(This article was first published on

**R Enthusiast and R/C++ hero**, and kindly contributed to R-bloggers)`mapply`

is a well known (or perhaps not) function in R. `mapply`

applies a function to extracts from one or more vectors. For example in R:

```
> mapply( function(x,y, z) x + y + z, 1:4, 4:1, 2)
# [1] 7 7 7 7
```

Notice how the last argument is recycled as we would expect in R.

I’ve recently updated `mapply`

in `Rcpp11`

to be as flexible as possible. It handles a variable number of parameters (which the `Rcpp`

version did not do), checks at compile time that it makes sense to apply the function to elements of the input vectors. It also handles primitives instead of vectors, doing recycling.

Here is an example using both vectors and a primitive.

```
#include <Rcpp.h>
using namespace Rcpp ;
// [[Rcpp::export]]
NumericVector mapply_example(NumericVector x, NumericVector y, double z){
auto fun = [](double a, double b, double c){ return a + b + c ;} ;
return mapply( fun, x, y, z ) ;
}
```

In this example, the applied function is a lambda, nicely captured with `auto`

because that’s awesome, but we can use a named function previously declared.

```
double fun(double a, double b, double c){
return a + b + c ;
}
// [[Rcpp::export]]
NumericVector mapply_example(NumericVector x, NumericVector y, double z){
return mapply( fun, x, y, z ) ;
}
```

To

**leave a comment**for the author, please follow the link and comment on their blog:**R Enthusiast and R/C++ hero**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...