Stata longwise average - date

Question

I'm using Stata and trying to compute conditional means based on time/date. For each store I want to calculate mean (inventory) per year. If there are missing year gaps, then I want to take the mean from the closest two dates' inventory values.
I've used (below) to get overall means per store, but I need more granularity.
egen mean_inv = mean(inventory), by (store)
I've also tried this loop with similar results:
by id, sort: gen v1'=_n'
forvalues x = 1/'=n'{
by store: sum inventory if v1==`x'
replace mean_inv= r(mean) if v1==`x'
}
Visually, I want mean inventory per store: (store id is not sequential)
5/1/2003 2/3/2006 8/9/2006 3/5/2007 6/9/2007 2/1/2008
13 18 12 15 24 11
[mean1] [mean2] [mean3] [mean4] [mean5]
store date inventory
1 16750 17
1 18234 16
1 15844 13
1 17111 14
1 17870 13
1 16929 13.5
1 17503 13
4 15987 18
4 15896 16
4 18211 16
4 17154 18
4 17931 24
4 16776 23
12 16426 26
12 17681 17
12 16386 17
12 16603 18
12 17034 16
12 17205 16
42 15798 18
42 16022 18
42 17496 16
42 17870 18
42 16204 18
42 16778 14
33 18053 23
33 16086 13
33 16450 21
33 17374 19
33 16814 19
33 15834 16
33 16167 16
56 17686 16
56 17623 18
56 17231 20
56 15978 16
56 16811 15
56 17861 20

Answer 1

It is hard to relate your code to the word description of your problem.
Your egen call calculates means by store, not year.
Your longer fragment does not make complete sense given lack of definitions and at least one typo.
Note that your variable v1 contains identifiers that run 1 up within groups of store, and does not distinguish different values of store, as you (seem to) imply. It strains credibility that it produces results anywhere near those by the egen call.
n is not defined and the code evaluating it is presumably intended to be
`=n'
If you calculate
by store: sum inventory if v1 == `x'
several means will be calculated in turn but only the last to be calculated will be accessible as r(mean).
The sample data are unrelated to the problem. There is no year variable and even if the dates are Stata daily dates, they are all dates within 1960.
Setting all that aside, suppose you have variables store, inventory and year. You can try
collapse inventory, by(store year)
fillin store year
ipolate inventory year, gen(inventory2) by(store)
The collapse produces a reduced dataset of means. The ipolate interpolates across gaps, as you ask. fillin may not be adequate to give all the store and year combinations you want and you may need to add further years manually before the interpolation. If you want to put these results back with the original data, that's a merge.
In total, this is a pretty messy question.