Replacing na.omit() with !is.na() appears to improve performance with time.
rm(list=ls())
test1 <- (rbind(c(0.1,0.2),0.3,0.1))
rownames(test1)=c('y1','y2','y3')
colnames(test1) = c('x1','x2');
test2 <- (rbind(c(0.8,0.9,0.5),c(0.5,0.1,0.6)))
rownames(test2) = c('y2','y5')
colnames(test2) = c('x1','x3','x2')
solution_3 <- function(test1, test2) {
lTest12 <- list(test1, test2)
namesRow <- unique( unlist( lapply(lTest12, rownames)))
namesCol <- unique( unlist( lapply(lTest12, colnames)))
tmp1 <- sapply(lTest12, function(x) as.vector(x[match(namesRow,
rownames(x)), match(namesCol, colnames(x))]))
tmp2 <- apply(tmp1, 1, function(x) { x[!is.na(x)] })
dimnames1 <- list(namesRow, namesCol)
tmp3 <- array(data = tmp2, dim = sapply(dimnames1, length), dimnames =
dimnames1)
tmp3
}
system.time(for(i in 1:10000) {solution_3(test1, test2)})
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