Insert specific elements in specific locations of a list - r

Question

I have unequal sized list and I want to append specific items from one list to specific positions in another list
First list
dat <- structure(list(supergrp = c("D", "A", "P", "B"),
clusters = c("1", "2", "3", "1"),
items = structure(list(`1.2` = c("a", "c", "d"),
`2.1` = "b", `3` = "e", `4` = c("e", "b")),
.Names = c("1.2", "2.1", "3", "4"))),
.Names = c("supergrp", "clusters", "items"),
row.names = c(NA, 4L), class = "data.frame")
second list
val_to_append <- structure(list(supergrp = c("D", "A"),
clusters = c(1, 2),
items = structure(list(`1.2` = c("c", "f"),
`2.1` = c("c", "d", "e")),
.Names = c("1.2", "2.1"))),
.Names = c("supergrp", "clusters", "items"),
row.names = c(NA, -2L), class = "data.frame")
I want to append val_to_append$item[[1]] to dat$item[[3]]
Similarly, I want to append item val_to_append$item[[2]] to dat$item[[1]]
The required output is
supergrp clusters items
1 D 1 a, c, d, e
2 A 2 b
3 P 3 e, c, f
4 B 1 e, b
I can do this in loop
dat_indx <- c(3,1)
val_indx <- c(1,2)
fin_result <- dat
for(i in seq_along(dat_indx)) {
out_put_indx <- dat_indx[[i]]
fin_result$items[[dat_indx[[i]]]] <- unique(c(fin_result$items[[dat_indx[[i]]]],
val_to_append$items[[val_indx[[i]]]]))
}
I tried normal vector indexing such as
append(fin_result$items[[dat_indx]], val_to_append$items[[val_indx]])
without success. Is there an efficient way to do this because my list, aka, dataframe is very large hundreds of thousands of samples.
I am thinking of sapply but don't have concrete idea

Answer 1

We can use mapply to achieve this. We append the values from val_to_append$items to dat$items using the index value which is known before hand.
dat_indx <- c(3,1)
val_indx <- c(1,2)
dat$items[dat_indx] <- mapply(function(x, y)
unique(c(dat$items[[x]], val_to_append$items[[y]])), dat_indx, val_indx)
dat
# supergrp clusters items
#1 D 1 a, c, d, e
#2 A 2 b
#3 P 3 e, c, f
#4 B 1 e, b
Although, this is another way of solving the problem I doubt how efficient it is going to be.