Statistical Modeling, Causal Inference, and Social Science

Gentleman et al. published a paper on visualizing genomic data. There are quite some issues that can be applied to other areas of data visualization. I particulary like the scatterplot examples on page 17. I [Gregor] often have massive datasets and it is hard to see anything there. smoothScatter from geneplotter R package can help a lot in producing more informative and eye candy graphs. Try the following (from smootScatter help page). And my examples--unfortunatelly not in English, but graphs show some context.

library("geneplotter") ## you need additionally annotate and Biobase ## from BioC and RColorBrewer if(interactive()) { x1 <- matrix(rnorm(1e4), ncol=2) x2 <- matrix(rnorm(1e4, mean=3, sd=1.5), ncol=2) x <- rbind(x1,x2)

layout(matrix(1:4, ncol=2, byrow=TRUE))
smoothScatter(x, nrpoints=0)
smoothScatter(x)
smoothScatter(x, nrpoints=Inf,
colramp=colorRampPalette(RColorBrewer::brewer.pal(9,
"YlOrRd")),
bandwidth=40)
colors <- densCols(x)
plot(x, col=colors, pch=20)
}