How can I filter the coordinates (lat, lon) in the data table?

TL; DR

This image of the left outer join displays exactly what I would like: deleting rows data.table

based on two columns lat, lon

that exactly match the lat, lon

columns of another data.table

.

Problem

Suppose I have the following data.table

"dt.master"

with over 1 million lines containing id

and coordinates of a specific location lat, lon

:

id    lat      lon
1     43.23    5.43
2     43.56    4.12
3     52.14   -9.85
4     43.56    4.12
5     43.83    9.43
...   ...      ...

      

        
        
        
      

    

What I would like to do is remove the lines that match a specific pair of coordinates. You might think that a pair of coordinates would be blacklisted (again a data.table

named a "dt.blacklist"

):

lat      lon
43.56    4.12
11.14   -5.85

      

        
        
        
      

    

In this case, when applying the blacklist, the answer should be:

id    lat      lon
1     43.23    5.43
3     52.14   -9.85
5     43.83    9.43
...   ...      ...  

      

        
        
        
      

    

Oddly enough, I cannot get it right.

What have I done so far

• Using merge
  
  for example:

  dt.result <- merge(dt.master, dt.blacklist[, c("lat", "lon")], by.x=c("lat", "lon"), by.y=c("lat", "lon"))
  
        
  
          
          
          
        
  
      

  But this gives lines that match and therefore are an inner join. I was thinking about deleting rows based on this result using subset
  
  :

  subset(dt.master, lat != dt.result$lat & lon != dt.result$lon)
  
        
  
          
          
          
        
  
      

  But the problem is that it partially works, as this example only deletes one line, not two lines as we would like. Somehow he only deletes the first "hit".

• Using a quick and dirty solution, concatenating lat, lon
  
  into a new named column "C"
  
  in both data tables and then deleting it as such:

  dt.master[C != dt.blacklist$C]
  
        
  
          
          
          
        
  
      

  However, the same problem occurs when only one of the two rows is deleted.