I want to perform a column-wise operation in R on column pairs. The function I actually want to use is not the one shown here, because it would complicate this example.
I have a dataframe:

df <- data.frame(p1 = c(-5, -4, 2, 0, -2, 1, 3, 4, 2, 7)
                 ,p2 = c(0, 1, 2, 0, -2, 1, 3, 3, 2, 0))

and a vector of the same length as the df :

tocompare <- c(0, 0, 2, 0, 2, 4, 16, 12, 6, 9)

I want to run a function that compares each column of df to the tocompare object. The steps I need to take is:

1. Make a two-element list. First element is a two-column dataframe x , in which the first column comes from the df and the second column is the tocompare object. Second element is a number. (this is needed for my actual function to work, I appreciate that it is not needed in this example). This number is constant for all iterations of this process (it's a number of rows in df / length of tocompare ) in this example, it's 10 .

data1 <- list(x = cbind(df %>% select(1), tocompare), N = length(tocompare))

# select(1) is used rather than df[,1] ensures the column header is kept

1. Compare the two columns of the first element (called x ) of the data1 list. The function that I use in real life is not cor ; this simplified example captures the problem. I wrote my_function in such a way that it needs the data1 object created above.

my_function <- function(data1){
x <- data1[[1]]
cr <- cor(x[,1], x[,2])
header <- colnames(x)[1]
print(c(header, cr))
}

cr_df1 <- my_function(data1)

I can do the same for the second df column:

data2 <- list(x = cbind(df %>% select(2), tocompare), N = length(tocompare))
cr_df2 <- my_function(data2)

And make a dataframe of final results:

final_df <- rbind(cr_df1, cr_df2) %>% 
`rownames<-`(NULL) %>% 
`colnames<-`(c("p", "R")) %>% 
as.data.frame()

the output will look like this:

> final_df 
   p         R
1 p1 0.7261224
2 p2 0.6233169

I would like to do this on a dataframe with thousands of columns. The bit I don't know is how to split the single dataframe into multiple two-column dataframes and then run my_function on these many small dataframes to return a single output. I think I would be able to do it with a loop and with transposing the df , but maybe there is a better way (I feel I should try to use map here)?