How to create missing value for repeated measurement data?

Question

I have a data set that not every subject’s observations were observed at the exact same time points, but I want to turn it in to a data set that every one’s observations were observed at the exact same time points (so that I can use it in SAS proc traj).

For example, suppose I have dataset "m":

id   <- c(1,1,1,1,2,2,3,3,3)
age  <- c(2,3,4,5,3,6,2,5,8)
IQ   <- c(3,4,5,4,6,5,3,8,10)
m    <- data.frame(id,age,IQ)
> m
  id age IQ
1  1   2  3
2  1   3  4
3  1   4  5
4  1   5  4
5  2   3  6
6  2   6  5
7  3   2  3
8  3   5  8
9  3   8 10
> unique(age)
[1] 2 3 4 5 6 8

I want to turn m to m2. But I can only do that manually.

id2   <- c(1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3)
age2  <- c(2,3,4,5,6,8,2,3,4,5,6,8,2,3,4,5,6,8) 
IQ2   <- c(3,4,5,4,NA,NA,6,5,NA,NA,NA,NA,3,8,10,NA,NA,NA) 
m2    <- data.frame(id2,age2,IQ2)    
m2
> m2
   id2 age2 IQ2
1    1    2   3
2    1    3   4
3    1    4   5
4    1    5   4
5    1    6  NA
6    1    8  NA
7    2    2   6
8    2    3   5
9    2    4  NA
10   2    5  NA
11   2    6  NA
12   2    8  NA
13   3    2   3
14   3    3   8
15   3    4  10
16   3    5  NA
17   3    6  NA
18   3    8  NA

Does anyone know a smarter way to do this?

Answer 1

Using tidyr, this is a one liner. You use the complete function, which creates rows with each combination of the columns passed to it, filling the rest of the rows with NA:

library(tidyr)
complete(m, id, age)

Source: local data frame [18 x 3]

      id   age    IQ
   (dbl) (dbl) (dbl)
1      1     2     3
2      1     3     4
3      1     4     5
4      1     5     4
5      1     6    NA
6      1     8    NA
7      2     2    NA
8      2     3     6
9      2     4    NA
10     2     5    NA
11     2     6     5
12     2     8    NA
13     3     2     3
14     3     3    NA
15     3     4    NA
16     3     5     8
17     3     6    NA
18     3     8    10