Filter directed co-occurrences in a table

Question

I have co-occurrence data that can be represented in two columns. The entries in each column are from the same set of possibilities. Ultimately I am aiming to plot a directed network but first I would like to split the table into those that reciprocal (i.e. both X->Y and Y->X) and those that occur in just one direction (i.e. only Y->Z). Here is an example:

library(tidyverse)

# Example data
from <-  c("A", "B", "F", "Q", "T", "S", "D", "E", "A", "T", "F")
to <- c("E", "D", "Q", "S", "F", "T", "B", "A", "D", "A", "E")
df <- data_frame(from, to)
df
# A tibble: 11 x 2
   from  to   
   <chr> <chr>
 1 A     E    
 2 B     D    
 3 F     Q    
 4 Q     S    
 5 T     F    
 6 S     T    
 7 D     B    
 8 E     A    
 9 A     D    
10 T     A    
11 F     E

and here is my desired output:

# Desired output 1 - reciprocal co-occurrences
df %>% 
  slice(c(1,2)) %>% 
  rename(item1 = from, item2 = to)

# A tibble: 2 x 2
  item1 item2
  <chr> <chr>
1 A     E    
2 B     D

# Desired output 2 - single occurrences
df %>% 
  slice(c(3,4,6,6,9,10,11))

# A tibble: 7 x 2
  from  to   
  <chr> <chr>
1 F     Q    
2 Q     S    
3 S     T    
4 S     T    
5 A     D    
6 T     A    
7 F     E

If the co-occurrences are reciprocal it does not matter what order the entries are in I only need their names co-occurrences are not I need to know the direction.

This feels like a graph problem so I have had a go but am unfamiliar with working with this type of data and most tutorials seem to cover undirected graphs. Looking at the tidygraph package which I understand uses the igraph package I have tried this:

library(tidygraph)

df %>% 
  as_tbl_graph(directed = TRUE) %>%
  activate(edges) %>% 
  mutate(recip_occur = edge_is_mutual()) %>% 
  as_tibble() %>%
  filter(recip_occur == TRUE) 
# A tibble: 4 x 3
   from    to recip_occur
  <int> <int> <lgl>      
1     1     8 TRUE       
2     2     7 TRUE       
3     7     2 TRUE       
4     8     1 TRUE

However this divorces the edges from the nodes and repeats reciprocal co-occurrences. Does anyone have experience with this sort of data?

score 1 · Accepted Answer · answered Jul 25 '18 at 08:19

try this:

data:

from <-  c("A", "B", "F", "Q", "T", "S", "D", "E", "A", "T", "F")
to <- c("E", "D", "Q", "S", "F", "T", "B", "A", "D", "A", "E")
df <- data_frame(from, to)

code:

recursive_IND <-
1:nrow(df) %>% 
sapply(function(x){
    if(any((as.character(df[x,]) == t(df[,c(2,1)])) %>% {.[1,] & .[2,]}))
        return(T) else return(F)
    })

df[recursive_IND,][!(df[recursive_IND,] %>% apply(1,sort) %>% t %>% duplicated(.)),]
df[!recursive_IND,]

result:

# A tibble: 2 x 2
#  from  to   
#  <chr> <chr>
#1 A     E    
#2 B     D    

# A tibble: 7 x 2
#  from  to   
#  <chr> <chr>
#1 F     Q    
#2 Q     S    
#3 T     F    
#4 S     T    
#5 A     D    
#6 T     A    
#7 F     E

Filter directed co-occurrences in a table

1 Answers1