Speed up pairwise counting of unique observations
I have a vector of objects ( object ) along with a 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 the code using for() loops, but it takes a long time to run as the number of unique objects increases. How might I change the code to speed up the run time?
Below is an example with 4 unique objects (in reality I have about 300). For example, objects a and c were both observed in time frames 1 and 2 , so they get a count of 2 . Objects b and d were never observed in the same time frame, so they get a count 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"
You can use crossproduct to get the counts of agreement. You can then reshape the 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's 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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