Splitting multiple values among multiple values

This is a follow-up question: Adjusting values in a table based on another table

I have two tables:

myt_repeating <- data.frame(
  name = c("a", "a", "a", "b", "b", "c", "c", "c", "d", "e", "e"),
  var1 = c(120, 80, 100, 20, 30, 15, 25, 35, 40, 50, 60), 
  var2 = c(90, 60, 70, 25, 35, 20, 30, 40, 45, 55, 65),
  var3 = c(110, 70, 90, 30, 40, 25, 35, 45, 50, 60, 70)
)

rel_table <- data.frame(
  name = c("a", "b", "c"),
  proportion = c(0.5, 0.4, 0.1)
)

My Question: For var1 and var2

• 50% of total "a" values stays with "a" rows (split equally among all "a" rows)
• 40% of total "a" values goes to "b" rows (split equally among all "b" rows)
• 10% of total "a" values goes to "c" rows (split equally among all "c" rows)
• In the end, sum(var1) and sum(var2) should be the same before/after

I could not think of any way to do this without a if loop:

redistribute <- function(x, val.cols, name.col, prop, from) {
   val.col <- match(val.cols, names(x))
   x.names <- x[[name.col]]
   prop.names <- prop[[1]]
   rows <- match(prop.names, x.names)
   
   from_totals <- colSums(x[x.names == from, val.col, drop=FALSE])
   
   for(i in seq_along(prop.names)) {
       group_name <- prop.names[i]
       group_prop <- prop[[2]][i]
       group_rows <- x.names == group_name
       n_rows <- sum(group_rows)
       
       if(group_name == from) {
           x[group_rows, val.col] <- x[group_rows, val.col] * group_prop
       } else {
           add_values <- (from_totals * group_prop) / n_rows
           x[group_rows, val.col] <- x[group_rows, val.col] + 
              matrix(add_values, nrow=n_rows, ncol=length(val.col), byrow=TRUE)
       }
   }
   x
}

result <- redistribute(myt_repeating, val.cols=c("var1", "var2"),
                       name.col="name", prop=rel_table, from="a")

Is there a more concise way to do this?

Same as previous question, here we just need to consider/divide by number of rows in each group.

library(dplyr)
library(rlang)

distribute_fn_multi <- function(dat, idCol, fromRow, toRows, colVars, props) {
  all_targets <- c(fromRow, toRows)
  prop_vec <- setNames(props, all_targets)
  source_totals <- dat %>% 
    filter({{ idCol }} == fromRow) %>% 
    summarise(across({{ colVars }}, sum)) %>%
    as.list()
  group_counts <- dat %>%
    filter({{ idCol }} %in% all_targets) %>%
    count({{ idCol }}, name = "n_rows")
  
  dat %>%
    left_join(group_counts, by = as_label(enquo(idCol))) %>%
    mutate(
      across({{ colVars }}, ~ {
        current_id <- {{ idCol }}
        case_when(
          current_id == fromRow ~ source_totals[[cur_column()]] * 
            prop_vec[as.character(fromRow)] / n_rows,
          current_id %in% toRows ~ .x + 
            (source_totals[[cur_column()]] * 
               prop_vec[as.character(current_id)] / n_rows),
          TRUE ~ .x
        )
      })
    ) %>%
    select(-n_rows)
}

distribute_fn_multi(
  dat = myt_repeating, 
  idCol = name, 
  fromRow = "a", 
  toRows = c("b", "c"), 
  colVars = c(var1, var2), 
  props = c(0.5, 0.4, 0.1)
)

# >    name var1     var2 var3
# > 1     a   50 36.66667  110
# > 2     a   50 36.66667   70
# > 3     a   50 36.66667   90
# > 4     b   80 69.00000   30
# > 5     b   90 79.00000   40
# > 6     c   25 27.33333   25
# > 7     c   35 37.33333   35
# > 8     c   45 47.33333   45
# > 9     d   40 45.00000   50
# > 10    e   50 55.00000   60
# > 11    e   60 65.00000   70