how to get the row difference within same group with different value in certain col

I have a df like below and would like to calculate the fee difference for each ID:

1. df %>% arrange (ID, Date) %>% group_by (ID);
2. Diff= Fee-lag(Fee) when Test != lag(Test)
3. if Test== lag(Test), then Diff equal to Fee- Fee from the most closest different Test

The outputs will looks like the one on the right. I am not sure how to get the orange part done. Could anyone guide me on this? Thanks.

df<- structure(list(ID = c("Tom", "Tom", "Tom", "Tom", "Jerry", "Jerry", 
"Jerry", "Jerry"), Date = c(20220901, 20220902, 20220903, 20220904, 
20220905, 20220906, 20220907, 20220908), Test = c("Math", "Art", 
"ELA", "ELA", "Art", "ELA", "ELA", "Math"), Fee = c(78, 98, 66, 
85, 78, 58, 96, 88)), row.names = c(NA, -8L), class = c("tbl_df", 
"tbl", "data.frame"))

>Solution :

Based on the expected, we get the difference between the ‘Fee’ grouped by ‘ID’ and then get the difference of the absolute in ‘Diff’ after adding ‘Test’ as grouping when there are more than 1 element per group

library(dplyr)
df %>% 
  group_by(ID) %>% 
  mutate(Diff = Fee - lag(Fee)) %>% 
  group_by(Test, .add = TRUE) %>% 
  mutate(Diff = if(n() > 1) c(first(Diff), diff(abs(Diff))) else Diff) %>% 
  ungroup

-output

# A tibble: 8 × 5
  ID        Date Test    Fee  Diff
  <chr>    <dbl> <chr> <dbl> <dbl>
1 Tom   20220901 Math     78    NA
2 Tom   20220902 Art      98    20
3 Tom   20220903 ELA      66   -32
4 Tom   20220904 ELA      85   -13
5 Jerry 20220905 Art      78    NA
6 Jerry 20220906 ELA      58   -20
7 Jerry 20220907 ELA      96    18
8 Jerry 20220908 Math     88    -8