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The recently added `RcppArmadillo::sample()`

functionality provides the same algorithm used in R’s `sample()`

to Rcpp-level code. Because R’s own `sample()`

is written in C with minimal work done in R, writing a wrapper around `RcppArmadillo::sample()`

to then call in R won’t get you much of a performance boost. However, if you need to repeatedly call `sample()`

, then calling a single function which performs everything in Rcpp-land (including multiple calls to `sample()`

) before returning to R can produce a noticeable speedup over a purely R-based solution.

### Accept-Reject Sampler Example

One place where this situation arises is in an accept-reject sampler where the candidate “draw” is the output of a call to `sample()`

. Concretely, let’s suppose we want to sample 20 integers (without replacement) from 1 to 50 such that the sum of the 20 integers is less than 400. Far fewer than 10% of randomly drawn samples will meet this constraint.

```
require(RcppArmadillo)
```

Loading required package: RcppArmadillo

Loading required package: Rcpp

```
require(rbenchmark)
```

Loading required package: rbenchmark

The R code is straightforward enough. It has been written to mirror the logic of the C++ code, although that doesn’t come at the cost of much performance.

```
r_getInts <- function(samples) {
thresh <- 400
results <- matrix(0, 20, samples) ;
cnt <- 0
while(cnt < samples) {
candidate = sample(1:50, 20)
if (sum(candidate) < thresh) {
results[, cnt + 1] <- candidate
cnt <- cnt + 1
}
}
return(results)
}
```

Although it is a bit longer, the logic of the C++ code is similar.

`#include `
// [[Rcpp::depends(RcppArmadillo)]]
using namespace Rcpp ;
// [[Rcpp::export]]
IntegerMatrix cpp_getInts(int samples
) {
RNGScope scope;
int cnt = 0 ;
IntegerMatrix results(20, samples) ;
IntegerVector frame = seq_len(50) ;
IntegerVector candidate(20) ;
int thresh = 400 ;
while (cnt < samples) {
candidate = RcppArmadillo::sample(frame,
20,
FALSE, NumericVector::create()
) ;
double sum = std::accumulate(candidate.begin(), candidate.end(), 0.0) ;
if (sum < thresh) {
results(_, cnt) = candidate ;
cnt++ ;
}
}
return results ;
}

### Performance

The Rcpp code tends to be about 7-9 times faster and this boost increases as the constraint becomes more complicated (and necessarily more costly in R).

```
benchmark(r = {set.seed(1); r_getInts(50)},
cpp = {set.seed(1); cpp_getInts(50)},
replications = 10,
order = 'relative',
columns = c("test", "replications", "relative", "elapsed")
)
```

test replications relative elapsed 2 cpp 10 1.00 0.036 1 r 10 11.97 0.431

### In the Real World …

Where might the structure in this problem arise in practice? One set of instances are those where “space” matters:

- sampling US cities such that no more than two are in any one state
- sampling cellphone towers such that no two are closer than
*X*miles apart - sampling nodes in a graph/network such that no one has more than
*K*edges

In these situations, R code to check the acceptance condition will likely be less efficient relative to the corresponding C++ code and so even larger speed-ups are realized.

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