[Mariia Chavez]

Hello again,

I wasn't able to get the command to work after all. When I put the two cities, for some reason it still shows three plus an extra mean and total that I don't know what is for. I have attached part of the datasetto this message, containing the information I want included in the summarize table which is the three cities, the three city variables (citygnd cityage citywealth_t3), the weight variable, and one of the variables that I want displayed as a mean (b02age_n). Can you please help me make a command that will make a table such as the one you made for the nlsw88, but with the attached dataset?

Best,
Maria

[Andrew Musau]

Import the data to Stata and use dataex to post this. Note the -count- option of dataex if the number of observations exceeds 100.

help dataex

[Mariia Chavez]

Like this?

* Example generated by -dataex-. For more info, type help dataex
clear
input float(city citygnd cityage) byte b02age_n float citywealth_t3
2 3 3 19 4
3 5 6 35 7
1 1 1 21 1
3 6 6 37 7
2 3 3 21 4
2 3 4 36 4
3 5 5 18 7
3 6 6 33 7
2 3 3 18 4
3 5 5 24 7
2 4 3 22 4
2 3 3 18 4
2 3 3 21 4
3 6 6 34 7
1 1 2 32 1
2 3 4 37 4
2 4 3 18 4
1 2 1 28 1
1 1 2 29 1
3 5 6 38 7
3 5 6 36 7
1 1 2 39 1
1 1 1 22 1
1 2 2 33 1
3 5 5 24 7
2 3 3 19 4
2 3 4 35 4
2 3 3 18 4
3 5 5 18 7
2 3 4 30 4
2 3 4 33 4
1 1 1 27 1
3 6 5 18 7
1 1 2 33 1
2 3 4 35 4
1 1 1 18 1
2 4 3 18 4
2 3 4 32 4
2 3 3 24 4
2 3 3 21 4
3 6 5 25 7
2 4 3 18 4
2 3 3 23 4
2 3 4 39 4
3 5 6 39 7
3 5 5 25 7
3 5 5 19 7
2 3 4 31 4
3 6 6 35 7
1 1 2 39 1
2 3 3 19 4
3 6 5 26 7
1 1 2 36 1
3 6 6 35 7
3 5 6 30 7
2 3 3 18 4
3 5 6 29 7
1 1 2 33 1
1 1 2 38 1
3 5 6 35 7
3 5 5 25 7
2 3 3 21 4
3 5 5 26 7
1 1 1 24 1
3 5 6 29 7
3 6 6 38 7
1 1 1 21 1
2 4 3 26 4
3 6 5 25 7
2 3 3 25 4
3 6 5 27 7
3 5 6 31 7
2 3 4 31 4
3 6 6 37 7
2 4 3 27 4
1 2 1 18 1
2 3 4 29 4
2 3 4 39 4
2 3 3 23 4
2 3 3 27 4
2 4 3 28 4
2 3 3 26 4
2 4 3 18 4
2 3 4 35 4
2 4 3 23 4
2 3 4 39 4
3 6 6 32 7
3 6 6 35 7
1 1 1 27 1
1 2 1 19 1
2 3 3 28 4
2 4 3 19 4
3 5 6 37 7
1 2 2 39 1
1 1 1 25 1
2 4 3 23 4
3 5 6 30 7
2 3 3 18 4
2 3 3 24 4
1 1 2 29 1
end
label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda, age 18-28", modify
label def cityage 2 "Serekunda, age 29-39", modify
label def cityage 3 "Tema, age 18-28", modify
label def cityage 4 "Tema, age 29-39", modify
label def cityage 5 "São Vicente, age 18-28", modify
label def cityage 6 "São Vicente, age 29-39", modify
label values b02age_n missonly
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda, poorer", modify
label def citywealth_t3 4 "Tema, poorer", modify
label def citywealth_t3 7 "São Vicente, poorer", modify

PS: I tried using it to run the command and it is still giving me the same error messages without any output

[Andrew Musau]

Thanks, that will do it. Some issues with the data: only the first variable of the local 'vars' is present and the weight variable is not present. Taking these into account, you can have:

* Example generated by -dataex-. For more info, type help dataex
clear
input float(city citygnd cityage) byte b02age_n float citywealth_t3
2 3 3 19 4
3 5 6 35 7
1 1 1 21 1
3 6 6 37 7
2 3 3 21 4
2 3 4 36 4
3 5 5 18 7
3 6 6 33 7
2 3 3 18 4
3 5 5 24 7
2 4 3 22 4
2 3 3 18 4
2 3 3 21 4
3 6 6 34 7
1 1 2 32 1
2 3 4 37 4
2 4 3 18 4
1 2 1 28 1
1 1 2 29 1
3 5 6 38 7
3 5 6 36 7
1 1 2 39 1
1 1 1 22 1
1 2 2 33 1
3 5 5 24 7
2 3 3 19 4
2 3 4 35 4
2 3 3 18 4
3 5 5 18 7
2 3 4 30 4
2 3 4 33 4
1 1 1 27 1
3 6 5 18 7
1 1 2 33 1
2 3 4 35 4
1 1 1 18 1
2 4 3 18 4
2 3 4 32 4
2 3 3 24 4
2 3 3 21 4
3 6 5 25 7
2 4 3 18 4
2 3 3 23 4
2 3 4 39 4
3 5 6 39 7
3 5 5 25 7
3 5 5 19 7
2 3 4 31 4
3 6 6 35 7
1 1 2 39 1
2 3 3 19 4
3 6 5 26 7
1 1 2 36 1
3 6 6 35 7
3 5 6 30 7
2 3 3 18 4
3 5 6 29 7
1 1 2 33 1
1 1 2 38 1
3 5 6 35 7
3 5 5 25 7
2 3 3 21 4
3 5 5 26 7
1 1 1 24 1
3 5 6 29 7
3 6 6 38 7
1 1 1 21 1
2 4 3 26 4
3 6 5 25 7
2 3 3 25 4
3 6 5 27 7
3 5 6 31 7
2 3 4 31 4
3 6 6 37 7
2 4 3 27 4
1 2 1 18 1
2 3 4 29 4
2 3 4 39 4
2 3 3 23 4
2 3 3 27 4
2 4 3 28 4
2 3 3 26 4
2 4 3 18 4
2 3 4 35 4
2 4 3 23 4
2 3 4 39 4
3 6 6 32 7
3 6 6 35 7
1 1 1 27 1
1 2 1 19 1
2 3 3 28 4
2 4 3 19 4
3 5 6 37 7
1 2 2 39 1
1 1 1 25 1
2 4 3 23 4
3 5 6 30 7
2 3 3 18 4
2 3 3 24 4
1 1 2 29 1
end
label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda, age 18-28", modify
label def cityage 2 "Serekunda, age 29-39", modify
label def cityage 3 "Tema, age 18-28", modify
label def cityage 4 "Tema, age 29-39", modify
label def cityage 5 "São Vicente, age 18-28", modify
label def cityage 6 "São Vicente, age 29-39", modify
label values b02age_n missonly
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda, poorer", modify
label def citywealth_t3 4 "Tema, poorer", modify
label def citywealth_t3 7 "São Vicente, poorer", modify


