Dataframe 新列基于 r 中的其他列和 groupby

Question

I have a question regarding simple data frame manipulation in R. I have the following df table (with more rows of course):

A_ID	B_ID	C_ID	Value
1	1	1	2
1	2	1	1
2	3	3	0
2	4	3	3

And I would like to have the following table:

A_ID	B_ID	C_ID	Value	Value_Equal	Value_NotEqual
1	1	1	2	2	1
1	2	1	1	2	1
2	3	3	0	0	3
2	4	3	3	0	3

So its like a group_by for A_ID, I want to check for each unique(A_ID) if B_ID=C_ID. If this is true, I want to have the Value for Value_Equal(Equal here means B_ID=C_ID), but not only for the row, but rather for the the other row with the same A_ID.

If its False, I want to have the Value for the column "Value_NotEqual", and again not just for the row, but rather for the other row with the same A_ID..

I hope it is clear what I mean. If you have any questions regarding my problem task, just ask. Thanks in advance!

Answer 1

Assuming there are only at most one case, grouped by 'A_ID', extract the 'Value' based on the logical expression ( B_ID == C_ID or B_ID != C_ID ) to create the 'Value_Equal/Value_NotEqual columns)

library(dplyr)
df1 %>% 
  group_by(A_ID) %>%
  mutate(Value_Equal = Value[B_ID == C_ID][1],
      Value_NotEqual = Value[B_ID != C_ID][1]) %>%
  ungroup

-output

# A tibble: 4 × 6
   A_ID  B_ID  C_ID Value Value_Equal Value_NotEqual
  <int> <int> <int> <int>       <int>          <int>
1     1     1     1     2           2              1
2     1     2     1     1           2              1
3     2     3     3     0           0              3
4     2     4     3     3           0              3

data

df1 <- structure(list(A_ID = c(1L, 1L, 2L, 2L), B_ID = 1:4, C_ID = c(1L, 
1L, 3L, 3L), Value = c(2L, 1L, 0L, 3L)), class = "data.frame", 
row.names = c(NA, 
-4L))