Showing a distribution over time: how many summary stats?

I saw this nice graph today on Twitter, by Thomas Forth:

but the more I looked at it, the more I felt it was hard to understand the changes over time across the income distribution from the Gini coefficient and the median. People started asking online for other percentiles, so I thought I would smooth each of them from the source data and plot them side by side:

Now, this has the advantage of showing exactly where in society the growth or contraction is, but it loses the engaging element of the wandering nation across economic space (cf Booze Space; where do we end up? washed up on the banks of the Walbrook?), which should not be sneezed at. Something engaging matters in dataviz.

Code (as you know, I’m a nuts ‘n’ bolts guy, so don’t go recommending ggplot2 to me):

library(foreign) library(splines) bluecol<-"#467db4" redcol<-"#b44f46" uk<-read.csv("uk_income.csv")[1:53,1:22] uk$Year <- as.numeric(substr(uk$Year,1,4)) sm<-apply(uk,2,function(z){smooth.spline(x=uk$Year,y=z)$y}) png("uk_income.png") par(yaxs="i") plot(uk$Year[1:3],sm[1:3,4],type="l", ylim=c(min(sm[,4:22]-1),max(sm[,4:22]+60)), xlim=c(1960,2015), col=bluecol, main="Percentiles of UK income over time", sub="(Colour indicates governing political party)", ylab="2013 GBP", xlab="Year") lines(uk$Year[4:10],sm[4:10,4],col=redcol) # Wilson I lines(uk$Year[11:14],sm[11:14,4],col=bluecol) # Heath lines(uk$Year[15:19],sm[15:19,4],col=redcol) # Wilson II, Callaghan lines(uk$Year[20:37],sm[20:37,4],col=bluecol) # Thatcher, Major lines(uk$Year[38:50],sm[38:50,4],col=redcol) # Blair, Brown lines(uk$Year[51:53],sm[51:53,4],col=bluecol) # cameron for(i in 5:22) { lines(uk$Year[1:3],sm[1:3,i],col=bluecol) # Macmillan, Douglas-Home lines(uk$Year[4:10],sm[4:10,i],col=redcol) # Wilson I lines(uk$Year[11:14],sm[11:14,i],col=bluecol) # Heath lines(uk$Year[15:19],sm[15:19,i],col=redcol) # Wilson II, Callaghan lines(uk$Year[20:37],sm[20:37,i],col=bluecol) # Thatcher, Major lines(uk$Year[38:50],sm[38:50,i],col=redcol) # Blair, Brown lines(uk$Year[51:53],sm[51:53,i],col=bluecol) # Cam'ron } dev.off()

(uk_income.csv is just the trimmed down source data spreadsheet)