estimates drop _all
local i 0
decode city, g(City)
replace City= strtoname(City)
local vars "b02age_n b37sblno_n"
foreach var1 of local vars{
    foreach var2 of varlist citygnd cityage citywealth_t3 {
        local ++i
        foreach city in Serekunda Tema São_Vicente{
            qui levelsof `var2', local(levs)
            foreach j of local levs{
                capture noisily{
                    eststo `city'`i'_`j': estpost summarize `var1' if `j'.`var2' & City=="`city'" 
                    mat mean= e(mean)
                    mat coleq mean= "`:lab (`var2') `j''"
                    estadd mat mean= mean, replace
                }
            }
        }
    }
    local opt= cond("`var1'"==word("`vars'", 1), "replace nonumb", "append eqlab(none) nonumb collab(none)")
    esttab Serekunda* Tema* São_Vicente* using testingall.csv, cells("mean(fmt(2)) count(label(N) fmt(0))") ///
    unstack `opt' mlab(none) noobs note("Extra Line") addnotes("Observations")
    estimates drop _all
}

[Andrew Musau]

Also, remove the commas from the value labels, as they cause the labels to split across multiple columns when opening the CSV file in Excel.

* Example generated by -dataex-. For more info, type help dataex
clear
input float(city citygnd cityage) byte b02age_n float citywealth_t3
2 3 3 19 4
3 5 6 35 7
1 1 1 21 1
3 6 6 37 7
2 3 3 21 4
2 3 4 36 4
3 5 5 18 7
3 6 6 33 7
2 3 3 18 4
3 5 5 24 7
2 4 3 22 4
2 3 3 18 4
2 3 3 21 4
3 6 6 34 7
1 1 2 32 1
2 3 4 37 4
2 4 3 18 4
1 2 1 28 1
1 1 2 29 1
3 5 6 38 7
3 5 6 36 7
1 1 2 39 1
1 1 1 22 1
1 2 2 33 1
3 5 5 24 7
2 3 3 19 4
2 3 4 35 4
2 3 3 18 4
3 5 5 18 7
2 3 4 30 4
2 3 4 33 4
1 1 1 27 1
3 6 5 18 7
1 1 2 33 1
2 3 4 35 4
1 1 1 18 1
2 4 3 18 4
2 3 4 32 4
2 3 3 24 4
2 3 3 21 4
3 6 5 25 7
2 4 3 18 4
2 3 3 23 4
2 3 4 39 4
3 5 6 39 7
3 5 5 25 7
3 5 5 19 7
2 3 4 31 4
3 6 6 35 7
1 1 2 39 1
2 3 3 19 4
3 6 5 26 7
1 1 2 36 1
3 6 6 35 7
3 5 6 30 7
2 3 3 18 4
3 5 6 29 7
1 1 2 33 1
1 1 2 38 1
3 5 6 35 7
3 5 5 25 7
2 3 3 21 4
3 5 5 26 7
1 1 1 24 1
3 5 6 29 7
3 6 6 38 7
1 1 1 21 1
2 4 3 26 4
3 6 5 25 7
2 3 3 25 4
3 6 5 27 7
3 5 6 31 7
2 3 4 31 4
3 6 6 37 7
2 4 3 27 4
1 2 1 18 1
2 3 4 29 4
2 3 4 39 4
2 3 3 23 4
2 3 3 27 4
2 4 3 28 4
2 3 3 26 4
2 4 3 18 4
2 3 4 35 4
2 4 3 23 4
2 3 4 39 4
3 6 6 32 7
3 6 6 35 7
1 1 1 27 1
1 2 1 19 1
2 3 3 28 4
2 4 3 19 4
3 5 6 37 7
1 2 2 39 1
1 1 1 25 1
2 4 3 23 4
3 5 6 30 7
2 3 3 18 4
2 3 3 24 4
1 1 2 29 1
end
label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda age 18-28", modify
label def cityage 2 "Serekunda age 29-39", modify
label def cityage 3 "Tema age 18-28", modify
label def cityage 4 "Tema age 29-39", modify
label def cityage 5 "São Vicente age 18-28", modify
label def cityage 6 "São Vicente age 29-39", modify
label values b02age_n missonly
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda poorer", modify
label def citywealth_t3 4 "Tema poorer", modify
label def citywealth_t3 7 "São Vicente poorer", modify


estimates drop _all
local i 0
decode city, g(City)
replace City= strtoname(City)
local vars "b02age_n b37sblno_n"
foreach var1 of local vars{
    foreach var2 of varlist citygnd cityage citywealth_t3 {
        local ++i
        foreach city in Serekunda Tema São_Vicente{
            qui levelsof `var2', local(levs)
            foreach j of local levs{
                capture noisily{
                    eststo `city'`i'_`j': estpost summarize `var1' if `j'.`var2' & City=="`city'"
                    mat mean= e(mean)
                    mat coleq mean= "`:lab (`var2') `j''"
                    estadd mat mean= mean, replace
                }
            }
        }
    }
    local opt= cond("`var1'"==word("`vars'", 1), "replace nonumb", "append eqlab(none) nonumb collab(none)")
    esttab Serekunda* Tema* São_Vicente* using testingall.csv, cells("mean(fmt(2)) count(label(N) fmt(0))") ///
    unstack `opt' mlab(none) noobs note("Extra Line") addnotes("Observations")
    estimates drop _all
}

[Mariia Chavez]

Dear Andrew,

Thank you so much for the tip about leaving out the commas in the labels! This was very helpful

As for the command, I am still having some issues.
There were some limitations in creating a dataset to share here, so that affected the dataset somewhat. For one, it said I was not allowed to include a weighting variable, which is why the weight is missing. Second, it had a cap of 100 observations for making the dataset. Thus, I also took out b37sblno_n to leave space for more of the others.

