I have a dataset which is in longformat in which Measurements (Time) are nested in Networkpartners (NP) which are nested in Persons (ID), here is an example of what it looks like (the real dataset has over thousands of rows):

```
ID  NP  Time Outcome1 Outcome2
1   11  1    4        NA
1   11  2    3        4
1   11  3    NA       NA
1   12  1    2        3
1   12  2    3        1
1   12  3    3        2
2   21  1    2        4
2   21  2    NA       NA
2   21  3    NA       NA
2   22  1    4        NA
2   22  2    4        3
2   22  3    NA       4

```

Now I would like to create the following new variable "NP.T":

The Number of Networkpartners (who have no NA in outcome1 AND outcome2 at this measurement) a specific person (ID) at a specific time. In other words: I want to count the NPs who have one or none NAs at the two outcome-variables. And I do not want to count those NPs who have NAs in BOTH outcome-variables.

So I would like to create a dataset like this:

```
ID  NP  Time Outcome1 Outcome2 NP.T
1   11  1    4        NA       2
1   11  2    3        4        2
1   11  3    NA       NA       1
1   12  1    2        3        2
1   12  2    3        1        2
1   12  3    3        2        1
2   21  1    2        4        2
2   21  2    NA       NA       1
2   21  3    NA       NA       1
2   22  1    4        NA       2
2   22  2    4        3        1
2   22  3    NA       4        1

```

I have the solution as provided in the earlier [question](https://stackoverflow.com/questions/18027823/creating-a-new-columns-from-a-data-frame) on how to create a variable that counts the number of Networkpartners (who have no NA in ONE of the two outcomes) a specific person (ID) at a specific time:

```
library(plyr)
mydata1<-ddply(mydata,.(ID,Time),transform, NP.T=length(Outcome[which(Outcome !   ="NA")]))

```

Now, I'd be very thankful if somebody could help me to find an answer on my specific problem!