Converting C code to C++ code: An example from plyr

[This article was first published on Rcpp Gallery, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The plyr package uses a couple of small C functions to optimise a number of particularly bad bottlenecks. Recently, two functions were converted to C++. This was mostly stimulated by a segmentation fault caused by some large inputs to the split_indices() function: rather than figuring out exactly what was going wrong with the complicated C code, it was easier to rewrite with simple, correct C++ code.

The job of split_indices() is simple: given a vector of integers, x, it returns a list where the i-th element of the list is an integer vector containing the positions of x equal to i. This is a useful building block for many of the functions in plyr.

It is fairly easy to see what is going on the in the C++ code:


using namespace Rcpp;

// [[Rcpp::exports]]
std::vector > split_indices(IntegerVector x, int n = 0) {
    if (n < 0) stop("n must be a positive integer");
    std::vector > ids(n);
    int nx = x.size();
    for (int i = 0; i < nx; ++i) {
        if (x[i] > n) {
        ids[x[i] - 1].push_back(i + 1);
    return ids;
  • We create a std::vector of integers called out. This will grow efficiently as we add new values, and Rcpp will automatically convert to a list of integer vectors when returned to R.

  • The loop iterates through each element of x, adding its index to the end of out. It also makes sure that out is long enough. (The plus and minus ones are needed because C++ uses 0 based indices and R uses 1 based indices.)

The code is simple, easy to understand (if one is a little familiar with the STL), and performant. Compare it to the original C code:


SEXP split_indices(SEXP group, SEXP n) {
    SEXP vec;
    int i, j, k;

    int nlevs = INTEGER(n)[0];
    int nobs = LENGTH(group);  
    int *pgroup = INTEGER(group);
    // Count number of cases in each group
    int counts[nlevs];
    for (i = 0; i < nlevs; i++)
        counts[i] = 0;
    for (i = 0; i < nobs; i++) {
        j = pgroup[i];
        if (j > nlevs) error("n smaller than largest index");
        counts[j - 1]++;

    // Allocate storage for results
    PROTECT(vec = allocVector(VECSXP, nlevs));
    for (i = 0; i < nlevs; i++) {
        SET_VECTOR_ELT(vec, i, allocVector(INTSXP, counts[i]));

    // Put indices in groups
    for (i = 0; i < nlevs; i++) {
        counts[i] = 0;
    for (i = 0; i < nobs; i++) {
        j = pgroup[i] - 1;
        k = counts[j];
        INTEGER(VECTOR_ELT(vec, j))[k] = i + 1;
    return vec;

This function is almost three times as long, and has a bug in it. It is substantially more complicated because it:

  • has to take care of memory management with PROTECT and UNPROTECT; Rcpp takes care of this for us

  • needs an additional loop through the data to determine how long each vector should be; the std::vector grows efficiently and eliminates this problem

Conversion to C++ can make code shorter and easier to understand and maintain, while remaining just as performant.

To leave a comment for the author, please follow the link and comment on their blog: Rcpp Gallery. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)