In making the dataset, these are therefore the commands I used to export the dataset:

keep b02age_n citygnd cityage citywealth_t3 city

dataex _all

That said, I see now that this still created some issues. First of all, it capped the citywealth_t3 so that it's just level 1, 4 and 7 "poorer" for all cities that are included, whereas "middle" and "wealthier" for all cities are excluded.

And when I tried to run the following command:

clear
fumi

label def cityage 1 "Serekunda age 18-28", modify
label def cityage 2 "Serekunda age 29-39", modify
label def cityage 3 "Tema age 18-28", modify
label def cityage 4 "Tema age 29-39", modify
label def cityage 5 "São Vicente age 18-28", modify
label def cityage 6 "São Vicente age 29-39", modify
label def citywealth_t3 1 "Serekunda poorer", modify
label def citywealth_t3 4 "Tema poorer", modify
label def citywealth_t3 7 "São Vicente poorer", modify
label def citywealth_t3 2 "Serekunda middle", modify
label def citywealth_t3 5 "Tema middle", modify
label def citywealth_t3 8 "São Vicente middle", modify
label def citywealth_t3 3 "Serekunda wealthier", modify
label def citywealth_t3 6 "Tema wealthier", modify
label def citywealth_t3 9 "São Vicente wealthier", modify


** Command suggested by Andrew Musau 13th of Nov

estimates drop _all
local i 0
decode city, g(City)
replace City= strtoname(City)
local vars "b02age_n b37sblno_n"
foreach var1 of local vars{
    foreach var2 of varlist citygnd cityage citywealth_t3 {
        local ++i
        foreach city in Serekunda Tema São_Vicente{
            qui levelsof `var2', local(levs)
            foreach j of local levs{
                capture noisily{
                    eststo `city'`i'_`j': estpost summarize `var1' if `j'.`var2' & City=="`city'" [aw = gweightcorr_n]
                    mat mean= e(mean)
                    mat coleq mean= "`:lab (`var2') `j''"
                    estadd mat mean= mean, replace
                }
            }
        }
    }
    local opt= cond("`var1'"==word("`vars'", 1), "replace nonumb", "append eqlab(none) nonumb collab(none)")
    esttab Serekunda* Tema* São_Vicente* using testingall.csv, cells("mean(fmt(2)) count(label(N) fmt(0))") ///
    unstack `opt' mlab(none) noobs note("Extra Line") addnotes("Observations")
    estimates drop _all
}

The column are completely messed up. For instance, it gives me multiple "Tema poor" that don't even match in the value. It start ok with Serekunda Female, then Serekunda Male, Serekunda [younger], Serekunda [older], Serekunda, poorer, ... middle, ... wealthier, but then it starts with Tema poor, does all the citywealth variables, and then goes back to doing it by city. In addition to this, the b37sblno_n have extra columns that have no label. I attached the output file so you can see for yourself.

I can try making a dataset similar to the last one with b37sblno_n instead of b02age_n, but do you have any suggestions for how to fix this?

Best,
Maria

[Mariia Chavez]

Here is the dataset with b37sblno_n instead of b02age_n

* Example generated by -dataex-. For more info, type help dataex
clear
input float(city citygnd cityage) byte b37sblno_n float citywealth_t3
3 5 6  7 7
2 3 3  3 4
1 1 1  2 1
3 6 6  6 7
2 3 4  5 4
2 3 3  6 4
3 5 5  2 7
3 6 6  4 7
2 3 3  8 4
3 5 5  2 7
2 3 3  4 4
2 3 3  3 4
2 4 3  2 4
3 6 6  6 7
1 1 2  4 1
2 4 3  2 4
2 3 4  2 4
1 2 1  8 1
1 1 2  7 1
3 5 6  6 7
3 5 6  7 7
1 1 2 19 1
1 1 1  6 1
1 2 2 11 1
3 5 5  4 7
2 3 3  7 4
2 3 4  9 4
2 3 3  5 4
3 5 5  6 7
2 3 4  5 4
2 3 4  7 4
1 1 1  6 1
3 6 5  8 7
1 1 2  5 1
2 3 4  6 4
1 1 1  5 1
2 4 3  7 4
2 3 4  6 4
2 3 3  8 4
2 3 3  6 4
3 6 5 10 7
2 4 3 14 4
2 3 3  9 4
2 3 4  7 4
3 5 5  3 7
3 5 6  4 7
3 5 5  6 7
2 3 4  2 4
3 6 6  5 7
1 1 2  0 1
2 3 3  3 4
3 6 5  9 7
1 1 2  8 1
3 6 6  7 7
3 5 6  7 7
2 3 3  1 4
3 5 6  6 7
1 1 2  5 1
1 1 2  1 1
3 5 6  5 7
3 5 5  4 7
2 3 3  3 4
3 5 5  3 7
1 1 1  4 1
3 5 6  8 7
3 6 6 24 7
1 1 1  3 1
2 4 3  4 4
3 6 5  3 7
2 3 3  4 4
3 6 5  3 7
3 5 6  8 7
2 3 4  4 4
3 6 6  5 7
2 4 3  4 4
1 2 1  4 1
2 4 3  3 4
2 3 4  7 4
2 3 3  8 4
2 3 4  6 4
2 3 3  5 4
2 3 3  6 4
2 4 3  5 4
2 3 4  4 4
2 4 3  6 4
2 3 4  8 4
3 6 6  3 7
3 6 6  2 7
1 1 1  7 1
1 2 1 16 1
2 3 3  3 4
2 4 3  6 4
3 5 6  7 7
1 2 2  4 1
1 1 1  7 1
2 4 3 10 4
3 5 6  3 7
2 3 3  2 4
2 3 3  8 4
1 1 2  6 1
end
label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda, age 18-28", modify
label def cityage 2 "Serekunda, age 29-39", modify
label def cityage 3 "Tema, age 18-28", modify
label def cityage 4 "Tema, age 29-39", modify
label def cityage 5 "São Vicente, age 18-28", modify
label def cityage 6 "São Vicente, age 29-39", modify
label values b37sblno_n missonly
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda, poorer", modify
label def citywealth_t3 4 "Tema, poorer", modify
label def citywealth_t3 7 "São Vicente, poorer", modify

Best,
Maria

[Andrew Musau]

If I append the extra variable or use it by itself with the code in #20, it runs perfectly. Unless you can look at my output and specifically point out something that is incorrect, my intention here is not to write the entire code for your project, but to provide guidance on how you can progress. I believe I have done that. If you struggle with making a few adjustments, consider discussing it with a colleague who may better understand the guidance.

