Dealing with `NA`

in `Rcpp`

can be a bit tricky to sort out, but the speed boosts are incredible and if you're dealing with large data - worth the time to figure it out. But usage cases are important to consider in the example below you can see that vectorized operations are still very competitive and very concise. `Rcpp`

doesn't shine so much in this example, but it can when you have a problem that requires a waterfall of results (one result relies on the next and so on).

Here's a basic function that takes `NA`

values into account and basically converts anything < 0 to -1 and anything > 0 to +1.

```
library(Rcpp)
library(microbenchmark)
signR <- function(x) {
for(i in 1:length(x)) {
if(is.na(x[i])) {
next
}
if (x[i] > 0) {
x[i] <- 1
} else {
x[i] <- -1
}
}
return(x)
}
signR2 <- function(x) {
x[x > 0] <- 1
x[x < 0] <- -1
x
}
cppFunction('
NumericVector signC(NumericVector x) {
int n = x.size();
NumericVector out(n);
for(int i = 0; i<n; ++i ) {
if (NumericVector::is_na(x[i])) {
out[i] = NA_REAL;
} else if (x[i] > 0) {
out[i] = 1;
} else {
out[i] = -1;
}
}
return out;
}')
test <- sample(c(rnorm(25),NA),100000, replace = TRUE)
microbenchmark(
signR(test),
signR2(test),
signC(test)
)
# Unit: milliseconds
# expr min lq mean median uq max neval
# signR(test) 197.676873 201.344711 205.951881 202.437384 204.712847 317.973634 100
# signR2(test) 4.267238 4.335184 4.424379 4.412983 4.467380 4.869512 100
# signC(test) 2.095438 2.120071 2.185537 2.160100 2.219835 2.676364 100
all.equal(signR(test),signR2(test),signC(test))
# TRUE
```