Find all sequences with the same column value

Question

I have the following data frame:

╔══════╦═════════╗
║ Code ║ Airline ║
╠══════╬═════════╣
║    1 ║ AF      ║
║    1 ║ KL      ║
║    8 ║ AR      ║
║    8 ║ A         


        

Answer 1

Several options using the data.table package:

1) Using strsplit, paste & operate by row:

library(data.table)
setDT(dat)[, Airline := trimws(Airline)  # this step is needed to remove the leading and trailing whitespaces
           ][, sharedwith := paste(Airline, collapse = ','), Code
            ][, sharedwith := paste(unlist(strsplit(sharedwith,','))[!unlist(strsplit(sharedwith,',')) %in% Airline], 
                                    collapse = ','), 1:nrow(dat)]

which gives:

> dat
   Code Airline sharedwith
1:    1      AF         KL
2:    1      KL         AF
3:    8      AR      AZ,DL
4:    8      AZ      AR,DL
5:    8      DL      AR,AZ

2) Using strsplit & paste with mapply instead of by = 1:nrow(dat):

setDT(dat)[, Airline := trimws(Airline)
           ][, sharedwith := paste(Airline, collapse = ','), Code
             ][, sharedwith := mapply(function(s,a) paste(unlist(strsplit(s,','))[!unlist(strsplit(s,',')) %in% a], 
                                                          collapse = ','),
                                      sharedwith, Airline)][]

which will give you the same result.

3) Or by using the CJ function with paste (inspired by the expand.grid solution of @zx8754):

library(data.table)
setDT(dat)[, Airline := trimws(Airline)
           ][, CJ(air=Airline, Airline,  unique=TRUE)[air!=V2][, .(shared=paste(V2,collapse=',')), air],
             Code]

which gives:

   Code air shared
1:    1  AF     KL
2:    1  KL     AF
3:    8  AR  AZ,DL
4:    8  AZ  AR,DL
5:    8  DL  AR,AZ

---

A solution with dplyr & tidyr to get the desired solution (inspired by @jaimedash):

library(dplyr)
library(tidyr)

dat <- dat %>% mutate(Airline = trimws(as.character(Airline)))

dat %>%
  mutate(SharedWith = Airline) %>% 
  group_by(Code) %>%
  nest(-Code, -Airline, .key = SharedWith) %>%
  left_join(dat, ., by = 'Code') %>%
  unnest() %>%
  filter(Airline != SharedWith) %>%
  group_by(Code, Airline) %>%
  summarise(SharedWith = toString(SharedWith))

which gives:

   Code Airline SharedWith
  (int)   (chr)      (chr)
1     1      AF         KL
2     1      KL         AF
3     8      AR     AZ, DL
4     8      AZ     AR, DL
5     8      DL     AR, AZ