Lesson 18: Functions with Blocks

Blocks are usually key in defining functions. Let’s generalize the above code in the Loops lesson, writing a function that replaces 0s by NAs in specified columns in general data frames, not just pima as before.

     1	zerosToNAs <- function(d,cols)
     2	{
     3	   for (j in cols) {
     4	      NArows <- which(d[,j] == 0)
     5	      d[NArows,j] <- NA
     6	   }
     7	   d
     8	}

(We’ve added line numbers to this display for convenence.)

Here the formal argument d is the data frame to be worked on, and cols specifies the columns in which 0s are to be replaced.

The loop goes through d, one column at a time. Since d[,j] == 0 means all of column j of d, then which(d[,j] == 0) will give us the indices in that column of elements that are 0s. Those indices in turn mean row numbers in d, which we’ve named NArows. In line 5, then, we replace the 0s we’ve found in column j by NAs. Before continuing, work through this little example in your mind:

> d <- data.frame(x=c(1,0,3),y=c(0,0,13)) 
> d
  x  y
1 1  0
2 0  0
> which(d[,2] == 0)
[1] 1 2  # ah yes; the 0 elements in column 2 are at indices 1 and 2

Returning the the above loop code, note that when we reach line 7, we’ve already finished the loop, and exited from it. So, we are ready to return the new value of d. Recall that we could do this via the expression return(d), but we can save ourselves some typing by simply writing d. That value becomes the last value computed, and R automatically returns that last value.

We could use this in the Pima data:

> pima <- zerosToNAs(pima,2:6)

There is an important subtlety here. All of this will produce a new data frame, rather than changing pima itself. That does look odd; isn’t d changing, and isn’t d the same as pima? Well, no; d is only a separate copy of pima. So, when d changes, pima does not. So, if we want pima to change, we must reassign the output of the function back to pima, as we did above.