R how to group column values by set theory - r

Question

I have dataset like this:
VisitID | Item |
1 | A |
1 | B |
1 | C |
1 | D |
2 | A |
2 | D |
2 | B |
3 | B |
3 | C |
4 | D |
4 | C |
I would like to create a classification column for items according to the set theory conditions:
VisitID contains A only, B only, C only, A&B, A&C, B&C, A&B&C, Others (Neither A,B,C exists)
The results should look like this:
VisitID | Item | Classification |
1 | A | A&B&C |
1 | B | A&B&C |
1 | C | A&B&C |
1 | D | A&B&C |
2 | A | A&B |
2 | D | A&B |
2 | B | A&B |
3 | B | B&C |
3 | C | B&C |
4 | D | C only |
4 | C | C only |
How can I do this in R, especially with dplyr?

Answer 1

You can use a left_join of the data with a group_by, filtered, summarised one.
library(dplyr)
data %>% left_join(
group_by(data, VisitID) %>%
distinct(VisitID, Item) %>%
filter(Item %in% c("A","B","C")) %>%
summarise(set=paste0(Item, collapse="&")),
by="VisitID")
Output:
VisitID Item set
1 1 A A&B&C
2 1 B A&B&C
3 1 C A&B&C
4 1 D A&B&C
5 2 A A&B
6 2 D A&B
7 2 B A&B
8 3 B B&C
9 3 C B&C
10 4 D C
11 4 C C
12 5 D <NA>
13 5 E <NA>
Data:
dput(data)
structure(list(VisitID = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L,
4L, 4L, 5L, 5L), Item = c("A", "B", "C", "D", "A", "D", "B",
"B", "C", "D", "C", "D", "E")), class = "data.frame", row.names = c(NA,
-13L))

Answer 2

We can write a custom function :
paste_values <- function(x) {
x1 <- x[x %in% c("A", "B", "C")]
if (n_distinct(x1) == 1)
#If want to keep in base R
#if (length(unique(x1) == 1)
paste0(unique(x1), " only")
else
paste0(unique(x1), collapse = " & ")
}
and apply it for each group.
library(dplyr)
df %>% group_by(VisitID) %>% mutate(Item = paste_values(Item))
# VisitID Item
# <int> <chr>
# 1 1 A & B & C
# 2 1 A & B & C
# 3 1 A & B & C
# 4 1 A & B & C
# 5 2 A & B
# 6 2 A & B
# 7 2 A & B
# 8 3 B & C
# 9 3 B & C
#10 4 C only
#11 4 C only
We can also use the same function in base R :
df$Item <- with(df, ave(Item, VisitID, FUN = paste_values))