Adjusting values in a table based on another table

I have these 2 tables:

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


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

I want to redistribute values from row "a" (myt) by keeping 50% for itself, giving 40% to row "b", and 10% to row "C" across variables var1 and var2 based on proportions defined in a relationship table (rel_table).

I tried seeing if there is some easy way to do this. I could not find one. I also tried to write a function:

redistribute_from_a <- function(df, rel_df) {
  a_var1 <- df[df$name == "a", "var1"]
  a_var2 <- df[df$name == "a", "var2"]
  
  df[df$name == "a", "var1"] <- a_var1 * rel_df[rel_df$name == "a", "proportion"]
  df[df$name == "a", "var2"] <- a_var2 * rel_df[rel_df$name == "a", "proportion"]
  
  for(target in c("b", "c")) {
    if(target %in% df$name) {
      prop <- rel_df[rel_df$name == target, "proportion"]
      df[df$name == target, "var1"] <- df[df$name == target, "var1"] + (a_var1 * prop)
      df[df$name == target, "var2"] <- df[df$name == target, "var2"] + (a_var2 * prop)
    }
  }
  
  return(df)
}

myt_redistributed <- redistribute_from_a(myt, rel_table)

Are there any standard ways to do this in R?

a_var1_orig <- myt$var1[myt$name == "a"]
a_var2_orig <- myt$var2[myt$name == "a"]

myt_redistributed <- myt %>%
 mutate(
   var1 = case_when(
     name == "a" ~ var1 * 0.5,
     name == "b" ~ var1 + (a_var1_orig * 0.4),
     name == "c" ~ var1 + (a_var1_orig * 0.1),
     TRUE ~ var1
   ),
   var2 = case_when(
     name == "a" ~ var2 * 0.5,
     name == "b" ~ var2 + (a_var2_orig * 0.4),
     name == "c" ~ var2 + (a_var2_orig * 0.1),
     TRUE ~ var2
   )
 )

print(myt_redistributed)

library(dplyr)

myt %>%
  left_join(rel_table, by = "name") %>%
  mutate(
    across(c(var1, var2), ~ ifelse(
      name == "a",
      .x * proportion,  
      .x + (myt[myt$name == "a", 
                c("var1", "var2")][[cur_column()]] * 
              coalesce(proportion, 0))  
    ))
  ) %>%
  select(-proportion)

#>   name var1 var2 var3
#> 1    a   50   40   90
#> 2    b   60   57   30
#> 3    c   40   43   40
#> 4    d   40   45   50
#> 5    e   50   55   60
#> 6    f   60   65   70

^{Created on 2025-07-22 with reprex v2.1.1}

Inspired by this comment, I wrote a function to make this a little more versatile;

library(rlang)
library(dplyr)

distribute_fn <- \(dat, idCol, fromRow, toRows, colVars, props) {
  all_targets <- c(fromRow, toRows)
  prop_vec <- setNames(props, all_targets)
  source_values <- dat %>% 
    filter({{ idCol }} == fromRow) %>% 
    select({{ colVars }})

  dat %>%
    mutate(
      across({{ colVars }}, ~ {
        current_id <- {{ idCol }}
        case_when(
          current_id == fromRow ~ .x * prop_vec[as.character(fromRow)],
          current_id %in% toRows ~ .x + (source_values[[cur_column()]] * 
                                           prop_vec[as.character(current_id)]),
          TRUE ~ .x)})
    )
}

distribute_fn(dat = myt, 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   40   90
#> 2    b   60   57   30
#> 3    c   40   43   40
#> 4    d   40   45   50
#> 5    e   50   55   60
#> 6    f   60   65   70

^{Created on 2025-07-22 with reprex v2.1.1}