Combination of vectors' values with the same positional indices in R - r

Question

Assuming we have the following simplified vectors (in reality, they contain much more values):
n <- c(1,2)
x <- c(4,5,6)
y <- c(7,8,9)
#to get all possible combinations, we can use expand.grid
df <- expand.grid(n=n,
                  x=x,
                  y=y
)
> df
n x y
1 4 7
2 4 7
1 5 7
2 5 7
1 6 7
2 6 7
1 4 8
2 4 8
1 5 8
2 5 8
1 6 8
2 6 8
1 4 9
2 4 9
1 5 9
2 5 9
1 6 9
2 6 9
However, I would like vectors x, y to have the combination where only elements with the same index values are considered, i.e. (x1, y1), (x2, y2), (x3, y3) but NOT (x1,y2), (x1,y3), etc.
while vector n is still used as usual (all its elements are 'paired' with the outcome of x and y combination).
In other words, I would like to get the following df:  
n x y
1 4 7
2 4 7
1 5 8
2 5 8
1 6 9
2 6 9
if n vector had 3 elements, i.e. n <- (1, 2, 3), then we would have:
n x y
1 4 7
2 4 7
3 4 7
1 5 8
2 5 8
3 5 8
1 6 9
2 6 9
3 6 9

Answer 1

You could combine list of pairs that need to be together and then use it in expand.grid
expand.grid(n, Map(c, x, y)) %>% tidyr::unnest_wider(Var2)
Or we can also use crossing using the same logic.
library(tidyverse)
crossing(n, x = map2(x, y, c)) %>%
unnest_wider(x) %>%
rename_at(-1, ~c("x", "y"))
# n x y
# <dbl> <dbl> <dbl>
#1 1 4 7
#2 1 5 8
#3 1 6 9
#4 2 4 7
#5 2 5 8
#6 2 6 9

Answer 2

We can create a function to do this
f1 <- function(vec1, vec2, n) {
d1 <- data.frame(x = vec1, y = vec2)
d2 <- transform(d1[rep(seq_len(nrow(d1)), each = length(n)), ], n = n)
row.names(d2) <- NULL
d2[c('n', 'x', 'y')]
}
f1(x, y, n = 1:2)
# n x y
#1 1 4 7
#2 2 4 7
#3 1 5 8
#4 2 5 8
#5 1 6 9
#6 2 6 9
f1(x, y, n = 1:3)
# n x y
#1 1 4 7
#2 2 4 7
#3 3 4 7
#4 1 5 8
#5 2 5 8
#6 3 5 8
#7 1 6 9
#8 2 6 9
#9 3 6 9
Or in tidyverse
library(dplyr)
library(tidyr)
tibble(x, y) %>%
uncount(length(n)) %>%
mutate(n = rep(n, length.out = n())) %>%
select(n, x, y)
# A tibble: 9 x 3
# n x y
# <int> <dbl> <dbl>
#1 1 4 7
#2 2 4 7
#3 3 4 7
#4 1 5 8
#5 2 5 8
#6 3 5 8
#7 1 6 9
#8 2 6 9
#9 3 6 9
Or create a tibble first and then use that with crossing
tibble(x, y) %>%
crossing(n)
data
n <- 1:3

Answer 3

Here's a tidyverse solution, using purrr::map_df:
library(tidyverse)
map_df(n, ~tibble(n=.x, x, y))
n x y
<dbl> <dbl> <dbl>
1 1 4 7
2 1 5 8
3 1 6 9
4 2 4 7
5 2 5 8
6 2 6 9
If you need the values sorted exactly like your example output, add %>% arrange(x, y) to the output of map.

Answer 4

One option would be to paste together x and y, then use expand grid and separate the columns using the separate function from the tidyr package.
library(dplyr) #for pipe
library(tidyr) #for separate
n <- c(1,2)
x <- c(4,5,6)
y <- c(7,8,9)
z <- paste(x, y, sep = "-")
expand.grid(n = n, xy = z) %>%
separate(xy, sep = "-", into = c("x", "y")) %>%
mutate(x = as.numeric(x), y = as.numeric(y)) %>%
as.tibble()