R fast cbind matrix using Rcpp

Question

cbind in R is time consuming for repeated calls, but also for different data types. I wrote code that is 3 times faster than cbind when linking two matrices. But the bind_cols in the dplyr package is just 100X faster than cbind . It is a pity that he can not accept the matrix as input. Can someone make the code below faster. Also, how to quickly snap a sparse matrix? Here is the code I used:

 require( Rcpp ) func <- 'NumericMatrix mmult(NumericMatrix a,NumericMatrix b) { //the colnumber of first matrix int acoln=a.ncol(); //the colnumber of second matrix int bcoln=b.ncol(); //build a new matrix, the dim is a.nrow() and acoln+bcoln NumericMatrix out(a.nrow(),acoln+bcoln) ; for (int j = 0; j < acoln + bcoln; j++) { if (j < acoln) { out(_,j) = a(_,j); } else { //put the context in the second matrix to the new matrix out(_,j) = b(_,j-acoln); } } return out ; }' a <- matrix(rep(1,2000*100),2000) b <- matrix(rep(2,2000*10),2000) cppFunction(func) system.time(for (i in seq(1,800)) {mmult(a,b)}) system.time(for (i in seq(1,800)) {cbind(a,b)}) identical(mmult(a,b),cbind(a,b)) 

Answer 1

Borrowing the idea from this comment by Romain Francois on one of my previous Rcpp adventures,

 func1 <- 'NumericMatrix mmult1(NumericMatrix a, NumericMatrix b) { int acoln = a.ncol(); int bcoln = b.ncol(); NumericMatrix out = no_init_matrix(a.nrow(), acoln + bcoln); for (int j = 0; j < acoln + bcoln; j++) { if (j < acoln) { out(_, j) = a(_, j); } else { out(_, j) = b(_, j - acoln); } } return out; }' cppFunction(func1) set.seed(42) a <- matrix(rnorm(1e7), 1e3) b <- matrix(runif(1e7), 1e3) identical(mmult(a, b), mmult1(a, b)) #TRUE library(microbenchmark) microbenchmark(mmult(a, b), mmult1(a, b), cbind(a, b), times = 10) #Unit: milliseconds # expr min lq mean median uq max neval # mmult(a, b) 69.64 70.52 89.71 72.28 128.8 136.6 10 # mmult1(a, b) 50.84 50.95 69.65 51.43 111.6 114.4 10 # cbind(a, b) 192.35 194.67 201.13 195.30 196.1 255.9 10 

Not much, but not bad for such a trivial change.