Dividing values from categories A, B, C based on category D whitout using pivot_wider

In the example dataframe below, I want to subtract the "revenue" of entries in category "D" from the revenue (of the same product) in entries of categories "A", "B" and "C". The actual dataframe has a bunch of other columns and I need to do this many times, so using pivot_wider operations is a bit inneficient. Is there a quick way to do this?

product category revenue
0001    A        78
0001    B        98
0001    C        103
0001    D        95
0002    A        81
 :      :        :

Results I’d like for the example dataframe:

product category revenue
0001    A        -7
0001    B        3
0001    C        8
0001    D        95
 :      :        :

What I’ve been doing so far:

df = df %>% pivot_wider(names_from = category, values_from = revenue)
df[,2:4] = df[,2:4] - df$D %>% as.data.frame()
df = df %>% pivot_longer(2:5, names_to = 'category', values_to = 'revenue')

Is there a better way of doing this?

>Solution :

df |>
  mutate(
    result = ifelse(category == "D", revenue, revenue - revenue[category == "D"]),
    .by = product
  )
#   product category revenue result
# 1       1        A      78    -17
# 2       1        B      98      3
# 3       1        C     103      8
# 4       1        D      95     95

Using this data:

df = read.table(text = 'product category revenue
0001    A        78
0001    B        98
0001    C        103
0001    D        95', header = T)