How to quantify observation pairs in individuals - r

Question

I'm looking for a way to quantify observation pairs in individuals (patients). In this example I have patients who each had two different diseases. The couple of disease(that is, in the same individuals) "a" and "b" is repeated 4 times, for example, in patients "G", "H", "I" and "J" and the couple "k" and "o" is repeated twice (patient "D" has done diseases "k" and "o" and patient "E" has also done these two diseases).
Patient_ID<- c("A","A","B","B","C","C","D","D","E","E","F","F",
"G","G","H","H","I","I","J","J")
Disease<-c("v","s","s","v","s","v" ,"k","o","k","o","o","s","a","b",
"a","b","b","a","b","a")
DATA<-data.frame(Patient_ID,Disease)
print(DATA)
Patient_ID Disease
1 A v
2 A s
3 B s
4 B v
5 C s
6 C v
7 D k
8 D o
9 E k
10 E o
11 F o
12 F s
13 G a
14 G b
15 H a
16 H b
17 I b
18 I a
19 J b
20 J a
With these statistics I would like to generate such a table below.
a b k o v s
a 0 4 0 0 0 0
b 4 0 0 0 0 0
k 0 0 0 2 0 0
o 0 0 2 0 0 1
v 0 0 0 0 0 3
s 0 0 0 1 3 0
Then generate a table for only levels that have count above a certain threshold (for example 2) like in the second table (below).
a b v s
a 0 4 0 0
b 4 0 0 0
v 0 0 0 3
s 0 0 3 0

Answer 1

Here is a base R option using table+crossprod, i.e.,
res <- `diag<-`(crossprod(table(DATA)),0)
which gives
> res
Disease
Disease a b k o s v
a 0 4 0 0 0 0
b 4 0 0 0 0 0
k 0 0 0 2 0 0
o 0 0 2 0 1 0
s 0 0 0 1 0 3
v 0 0 0 0 3 0
For the subset by given threshold, you can use
th <- 2
inds <- rowSums(res > th)>0
subset_res <- subset(res,inds,inds)
which gives
> subset_res
Disease
Disease a b s v
a 0 4 0 0
b 4 0 0 0
s 0 0 0 3
v 0 0 3 0

Answer 2

At first, use unstack() to transform Disease to a data frame with 2 columns. Remember to make both columns have equal levels. This step is to prevent dropping levels in the following operation. Then input the data frame into table() and it'll create a contingency table. In this table, "a & b" and "b & a" are different. To compute the total counts, you need tab + t(tab).
pair <- data.frame(t(unstack(DATA, Disease ~ Patient_ID)))
pair[] <- lapply(pair, factor, levels = levels(DATA$Disease))
tab <- table(pair)
tab + t(tab)
# X2
# X1 a b k o s v
# a 0 4 0 0 0 0
# b 4 0 0 0 0 0
# k 0 0 0 2 0 0
# o 0 0 2 0 1 0
# s 0 0 0 1 0 3
# v 0 0 0 0 3 0