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Recall that factors are really just integer vectors with ‘levels’, i.e., character labels that get mapped to each integer in the vector. How can we take an arbitrary character, integer, numeric, or logical vector and coerce it to a factor with Rcpp? It’s actually quite easy with Rcpp sugar:

```
#include <Rcpp.h>
using namespace Rcpp;
template <int RTYPE>
IntegerVector fast_factor_template( const Vector<RTYPE>& x ) {
Vector<RTYPE> levs = sort_unique(x);
IntegerVector out = match(x, levs);
out.attr("levels") = as<CharacterVector>(levs);
out.attr("class") = "factor";
return out;
}
// [[Rcpp::export]]
SEXP fast_factor( SEXP x ) {
switch( TYPEOF(x) ) {
case INTSXP: return fast_factor_template<INTSXP>(x);
case REALSXP: return fast_factor_template<REALSXP>(x);
case STRSXP: return fast_factor_template<STRSXP>(x);
}
return R_NilValue;
}
```

Note a few things:

We template over the

`RTYPE`

; i.e., the internal type that R assigns to its objects. For this example, we just need to know that the R types (as exposed in an R session) map to internal C types as`integer -> INTSXP`

,`numeric -> REALSXP`

, and`character -> STRSXP`

.We return an IntegerVector. Remember that factors are just integer vectors with a

`levels`

attribute and class`factor`

.To generate our factor, we simply need to calculate the sorted unique values (the levels), and then match our vector back to those levels.

Next, we can just set the attributes on the object so that R will interpret it as a factor, rather than a plain old integer vector, when it’s returned.

And a quick test:

```
library(microbenchmark)
all.equal( factor( 1:10 ), fast_factor( 1:10 ) )
```

[1] TRUE

```
all.equal( factor( letters ), fast_factor( letters ) )
```

[1] TRUE

```
lets <- sample( letters, 1E5, replace=TRUE )
microbenchmark( factor(lets), fast_factor(lets) )
```

Unit: milliseconds expr min lq median uq max 1 factor(lets) 5.315 5.766 5.930 6.069 32.93 2 fast_factor(lets) 1.420 1.458 1.474 1.486 28.85

(However, note that this doesn’t handle `NA`

s – fixing that is left as an exercise. Similarily for logical vectors – it’s not quite as simple as just adding a call to a `LGLSXP`

templated call, but it’s still not tough – use `INTSXP`

and set set the levels to FALSE and TRUE.)

We can demonstrate a simple example of where this might be useful with tapply. `tapply(x, group, FUN)`

is really just a wrapper to `lapply( split(x, group), FUN )`

, and `split`

relies on coercing ‘group’ to a factor. Otherwise, `split`

calls `.Internal( split(x, group) )`

, and trying to do better than an internal C function is typically a bit futile. So, now that we’ve written this, we can test a couple ways of performing a `tapply`

-like function:

```
x <- rnorm(1E5)
gp <- sample( 1:1000, 1E5, TRUE )
all( tapply(x, gp, mean) == unlist( lapply( split(x, fast_factor(gp)), mean ) ) )
```

[1] TRUE

```
all( tapply(x, gp, mean) == unlist( lapply( split(x, gp), mean ) ) )
```

[1] TRUE

```
rbenchmark::benchmark( replications=20, order="relative",
tapply(x, gp, mean),
unlist( lapply( split(x, fast_factor(gp)), mean) ),
unlist( lapply( split(x, gp), mean ) )
)[,1:4]
```

test replications elapsed 2 unlist(lapply(split(x, fast_factor(gp)), mean)) 20 0.200 3 unlist(lapply(split(x, gp), mean)) 20 0.731 1 tapply(x, gp, mean) 20 1.444 relative 2 1.000 3 3.655 1 7.220

To be fair, tapply actually returns a 1-dimensional array rather than a vector, and also can operate on more general arrays. However, we still do see a modest speedup both for using lapply, and for taking advantage of our fast factor generator.

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