Learning R

Chapter 9. Advanced Looping

R’s looping capability goes far beyond the three standard-issue loops seen in the last chapter. It gives you the ability to apply functions to each element of a vector, list, or array, so you can write pseudo-vectorized code where normal vectorization isn’t possible. Other loops let you calculate summary statistics on chunks of data.

Chapter Goals

After reading this chapter, you should:

• Be able to apply a function to every element of a list or vector, or to every row or column of a matrix
• Be able to solve split-apply-combine problems
• Be able to use the plyr package

Replication

Cast your mind back to Chapter 4 and the rep function. rep repeats its input several times. Another related function, replicate, calls an expression several times. Mostly, they do exactly the same thing. The difference occurs when random number generation is involved. Pretend for a moment that the uniform random number generation function, runif, isn’t vectorized. rep will repeat the same random number several times, but replicate gives a different number each time (for historical reasons, the order of the arguments is annoyingly back to front):

rep(runif(1), 5)

## [1] 0.04573 0.04573 0.04573 0.04573 0.04573

replicate(5, runif(1))

## [1] 0.5839 0.3689 0.1601 0.9176 0.5388

replicate comes into its own in more complicated examples: its main use is in Monte Carlo analyses, where you repeat an analysis a known number of times, and each iteration is independent of the others.

This next example ...