Tidyverse friendly hack on combn - r

Question

Problem is simple and in many other posts, but I haven't found satisfactory answer.
Say you have a tibble with one column of labels (here letters) and other values in other columns (here just one 'value').
data <- tibble(letter = letters[1:5], value = 1:5)
Now what you want is generate all the pairs without permutations and keep the value attached to each of the pair element. Here's the solution I have and which I believe is valid but...inelegant.
combn(data$letter, m = 2) %>%
t() %>%
as_tibble() %>%
rename(letter_1 = V1, letter_2 = V2) %>%
left_join(data, by = c("letter_1" = "letter")) %>%
left_join(data, by = c("letter_2" = "letter"), suffix = c("_1", "_2"))
Which outputs the desired result:
# A tibble: 10 x 4
letter_1 letter_2 value_1 value_2
<chr> <chr> <int> <int>
1 a b 1 2
2 a c 1 3
3 a d 1 4
4 a e 1 5
5 b c 2 3
6 b d 2 4
7 b e 2 5
8 c d 3 4
9 c e 3 5
10 d e 4 5
I'm really looking for a tidyverse approach. I'm a fan boy :)
Thank you in advance for any help.

Answer 1

Here is a tidyverse solution using expand (instead of combn):
data %>%
expand(letter_1 = letter, letter_2 = letter) %>%
mutate(
value_1 = match(letter_1, letters),
value_2 = match(letter_2, letters)) %>%
filter(letter_1 != letter_2) %>%
rowwise() %>%
mutate(id = paste0(sort(c(letter_1, letter_2)), collapse = " ")) %>%
distinct(id, .keep_all = TRUE) %>%
select(-id)
## A tibble: 15 x 4
# letter_1 letter_2 value_1 value_2
# <chr> <chr> <int> <int>
# 2 a b 1 2
# 3 a c 1 3
# 4 a d 1 4
# 5 a e 1 5
# 7 b c 2 3
# 8 b d 2 4
# 9 b e 2 5
#11 c d 3 4
#12 c e 3 5
#13 d d 4 4
#14 d e 4 5

Answer 2

One option could be using combn as:
data <- tibble(letter = letters[1:5], value = 1:5)
res <- cbind(data.frame(t(combn(data$letter, 2))), data.frame(t(combn(data$value, 2))))
names(res) <- c("letter_1", "letter_2", "value_1", "value_2")
res
# letter_1 letter_2 value_1 value_2
# 1 a b 1 2
# 2 a c 1 3
# 3 a d 1 4
# 4 a e 1 5
# 5 b c 2 3
# 6 b d 2 4
# 7 b e 2 5
# 8 c d 3 4
# 9 c e 3 5
# 10 d e 4 5

Answer 3

I find the rowwise() function to work inconsistently in my machine. You might want to try map() functions in the purrr pacakge.
Here's a way to implement this:
library(purrr)
data %>%
expand(letter_1 = letter, letter_2 = letter) %>%
mutate(
value_1 = match(letter_1, letters),
value_2 = match(letter_2, letters)) %>%
filter(letter_1 != letter_2) %>%
mutate(
id = map2_chr(letter_1, letter_2, function(x, y) {
paste(sort(c(x, y)), collapse = " ")
})
) %>%
distinct(id, .keep_all = TRUE) %>%
select(-id)
# # A tibble: 10 x 4
# letter_1 letter_2 value_1 value_2
# <chr> <chr> <int> <int>
# 1 a b 1 2
# 2 a c 1 3
# 3 a d 1 4
# 4 a e 1 5
# 5 b c 2 3
# 6 b d 2 4
# 7 b e 2 5
# 8 c d 3 4
# 9 c e 3 5
# 10 d e 4 5