Convenient access to Gapminder’s datasets from R

In April, Hans Rosling examined the influence of religion on fertility. I used R to replicate a graphic of his talk:

> library(datamart)
> gm <- gapminder()
> #queries(gm)
> #
> # babies per woman
> tmp <- query(gm, "TotalFertilityRate")
> babies <- as.vector(tmp["2008"])
> names(babies) <- names(tmp)
> babies <- babies[!is.na(babies)]
> countries <- names(babies)
> #
> # income per capita, PPP adjusted
> tmp <- query(gm, "IncomePerCapita")
> income <- as.vector(tmp["2008"])
> names(income) <- names(tmp)
> income <- income[!is.na(income)]
> countries <- intersect(countries, names(income))
> #
> # religion
> tmp <- query(gm, "MainReligion")
> religion <- tmp[,"Group"]
> names(religion) <- tmp[,"Entity"]
> religion[religion==""] <- "unknown"
> colcodes <- c(
+   Christian="blue", 
+   "Eastern religions"="red", 
+   Muslim="green", "unknown"="grey"
+ )
> countries <- intersect(countries, names(religion))
> #
> # plot
> par(mar=c(4,4,0,0)+0.1)
> plot(
+   x=income[countries], 
+   y=babies[countries], 
+   col=colcodes[religion[countries]], 
+   log="x",
+   xlab="Income per Person, PPP-adjusted", 
+   ylab="Babies per Woman"
+ )
> legend(
+   "topright", 
+   legend=names(colcodes), 
+   fill=colcodes, 
+   border=colcodes
+ )

One of the points Rosling wanted to make is: Religion has no or very little influence on fertility, but economic welfare has. I wonder if demographs agree and take this economic effect into account.

If you want to know more about that gapminder function and that query method, read on.