Question

I have a long format unbalanced longitudinal data. I would like to exclude all the cases that do not contain complete information. By that I mean all cases that do not repeat 8 times. Someone can help me finding a solution?

Below an example: I have three subjects {A, B, and C}. I have 8 information for A and B, but only 2 for C. How can I delete rows in which C is present based on the information it has less than 8 repeated measurements?

temp = scan()
A 1 1 1 0
A 1 1 0 1
A 1 0 0 0
A 1 1 1 1
A 0 1 0 0
A 1 1 1 0
A 1 1 0 1
A 1 0 0 0
B 1 1 1 0
B 1 1 0 1
B 1 0 0 0
B 1 1 1 1
B 0 1 0 0
B 1 1 1 0
B 1 1 0 1
B 1 0 0 0
C 1 1 1 1
C 0 1 0 0

Any help?

Answer 1

Assuming your variable names are V1, V2… and so on, here’s one approach:

temp[temp$V1 %in% names(which(table(temp$V1) == 8)), ]

The table(temp$V1) == 8 matches the values in the V1 column that have exactly 8 cases. The names(which(... part creates a basic character vector that we can match using %in%.

And another:

temp[ave(as.character(temp$V1), temp$V1, FUN = length) == "8", ]