Run Functions with Rcpp

Ross Bennett — written Dec 28, 2012 — source

Writing running functions in R can be slow because of the loops involved. The TTR package contains several run functions that are very fast because they call Fortran and C routines. With Rcpp and the C++ STL one can easily write run functions to use in R.

#include <Rcpp.h>
#include <numeric>      // for accumulate

using namespace Rcpp;

// [[Rcpp::export]]
NumericVector run_sum(NumericVector x, int n) {
    int sz = x.size();
    NumericVector res(sz);

    res[n-1] = std::accumulate(x.begin(), x.end()-sz+n, 0.0);

    for(int i = n; i < sz; i++) {
       res[i] = res[i-1] + x[i] - x[i-n];
    }
    // pad the first n-1 elements with NA
    std::fill(res.begin(), res.end()-sz+n-1, NA_REAL);
    return res;
}

suppressMessages(library(TTR))

# use a small sample set for demonstration purposes
set.seed(123)
x <- rnorm(10)
x

 [1] -0.5604756 -0.2301775  1.5587083  0.0705084  0.1292877  1.7150650
 [7]  0.4609162 -1.2650612 -0.6868529 -0.4456620

n <- 4

# Check to make sure that the result of run_sum matches
# runSum from the TTR package
stopifnot(all.equal(run_sum(x, n), runSum(x, n)))

run_sum(x, n)

 [1]        NA        NA        NA  0.838564  1.528327  3.473569  2.375777
 [8]  1.040208  0.224067 -1.936660

Now that we have the function run_sum written, we can easily use this to write the function run_mean just by dividing run_sum by n. Another point to note is the syntax to cast n to a double so we do not lose the decimal points with integer division.

// [[Rcpp::export]]
NumericVector run_mean(NumericVector x, int n) {
    return run_sum(x, n) / (double)n;
}

# Check to make sure that the result of run_mean matches
# runMean from the TTR package
stopifnot(all.equal(run_mean(x, n), runMean(x, n)))

run_mean(x, n)

 [1]         NA         NA         NA  0.2096409  0.3820817  0.8683924
 [7]  0.5939443  0.2600519  0.0560168 -0.4841650

With min_element and max_element from the algorithm header, one can also easily write a function that calculates the min and the max of a range over a running window. Note the * to dereference the iterator to obtain the value. See Finding the minimum of a vector for another example of using min_element.

// [[Rcpp::export]]
NumericVector run_min(NumericVector x, int n){
    int sz = x.size();
    NumericVector res(sz);
    for(int i = 0; i < (sz-n+1); i++){
        res[i+n-1] = *std::min_element(x.begin() + i, x.end() - sz + n + i);
    }
    // pad the first n-1 elements with NA
    std::fill(res.begin(), res.end()-sz+n-1, NA_REAL);
    return res;
}

# Check to make sure that the result of run_min matches
# runMin from the TTR package
stopifnot(all.equal(run_min(x, n), runMin(x, n)))

run_min(x, n)

 [1]         NA         NA         NA -0.5604756 -0.2301775  0.0705084
 [7]  0.0705084 -1.2650612 -1.2650612 -1.2650612

// [[Rcpp::export]]
NumericVector run_max(NumericVector x, int n){
    int sz = x.size();
    NumericVector res(sz);
    for(int i = 0; i < (sz-n+1); i++){
        res[i+n-1] = *std::max_element(x.begin() + i, x.end() - sz + n + i);
    }
    // pad the first n-1 elements with NA
    std::fill(res.begin(), res.end()-sz+n-1, NA_REAL);
    return res;
}

# Check to make sure that the result of run_max matches
# runMax from the TTR package
stopifnot(all.equal(run_max(x, n), runMax(x, n)))

run_max(x, n)

 [1]       NA       NA       NA 1.558708 1.558708 1.715065 1.715065
 [8] 1.715065 1.715065 0.460916

This post demonstrates how to incorporate a few useful functions from the STL, accumulate, min_element, and max_element, to write ‘run’ functions with Rcpp.

tags: stl

Extending R with C++ and Fortran — Dirk Eddelbuettel and JBrandon Duck-Mayr
Benchmarking Rcpp code with RcppClock — Zach DeBruine
Simulation Smoother using RcppArmadillo — Tomasz Woźniak

Run Functions with Rcpp

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