# if ... else and ifelse

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] 4.215`

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] 13.021`

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] 15.023`

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] 8.381`