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Data frames are one of R’s distinguishing features. Exposing a list of lists as an array of cases, they make many formal operations such as regression or optimization easy to represent.

The R data.frame operation for lists is quite slow, in large part because it exposes a vast amount of functionality. This sample shows one way to write a much faster data.frame creator in C++ if one is willing to forego that generality.

#include

using namespace Rcpp;

// [[Rcpp::export]]
List CheapDataFrameBuilder(List a) {
List returned_frame = clone(a);
GenericVector sample_row = returned_frame(0);

StringVector row_names(sample_row.length());
for (int i = 0; i < sample_row.length(); ++i) {
char name[5];
sprintf(&(name[0]), "%d", i);
row_names(i) = name;
}
returned_frame.attr("row.names") = row_names;

StringVector col_names(returned_frame.length());
for (int j = 0; j < returned_frame.length(); ++j) {
char name[6];
sprintf(&(name[0]), "X.%d", j);
col_names(j) = name;
}
returned_frame.attr("names") = col_names;
returned_frame.attr("class") = "data.frame";

return returned_frame;
}


Here is the result of comparing the native function to this version.

library(rbenchmark)
a <- replicate(250, 1:100, simplify=FALSE)

res <- benchmark(as.data.frame(a),
CheapDataFrameBuilder(a),
order="relative", replications=500)
res[,1:4]

test replications elapsed relative
2 CheapDataFrameBuilder(a)          500   0.104      1.0
1         as.data.frame(a)          500  16.730    160.9


There are some subtleties in this code:

— It turns out that one can’t send super-large data frames to it because of possible buffer overflows. I’ve never seen that problem when I’ve written Rcpp functions which exchanged SEXPs with R, but this one uses Rcpp:export in order to use sourceCpp.

— Notice the invocation of clone() in the first line of the code. If you don’t do that, you wind up side-effecting the parameter, which is not what most people would expect.