How can I operate on all combinations of 2 list elements?

Question

I have to compare all the elements in the list with each other, dog vs cat, dog vs mouse, cat vs mouse.

animals <- list(
  dog = data.frame(
    col_1 = sample(c(100:200), size = 10, replace = TRUE),
    col_2 = sample(c(100:200), size = 10, replace = TRUE),
    col_3 = sample(c(100:200), size = 10, replace = TRUE),
    col_4 = sample(c(100:200), size = 10, replace = TRUE)
  ),
  cat = data.frame(
    col_1 = sample(c(100:200), size = 10, replace = TRUE),
    col_2 = sample(c(100:200), size = 10, replace = TRUE),
    col_3 = sample(c(100:200), size = 10, replace = TRUE),
    col_4 = sample(c(100:200), size = 10, replace = TRUE)
  ),
  mouse = data.frame(
    col_1 = sample(c(100:200), size = 10, replace = TRUE),
    col_2 = sample(c(100:200), size = 10, replace = TRUE),
    col_3 = sample(c(100:200), size = 10, replace = TRUE),
    col_4 = sample(c(100:200), size = 10, replace = TRUE)
   )
)

I want to compare every combination of two dataframes from the above. The type of comparison isn't important - it's sufficient to say this can be done via a generic_function() like so:

generic_function(animals$dog[, c("col_1", "col_2")],
                 animals$cat[, c("col_1", "col_2")])

Since there is a lot of data to compare, I would like to use lapply function, but I don't know how to create the intersections between the various levels of the list. Can you help me?

I think we need some more information. Do you want to compare every dataframe with every other dataframe, whilst also taking every possible combination of two columns from each dataframe? — wurli
– wurli, Commented Mar 14, 2024 at 15:40
Additionally {MsCoreUtils} doesn't seem to be on CRAN - please provide a link to the package. If the question is the same if you replace ndotproduct() with any function which accepts two dataframes, I'd suggest editing it to remove this detail. — wurli
– wurli, Commented Mar 14, 2024 at 15:41
@wurli not need every column combination, but only between specific column as in the example. And yes, this should work with any generic function that accept 2 dataframes. — Riccardo
– Riccardo, Commented Mar 14, 2024 at 15:45
combn(names(animals), 2L) and then iterate over the columns? — Friede
– Friede, Commented Mar 14, 2024 at 15:51

wurli · Accepted Answer · 2024-03-14 15:50:21Z

You can generate combinations with combn() and then iterate over combinations using lapply():

animals <- list(
  dog = data.frame(
    col_1 = sample(c(100:200), size = 10, replace = TRUE),
    col_2 = sample(c(100:200), size = 10, replace = TRUE),
    col_3 = sample(c(100:200), size = 10, replace = TRUE),
    col_4 = sample(c(100:200), size = 10, replace = TRUE)
  ),
  cat = data.frame(
    col_1 = sample(c(100:200), size = 10, replace = TRUE),
    col_2 = sample(c(100:200), size = 10, replace = TRUE),
    col_3 = sample(c(100:200), size = 10, replace = TRUE),
    col_4 = sample(c(100:200), size = 10, replace = TRUE)
  ),
  mouse = data.frame(
    col_1 = sample(c(100:200), size = 10, replace = TRUE),
    col_2 = sample(c(100:200), size = 10, replace = TRUE),
    col_3 = sample(c(100:200), size = 10, replace = TRUE),
    col_4 = sample(c(100:200), size = 10, replace = TRUE)
  )
)


combinations <- asplit(combn(names(animals), 2), 2)

print(combinations)
#> [[1]]
#> [1] "dog" "cat"
#> 
#> [[2]]
#> [1] "dog"   "mouse"
#> 
#> [[3]]
#> [1] "cat"   "mouse"

lapply(combinations, function(x) {
  generic_function(
    animals[[x[1]]][, c("col_1", "col_2")],
    animals[[x[2]]][, c("col_1", "col_2")]
  )
})

^{Created on 2024-03-14 with reprex v2.1.0}

s_baldur · Accepted Answer · 2024-03-14 16:27:40Z

1

n <- length(animals)
cols <- c("col_1", "col_2")
lapply(1:(n-1), \(i) lapply((i+1):n, \(j) generic_function(animals[[i]][cols], animals[[j]][cols]))) |> 
  unlist(recursive = FALSE)

answered Mar 14, 2024 at 16:27

s_baldur

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Comments

ThomasIsCoding · Accepted Answer · 2024-03-14 21:45:46Z

1

You can use combn like below

combn(
   lapply(animals, `[`, c("col_1", "col_2")),
   2,
   FUN = \(x) do.call(generic_function, x),
   simplify = FALSE
)

answered Mar 14, 2024 at 21:45

ThomasIsCoding

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