Does ifelse really calculate both of its vectors every time? Is it slow?

Question

Does ifelse really calculate both the yes and no vectors -- as in, the entirety of each vector? Or does it just calculate some values from each vector?

Also, is ifelse really that slow?

Answer 1

Yes. (With exception)

ifelse calculates both its yes value and its no value. Except in the case where the test condition is either all TRUE or all FALSE.

We can see this by generating random numbers and observing how many numbers are actually generated. (by reverting the seed).

# TEST CONDITION, ALL TRUE set.seed(1) dump  <- ifelse(rep(TRUE, 200), rnorm(200), rnorm(200)) next.random.number.after.all.true <- rnorm(1)  # TEST CONDITION, ALL FALSE set.seed(1) dump  <- ifelse(rep(FALSE, 200), rnorm(200), rnorm(200)) next.random.number.after.all.false <- rnorm(1)  # TEST CONDITION, MIXED set.seed(1) dump   <- ifelse(c(FALSE, rep(TRUE, 199)), rnorm(200), rnorm(200)) next.random.number.after.some.TRUE.some.FALSE <- rnorm(1)  # RESET THE SEED, GENERATE SEVERAL RANDOM NUMBERS TO SEARCH FOR A MATCH set.seed(1) r.1000 <- rnorm(1000)   cat("Quantity of random numbers generated during the `ifelse` statement when:",      "\n\tAll True  ", which(r.1000 == next.random.number.after.all.true) - 1,     "\n\tAll False ", which(r.1000 == next.random.number.after.all.false) - 1,     "\n\tMixed T/F ", which(r.1000 == next.random.number.after.some.TRUE.some.FALSE) - 1    ) 

Gives the following output:

Quantity of random numbers generated during the `ifelse` statement when:    All True   200    All False  200    Mixed T/F  400   <~~ Notice TWICE AS MANY numbers were                        generated when `test` had both                        T & F values present 

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We can also see it in the source code itself:

. . if (any(test[!nas]))         ans[test & !nas] <- rep(yes, length.out = length(ans))[test &   # <~~~~ This line and the one below         !nas] if (any(!test[!nas]))      ans[!test & !nas] <- rep(no, length.out = length(ans))[!test &  # <~~~~ ... are the cluprits         !nas] . . 

Notice that yes and no are computed only if there is some non-NA value of test that is TRUE or FALSE (respectively).
At which point -- and this is the imporant part when it comes to efficiency -- the entirety of each vector is computed.

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Ok, but is it slower?

Lets see if we can test it:

library(microbenchmark)  # Create some sample data   N <- 1e4   set.seed(1)   X <- sample(c(seq(100), rep(NA, 100)), N, TRUE)   Y <- ifelse(is.na(X), rnorm(X), NA)  # Y has reverse NA/not-NA setup than X 

These two statements generate the same results

yesifelse <- quote(sort(ifelse(is.na(X), Y+17, X-17 ) )) noiflese  <- quote(sort(c(Y[is.na(X)]+17, X[is.na(Y)]-17)))  identical(eval(yesifelse), eval(noiflese)) # [1] TRUE 

but one is twice as fast as the other

microbenchmark(eval(yesifelse), eval(noiflese), times=50L)  N = 1,000 Unit: milliseconds             expr      min       lq   median       uq      max neval  eval(yesifelse) 2.286621 2.348590 2.411776 2.537604 10.05973    50   eval(noiflese) 1.088669 1.093864 1.122075 1.149558 61.23110    50  N = 10,000 Unit: milliseconds             expr      min       lq   median       uq      max neval  eval(yesifelse) 30.32039 36.19569 38.50461 40.84996 98.77294    50   eval(noiflese) 12.70274 13.58295 14.38579 20.03587 21.68665    50