cbind

Given three (or n lists): one <- list(a=1:2,b="one") two <- list(a=2:3,b="two") three <- list(a=3:4,b="three") What would be a more efficient way of cbind ind each list item across the n lists, to get this result? mapply(cbind,mapply(cbind,one,two,SIMPLIFY=FALSE),three,SIMPLIFY=FALSE) $a [,1] [,2] [,3] [1,] 1 2 3 [2,] 2 3 4 $b [,1] [,2] [,3] [1,] "one" "two" "three" This works okay when n is 2 or 3 but is quickly going to become ludicrously complex. Is there a more efficient variation on this? I have seen similar questions on S.O. but have struggled to adapt them. Or like this: mapply(cbind,

I have two sparse matrices, m1 and m2 : > m1 <- Matrix(data=0,nrow=2, ncol=1, sparse=TRUE, dimnames=list(c("b","d"),NULL)) > m2 <- Matrix(data=0,nrow=2, ncol=1, sparse=TRUE, dimnames=list(c("a","b"),NULL)) > m1["b",1]<- 4 > m2["a",1]<- 5 > m1 2 x 1 sparse Matrix of class "dgCMatrix" b 4 d . > m2 2 x 1 sparse Matrix of class "dgCMatrix" a 5 b . > and I want to cbind() them to make a sparse matrix like: [,1] [,2] a . 5 b 4 . d . . however cbind() ignores the named rows: > cbind(m1[,1],m2[,1]) [,1] [,2] b 4 5 d 0 0 is there some way to do this without a brute force loop? You should send the

cbind in R is relatively time consuming in repeated calls, but it also is powerful for various data types. I have written code that is 3X faster than cbind when binding two matrices. But bind_cols in dplyr package is merely 100X faster than cbind . It is only a pity that it cannot take matrix as input. Can someone make the code below more fast. Also, how do I fast bind 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(); /