# Using the Rcpp sugar function clamp

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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.

#includeusing 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.

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