically looking at regional variation in particular responses. Because there are only around 1800 observations in the survey, which are split across 66 subregions of Poland (NUTS-3, specifically), I suspected there would be a large degree of variation in how these interviews were distributed across regions. Typically, I would just use densityplot from the lattice package to get some idea of how a continuous variable is distributed. Of course, with discrete data, table would work well when the variable only takes on few possible values. When the variable can take on a larger number of values, barchart (also from lattice) may also work. However, none of these seemed to provide the type of information I wanted. densityplot obscured the distribution of the data, there were too many categories for table to be all that useful, and I found the barchart to be ugly and not that informative. What I came up with was the following (click the image to get the PDF version):

This is a simple variation of a frequency plot (I like simple plots), but I found it to be much more informative than the alternatives. I hope it’s obvious that each dot represents a NUTS-3 region in Poland, the x-axis, as the label states, is the number of interviews conducted in each region. The function I used to create this plot is as follows:

distplot <- function(x, ...) {
   d <- table(x)
   d <- do.call(rbind, tapply(d, d, function(x) cbind(x, 1:length(x))))
   xyplot(d[,2] ~ d[,1], ...)

And this is how it was called:

distplot(Data$subreg.id, ylab = NULL,
   xlab = "Number of interviews conducted",
   scales = list(x = list(at = seq(0, 80, 5)),
   y = list(at = seq(0, 20, 5))), col = red[7], pch = 16,
   ylim = c(0,15))

It is quite possible (even likely) that there is a more elegant way to produce a similar plot—there may even be a built-in function somewhere. But sometimes it’s just quicker to code something yourself than spending a bunch of time looking for “a better way”.

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