How to create multiple columns with across?

I'd like to write the equivalent of this R code in a more synthetic way because in my real example I have a lot of var1, var2 columns.

I thought it would be possible with across?

library(dplyr)

price <- tribble(
  ~code, ~var1, ~var2,
  "a", 1, 8,
  "b", 4, 5
)

quant <- tribble(
  ~code, ~var1, ~var2,
  "a", 10, 90,
  "b", 12, 88
)

# what I want to do in a more synthetic way
tab3 <- data.frame(
  code = price$code,
  var1 = price$var1 * quant$var1,
  var2 = price$var2 * quant$var2
)

Many thanks in advance !

We can left-join and mutate across the known variables.

library(dplyr)
left_join(price, quant, by = "code", suffix = c("", ".y")) %>%
  mutate(across(c(var1, var2), ~ . * cur_data()[[paste0(cur_column(), ".y")]])) %>%
  select(-ends_with(".y"))
# # A tibble: 2 × 3
#   code   var1  var2
#   <chr> <dbl> <dbl>
# 1 a        10   720
# 2 b        48   440

Explanation:

• across only allows us to work on one column at a time, values only
• however, we can get the current column's name with cur_column()
• since we are controlling the suffixes of the columns created in the join (using suffix=), we know that var1 and var2 both have a .y-complement
• knowing the other column's name, we can use standard [[ access on cur_data(), which is generally the same as the original data coming into the pipe, in full context of grouping, arranging, and other columns recently added to the data within this or previous calls to mutate