* Example generated by -dataex-. For more info, type help dataex
clear
input float(city citygnd cityage) byte(b02age_n b37sblno_n) float citywealth_t3 str12 City
3 5 6 19  7 7 "São_Vicente"
2 3 3 35  3 4 "Tema"        
1 1 1 21  2 1 "Serekunda"   
3 6 6 37  6 7 "São_Vicente"
2 3 4 21  5 4 "Tema"        
2 3 3 36  6 4 "Tema"        
3 5 5 18  2 7 "São_Vicente"
3 6 6 33  4 7 "São_Vicente"
2 3 3 18  8 4 "Tema"        
3 5 5 24  2 7 "São_Vicente"
2 3 3 22  4 4 "Tema"        
2 3 3 18  3 4 "Tema"        
2 4 3 21  2 4 "Tema"        
3 6 6 34  6 7 "São_Vicente"
1 1 2 32  4 1 "Serekunda"   
2 4 3 37  2 4 "Tema"        
2 3 4 18  2 4 "Tema"        
1 2 1 28  8 1 "Serekunda"   
1 1 2 29  7 1 "Serekunda"   
3 5 6 38  6 7 "São_Vicente"
3 5 6 36  7 7 "São_Vicente"
1 1 2 39 19 1 "Serekunda"   
1 1 1 22  6 1 "Serekunda"   
1 2 2 33 11 1 "Serekunda"   
3 5 5 24  4 7 "São_Vicente"
2 3 3 19  7 4 "Tema"        
2 3 4 35  9 4 "Tema"        
2 3 3 18  5 4 "Tema"        
3 5 5 18  6 7 "São_Vicente"
2 3 4 30  5 4 "Tema"        
2 3 4 33  7 4 "Tema"        
1 1 1 27  6 1 "Serekunda"   
3 6 5 18  8 7 "São_Vicente"
1 1 2 33  5 1 "Serekunda"   
2 3 4 35  6 4 "Tema"        
1 1 1 18  5 1 "Serekunda"   
2 4 3 18  7 4 "Tema"        
2 3 4 32  6 4 "Tema"        
2 3 3 24  8 4 "Tema"        
2 3 3 21  6 4 "Tema"        
3 6 5 25 10 7 "São_Vicente"
2 4 3 18 14 4 "Tema"        
2 3 3 23  9 4 "Tema"        
2 3 4 39  7 4 "Tema"        
3 5 5 39  3 7 "São_Vicente"
3 5 6 25  4 7 "São_Vicente"
3 5 5 19  6 7 "São_Vicente"
2 3 4 31  2 4 "Tema"        
3 6 6 35  5 7 "São_Vicente"
1 1 2 39  0 1 "Serekunda"   
2 3 3 19  3 4 "Tema"        
3 6 5 26  9 7 "São_Vicente"
1 1 2 36  8 1 "Serekunda"   
3 6 6 35  7 7 "São_Vicente"
3 5 6 30  7 7 "São_Vicente"
2 3 3 18  1 4 "Tema"        
3 5 6 29  6 7 "São_Vicente"
1 1 2 33  5 1 "Serekunda"   
1 1 2 38  1 1 "Serekunda"   
3 5 6 35  5 7 "São_Vicente"
3 5 5 25  4 7 "São_Vicente"
2 3 3 21  3 4 "Tema"        
3 5 5 26  3 7 "São_Vicente"
1 1 1 24  4 1 "Serekunda"   
3 5 6 29  8 7 "São_Vicente"
3 6 6 38 24 7 "São_Vicente"
1 1 1 21  3 1 "Serekunda"   
2 4 3 26  4 4 "Tema"        
3 6 5 25  3 7 "São_Vicente"
2 3 3 25  4 4 "Tema"        
3 6 5 27  3 7 "São_Vicente"
3 5 6 31  8 7 "São_Vicente"
2 3 4 31  4 4 "Tema"        
3 6 6 37  5 7 "São_Vicente"
2 4 3 27  4 4 "Tema"        
1 2 1 18  4 1 "Serekunda"   
2 4 3 29  3 4 "Tema"        
2 3 4 39  7 4 "Tema"        
2 3 3 23  8 4 "Tema"        
2 3 4 27  6 4 "Tema"        
2 3 3 28  5 4 "Tema"        
2 3 3 26  6 4 "Tema"        
2 4 3 18  5 4 "Tema"        
2 3 4 35  4 4 "Tema"        
2 4 3 23  6 4 "Tema"        
2 3 4 39  8 4 "Tema"        
3 6 6 32  3 7 "São_Vicente"
3 6 6 35  2 7 "São_Vicente"
1 1 1 27  7 1 "Serekunda"   
1 2 1 19 16 1 "Serekunda"   
2 3 3 28  3 4 "Tema"        
2 4 3 19  6 4 "Tema"        
3 5 6 37  7 7 "São_Vicente"
1 2 2 39  4 1 "Serekunda"   
1 1 1 25  7 1 "Serekunda"   
2 4 3 23 10 4 "Tema"        
3 5 6 30  3 7 "São_Vicente"
2 3 3 18  2 4 "Tema"        
2 3 3 24  8 4 "Tema"        
1 1 2 29  6 1 "Serekunda"   
end
label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda age 18-28", modify
label def cityage 2 "Serekunda age 29-39", modify
label def cityage 3 "Tema age 18-28", modify
label def cityage 4 "Tema age 29-39", modify
label def cityage 5 "São Vicente age 18-28", modify
label def cityage 6 "São Vicente age 29-39", modify
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda poorer", modify
label def citywealth_t3 4 "Tema poorer", modify
label def citywealth_t3 7 "São Vicente poorer", modify


label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda age 18-28", modify
label def cityage 2 "Serekunda age 29-39", modify
label def cityage 3 "Tema age 18-28", modify
label def cityage 4 "Tema age 29-39", modify
label def cityage 5 "São Vicente age 18-28", modify
label def cityage 6 "São Vicente age 29-39", modify
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda poorer", modify
label def citywealth_t3 4 "Tema poorer", modify
label def citywealth_t3 7 "São Vicente poorer", modify


estimates drop _all
local i 0
*decode city, g(City)
*replace City= strtoname(City)
local vars "b02age_n b37sblno_n"
foreach var1 of local vars{
    foreach var2 of varlist citygnd cityage citywealth_t3 {
        local ++i
        foreach city in Serekunda Tema São_Vicente{
            qui levelsof `var2', local(levs)
            foreach j of local levs{
                capture noisily{
                    eststo `city'`i'_`j': estpost summarize `var1' if `j'.`var2' & City=="`city'"
                    mat mean= e(mean)
                    mat coleq mean= "`:lab (`var2') `j''"
                    estadd mat mean= mean, replace
                }
            }
        }
    }
    local opt= cond("`var1'"==word("`vars'", 1), "replace nonumb", "append eqlab(none) nonumb collab(none)")
    esttab Serekunda* Tema* São_Vicente* using testingall.csv, cells("mean(fmt(2)) count(label(N) fmt(0))") ///
    unstack `opt' mlab(none) noobs nonote
    estimates drop _all
}

As I pointed out, you need to get rid of commas in the value labels, otherwise the column labels will be messed up as CSV files use the comma as a separator. You appear not to have done this on the basis of your most recent data example.

