I apologize if this is duplicative inquiry. I am working with the 2017-2018 Area Health Resource File which is county data. I reshaped the the data set from in long layout, and it have been set up for longitudinal (panel) data analysis. For this question, I have looked at several previous entries and have not been able to find one that provides an answer to my question.

I have 3 separate variables for education created in my dataset which is the number of non-veterans in a county that had either a high school, some college or college or more education from 2012-2016. I am hoping to create a categorical variable called "educ" that would be a combination of these three variables.

I tried using the following format for generating a new variable but i haven't had any luck:

gen educ=1 if nonvetedu_hs>1
replace educ=2 if nonvetedu_hsplus>1
replace educ=3 if nonvetedu_college>1
replace educ=4 if nonvetedu_hs<. & nonvetedu_hsplus<. & nonvetedu_college<.

My goal is to create categorical variables for similar other variables (race, gender, income, etc) which are all formatted the same way so that I can use it in regression model. I included the three variables of interest plus the year variable. I previously reshaped from wide to long and the data for 2013-2016 is missing. Not sure how I fix this (or if i need to fix this) for the model ...but I will follow up on that in a separate post.

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long(nonvetedu_college nonvetedu_hsplus nonvetedu_hs) byte year
    .      .     . 10
    .      .     . 11
 6783  26851  4274 12
    .      .     . 13
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    .      .     . 16
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    .      .     . 10
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35408 107434 13014 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 2074  12031  4616 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 1667  11594  2881 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 4499  27648  7355 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
  672   4530  2397 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 1996  10117  2482 12
    .      .     . 13
    .      .     . 14
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    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
11694  55134 13080 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 2574  17094  4404 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 2293  13272  3196 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
    .      .     . 11
 3855  21120  5630 12
    .      .     . 13
    .      .     . 14
    .      .     . 15
    .      .     . 16
    .      .     . 17
    .      .     . 18
    .      .     . 10
end
Thank you in advance