cxxfunction vs cppFunction

cxxfunction_vs_cppFunction.html

cxxfunction vs cppFunction
Lili Wang

May 4, 2018

Motivation
cppFucntion() is by defaut available in Rcpp library; cxxfunction() is from the inline library. Note that, for the latter, you will need to require both Rcpp and inline libraries. Both dynamically define an R function with inlined C++ code using the .Call calling convention. I’m curious about how the two function differ from the other, and which should I choose in practice.

For below, I will implement both functions to construct a function in C++

R
min_flag_R=function(x){
  y=(min(x)<0)
  return(y)
}
cppFunction
Rcpp in cppFunction

library(Rcpp)
src<-'bool min_flag_cpp_cpp( NumericVector x){return(min(x)<0);}'
cppFunction(src) #cppFunction(src,depends="Rcpp")
RcppArmadillo in cppFunction

src<-'bool min_flag_cpp_arma( arma::vec x){
      return(min(x)<0);
}'
cppFunction(src,depends="RcppArmadillo")# plugins=c("cpp11") 
cxxfunction
Rcpp in cxxfunction
library(inline)
## 
## Attaching package: 'inline'
## The following object is masked from 'package:Rcpp':
## 
##     registerPlugin
src<-'
return wrap(min(as<NumericVector>(x))<0);
'
rcpp_inc <- 'using namespace Rcpp;'
min_flag_cxx_cpp<-cxxfunction(signature(x="numeric"),src,plugin = "Rcpp",includes = rcpp_inc)
## ld: warning: directory not found for option '-L/usr/local/opt/openssl/lib'
# the type in R
Armadillo in cxxfunction
library(inline)
src<-'
return(wrap(min(as<vec>(x))<0)); 
' #need to transform into the type in RcppArmadillo
rcpp_inc <- 'using namespace Rcpp;
             using namespace arma;'
min_flag_cxx_arma<-cxxfunction(signature(x="NumericVector"),src,plugin =  "RcppArmadillo",includes=rcpp_inc) #input type in R
## ld: warning: directory not found for option '-L/usr/local/opt/openssl/lib'
Comparison
library(rbenchmark)
set.seed(100)
benchmark("R" = {
            x <- rnorm(1000)
            min_flag_R(x)
          },
          "cpp_cpp" = {
            x <- rnorm(1000)
            min_flag_cpp_cpp(x)
          },
          "cpp_arma" = {
            x <- rnorm(1000)
            min_flag_cpp_arma(x)
          },
          "cxx_cpp" = {
            x <- rnorm(1000)
            min_flag_cxx_cpp(x)
          },
          "cxx_arma" = {
            x <- rnorm(1000)
            min_flag_cxx_arma(x)
          },          
          replications = 1000,
          order="user.self",
          columns = c("test", "replications","relative", "user.self","elapsed",
                      "sys.self"),
          relative = "user.self")
##       test replications relative user.self elapsed sys.self
## 5 cxx_arma         1000    1.000     0.080   0.082    0.001
## 3 cpp_arma         1000    1.012     0.081   0.082    0.000
## 1        R         1000    1.062     0.085   0.088    0.004
## 4  cxx_cpp         1000    1.100     0.088   0.088    0.000
## 2  cpp_cpp         1000    1.300     0.104   0.108    0.003

cxxfunction_vs_cppFunction.Rmd

---
title: "cxxfunction vs cppFunction"
author: "Lili Wang"
date: "May 4, 2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Motivation
cppFucntion() is by defaut available in Rcpp library; cxxfunction() is from the inline library. Note that, for the latter, you will need to require both Rcpp and inline libraries. Both dynamically define an R function with inlined C++ code using the .Call calling convention. I'm curious about how the two function differ from the other, and which should I choose in practice.

For below, I will implement both functions to construct a function in C++

### R

```{r rcode}
min_flag_R=function(x){
  y=(min(x)<0)
  return(y)
}
```

### cppFunction

#### Rcpp in cppFunction
```{r Rcpp cppFuncntion}
library(Rcpp)
src<-'bool min_flag_cpp_cpp( NumericVector x){return(min(x)<0);}'
cppFunction(src) #cppFunction(src,depends="Rcpp")

```


#### RcppArmadillo in cppFunction
```{r Arma cppFunction}
src<-'bool min_flag_cpp_arma( arma::vec x){
      return(min(x)<0);
}'
cppFunction(src,depends="RcppArmadillo")# plugins=c("cpp11") 

```

## cxxfunction

### Rcpp in cxxfunction
```{r Rcpp cxxfunction}
library(inline)
src<-'
return wrap(min(as<NumericVector>(x))<0);
'
rcpp_inc <- 'using namespace Rcpp;'
min_flag_cxx_cpp<-cxxfunction(signature(x="numeric"),src,plugin = "Rcpp",includes = rcpp_inc)
# the type in R
```

### Armadillo in cxxfunction
```{r Arma cxxfunction}
library(inline)
src<-'
return(wrap(min(as<vec>(x))<0)); 
' #need to transform into the type in RcppArmadillo
rcpp_inc <- 'using namespace Rcpp;
             using namespace arma;'
min_flag_cxx_arma<-cxxfunction(signature(x="NumericVector"),src,plugin =  "RcppArmadillo",includes=rcpp_inc) #input type in R

```
## Comparison

```{r compare}
library(rbenchmark)
set.seed(100)
benchmark("R" = {
            x <- rnorm(1000)
            min_flag_R(x)
          },
          "cpp_cpp" = {
            x <- rnorm(1000)
            min_flag_cpp_cpp(x)
          },
          "cpp_arma" = {
            x <- rnorm(1000)
            min_flag_cpp_arma(x)
          },
          "cxx_cpp" = {
            x <- rnorm(1000)
            min_flag_cxx_cpp(x)
          },
          "cxx_arma" = {
            x <- rnorm(1000)
            min_flag_cxx_arma(x)
          },          
          replications = 1000,
          order="user.self",
          columns = c("test", "replications","relative", "user.self","elapsed",
                      "sys.self"),
          relative = "user.self")


```