Generalize R %in% operator to match tuples - r

Question

I spent a while the other day looking for a way to check if a row vector is contained in some set of row vectors in R. Basically, I want to generalize the %in% operator to match a tuple instead of each entry in a vector. For example, I want:
row.vec = c("A", 3)
row.vec
# [1] "A" "3"
data.set = rbind(c("A",1),c("B",3),c("C",2))
data.set
# [,1] [,2]
# [1,] "A" "1"
# [2,] "B" "3"
# [3,] "C" "2"
row.vec %tuple.in% data.set
# [1] FALSE
for my made-up operator %tuple.in% because the row vector c("A",3) is not a row vector in data.set. Using the %in% operator gives:
row.vec %in% data.set
# [1] TRUE TRUE
because "A" and 3 are in data.set, which is not what I want.
I have two questions. First, are there any good existing solutions to this?
Second, since I couldn't find them (even if they exist), I tried to write my own function to do it. It works for an input matrix of row vectors, but I'm wondering if any experts have proposed improvements:
is.tuple.in <- function(matrix1, matrix2){
# Apply rbind() so that matrix1 has columns even if it is a row vector.
matrix1 = rbind(matrix1)
if(ncol(matrix1) != ncol(matrix2)){
stop("Matrices must have the same number of columns.") }
# Now check for the first row and handle other rows recursively
row.vec = matrix1[1,]
tuple.found = FALSE
for(i in 1:nrow(matrix2)){
# If we find a match, then this row exists in matrix 2 and we can break the loop
if(all(row.vec == matrix2[i,])){
tuple.found = TRUE
break
}
}
# If there are more rows to be checked, use a recursive call
if(nrow(matrix1) > 1){
return(c(tuple.found, is.tuple.in(matrix1[2:nrow(matrix1),],matrix2)))
} else {
return(tuple.found)
}
}
I see a couple problems with that that I'm not sure how to fix. First, I'd like the base case to be clear at the start of the function. I didn't manage to do this because I pass matrix1[2:nrow(matrix1),] in the recursive call, which produces an error if matrix1 has one row. So instead of getting to a case where matrix1 is empty, I have an if condition at the end deciding if more iterations are necessary.
Second, I think the use of rbind() at the start is sloppy, but I needed it for when matrix1 had been reduced to a single row. Without using rbind(), ncol(matrix1) produced an error in the 1-row case. I figure my trouble here has to do with a lack of knowledge about R data types.
Any help would be appreciated.

Answer 1

I'm wondering if you have made this a bit more complicated than it is. For example,
set.seed(1618)
vec <- c(1,3)
mat <- matrix(rpois(1000,3), ncol = 2)
rownames(mat) <- 1:nrow(mat)
mat[sapply(1:nrow(mat), function(x) all(vec %in% mat[x, ])), ]
# gives me this
# [,1] [,2]
# 6 3 1
# 38 3 1
# 39 3 1
# 85 1 3
# 88 1 3
# 89 1 3
# 95 3 1
# 113 1 3
# ...
you could subset this further if you care about the order
or you could modify the function slightly:
mat[sapply(1:nrow(mat), function(x)
all(paste(vec, collapse = '') %in% paste(mat[x, ], collapse = ''))), ]
# [,1] [,2]
# 85 1 3
# 88 1 3
# 89 1 3
# 113 1 3
# 133 1 3
# 139 1 3
# 187 1 3
# ...
another example with a longer vector
set.seed(1618)
vec <- c(1,4,5,2)
mat <- matrix(rpois(10000, 3), ncol = 4)
rownames(mat) <- 1:nrow(mat)
mat[sapply(1:nrow(mat), function(x) all(vec %in% mat[x, ])), ]
# [,1] [,2] [,3] [,4]
# 57 2 5 1 4
# 147 1 5 2 4
# 279 1 2 5 4
# 303 1 5 2 4
# 437 1 5 4 2
# 443 1 4 5 2
# 580 5 4 2 1
# ...
I see a couple that match:
mat[sapply(1:nrow(mat), function(x)
all(paste(vec, collapse = '') %in% paste(mat[x, ], collapse = ''))), ]
# [,1] [,2] [,3] [,4]
# 443 1 4 5 2
# 901 1 4 5 2
# 1047 1 4 5 2
but only three
for your single row case:
vec <- c(1,4,5,2)
mat <- matrix(c(1,4,5,2), ncol = 4)
rownames(mat) <- 1:nrow(mat)
mat[sapply(1:nrow(mat), function(x)
all(paste(vec, collapse = '') %in% paste(mat[x, ], collapse = ''))), ]
# [1] 1 4 5 2
here is a simple function with the above code
is.tuplein <- function(vec, mat, exact = TRUE) {
rownames(mat) <- 1:nrow(mat)
if (exact)
tmp <- mat[sapply(1:nrow(mat), function(x)
all(paste(vec, collapse = '') %in% paste(mat[x, ], collapse = ''))), ]
else tmp <- mat[sapply(1:nrow(mat), function(x) all(vec %in% mat[x, ])), ]
return(tmp)
}
is.tuplein(vec = vec, mat = mat)
# [1] 1 4 5 2
seems to work, so let's make our own %in% operator:
`%tuple%` <- function(x, y) is.tuplein(vec = x, mat = y, exact = TRUE)
`%tuple1%` <- function(x, y) is.tuplein(vec = x, mat = y, exact = FALSE)
and try her out
set.seed(1618)
c(1,2,3) %tuple% matrix(rpois(1002,3), ncol = 3)
# [,1] [,2] [,3]
# 133 1 2 3
# 190 1 2 3
# 321 1 2 3
set.seed(1618)
c(1,2,3) %tuple1% matrix(rpois(1002,3), ncol = 3)
# [,1] [,2] [,3]
# 48 2 3 1
# 64 2 3 1
# 71 1 3 2
# 73 3 1 2
# 108 3 1 2
# 112 1 3 2
# 133 1 2 3
# 166 2 1 3

Answer 2

Does this do what you want (even for more than 2 columns)?
paste(row.vec,collapse="_") %in% apply(data.set,1,paste,collapse="_")