Extract elements common in all column groups

Question

I have a R dataset x as below:

  ID Month
1   1   Jan
2   3   Jan
3   4   Jan
4   6   Jan
5   6   Jan
6   9   Jan
7   2   Feb
8   4   Feb
9   6   Feb
10  8           


        

Answer 1

We can use data.table. Convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'ID', get the row index (.I) where the number of unique 'Months' are equal to the number of unique 'Months' in the whole dataset and subset the data based on this

library(data.table)
setDT(df1)[df1[, .I[uniqueN(Month) == uniqueN(df1$Month)], ID]$V1]
#    ID Month
# 1:  4   Jan
# 2:  4   Feb
# 3:  4   Mar
# 4:  4   Apr
# 5:  4   May
# 6:  4   Jun
# 7:  6   Jan
# 8:  6   Jan
# 9:  6   Feb
#10:  6   Mar
#11:  6   Apr
#12:  6   May
#13:  6   Jun

To extract the 'ID's

setDT(df1)[, ID[uniqueN(Month) == uniqueN(df1$Month)], ID]$V1
#[1] 4 6

---

Or with base R

1) Using table with rowSums

v1 <- rowSums(table(df1) > 0)
names(v1)[v1==max(v1)]
#[1] "4" "6"

This info can be used for subsetting the data

subset(df1, ID %in% names(v1)[v1 == max(v1)])

2) Using tapply

lst <- with(df1, tapply(Month, ID, FUN = unique))
names(which(lengths(lst) == length(unique(df1$Month))))
#[1] "4" "6"

---

Or using dplyr

library(dplyr)
df1 %>%
     group_by(ID) %>%
     filter(n_distinct(Month)== n_distinct(df1$Month)) %>%
     .$ID %>%
     unique
#[1] 4 6

or if we need to get the rows

df1 %>%
     group_by(ID) %>%
     filter(n_distinct(Month)== n_distinct(df1$Month))
# A tibble: 13 x 2
# Groups:   ID [2]
#      ID Month
#    
# 1     4   Jan
# 2     6   Jan
# 3     6   Jan
# 4     4   Feb
# 5     6   Feb
# 6     4   Mar
# 7     6   Mar
# 8     4   Apr
# 9     6   Apr
#10     4   May
#11     6   May
#12     4   Jun
#13     6   Jun