Exclude rows where value used in another row

Question

Imagine you have the following data set:

df = data.frame(ID = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20), gender= c(1,2,1,2,2,2,2,1,1,2,1,2,1,2,2,2,2,1,1,2),
                PID = c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10))
                    

how can I write a code that removes the rows in the df whose gender and PID are the same (see picture). Please imagine that the code is over 1000 rows long (so it should be a solution that automatically searches for the right values to exclude).

Answer 1

base R

df[ave(rep(TRUE, nrow(df)), df[,c("gender","paar")], FUN = function(z) !any(duplicated(z))),]
#    ID gender paar
# 1   1      1    1
# 2   2      2    1
# 3   3      1    2
# 4   4      2    2
# 7   7      2    4
# 8   8      1    4
# 9   9      1    5
# 10 10      2    5
# 11 11      1    6
# 12 12      2    6
# 13 13      1    7
# 14 14      2    7
# 17 17      2    9
# 18 18      1    9
# 19 19      1   10
# 20 20      2   10

dplyr

library(dplyr)
df %>%
  group_by(gender, paar) %>%
  filter(!any(duplicated(cbind(gender, paar)))) %>%
  ungroup()

Answer 2

In base R , we may use subset after removing the observations where the group count for 'gender' and 'paar' are not 1

subset(df, ave(seq_along(gender), gender, paar, FUN = length) == 1)

---

Or with duplicated

df[!(duplicated(df[-1])|duplicated(df[-1], fromLast = TRUE)),]

-output

   ID gender paar
1   1      1    1
2   2      2    1
3   3      1    2
4   4      2    2
7   7      2    4
8   8      1    4
9   9      1    5
10 10      2    5
11 11      1    6
12 12      2    6
13 13      1    7
14 14      2    7
17 17      2    9
18 18      1    9
19 19      1   10
20 20      2   10

Answer 3

Using aggregate

na.omit(aggregate(. ~ gender + PID, df, function(x) 
  ifelse(length(x) == 1, x, NA)))
   gender PID ID
1       1   1  1
2       2   1  2
3       1   2  3
4       2   2  4
6       1   4  8
7       2   4  7
8       1   5  9
9       2   5 10
10      1   6 11
11      2   6 12
12      1   7 13
13      2   7 14
15      1   9 18
16      2   9 17
17      1  10 19
18      2  10 20

With dplyr

library(dplyr)

df %>% 
  group_by(gender, PID) %>% 
  filter(n() == 1) %>% 
  ungroup()
# A tibble: 16 × 3
      ID gender   PID
   <dbl>  <dbl> <dbl>
 1     1      1     1
 2     2      2     1
 3     3      1     2
 4     4      2     2
 5     7      2     4
 6     8      1     4
 7     9      1     5
 8    10      2     5
 9    11      1     6
10    12      2     6
11    13      1     7
12    14      2     7
13    17      2     9
14    18      1     9
15    19      1    10
16    20      2    10

Answer 4

Another dplyr option could be:

df %>%
 filter(with(rle(paste0(gender, PID)), rep(lengths == 1, lengths)))

   ID gender PID
1   1      1   1
2   2      2   1
3   3      1   2
4   4      2   2
5   7      2   4
6   8      1   4
7   9      1   5
8  10      2   5
9  11      1   6
10 12      2   6
11 13      1   7
12 14      2   7
13 17      2   9
14 18      1   9
15 19      1  10
16 20      2  10

If the duplicated values can occur also between non-consecutive rows:

df %>%
 arrange(gender, PID) %>%
 filter(with(rle(paste0(gender, PID)), rep(lengths == 1, lengths)))

Answer 5

Here is one more: :-)

library(dplyr)
df %>%
  group_by(gender, PID) %>%  
  filter(is.na(ifelse(n()>1, 1, NA))) 

     ID gender   PID
   <dbl>  <dbl> <dbl>
 1     1      1     1
 2     2      2     1
 3     3      1     2
 4     4      2     2
 5     7      2     4
 6     8      1     4
 7     9      1     5
 8    10      2     5
 9    11      1     6
10    12      2     6
11    13      1     7
12    14      2     7
13    17      2     9
14    18      1     9
15    19      1    10
16    20      2    10