R - subset column based on condition on duplicate rows

Question

I have a dataframe with an id column that is repeated, with site counts. I want to know how I can remove the duplicates ID records only when Site_Count record is more than 0.

Generate DF:

DF <- data.frame(
    'ID' = sample(100:300, 100, replace=T),
    'Site_count' = sample(0:1, 100, replace=T)
)

My attempt at the subset:

subset(DF[!duplicated(DF$ID),], site_count > 0)

But in this case it will remove all 0 site counts - I want to subset to only remove the record when there is a duplicate record with more than 0 site count.

Desirable results would look something like this (notice there site IDs with 0 site count, but no duplicate IDs with 0 and another site count):

ID    site count
--    ----------
1        0
2        1
3        1
4        0
5        5

Answer 1

The expected output is not very clear. May be this helps:

 indx <- with(DF, ave(!Site_count, ID, FUN=function(x) sum(x)>1))
 DF[!(duplicated(DF$ID) & indx),]

Update

After re-reading the description, your expected answer could also be:

 indx <- with(DF, ave(Site_count, ID, FUN=function(x) any(x>0)))
 DF[!(duplicated(DF$ID) & indx),]

Answer 2

Possibly this:

set.seed(42)
DF <- data.frame(
  'ID' = c(sample(1:3, 10, replace=T), 4),
  'Site_count' = c(sample(0:3, 10, replace=T), 0)
)

#   ID Site_count
#1   3          1
#2   3          2
#3   1          3
#4   3          1
#5   2          1
#6   2          3
#7   3          3
#8   1          0
#9   2          1
#10  3          2
#11  4          0


fun <- function(x) {
  if (length(x) == 1L) return(x) else {
    return(x[which.max(x > 0)])
  }
}
library(plyr)
ddply(DF, .(ID), summarise, Site_count = fun(Site_count))
#  ID Site_count
#1  1          3
#2  2          1
#3  3          1
#4  4          0