I have a fairly large data set with nearly 6000 observations in its entirety. I was provided another data set that has missing observations. Instead of filling the rows/observations with NA if measurement did not occur, the entire row/observation was omitted. This second data set has 5500 rows.
I need to determine which observations were not recorded, or in other words, which rows are missing in the second data set. I'm not referring to NAs or missing values, but observations that did not make it into the data set.
In my example below, every observation (ID) should also have "group' record of 11,12,13,14,21,22,23,24. However, ID 206902 only has groups 11, 12, 14, 21, 22, 23, 24. It's missing 13
In this example, the ID's aren't truly unique, so there should be 8 ID's. For example, ID 206901 & group 11; ID 206901 & group 12, etc.
How can I easily determine which observations (ID) are missing? Again, there should be 8 records for each ID.
example <- structure(list(ID = c(206901L, 206901L, 206901L, 206901L, 206901L,
206901L, 206901L, 206901L, 206902L, 206902L, 206902L, 206902L,
206902L, 206902L, 206902L), group = c(11L, 12L, 13L, 14L, 21L,
22L, 23L, 24L, 11L, 12L, 14L, 21L, 22L, 23L, 24L)), .Names = c("ID",
"group"), sorted = "ID", class = c("tbl_dt", "tbl", "data.table",
"data.frame"), row.names = c(NA, -15L), .internal.selfref = <pointer: 0x0000000000100788>)