R: Find and add missing (/non existing) rows in time related data frame - r

Question

I'm struggling with the following.
If have a (big) data frame with the following:
several columns for which the combination of columns is a 'unique' combination, say ID
a time related column
a measure related column
I want to make sure that for each unique ID for each time interval a measure is available in the data frame. And if it is not, I want to add a 0 (or NA) measure for that time/ID.
To illustrate the problem, create the following test data frame:
test <- data.frame(
YearWeek =rep(c("2012-01","2012-02"),each=4),
ProductID =rep(c(1,2), times=4),
CustomerID =rep(c("a","b"), each=2, times=2),
Quantity =5:12
)[1:7,]
YearWeek ProductID CustomerID Quantity
1 2012-01 1 a 5
2 2012-01 2 a 6
3 2012-01 1 b 7
4 2012-01 2 b 8
5 2012-02 1 a 9
6 2012-02 2 a 10
7 2012-02 1 b 11
The 8th row is left out, on purpose. This way I simulate a 'missing value' (missing Quantity) for ID '2-b' (ProductID-CustomerID) for the time value "2012-02".
What I want to do is adjust the data.frame in such a way that for all time values (these are known, in this example just "2012-01" and "2012-02"), for all ID-combinations (these are not known upfront, but this is 'all unique ID combinations in the data frame', thus the unique set on the ID columns), a Quantity is available in the data frame.
This should result for this example (if we choose NA for the missing value, typically I want to have control on that):
YearWeek ProductID CustomerID Quantity
1 2012-01 1 a 5
2 2012-01 2 a 6
3 2012-01 1 b 7
4 2012-01 2 b 8
5 2012-02 1 a 9
6 2012-02 2 a 10
7 2012-02 1 b 11
8 2012-02 2 b NA
The ultimate goal is to create time series for these ID combinations and I therefore want to have Quantities for all time values. I need to do different aggregations (on time) and using different levels of ID's from a big dataset
I tried several things, for instance with melt and cast from the reshape package. But so far I didn't manage to do it. The next step is creating a function, with for-loops etc. but that is not really useful from a performance perspective.
Maybe there is an easier way to create time series instantly, giving a data.frame like test. Does anybody have an idea on this one??
Thanks in advance!
Note that in the actual problem there are more than two 'ID columns'.
EDIT:
I should describe the problem further. There is a difference between the 'time' column and the 'ID' columns. The first (and great!) answer on the question by joran, maybe didn't get a clear understanding from what I want (and the example I gave didn't made the difference clear). I said above:
for all ID-combinations (these are not known upfront, but this is 'all
unique ID combinations in the data frame', thus the unique set on the
ID columns)
So I do not want 'all possible ID combinations' but 'all ID combinations within the data'.
For each of those combinations I want a value for every unique time-value.
Let me make it clear by expanding test to test2, as follows
> test2 <- rbind(test, c("2012-02", 3, "a", 13))
> test2
YearWeek ProductID CustomerID Quantity
1 2012-01 1 a 5
2 2012-01 2 a 6
3 2012-01 1 b 7
4 2012-01 2 b 8
5 2012-02 1 a 9
6 2012-02 2 a 10
7 2012-02 1 b 11
8 2012-02 3 a 13
Which means I want in the resulting data frame no '3-b' ID combination, because this combination is not within test2. If I use the method of the first answer I will get the following:
> vals2 <- expand.grid(YearWeek = unique(test2$YearWeek),
ProductID = unique(test2$ProductID),
CustomerID = unique(test2$CustomerID))
> merge(vals2,test2,all = TRUE)
YearWeek ProductID CustomerID Quantity
1 2012-01 1 a 5
2 2012-01 1 b 7
3 2012-01 2 a 6
4 2012-01 2 b 8
5 2012-01 3 a <NA>
6 2012-01 3 b <NA>
7 2012-02 1 a 9
8 2012-02 1 b 11
9 2012-02 2 a 10
10 2012-02 2 b <NA>
11 2012-02 3 a 13
12 2012-02 3 b <NA>
So I don't want the rows 6 and 12 to be here.
To overcome this problem I found a solution in the one below. In here I split the 'unique time column' and the 'unique ID combination'. The difference with above is thus the word 'combination' and not unique for every ID column.
> temp_merge <- merge(unique(test2["YearWeek"]),
unique(test2[c("ProductID", "CustomerID")]))
> merge(temp_merge,test2,all = TRUE)
YearWeek ProductID CustomerID Quantity
1 2012-01 1 a 5
2 2012-01 1 b 7
3 2012-01 2 a 6
4 2012-01 2 b 8
5 2012-01 3 a <NA>
6 2012-02 1 a 9
7 2012-02 1 b 11
8 2012-02 2 a 10
9 2012-02 2 b <NA>
10 2012-02 3 a 13
What are the comments on this one?
Is this an elegant way, or are there better ways?

Answer 1

Use expand.grid and merge:
vals <- expand.grid(YearWeek = unique(test$YearWeek),
ProductID = unique(test$ProductID),
CustomerID = unique(test$CustomerID))
> merge(vals,test,all = TRUE)
YearWeek ProductID CustomerID Quantity
1 2012-01 1 a 5
2 2012-01 1 b 7
3 2012-01 2 a 6
4 2012-01 2 b 8
5 2012-02 1 a 9
6 2012-02 1 b 11
7 2012-02 2 a 10
8 2012-02 2 b NA
The NAs can be replaced after the fact with whatever values you choose using subsetting and is.na.