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Since the 0.10.* release series, Rcpp contains a new sugar function `clamp` which can be used to limit vectors to both a minimum and maximim value. This recent StackOverflow question permitted `clamp` to shine. We retake some of the answers, including the `clamp` entry by Romain.

We first define the three R versions.

```pminpmaxClamp <- function(x, a, b) {
pmax(a, pmin(x, b) )
}

ifelseClamp <- function(x, a, b) {
ifelse(x <= a,  a, ifelse(x >= b, b, x))
}

operationsClamp <- function(x, a, b) {
a + (x-a > 0)*(x-a) - (x-b > 0)*(x-b)
}
```

We then define some data, and ensure that these versions all producing identical results.

```set.seed(42)
x <- rnorm(100000)

a <- -1.0
b <- 1.0
stopifnot(all.equal(pminpmaxClamp(x,a,b), ifelseClamp(x,a,b), operationsClamp(x,a,b)))
```

Next is the C++ solution: a one-liner thanks to the existing sugar function.

```#include

using namespace Rcpp;

// [[Rcpp::export]]
NumericVector rcppClamp(NumericVector x, double mi, double ma) {
return clamp(mi, x, ma);
}
```

We can then check and benchmark the new C++ version.

```stopifnot(all.equal(pminpmaxClamp(x,a,b), rcppClamp(x,a,b)))

library(rbenchmark)
benchmark(pminpmaxClamp(x, a, b),
ifelseClamp(x, a, b),
operationsClamp(x, a, b),
rcppClamp(x, a, b),
order="relative")[,1:4]

test replications elapsed relative
4       rcppClamp(x, a, b)          100   0.119    1.000
3 operationsClamp(x, a, b)          100   0.505    4.244
1   pminpmaxClamp(x, a, b)          100   0.530    4.454
2     ifelseClamp(x, a, b)          100   5.268   44.269
```

We see a decent gain of the Rcpp version even relative to these vectorised R solutions. Among these, the simplest (based on `ifelse`) is by far the slowest. The parallel min/max version is about as faster as the clever-but-less-readable expression-based solution.

Real “production” solutions will of course need some more testing of inputs etc. However, as an illustration of `clamp` this example has hopefully been compelling.