r - ifelse 真的每次都计算它的两个向量吗？慢吗？

Question

是否ifelse真的计算了yes和no向量——就像每个向量的整体一样？或者它只是从每个向量中计算一些值？

还有，ifelse真的那么慢吗？

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

---

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.

---

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