R Calculate sum of values by unique column PAIRS (B-A and A-B) while keeping both pairs

Question

I'm dealing with the following issue. I would like to sum the count by Date, and unique pair of ID1 and ID2, meaning that AB and BA are ONE pair. However, I want to keep both pairs and their sum in my dataset.

My Dataset looks like this:

Date ID1 ID2 Count
12-1   A   B   1
12-1   B   A   1
12-1   D   E   1
12-1   E   D   2
12-2   Y   Z   2
12-2   Z   Y   3

An expected output looks like this:

Date ID1 ID2 SUM
12-1   A   B   2
12-1   B   A   2
12-1   D   E   3
12-1   E   D   3
12-2   Y   Z   5
12-2   Z   Y   5

My Question can be seen as an extension of this previous question:

R sum observations by unique column PAIRS (BA and AB) and NOT unique combinations (BA or AB)

Many thanks in advance.

Answer 1

Here is a way.
First, create a vector of sorted values in the columns ID1 and ID2 , and paste them together. Then group with ave . Finally, remove the vector of unique values.

df1$unique <- apply(df1[c("ID1", "ID2")], 1, \(x) paste(sort(x), collapse = ""))
df1$Sum <- with(df1, ave(Count, unique, FUN = sum))
df1$unique <- NULL
df1
#  Date ID1 ID2 Count Sum
#1 12-1   A   B     1   2
#2 12-1   B   A     1   2
#3 12-1   D   E     1   3
#4 12-1   E   D     2   3
#5 12-2   Y   Z     2   5
#6 12-2   Z   Y     3   5

Answer 2

This may also be done with pmin/pmax to create a grouping column

library(dplyr)
library(stringr)
df1 %>% 
   group_by(Date, grp = str_c(pmin(ID1, ID2), pmax(ID1, ID2))) %>% 
   mutate(Sum = sum(Count)) %>%
   ungroup %>%
   select(-grp)

-output

# A tibble: 6 × 5
  Date  ID1   ID2   Count   Sum
  <chr> <chr> <chr> <int> <int>
1 12-1  A     B         1     2
2 12-1  B     A         1     2
3 12-1  D     E         1     3
4 12-1  E     D         2     3
5 12-2  Y     Z         2     5
6 12-2  Z     Y         3     5

data

df1 <- structure(list(Date = c("12-1", "12-1", "12-1", "12-1", "12-2", 
"12-2"), ID1 = c("A", "B", "D", "E", "Y", "Z"), ID2 = c("B", 
"A", "E", "D", "Z", "Y"), Count = c(1L, 1L, 1L, 2L, 2L, 3L)),
 class = "data.frame", row.names = c(NA, 
-6L))

Answer 3

Here is a dplyr solution making use of lapply :

In essence we create a new column y that orders the characters in alphabetically order, so that we can group also for this column:

library(dplyr)
library(stringr)

df %>% 
  mutate(x = paste(ID1, ID2)) %>% 
  mutate(y = str_split(x, ' ') %>% lapply(., 'sort') %>%  lapply(., 'paste', collapse=' ')) %>% 
  group_by(Date, y) %>% 
  mutate(SUM = sum(Count)) %>% 
  ungroup() %>% 
  select(-c(x, y, Count))
  

  Date  ID1   ID2     SUM
  <chr> <chr> <chr> <int>
1 12-1  A     B         2
2 12-1  B     A         2
3 12-1  D     E         3
4 12-1  E     D         3
5 12-2  Y     Z         5
6 12-2  Z     Y         5