[Mariia Chavez]

Hello again,

It appears I sent you the wrong file above, I apologize for this, and have attached the correct one to this message.
To make the dataset I sent you I ran these commands:
keep b02age_n citywealth_t3 citygnd cityage city
dataex _all

That said, the dataset was still to big, hence not all subvalues of citywealth_t3 (specifically middle and wealthier) were included in the exported dataset.
When I run your suggested command for the exported dataset, I get the same table as you.
However if I run the following commands:

keep b02age_n citywealth_t3 citygnd cityage city

* Removing commas from value labels
label def cityage 1 "Serekunda age 18-28", modify
label def cityage 2 "Serekunda age 29-39", modify
label def cityage 3 "Tema age 18-28", modify
label def cityage 4 "Tema age 29-39", modify
label def cityage 5 "São Vicente age 18-28", modify
label def cityage 6 "São Vicente age 29-39", modify
label def citywealth_t3 1 "Serekunda poorer", modify
label def citywealth_t3 4 "Tema poorer", modify
label def citywealth_t3 7 "São Vicente poorer", modify
label def citywealth_t3 2 "Serekunda middle", modify
label def citywealth_t3 5 "Tema middle", modify
label def citywealth_t3 8 "São Vicente middle", modify
label def citywealth_t3 3 "Serekunda wealthier", modify
label def citywealth_t3 6 "Tema wealthier", modify
label def citywealth_t3 9 "São Vicente wealthier", modify

*Andrew Musau's command
estimates drop _all
local i 0
decode city, g(City)
replace City= strtoname(City)
local vars "b02age_n"
foreach var1 of local vars{
foreach var2 of varlist citygnd cityage citywealth_t3 {
local ++i
foreach city in Serekunda Tema São_Vicente{
qui levelsof `var2', local(levs)
foreach j of local levs{
capture noisily{
eststo `city'`i'_`j': estpost summarize `var1' if `j'.`var2' & City=="`city'"
mat mean= e(mean)
mat coleq mean= "`:lab (`var2') `j''"
estadd mat mean= mean, replace
}
}
}
}
local opt= cond("`var1'"==word("`vars'", 1), "replace nonumb", "append eqlab(none) nonumb collab(none)")
esttab Serekunda* Tema* São_Vicente* using testing1911.csv, cells("mean(fmt(2)) count(label(N) fmt(0))") ///
unstack `opt' mlab(none) noobs note("Extra Line") addnotes("Observations")
estimates drop _all
}



I get the table presented in the attached Excel file. I have asked several colleagues for help and tried to investigate using other tools on the internet without any luck. That said, I recognize that you would be very limited in attempting to solve or uncover the problem seeing as your formula is working for the dataset I am able to export and send to you. Thus, I want to take this moment to thank you so much for your help. It has really been invaluable. I will continue working on it, in the hopes that I find where the problem is.

All the best,
Maria

[Andrew Musau]

Again, dataex is the way to provide data examples on Statalist (refer to FAQ Advice #12 for reasons).

[Mariia Chavez]

Hello again
I was able to make the following dataset including weights, and all the variables. With this dataset, the command you suggested (included below) make multiple columns, seemingly of the same variable means, but with different values. Are you able to see why?
PS: I made sure that these value labels do not contain the commas, like you suggested

I have already tried testing for errors in the dataset, and errors in the command, but all tests I have thought of (been suggested) to try come up saying, that everything should be working fine.
I have split the full dataset command as it did not fit in one message. But you should be able to just copy paste the last part after the first.

estimates drop _all
local i 0
decode city, g(City)
replace City= strtoname(City)
local vars "b02age_n"
foreach var1 of local vars{
foreach var2 of varlist citygnd cityage citywealth_t3 {
local ++i
foreach city in Serekunda Tema São_Vicente{
qui levelsof `var2', local(levs)
foreach j of local levs{
capture noisily{
eststo `city'`i'_`j': estpost summarize `var1' if `j'.`var2' & City=="`city'"
mat mean= e(mean)
mat coleq mean= "`:lab (`var2') `j''"
estadd mat mean= mean, replace
}
}
}
}
local opt= cond("`var1'"==word("`vars'", 1), "replace nonumb", "append eqlab(none) nonumb collab(none)")
esttab Serekunda* Tema* São_Vicente* using testing1911.csv, cells("mean(fmt(2)) count(label(N) fmt(0))") ///
unstack `opt' mlab(none) noobs note("Extra Line") addnotes("Observations")
estimates drop _all
}


