Remove NA values efficiently - r

Question

I need to remove NA values efficiently from vectors inside a function which is implemented with RcppEigen. I can of course do it using a for loop, but I wonder if there is a more efficient way.
Here is an example:
library(RcppEigen)
library(inline)
incl <- '
using Eigen::Map;
using Eigen::VectorXd;
typedef Map<VectorXd> MapVecd;
'
body <- '
const MapVecd x(as<MapVecd>(xx)), y(as<MapVecd>(yy));
VectorXd x1(x), y1(y);
int k(0);
for (int i = 0; i < x.rows(); ++i) {
if (x.coeff(i)==x.coeff(i) && y.coeff(i)==y.coeff(i)) {
x1(k) = x.coeff(i);
y1(k) = y.coeff(i);
k++;
};
};
x1.conservativeResize(k);
y1.conservativeResize(k);
return Rcpp::List::create(Rcpp::Named("x") = x1,
Rcpp::Named("y") = y1);
'
na.omit.cpp <- cxxfunction(signature(xx = "Vector", yy= "Vector"),
body, "RcppEigen", incl)
na.omit.cpp(c(1.5, NaN, 7, NA), c(7.0, 1, NA, 3))
#$x
#[1] 1.5
#
#$y
#[1] 7
In my use case I need to do this about one million times in a loop (inside the Rcpp function) and the vectors could be quite long (let's assume 1000 elements).
PS: I've also investigated the route to find all NA/NaN values using x.array()==x.array(), but was unable to find a way to use the result for subsetting with Eigen.

Answer 1

Perhaps I am not understanding the question correctly, but within Rcpp, I don't see how you could possibly do this more efficiently than a for loop. for loops are generally inefficient in R only because iterating through a loop in R requires a lot of heavy interpreted machinery. But this is not the case once you are down at the C++ level. Even natively vectorized R functions ultimately are implemented with for loops in C. So the only way I can think to make this more efficient is to try to do it in parallel.
For example, here's a simple na.omit.cpp function that omits NA values from a single vector:
rcppfun<-"
Rcpp::NumericVector naomit(Rcpp::NumericVector x){
std::vector<double> r(x.size());
int k=0;
for (int i = 0; i < x.size(); ++i) {
if (x[i]==x[i]) {
r[k] = x[i];
k++;
}
}
r.resize(k);
return Rcpp::wrap(r);
}"
na.omit.cpp<-cppFunction(rcppfun)
This runs even more quickly than R's built in na.omit:
> set.seed(123)
> x<-1:10000
> x[sample(10000,1000)]<-NA
> y1<-na.omit(x)
> y2<-na.omit.cpp(x)
> all(y1==y2)
[1] TRUE
> require(microbenchmark)
> microbenchmark(na.omit(x),na.omit.cpp(x))
Unit: microseconds
expr min lq median uq max neval
na.omit(x) 290.157 363.9935 376.4400 401.750 6547.447 100
na.omit.cpp(x) 107.524 168.1955 173.6035 210.524 222.564 100

Answer 2

I do not know if I understand the problem correctly or not but you can use the following arguments:
a = c(1.5, NaN, 7, NA)
a[-which(is.na(a))]
[1] 1.5 7.0
It might be useful to use `rinside' if you want to use it in C++.