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Speed ​​up counting unique observations

I have a vector of objects ( object ) along with the corresponding vector of time frames ( tframe ) in which the objects were observed. For each unique pair of objects, I want to calculate the number of time frames in which both objects were observed.

I can write code using for() loops, but it takes a long time to run as the number of unique objects increases. How can I change the code to speed up the execution time?

Below is an example with 4 unique objects (in reality I have about 300). For example, objects a and c were observed in time frames 1 and 2 , so they get counter 2 . Objects b and d never observed in the same time frame, so they get a counter of 0 .

 object <- c("a", "a", "a", "b", "b", "c", "c", "c", "c", "d") tframe <- c(1, 1, 2, 2, 3, 1, 2, 2, 3, 1) uo <- unique(object) n <- length(uo) mpairs <- matrix(NA, nrow=n*(n-1)/2, ncol=3, dimnames=list(NULL, c("obj1", "obj2", "sametf"))) row <- 0 for(i in 1:(n-1)) { for(j in (i+1):n) { row <- row+1 mpairs[row, "obj1"] <- uo[i] mpairs[row, "obj2"] <- uo[j] # no. of time frames in which both objects in a pair were observed intwin <- intersect(tframe[object==uo[i]], tframe[object==uo[j]]) mpairs[row, "sametf"] <- length(intwin) }} data.frame(object, tframe) object tframe 1 a 1 2 a 1 3 a 2 4 b 2 5 b 3 6 c 1 7 c 2 8 c 2 9 c 3 10 d 1 mpairs obj1 obj2 sametf [1,] "a" "b" "1" [2,] "a" "c" "2" [3,] "a" "d" "1" [4,] "b" "c" "2" [5,] "b" "d" "0" [6,] "c" "d" "1"
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You can use crossproduct to get matches. Then you can change the form data if required.

Example

 object <- c("a", "a", "a", "b", "b", "c", "c", "c", "c", "d") tframe <- c(1, 1, 2, 2, 3, 1, 2, 2, 3, 1) # This will give you the counts # Use code from Jean comment tab <- tcrossprod(table(object, tframe)>0) # Reshape the data tab[lower.tri(tab, TRUE)] <- NA reshape2::melt(tab, na.rm=TRUE)
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