ifelse는 실제로 매번 두 벡터를 모두 계산합니까?

ifelse는 실제로 매번 두 벡터를 모두 계산합니까? 느린가요?

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ifelse실제로 각 벡터의 전체와 같이 yes및 no벡터를 모두 계산 합니까 ? 아니면 각 벡터에서 일부 값을 계산합니까?

또한 ifelse정말 느린가요?

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예. (예외 있음)

ifelse그 yes가치와 가치를 모두 계산합니다 no. test조건이 all TRUE또는 all 인 경우를 제외하고 FALSE.

난수를 생성하고 실제로 생성 된 수를 관찰하여이를 확인할 수 있습니다. (을 되돌려 서 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 
  )

다음 출력을 제공합니다.

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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소스 코드 자체에서도 볼 수 있습니다.

.
.
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

참고URL : https://stackoverflow.com/questions/16275149/does-ifelse-really-calculate-both-of-its-vectors-every-time-is-it-slow