[CODE]
* Example generated by -dataex-. For more info, type help dataex
clear
input byte b02age_n float(gweightcorr_n citywealth_t3 citygnd cityage city)
19 318.0764 4 3 3 2
35 87.06275 7 5 6 3
21 232.0856 1 1 1 1
37 711.2202 7 6 6 3
21 377.3697 4 3 3 2
36 380.876 4 3 4 2
18 87.7949 7 5 5 3
33 350.1273 7 6 6 3
18 488.92505 4 3 3 2
24 174.1255 7 5 5 3
22 163.15567 4 4 3 2
18 377.3697 4 3 3 2
21 251.5798 4 3 3 2
34 364.9541 7 6 6 3
32 232.08563 1 1 2 1
37 106.02544 4 3 4 2
18 235.16953 4 4 3 2
28 490.9393 1 2 1 1
29 77.36187 1 1 2 1
38 87.06275 7 5 6 3
36 131.09586 7 5 6 3
39 232.08563 1 1 2 1
22 232.0856 1 1 1 1
33 245.46967 1 2 2 1
24 87.06275 7 5 5 3
19 424.1018 4 3 3 2
35 212.0509 4 3 4 2
18 212.0509 4 3 3 2
18 262.1917 7 5 5 3
30 106.02544 4 3 4 2
33 341.4781 4 3 4 2
27 77.36188 1 1 1 1
18 563.541 7 6 5 3
33 77.36187 1 1 2 1
35 636.1527 4 3 4 2
18 232.08563 1 1 1 1
18 564.28125 4 4 3 2
32 251.5798 4 3 4 2
24 138.2268 4 3 3 2
21 265.27042 4 3 3 2
25 298.35553 7 6 5 3
18 654.4615 4 4 3 2
23 424.1018 4 3 3 2
39 106.02544 4 3 4 2
39 131.09586 7 5 6 3
25 295.47217 7 5 5 3
19 292.79822 7 5 5 3
31 212.0509 4 3 4 2
35 611.2264 7 6 6 3
39 541.5331 1 1 2 1
19 125.7899 4 3 3 2
26 474.1468 7 6 5 3
36 154.72374 1 1 2 1
35 262.59543 7 6 6 3
30 784.082 7 5 6 3
18 108.72178 4 3 3 2
29 92.96252 7 5 6 3
33 154.72375 1 1 2 1
38 232.08563 1 1 2 1
35 92.78071 7 5 6 3
25 357.0339 7 5 5 3
21 125.7899 4 3 3 2
26 131.09586 7 5 5 3
24 773.6188 1 1 1 1
29 87.7949 7 5 6 3
38 404.3657 7 6 6 3
21 154.72374 1 1 1 1
26 489.467 4 4 3 2
25 402.67615 7 6 5 3
25 106.02544 4 3 3 2
27 202.3674 7 6 5 3
31 87.7949 7 5 6 3
31 318.77335 4 3 4 2
37 194.76015 7 6 6 3
27 545.4112 4 4 3 2
18 122.73486 1 2 1 1
29 366.6938 4 3 4 2
39 122.23126 4 3 4 2
23 251.5798 4 3 3 2
27 122.23126 4 3 3 2
28 326.3113 4 4 3 2
26 212.0509 4 3 3 2
18 658.1558 4 4 3 2
35 455.3042 4 3 4 2
23 232.8463 4 4 3 2
39 251.5798 4 3 4 2
32 404.7348 7 6 6 3
35 348.4311 7 6 6 3
27 232.08563 1 1 1 1
19 981.8787 1 2 1 1
28 377.3697 4 3 3 2
19 774.2797 4 4 3 2
37 228.5397 7 5 6 3
39 245.46973 1 2 2 1
25 154.72374 1 1 1 1
23 326.3113 4 4 3 2
30 312.0889 7 5 6 3
18 106.02544 4 3 3 2
24 125.7899 4 3 3 2
29 386.8094 1 1 2 1
18 106.02544 4 3 3 2
34 125.7899 4 3 4 2
27 99.28418 4 3 3 2
26 251.5798 4 3 3 2
18 350.319 4 4 3 2
22 424.1018 4 3 3 2
22 503.2894 7 6 5 3
18 245.46973 1 2 1 1
25 309.4475 1 1 1 1
39 332.4214 7 5 6 3
23 212.0509 4 3 3 2
19 122.73483 1 2 1 1
32 77.36188 1 1 2 1
27 245.46967 1 2 1 1
35 377.3697 4 3 4 2
19 190.44777 7 5 5 3
26 253.9174 4 3 3 2
26 302.62674 4 3 3 2
18 326.3113 4 4 3 2
21 366.6938 4 3 3 2
34 318.0764 4 3 4 2
39 125.7899 4 3 4 2
30 77.36188 1 1 2 1
31 113.82605 4 3 4 2
23 525.4785 4 4 3 2
32 212.0509 4 3 4 2
30 154.72375 1 1 2 1
18 122.73486 1 2 1 1
37 265.27042 4 3 4 2
21 154.72374 1 1 1 1
30 167.80746 7 5 6 3
21 318.0764 4 3 3 2
22 503.1596 4 3 3 2
34 77.36188 1 1 2 1
27 387.1398 4 4 3 2
29 126.9587 4 3 4 2
22 580.7098 4 4 3 2
23 113.23483 4 4 3 2
39 106.02544 4 3 4 2
33 424.1018 4 3 4 2
32 628.9496 4 3 4 2
32 163.64055 7 5 6 3
35 77.36187 1 1 2 1
37 325.9908 7 6 6 3
18 164.53896 4 4 3 2
22 488.92505 4 3 3 2
25 469.8559 7 6 5 3
21 537.4653 7 5 5 3
30 148.88603 7 6 6 3
33 332.4214 7 5 6 3
23 201.33807 7 6 5 3
18 106.2578 4 3 3 2
28 251.5798 4 3 3 2
18 464.1713 1 1 1 1
19 326.3113 4 4 3 2
34 451.3719 7 6 6 3
21 332.4214 7 5 5 3
25 309.4475 1 1 1 1
21 350.319 4 4 3 2
18 154.72374 1 1 1 1
18 154.72375 1 1 1 1
19 77.36187 1 1 1 1
19 106.92438 4 3 3 2
28 350.319 4 4 3 2
28 245.46973 1 2 1 1
20 212.0509 4 3 3 2
37 664.8428 7 5 6 3
34 212.5156 4 3 4 2
24 383.4704 7 5 5 3
31 232.0856 1 1 2 1
32 194.76015 7 6 6 3
24 366.6938 4 3 3 2
37 309.4475 1 1 2 1
23 232.0856 1 1 1 1
32 245.46967 1 2 2 1
35 77.36188 1 1 2 1
30 356.5487 7 6 6 3
29 696.2569 1 1 2 1
32 251.6447 7 6 6 3
32 77.36187 1 1 2 1
22 620.2624 7 5 5 3
20 529.2686 7 6 5 3
30 232.99884 7 5 6 3
25 251.5798 4 3 3 2
33 386.8094 1 1 2 1
23 77.36188 1 1 1 1
25 152.78197 4 4 3 2
22 533.4396 7 6 5 3
32 525.4785 4 4 4 2
18 377.3697 4 3 3 2
22 126.1085 7 5 5 3
20 387.1398 4 4 3 2
26 77.36187 1 1 1 1
24 316.3625 7 5 5 3
28 489.467 4 4 3 2
29 245.46967 1 2 2 1
38 309.4475 1 1 2 1
21 77.36187 1 1 1 1
25 251.5798 4 3 3 2
37 541.5331 1 1 2 1
20 479.4499 7 5 5 3
19 154.72374 1 1 1 1
19 490.93945 1 2 1 1
22 122.73486 1 2 1 1
23 77.36188 1 1 1 1
35 470.4492 7 5 6 3
19 454.4835 7 5 5 3
25 326.3113 4 4 3 2
37 232.0856 1 1 2 1
23 106.02544 4 3 3 2
20 154.72374 1 1 1 1
28 305.6462 4 3 3 2
21 530.1272 4 3 3 2
32 386.8094 1 1 2 1
18 236.2875 4 3 3 2
18 79.90833 7 5 5 3
27 613.6742 1 2 1 1
23 251.5798 4 3 3 2
24 489.467 4 4 3 2
28 158.18123 7 5 5 3
27 559.3583 7 5 5 3
34 327.2811 7 5 6 3
26 489.467 4 4 3 2
28 628.9496 4 3 3 2
20 490.9393 1 2 1 1
27 163.64055 7 5 5 3
32 159.81665 7 5 6 3
18 368.2046 1 2 1 1
25 106.02544 4 3 3 2
26 309.8629 7 5 5 3
20 940.4686 4 4 3 2
32 113.82605 4 3 4 2
34 175.1595 4 4 4 2
31 106.02544 4 3 4 2
35 469.8559 7 6 6 3
31 465.8331 7 6 6 3
23 533.4396 7 6 5 3
33 113.82605 4 3 4 2
33 147.73608 7 5 6 3
20 700.638 4 4 3 2
35 174.1255 7 5 6 3
39 628.9496 4 3 4 2
30 163.15567 4 4 4 2
21 368.2046 1 2 1 1
33 809.4697 7 6 6 3
37 245.46967 1 2 2 1
28 163.15567 4 4 3 2
19 212.5156 4 3 3 2
22 363.6075 4 4 3 2
28 384.913 7 5 5 3
