Applying an existing multi-argument function to multiple dataframes, row by row, with a joint output dataframe - r

Question

I have a function taking four arguments,
h(a, b, c, d)
Where a and b are the i-th and the i+1-th row of df1 and c and d are the i-th and i+1-th row of df2, and the output has four variables and i-1 results.
The idea is the following: I want to use the function h to each combination of these four arguments where i is common, and so:
- for the first iteration it will take the 1st and 2nd row of df1 and 1st and 2nd row of df2
- for the second iteration it will take the 2nd and 3rd row of df1 and 2nd and 3rd row of df2
...
Afterward, perfectly, the results will be stored in a separate data frame, with 4 columns and i-1 rows.
I tried making use of apply function and of a for loop, yet my attempts failed me. I don't necessarily need a readymade solution, a hint would be nice. Thanks!
EDIT: reproducible example:
df1 <- data.frame(a = c(1, 2, 3, 4), b = c(5, 6, 7, 8))
df2 <- data.frame(c = c(4, 3, 2, 1), d = c(8, 7, 6, 5))
h <- function (a, b, c, d) {
vector <- (a + b) / (c - d)
vector
}
I would like to get a function that uses h until b and d reach the last row of df1/df2 (they have the same number of rows), and for each such combination generate vector and add it to some new data frame as a next row.

Answer 1

With apply you could do something like this:
df1 <- data.frame(a = c(1, 2, 3, 4), b = c(5, 6, 7, 8))
df2 <- data.frame(c = c(4, 3, 2, 1), d = c(8, 7, 6, 5))
h <- function (a, b, c, d) {
(a + b) / (c - d)
}
apply(cbind(df1, df2), 1, function(x) h(x["a"], x["b"], x["c"], x["d"]))
[1] -1.5 -2.0 -2.5 -3.0
If h is a vectorized function (as in your example) it would be better to
do.call(h, cbind(df1, df2))
Of course, I am not assuming that h is that simple, in which case (df1$a + df1$b) / (df2$c - df2$d) would suffice.
However, I advise learning about the purrr package. It is great for this kind of situation and mainly: you can define what type of output you are expecting (with purrr::map_*) to ensure consistency and avoid unexpected results.
For multiple arguments of a dataframe, use purrr::pmap_*:
# `pmap` returns a list
purrr::pmap(cbind(df1, df2), h)
[[1]]
[1] -1.5
[[2]]
[1] -2
[[3]]
[1] -2.5
[[4]]
[1] -3
# `pmap_dbl` returns a double vector or throws an error otherwise
purrr::pmap_dbl(cbind(df1, df2), h)
[1] -1.5 -2.0 -2.5 -3.0