in R community there is often a preference to work at table level in tidy ways using functional programming styles. This has been facilitated by tidyverse packages etc. I would think you could achieve the sort of calculations you have in mind through this style and avoid all for loops, the resulting code will likely be easier to follow.
However , its challenging to suggest solutions over unseen data and data structures.
Please follow this attached guide on how to make a reproducible example, which means data as well as code.
Short Version
You can share your data in a forum friendly way by passing the data to share to the dput() function.
If your data is too large you can use standard methods to reduce it before sending to dput().
When you come to share the dput() text that represents your data, please be sure to format your post with triple backticks on the line before your code begins to format it appropriately.
```
( example_df <- structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6,
5, 4.4, 4.9), Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9, 3.4,
3.4, 2.9, 3.1), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7,
1.4, 1.5, 1.4, 1.5), Petal.Width = c(0.2, 0.2, 0.2, 0.2, 0.2,
0.4, 0.3, 0.2, 0.2, 0.1), Species = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("setosa", "versicolor", "virginica"
), class = "factor")), row.names = c(NA, -10L), class = c("tbl_df",
"tbl", "data.frame")))
```