R, Stata and descriptive stats

| Pierre |

It’s amazing how R can make complicated things look simple and simple things look complicated.

I tried to explain in my previous post that R could have important advantages over Stata when it came to managing weird, “non-rectangular” datasets that need to be transformed/combined/reshaped in non-trivial ways: R makes it much easier to work on several datasets at the same time, and different types of objects can be used in consistent ways.

Still, I haven’t completely stopped using Stata: one of the things that bother me when I use R is the lack of nice and quick descriptive statistics functions like “tabulate” or “tabstat”. Of course, it is possible to use standard R functions to get about the same desired output, but they tend to be quite a bit more cumbersome. Here’s an example:

tabstat y, by(group) stats(N mean p10 median p90)

could be translated into R as:

tapply(levels(group), levels(group), function(i)
cbind(N=length(y[group == i],
mean(y[group == i]),
quantile(y[group == i], c(.1,.5,.9)))

or, for a more concise version:

by(y, group, function(x)
c(N=length(x),mean=mean(x),quantile(x,c(.1,.5,.9)))

That’s quite ugly compared to the simple tabstat command, but I could deal with it… Now suppose I am working on survey data and observations have sampling weights, and the syntax will have to get even more complicated — I’d have to think about something for a few minutes, when all Stata would need is a quick [fw=weight] statement added before the comma.

True, R can deal with survey weights, but it almost never matches the simplicity of Stata when all I am trying to do is get a few simple descriptive statistics on survey data:

One of my latest problems with R involved trying to make a two-way table of relative frequencies by column with weighted data… yes, a simple contingency table! The table() function cannot even compare with Stata’s tabulate twoway command, since:

1. it does not handle weights;
2. it does not report marginal distributions in the last row and column of the table (which I always find helpful);
3. it calculates cell frequencies but not relative frequencies by row or column.

Luckily, writing an R function that can achieve this is not too hard:

col.table <- function(var1, var2, weights=rep(1,length(var1)), margins=TRUE){
# Creating table of (weighted) relative frequencies by column, and adding row variable margins as the last column
crosstab <- prop.table(xtabs(weights ~ var1 + var2), margin=2)
t <- cbind(crosstab, Total=prop.table(xtabs(weights ~ var1)))
# Adding column sums in the last row
t <- rbind(t,Total = colSums(t))
# Naming rows and columns of the table after var1 and var2 used, and returning result
names(dimnames(t)) <- c(deparse(substitute(var1)), deparse(substitute(var2)))
return(round(100*t,2))
}

col.table(x,y,w) gives the same output as Stata’s “tabulate x y [fw=w], col nofreq”. Note that the weight argument is optional so that: col.table(x,y) is equivalent to tabulate x y, col nofreq.

Here’s the same function, but for relative distributions by row:

row.table <- function(var1, var2, weights=rep(1,length(var1)), margins=TRUE){
t <- rbind(prop.table(xtabs(weights ~ var1 + var2), margin=1),
Total=prop.table(xtabs(weights ~ var2)))
t <- cbind(t,Total = rowSums(t))
names(dimnames(t)) <- c(deparse(substitute(var1)), deparse(substitute(var2)))
return(round(100*t,2))
}