Inspired by Mark Reid’s post illustrating the bimodal relationship between the density of guns in a population and the number of gun homicides, I’ve created a slightly different plot from the same data, designed to illustrate a slightly… muddier relationship. This is an expanded variant of homicides vs guns, all countries, but scaling firearm homicides by log(10) shows the relationships between low-homicide countries.

library(directlabels) library(lattice) guns <- read.table("guns/data/guns.csv", sep="\t", header=TRUE) deaths <- read.table("guns/data/deaths.csv", sep="\t", header=TRUE) oecd <- read.table("guns/data/oecd.csv", sep="\t", header=TRUE) data <- merge(guns, deaths, by="Country") data$OECD <- data$Country %in% oecd$Country plot( direct.label( xyplot(Homicides ~ Guns, data, group=Country, main="Homicides vs. Guns", xlab="Guns per 100 people", ylab="Homicides vs 100k people", scales=list(y = list(log = 10))), "top.points"))


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