How to find values shared between groups in a data frame? [duplicate]

Answer 1

Using base R you can use table:

as.numeric(colnames(a<-table(df))[colSums(a>0)==nrow(a)])
[1] 10 15

you can also do:

df %>%
   mutate(s = val %in% as.numeric(colnames(a<-table(df))[colSums(a>0)==nrow(a)]))
   exp val     s
1    A  10  TRUE
2    A  20 FALSE
3    A  15  TRUE
4    A  10  TRUE
5    B  10  TRUE
6    B  15  TRUE
7    B  99 FALSE
8    B   2 FALSE
9    C  15  TRUE
10   C  20 FALSE
11   C  10  TRUE
12   C   4 FALSE

Answer 2

We can go through the data.frame row by row and count up how many times that row's value is found in the vector df$val.

To deal with possible repeat values, we have to use group_by %>% distinct to remove repeated values of val within groups. But then to get just the values of val as a vector, we need to ungroup %>% select(val) %>% unlist, which just seems needlessly complicated.

Finally, we can check whether the number of groups the value is found in equals the total number of groups.

df %>%
    rowwise() %>%
    mutate(num_groups = sum(group_by(., exp) %>%
                                distinct(val) %>%
                                ungroup() %>%
                                select(val) %>%
                                unlist() %in% val),
           shared = num_groups == length(unique(.$exp)))


# A tibble: 12 x 4
   exp     val num_groups shared
   <fct> <dbl>      <int> <lgl> 
 1 A        10          3 TRUE  
 2 A        20          2 FALSE 
 3 A        15          3 TRUE  
 4 A        10          3 TRUE  
 5 B        10          3 TRUE  
 6 B        15          3 TRUE  
 7 B        99          1 FALSE 
 8 B         2          1 FALSE 
 9 C        15          3 TRUE  
10 C        20          2 FALSE 
11 C        10          3 TRUE  
12 C         4          1 FALSE 

Answer 3

Or you can group by val and then check whether the number of distinct exp for that val is equal to the data frame level number of distinct exp:

df %>% 
    group_by(val) %>% 
    mutate(shared = n_distinct(exp) == n_distinct(.$exp))
    # notice the first exp refers to exp for each group while .$exp refers 
    # to the overall exp column in the data frame

# A tibble: 12 x 3
# Groups:   val [6]
#   exp     val shared
#   <fct> <dbl> <lgl> 
# 1 A        10 TRUE  
# 2 A        20 FALSE 
# 3 A        15 TRUE  
# 4 A        10 TRUE  
# 5 B        10 TRUE  
# 6 B        15 TRUE  
# 7 B        99 FALSE 
# 8 B         2 FALSE 
# 9 C        15 TRUE  
#10 C        20 FALSE 
#11 C        10 TRUE  
#12 C         4 FALSE

Answer 4

Here is an other base R solution:

x <- split(df$val, df$exp)
Reduce(intersect, x)
## [1] 10 15