performing something similar to rbind in dplyr or tidyr?

Question

Say with the following data, I am interested in the question of how many unique partners each fruit has?

my df:

       fruit1 fruit2
    1   guava   kiwi
    2   lemon   pear
    3    pear  apple
    4   guava   kiwi
    5    pear  guava
    6   apple   kiwi
    7  banana  lemon
    8   lemon   kiwi
    9   apple banana
    10  lemon  guava

I am trying to get to grips with dplyr and tidyr. To do this, I thought it would be good to use the n_distinct() in dplyr. I did the following:

rbind (df %>%select(fruita=fruit1,fruitb=fruit2), 
       df %>%select(fruita=fruit2,fruitb=fruit1)) %>%
  group_by(fruita) %>%
  summarise(Partners=n_distinct(fruitb)) %>%
  arrange(desc(Partners))

This essentially duplicates the 10 rows underneath but switches the order of the fruits in the bottom half. I then count for each fruit in the new first column, how many unique partner fruits it has in the new second column using n_distinct().

This works fine, but given how elegant dplyr and tidyr are, I am wondering if there is a more efficient way of doing this, and especially if there is a way of performing an rbind such as this using one of these packages?

The final data look like this:

  fruita Partners
1  lemon        4
2  apple        3
3  guava        3
4   pear        3
5   kiwi        3
6 banana        2

data for reproducing:

structure(list(fruit1 = structure(c(3L, 4L, 5L, 3L, 5L, 1L, 2L, 
4L, 1L, 4L), .Label = c("apple", "banana", "guava", "lemon", 
"pear"), class = "factor"), fruit2 = structure(c(4L, 6L, 1L, 
4L, 3L, 4L, 5L, 4L, 2L, 3L), .Label = c("apple", "banana", "guava", 
"kiwi", "lemon", "pear"), class = "factor")), .Names = c("fruit1", 
"fruit2"), class = "data.frame", row.names = c(NA, -10L))

Answer 1

Not sure if this helps:

df %>% 
do(data.frame(fruita=unlist(.), fruitb=unlist(.[,2:1]))) %>%
group_by(fruita) %>% 
summarise(Partners=n_distinct(fruitb)) %>% 
arrange(desc(Partners))
#Source: local data frame [6 x 2]

#    fruita Partners
#  1  lemon        4
#  2  apple        3
#  3  guava        3
#  4   pear        3
#  5   kiwi        3
#  6 banana        2