My understanding is that you want to correlate column 1 of `A`

and column 1 of `B`

, and so forth. I think you'll find that the approach I showed before achieves exactly this, plus it provides all other pairwirse correlations, which you can choose to ignore.

If you truly only want the column-wise correlations, see `# Column-wise correlations`

below. However, note that the 5 column-wise values are included in the full table. For example, the pairwise correlation of `M_1 vs. I_1`

is row 6 in the table, and the first value in the vector of column-wise correlations.

```
A <- data.frame(rnorm(10000),
rnorm(10000),
rnorm(10000),
rnorm(10000),
rnorm(10000))
row.names(A) <- paste0("G_", 1:10000)
colnames(A) <- paste0("M_", 1:5)
B <- data.frame(rnorm(10000),
rnorm(10000),
rnorm(10000),
rnorm(10000),
rnorm(10000))
row.names(B) <- paste0("g_", 1:10000)
colnames(B) <- paste0("I_", 1:5)
library(dplyr)
library(tidyr)
library(corrr)
# Pair-wise correlations
bind_cols(A, B) %>%
correlate() %>%
stretch(remove.dups = TRUE)
#>
#> Correlation method: 'pearson'
#> Missing treated using: 'pairwise.complete.obs'
#> # A tibble: 55 x 3
#> x y r
#> <chr> <chr> <dbl>
#> 1 M_1 M_1 NA
#> 2 M_1 M_2 0.00328
#> 3 M_1 M_3 0.00822
#> 4 M_1 M_4 -0.00157
#> 5 M_1 M_5 -0.0137
#> 6 M_1 I_1 0.00345
#> 7 M_1 I_2 0.00611
#> 8 M_1 I_3 -0.00776
#> 9 M_1 I_4 0.000908
#> 10 M_1 I_5 -0.0144
#> # … with 45 more rows
# Column-wise correlations
purrr::map2_dbl(A, B, cor) %>%
setNames(nm = paste0(names(A), " vs. ", names(B)))
#> M_1 vs. I_1 M_2 vs. I_2 M_3 vs. I_3 M_4 vs. I_4 M_5 vs. I_5
#> 0.003447392 -0.011436215 0.003282041 -0.002950399 0.018201852
```