Suppose you have a dataframe with ids and elements prescripted to each id. For example:
example <- data.frame(id = c(1,1,1,1,1,2,2,2,3,4,4,4,4,4,4,4,5,5,5,5),
vals = c("a","b",'c','d','e','a','b','d','c',
'd','f','g','h','a','k','l','m', 'a',
'b', 'c'))
I want to find all possible pair combinations. The main struggle here is not the functional of R language that I can use, but the logic. How can I iterate through all elements and find patterns? For instance, a was picked with b 3 times in my sample dataframe. But original dataframe is more than 30k rows, so I cannot count these combinations manually. How do I automatize this process of finding the number of picks of each elements?
I was thinking about widening my df with pivot_wider and then using map_lgl to find matches. Then I faced the problem that it will take a lot of time for me to find all possible combinations, applying map_lgl for every pair of elements.
I was asking nearly the same question less than a month ago, fellow users answered it but the result is not anything I really need.
Do you have any ideas how to create a dataframe with all possible combinations of values for all ids?
This won't (can't) be fast for many IDs. If it is too slow, you need to parallelize or implement it in a compiled language (e.g., using Rcpp).
We sort vals. We can then create all combination of two items grouped by ID. We exclude ID's with 1 item. Finally we tabulate the result.
library(data.table)
setDT(example)
setorder(example, id, vals)
example[, if (.N > 1) split(combn(vals, 2), 1:2), by = id][, .N, by = c("1", "2")]
# 1 2 N
# 1: a b 3
# 2: a c 2
# 3: a d 3
# 4: a e 1
# 5: b c 2
# 6: b d 2
# 7: b e 1
#<...>