Consider that we have a matrix like below:
my_mat <- matrix(c(1, 2, 3, 4), 2, 2)
And, we have another matrix MM, which has dimension 50x2.
dim(MM)
[1] 50 2
Here, I want to check whether MM has rows that are the same as that of my_may.
That is, I want to check whether MM has the rows c(1, 3) or c(2, 4).
One direct way could be
my_mat[1, ] == t(MM)
my_mat[2, ] == t(MM)
But, in my problem, my_mat also has very large number of rows.
So, how can I do this compactly?
Maybe simply using base::merge() is enough to do what you want? For example:
set.seed(123)
my_mat <- matrix(c(1, 2, 3, 4), 2, 2)
large_mat <- matrix(sample(1:4, 100, replace = TRUE), ncol = 2)
head(large_mat)
#> [,1] [,2]
#> [1,] 3 4
#> [2,] 3 1
#> [3,] 3 3
#> [4,] 2 1
#> [5,] 3 3
#> [6,] 2 4
any_common <- if (nrow(merge(large_mat, my_mat)) == 0) FALSE else TRUE
any_common
#> [1] TRUE
If you want to know which rows in the large matrix match, then a bit more work is required:
set.seed(123)
my_mat <- matrix(c(1, 2, 3, 4), 2, 2)
large_mat <- matrix(sample(1:4, 100, replace = TRUE), ncol = 2)
large_mat <- as.data.frame(large_mat)
large_mat$row_num <- 1:nrow(large_mat)
head(large_mat)
#> V1 V2 row_num
#> 1 3 4 1
#> 2 3 1 2
#> 3 3 3 3
#> 4 2 1 4
#> 5 3 3 5
#> 6 2 4 6
which_common <- merge(large_mat, my_mat)$row_num
which_common
#> [1] 14 23 25 12 13 6 30
any_common <- if (length(which_common) == 0) FALSE else TRUE
any_common
#> [1] TRUE
Created on 2023-11-01 with reprex v2.0.2