(You have two viable strategies already, but here is another which may be conceptually easier to follow if you are relatively new to R and aren't familiar with the way plyr works.)
I often need to know how many NA
s I have in different variables, so here is a convenience function I use standard:
sna <- function(x){
sum(is.na(x))
}
From there, I sometimes use aggregate()
, but sometimes I find ?summaryBy in the doBy package to be more convenient. Here's an example:
library(doBy)
z <- summaryBy(Diff~ID, data=my.data, FUN=sna)
z
ID Diff.sna
1 11 1
2 189 0
3 487 0
4 566 0
5 789 1
After this, you just need to use ?merge and convert the count of NA
s to a logical to get your final data frame:
my.data <- merge(my.data, z, by="ID")
my.data$Diff.sna <- my.data$Diff.sna>0
my.data
ID Diff Diff.sna
1 11 445 TRUE
2 11 NA TRUE
3 189 578 FALSE
4 189 810 FALSE
5 487 860 FALSE
6 487 780 FALSE
7 566 100 FALSE
8 566 277 FALSE
9 789 529 TRUE
10 789 43 TRUE
11 789 NA TRUE