22 725.9514 7 5 5 3
28 262.1917 7 5 5 3
31 1369.987 4 4 4 2
26 181.80376 4 4 3 2
18 125.7899 4 3 3 2
18 397.1367 4 3 3 2
23 89.99535 7 5 5 3
19 239.72495 7 5 5 3
21 329.0779 4 4 3 2
22 618.895 1 1 1 1
25 295.47217 7 5 5 3
27 166.2107 7 5 5 3
36 125.7899 4 3 4 2
35 77.36188 1 1 2 1
38 387.1398 4 4 4 2
32 244.46252 4 3 4 2
18 106.92438 4 3 3 2
25 77.36188 1 1 1 1
21 462.3468 7 6 5 3
21 188.13193 7 5 5 3
38 125.7899 4 3 4 2
30 154.72375 1 1 2 1
22 122.73483 1 2 1 1
31 566.2004 7 6 6 3
30 912.3854 7 6 6 3
39 106.2578 4 3 4 2
22 122.23126 4 3 3 2
20 376.1875 4 4 3 2
32 159.81665 7 5 6 3
31 217.1304 7 5 6 3
26 212.0509 4 3 3 2
26 541.5331 1 1 1 1
19 154.72374 1 1 1 1
39 217.44356 4 3 4 2
25 122.73486 1 2 1 1
31 318.0764 4 3 4 2
21 613.6742 1 2 1 1
18 154.72375 1 1 1 1
18 125.7899 4 3 3 2
29 265.27042 4 3 4 2
33 175.1595 4 4 4 2
28 227.6521 4 3 3 2
30 77.36188 1 1 2 1
27 154.72375 1 1 1 1
37 154.72374 1 1 2 1
31 212.0509 4 3 4 2
25 251.5798 4 3 3 2
25 525.4785 4 4 3 2
31 244.46252 4 3 4 2
39 613.6743 1 2 2 1
27 530.1272 4 3 3 2
33 122.73486 1 2 2 1
27 613.6743 1 2 1 1
39 188.09375 4 4 4 2
19 251.5798 4 3 3 2
36 113.82605 4 3 4 2
23 77.36188 1 1 1 1
18 122.73486 1 2 1 1
28 106.02544 4 3 3 2
19 366.6938 4 3 3 2
26 212.0509 4 3 3 2
31 77.36188 1 1 2 1
20 154.72375 1 1 1 1
21 251.5798 4 3 3 2
26 204.1037 4 4 3 2
37 244.46252 4 3 4 2
19 603.7939 7 6 5 3
25 424.1018 4 3 3 2
26 251.5798 4 3 3 2
19 245.46973 1 2 1 1
19 981.0791 4 4 3 2
18 850.9805 1 1 1 1
30 313.6328 7 5 6 3
24 326.3113 4 4 3 2
31 212.0509 4 3 4 2
30 251.5798 4 3 4 2
20 163.15567 4 4 3 2
24 227.6521 4 3 3 2
27 77.36188 1 1 1 1
38 87.06275 7 5 6 3
32 77.36188 1 1 2 1
30 113.71352 7 5 6 3
32 175.06363 7 6 6 3
18 266.7198 7 6 5 3
20 77.36187 1 1 1 1
34 212.0509 4 3 4 2
38 354.4312 4 3 4 2
37 281.27963 7 5 6 3
30 425.0312 4 3 4 2
28 122.23126 4 3 3 2
38 725.9514 7 5 6 3
21 77.36187 1 1 1 1
20 443.20825 7 5 5 3
21 773.6187 1 1 1 1
33 152.8231 4 3 4 2
27 138.37724 7 5 5 3
24 252.217 7 5 5 3
32 350.319 4 4 4 2
30 77.36188 1 1 2 1
38 154.72374 1 1 2 1
29 77.36187 1 1 2 1
38 309.4475 1 1 2 1
34 264.6343 7 6 6 3
20 154.72375 1 1 1 1
18 77.36188 1 1 1 1
22 212.0509 4 3 3 2
21 631.112 7 6 5 3
28 92.96252 7 5 5 3
34 154.93146 7 5 6 3
28 362.9757 7 5 5 3
27 248.6199 7 6 5 3
22 125.7899 4 3 3 2
24 77.36187 1 1 1 1
21 77.36188 1 1 1 1
35 77.36187 1 1 2 1
24 541.5331 1 1 1 1
19 489.467 4 4 3 2
24 122.73486 1 2 1 1
36 106.92438 4 3 4 2
34 204.1037 4 4 4 2
33 154.72374 1 1 2 1
34 154.72375 1 1 2 1
25 77.36187 1 1 1 1
19 251.5798 4 3 3 2
30 111.09453 7 5 6 3
30 232.08563 1 1 2 1
29 136.5389 7 5 6 3
38 152.55225 7 5 6 3
33 245.46973 1 2 2 1
19 232.0856 1 1 1 1
30 166.2107 7 5 6 3
18 245.46967 1 2 1 1
27 302.62674 4 3 3 2
26 224.17076 4 3 3 2
35 163.15567 4 4 4 2
18 154.72375 1 1 1 1
23 613.6742 1 2 1 1
23 490.9393 1 2 1 1
26 125.7899 4 3 3 2
38 77.36188 1 1 2 1
18 172.4809 4 4 3 2
25 232.08563 1 1 1 1
23 99.28418 4 3 3 2
32 126.9587 4 3 4 2
28 118.14375 4 3 3 2
18 77.36188 1 1 1 1
37 106.02544 4 3 4 2
19 978.934 4 4 3 2
30 387.1398 4 4 4 2
19 489.467 4 4 3 2
27 212.0509 4 3 3 2
33 132.63521 4 3 4 2
21 387.1398 4 4 3 2
27 108.5652 7 5 5 3
38 140.63982 7 5 6 3
34 265.27042 4 3 4 2
23 341.4781 4 3 3 2
32 163.15567 4 4 4 2
32 193.5699 4 4 4 2
33 455.3042 4 3 4 2
31 245.46967 1 2 2 1
36 154.72375 1 1 2 1
19 212.0509 4 3 3 2
22 251.5798 4 3 3 2
19 632.7249 7 5 5 3
35 316.3625 7 5 6 3
27 113.82605 4 3 3 2
19 368.2045 1 2 1 1
33 113.82605 4 3 4 2
22 212.0509 4 3 3 2
23 613.6742 1 2 1 1
21 87.7949 7 5 5 3
34 77.36187 1 1 2 1
31 156.8164 7 5 6 3
38 77.36187 1 1 2 1
23 580.7098 4 4 3 2
33 227.6521 4 3 4 2
28 113.82605 4 3 3 2
27 77.36187 1 1 1 1
18 118.14375 4 3 3 2
32 77.36187 1 1 2 1
22 239.72495 7 5 5 3
32 189.6587 7 6 6 3
38 808.7314 7 6 6 3
38 106.2578 4 3 4 2
28 146.39911 7 5 5 3
27 222.18906 7 5 5 3
30 113.82605 4 3 4 2
30 235.16953 4 4 4 2
23 309.4475 1 1 1 1
32 774.2797 4 4 4 2
28 105.45416 7 5 5 3
27 106.2578 4 3 3 2
18 113.82605 4 3 3 2
38 265.27042 4 3 4 2
34 496.4208 4 3 4 2
39 125.7899 4 3 4 2
24 77.36187 1 1 1 1
33 111.09453 7 5 6 3
19 376.2639 7 5 5 3
23 125.7899 4 3 3 2
34 455.3042 4 3 4 2
26 99.28418 4 3 3 2
35 218.1874 7 5 6 3
24 424.1018 4 3 3 2
21 327.2811 7 5 5 3
32 77.36187 1 1 2 1
21 106.2578 4 3 3 2
29 183.36983 7 6 6 3
35 77.36187 1 1 2 1
25 490.53955 4 4 3 2
19 658.1558 4 4 3 2
30 580.7098 4 4 4 2
23 122.73486 1 2 1 1
35 266.7198 7 6 6 3
27 736.4091 1 2 1 1
29 106.2578 4 3 4 2
24 193.5699 4 4 3 2
18 77.36188 1 1 1 1
23 154.72374 1 1 1 1
39 244.46252 4 3 4 2
21 490.93945 1 2 1 1
32 156.8164 7 5 6 3
37 128.22958 7 6 6 3
20 212.0509 4 3 3 2
24 122.23126 4 3 3 2
29 245.46967 1 2 2 1
23 222.18906 7 5 5 3
28 147.16956 4 3 3 2
23 109.0937 7 5 5 3
32 106.02544 4 3 4 2
38 210.3707 7 6 6 3
34 225.68596 7 6 6 3
24 1316.656 4 4 3 2
23 99.28418 4 3 3 2
18 77.36187 1 1 1 1
25 99.28418 4 3 3 2
35 154.72374 1 1 2 1
19 232.0856 1 1 1 1
19 309.4475 1 1 1 1
18 77.36188 1 1 1 1
24 541.5331 1 1 1 1
27 212.5156 4 3 3 2
36 222.18906 7 5 6 3
29 470.33905 4 4 4 2
19 309.4475 1 1 1 1
26 224.17076 4 3 3 2
18 305.6462 4 3 3 2
21 261.1882 7 5 5 3
29 256.60864 7 5 6 3
19 92.96252 7 5 5 3
30 263.3847 7 5 6 3
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35 156.8164 7 5 6 3
25 439.1973 7 5 5 3

