# if … else and ifelse

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Let’s make this a quick and quite basic one. There is this incredibly useful function in R called `ifelse()`

. It’s basically a vectorized version of an if … else control structure every programming language has in one way or the other. `ifelse()`

has, in my view, *two* major advantages over if … else:

- It’s super fast.
- It’s more convenient to use.

The basic idea is that you have a vector of values and whenever you want to test these values against some kind of condition, you want to have a specific value in another vector. An example follows below. First, let’s load the `{rbenchmark}`

package to see the speed benefits.

library(rbenchmark)

Now, the toy example: I am creating a vector of half a million random normally distributed values. For each of these values, I want to know whether the value is below or above zero.

x <- rnorm(500000)

`ifelse()`

is used as `ifelse(<TEST>, <OUTCOME IF TRUE>, <OUTCOME IF FALSE>)`

, so we need three arguments. My test is `x < 0`

and I want to have the string `"negative"`

in `y`

whenever the corresponding value in `x`

is smaller than zero. If this is not the case, then `y`

should have a `"positive"`

in this position. `ifelse()`

only needs one line of code for this.

benchmark(replications = 50, { y <- ifelse(x < 0, "negative", "positive") })$user.self ## [1] 5.88

We could also solve this with a `for`

loop. But, as you can see, this takes approx. 3 times as long.

benchmark(replications = 50, { y <- c() for (i in x) { if (i < 0) { y[length(y)+1] <- "negative" } else { y[length(y)+1] <- "negative" } } })$user.self ## [1] 16.938

The same is true for an `sapply()`

version. `sapply()`

even consistently takes a little longer than a `for`

loop in this case - to my surprise.

benchmark(replications = 50, { y <- sapply(x, USE.NAMES = F, FUN = function (i) { if (i < 0) { "negative" } else { "positive" } } ) })$user.self ## [1] 20.423

It’s highly unlikely that `rnorm()`

produces a value of *exactly* zero. But we could also check for this by simply nesting calls to `ifelse()`

. If you want to do this, you simply add another `ifelse()`

in the “FALSE” part of the previous `ifelse()`

as I did below. In this little toy example, this nested test is still considerably faster than the `for`

or `sapply()`

versions of the single test.

benchmark(replications = 50, { y <- ifelse(x < 0, "negative", ifelse(x > 0, "positive", "exactly zero")) })$user.self ## [1] 12.197

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