How to assign values to cases by group

Question

This is sample of data I am working with:

Here is the data splited into different files, one for 2 elements by group and anothe one with 3 elements by group

https://drive.google.com/drive/folders/13NcZ1eDRz7RfPIV0w-bOsF6kS1ePSQ9j?usp=sharing

In the column inspection I want to assing this codes by group (group variables are Code, Area, Floor and Box).

For each group combination:

• If result=1 in one the cases and the other has result=4, inspection=1 to case with result=4 and inspection=2 to result=1

• If result=1 in one the cases and the other has result=3, inspection=1 to case with result=3 and inspection=2 to result=1

• If result=1 in one the cases and the other has result=1, inspection=1 one of the cases and to the other inspection=2.

How can I make this adjustment to inspection variable by each case groups to get the result in this image?

Then, I have cases with 3 elements per group:

For each group combination:

• If result=1 in one the cases and the other two has result=4 and result=4, inspection=1 to first case with result=4, the second case with result=4 asign inspection=0 and inspection=2 to result=1

• If result=1 in one the cases and the other two has result=4 and result=0, inspection=1 to the case with result=4, inspection=0 to case with result=0 and inspection=2 to result=1

• If result=1 in all the cases, inspection=1 in one of the cases and to the other inspection=2, as we can see on the image below

How can I do that asignation?

Answer 1

How about just using the order of Result to rearrange c(2,1), like this:

df %>% 
  group_by(Code, Area, Floor, Box) %>% 
  mutate(Inspection = c(2,1)[order(Result)])

Output:

  Code   Area Floor   Box Result Inspection
  <chr> <dbl> <dbl> <dbl>  <dbl>      <dbl>
1 10101    11   108     1      4          1
2 10101    11   108     1      1          2
3 10101    11    89     1      1          2
4 10101    11    89     1      1          1
5 10101    11    90     1      1          2
6 10101    11    90     1      3          1