Dirk Eddelbuettel — written Dec 30, 2012 — source

The STL also contains random sampling and shuffling algorithms.
We start by looking at `random_shuffle`

.

There are two forms. The first uses an internal RNG with its own
seed; the second form allows for a function object conformant to
the STLâ€™s requirements (essentially, given `N`

produce a uniform
draw greater or equal to zero and less than `N`

). This is useful
for us as it lets us tie this to the same RNG which R uses.

```
#include <Rcpp.h>
// wrapper around R's RNG such that we get a uniform distribution over
// [0,n) as required by the STL algorithm
inline int randWrapper(const int n) { return floor(unif_rand()*n); }
// [[Rcpp::export]]
Rcpp::NumericVector randomShuffle(Rcpp::NumericVector a) {
// clone a into b to leave a alone
Rcpp::NumericVector b = Rcpp::clone(a);
std::random_shuffle(b.begin(), b.end(), randWrapper);
return b;
}
```

We can illustrate this on a simple example or two:

```
a <- 1:8
set.seed(42)
randomShuffle(a)
```

[1] 1 4 3 7 5 8 6 2

```
set.seed(42)
randomShuffle(a)
```

[1] 1 4 3 7 5 8 6 2

By tieing the STL implementation of the random permutation to the RNG from R, we are able to compute reproducible permutations, fast and from C++.

**tags:**
stl

- Serialize and Deserialize a C++ Object in Rcpp — Wush Wu
- Stochastic SIR Epidemiological Compartment Model — Christian Gunning
- Call matplotlib from R — Matti Pastell