[Mariia Chavez]

39 106.02544 5 3 4 2
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39 188.13193 8 5 6 3

[Mariia Chavez]

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end
label values b02age_n missonly
label def missonly .b "Refuse to answer", modify
label values citywealth_t3 citywealth_t3
label def citywealth_t3 1 "Serekunda poorer", modify
label def citywealth_t3 2 "Serekunda middle", modify
label def citywealth_t3 3 "Serekunda wealthier", modify
label def citywealth_t3 4 "Tema poorer", modify
label def citywealth_t3 5 "Tema middle", modify
label def citywealth_t3 6 "Tema wealthier", modify
label def citywealth_t3 7 "São Vicente poorer", modify
label def citywealth_t3 8 "São Vicente middle", modify
label def citywealth_t3 9 "São Vicente wealthier", modify
label values citygnd citygnd
label def citygnd 1 "Serekunda Female", modify
label def citygnd 2 "Serekunda Male", modify
label def citygnd 3 "Tema Female", modify
label def citygnd 4 "Tema Male", modify
label def citygnd 5 "São Vicente Female", modify
label def citygnd 6 "São Vicente Male", modify
label values cityage cityage
label def cityage 1 "Serekunda age 18-28", modify
label def cityage 2 "Serekunda age 29-39", modify
label def cityage 3 "Tema age 18-28", modify
label def cityage 4 "Tema age 29-39", modify
label def cityage 5 "São Vicente age 18-28", modify
label def cityage 6 "São Vicente age 29-39", modify
label values city city
label def city 1 "Serekunda", modify
label def city 2 "Tema", modify
label def city 3 "São Vicente", modify
[/CODE]

I had to divide the code into three, so this is the final part of the code, the former message contains part 2/3