Consider this data set:
> DATA <- data.frame(Agreement_number = c(1,1,1,1,2,2,2,2),
+ country = c("Canada","Canada", "USA", "USA", "Canada","Canada", "USA", "USA"),
+ action = c("signature", "ratification","signature", "ratification", "signature", "ratification","signature", "ratification"),
+ signature_date = c(2000,NA,2000,NA, 2001, NA, 2002, NA),
+ ratification_date = c(NA, 2001, NA, 2002, NA, 2001, NA, 2002))
> DATA
Agreement_number country action signature_date ratification_date
1 Canada signature 2000 NA
1 Canada ratification NA 2001
1 USA signature 2000 NA
1 USA ratification NA 2002
2 Canada signature 2001 NA
2 Canada ratification NA 2001
2 USA signature 2002 NA
2 USA ratification NA 2002
As you can see, half of the rows have duplicate information. For a small data set like this it is really easy to remove duplicates. I could use the coalesce function (dplyr package), get rid of the "action" column and then erase all the irrelevant rows. Though, there many other ways. The final result should look like this:
> DATA <- data.frame( Agreement_number = c(1,1,2,2),
+ country = c("Canada", "USA", "Canada","USA"),
+ signature_date = c(2000,2000,2001,2002),
+ ratification_date = c(2001, 2002, 2001, 2002))
> DATA
Agreement_number country signature_date ratification_date
1 Canada 2000 2001
1 USA 2000 2002
2 Canada 2001 2001
2 USA 2002 2002
The problem, is that my real data set is MUCH bigger (102000 x 270) and there are many more variables. The real data is also more irregular and there are more absent values. The coalesce function seems very slow. The best loop I could make so far still takes up to 5-10 minutes to run.
Is there a simple way of doing this which would be faster? I have the feeling that there must be some function in R for that kind of operation, but I couldn't find any.