Stata and R:
I have two cross-sectional datasets I'm merging. The two datasets have an equal amount of countries and only one dataset has zero missing years (year). The problem is that the missing years are simply not recorded, so I need to make a new variable that would add the years where there is no other data. Otherwise, I cannot merge the datasets according to the two keys, country and year.
Not so -- in Stata (and I would be surprised at a problem in R, but others must speak to that).
Missing observations -- in this context and any similar better called absent -- are not a problem. Here's a demonstration. merge is smart enough to notice gaps and make them explicit as missings. You could "fix" them yourself ahead of the merge, but that is pointless.
clear
input state year y
1 2019 1
1 2020 2
2 2019 3
2 2020 4
end
save tomerge
clear
input state year x
1 2019 42
2 2019 84
end
merge 1:1 state year using tomerge
list
Results
. merge 1:1 state year using tomerge
Result Number of obs
-----------------------------------------
Not matched 2
from master 0 (_merge==1)
from using 2 (_merge==2)
Matched 2 (_merge==3)
-----------------------------------------
.
. list
+----------------------------------------+
| state year x y _merge |
|----------------------------------------|
1. | 1 2019 42 1 Matched (3) |
2. | 2 2019 84 3 Matched (3) |
3. | 1 2020 . 2 Using only (2) |
4. | 2 2020 . 4 Using only (2) |
+----------------------------------------+
Otherwise put, 1:1 as syntax specifies the overall pattern and doesn't rule out 0:1 or 1:0 matches. merge will actually append if identifiers don't match at all. You do need the key variables to exist under identical names in both datasets.
