R exact selection based on %in%

Question

I am having some trouble doing a simple operation.

What I want is to simply select the rows, grouped by id, that have all of the values (-1, -2, -3, -4), and apply a tag based on this condition.

   id time
1   1   -4
2   1   -3
3   1   -2
4   1   -1
5   2   -1
6   2   -2
7   3   -1
8   3   -3
9   4   -1
10  4   -2
11  4   -3
12  4   -4

Intuitively I was going for

rb[rb$time %in% c(-1, -2, -3, -4), ]

However, when I do that, all id are selected. So, the %in% operator uses the OR | operator.

So, I thought I could do this then, to use the AND & operator

rb[rb$time == -1 & 
 rb$time == -2 & 
 rb$time == -3 & 
 rb$time == -4, ]

But it does not work.

Any clue how I could tag or subset these id, such that

   id time tag
1   1   -4   1
2   1   -3   1
3   1   -2   1
4   1   -1   1
5   2   -1   0
6   2   -2   0
7   3   -1   0
8   3   -3   0
9   4   -1   1
10  4   -2   1
11  4   -3   1
12  4   -4   1

Answer 1

One dplyr possibility could be:

df %>%
 group_by(id) %>%
 mutate(tag = all(c(-1, -2, -3, -4) %in% time) * 1)

      id  time   tag
   <int> <int> <dbl>
 1     1    -4     1
 2     1    -3     1
 3     1    -2     1
 4     1    -1     1
 5     2    -1     0
 6     2    -2     0
 7     3    -1     0
 8     3    -3     0
 9     4    -1     1
10     4    -2     1
11     4    -3     1
12     4    -4     1

And the same with base R could be:

with(df, ave(time, id, FUN = function(x) all(c(-1, -2, -3, -4) %in% x) * 1))

Answer 2

One of many base R options is to use aggregate (we could also rename and convert to integer to get 0 or 1, but booleans seem better fit for the purpose):

tags <- aggregate(df$time, by = df["id"], FUN = function(el) all(-1:-4 %in% el))
df <- merge(df, tags)

Result:

df
#    id time     x
# 1   1   -4  TRUE
# 2   1   -3  TRUE
# 3   1   -2  TRUE
# 4   1   -1  TRUE
# 5   2   -1 FALSE
# 6   2   -2 FALSE
# 7   2   -1 FALSE
# 8   2   -3 FALSE
# 9   3   -1  TRUE
# 10  3   -2  TRUE
# 11  3   -3  TRUE
# 12  3   -4  TRUE