I’m starting to incorporate more Rcpp in my R work, and so decided to spend some time exploring the Rcpp Gallery. One example by John Merrill caught my eye. He provides a C++ solution to transforming an list of lists into a data frame, and shows impressive speed savings compared to as.data.frame.
This got me thinking about how I do this operation currently. I tend to rely on the
do.call method. To mimic the example in the Rcpp example:
a <- replicate(250, 1:100, simplify=FALSE) b <- do.call(cbind, a)
For fairness, I should get a data frame rather than a matrix, so for my comparisons, I do convert
b into a data frame. I follow the original coding in the example, adding my method above into the mix. Comparing times:
res <- benchmark(as.data.frame(a), CheapDataFrameBuilder(a), as.data.frame(do.call(cbind, a)), order="relative", replications=500) res[,1:4]
The results were quite interesting to me :)
test replications elapsed relative 3 as.data.frame(do.call(cbind, a)) 500 0.36 1.000 2 CheapDataFrameBuilder(a) 500 0.52 1.444 1 as.data.frame(a) 500 7.28 20.222
I think part of what’s happening here is that as.data.frame.list expends overhead checking for different aspects of making a legit data frame, including naming conventions. The comparison to
CheapDataFrameBuilder should really be with my barebones strategy. Having said that, the example does provide great value in showing what can be done using Rcpp.