Add missing values in time series efficiently

I have 500 datasets (panel data). In each I have a time series (week) across different shops (store). Within each shop, I would need to add missing time series observations.

A sample of my data would be:

store   week           value
1           1          50
1           3          52
1           4          10
2           1          4
2           4          84
2           5          2

which I would like to look like:

store   week        value
1           1       50
1           2       0
1           3       52
1           4       10
2           1       4
2           2       0
2           3       0
2           4       84
2           5       2

I currently use the following code (which works, but takes very very long on my data):

  stores<-unique(mydata$store)

  for (i in 1:length(stores)){ 
  mydata <- merge(
    expand.grid(week=min(mydata$week):max(mydata$week)),
    mydata, all=TRUE)
  mydata[is.na(mydata)] <- 0
  }

Are there better and more efficient ways to do so?