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  • #16
    How do you decide which importer matches with which exporter. In other words, why are Germany and Albania on the same row instead of, say, Germany and Bangladesh? Unless there is some systematic way of deciding this, you are creating data salad in which unrelated information is being paired up. That is a recipe for disaster.

    Comment

    • #17
      No, I just gave you an example. My data has structure and I collected Importers and Exporters from Un Comtrade database. Now I want to separate my n_cases and n_deaths fro Exporters and Importers based on this result.

      Code:
      * Example generated by -dataex-. To install: ssc install dataex
clear
input int Year long Month str24 Importers str44 Exporters double Trade
2019 201901 "Australia" "Serbia"                                  2706433
2019 201901 "Australia" "El Salvador"                              341597
2019 201901 "Australia" "Timor-Leste"                              335021
2019 201901 "Australia" "Cabo Verde"                                 5757
2019 201901 "Australia" "Vanuatu"                                   64854
2019 201901 "Australia" "Tunisia"                                 3109798
2019 201901 "Australia" "Liberia"                                    1345
2019 201901 "Australia" "Malta"                                   1691151
2019 201901 "Australia" "Libya"                                  61504797
2019 201901 "Australia" "Suriname"                                   1077
2019 201901 "Australia" "Kuwait"                                 10274753
2019 201901 "Australia" "Cambodia"                               17118371
2019 201901 "Australia" "New Zealand"                           429702886
2019 201901 "Australia" "Brazil"                                 69890417
2019 201901 "Australia" "Uganda"                                   550144
2019 201901 "Australia" "Slovenia"                               11298070
2019 201901 "Australia" "Niger"                                     10315
2019 201901 "Australia" "Comoros"                                    5600
2019 201901 "Australia" "Cuba"                                    1176378
2019 201901 "Australia" "Congo"                                    125747
2019 201901 "Australia" "Rep. of Moldova"                          171228
2019 201901 "Australia" "Nauru"                                      1880
2019 201901 "Australia" "Italy"                                 479991405
2019 201901 "Australia" "Somalia"                                    5766
2019 201901 "Australia" "Gambia"                                     1991
2019 201901 "Australia" "Sri Lanka"                              17304844
2019 201901 "Australia" "Ireland"                               122058289
2019 201901 "Australia" "Maldives"                                 139068
2019 201901 "Australia" "Seychelles"                               132510
2019 201901 "Australia" "Curaçao"                                   1869
2019 201901 "Australia" "Haiti"                                    206319
2019 201901 "Australia" "French Polynesia"                          12329
2019 201901 "Australia" "Lao People's Dem. Rep."                   490843
2019 201901 "Australia" "Brunei Darussalam"                      97923810
2019 201901 "Australia" "Indonesia"                             259529276
2019 201901 "Australia" "Luxembourg"                              1933694
2019 201901 "Australia" "Br. Virgin Isds"                          439710
2019 201901 "Australia" "Nigeria"                                86426658
2019 201901 "Australia" "Syria"                                    211276
2019 201901 "Australia" "Azerbaijan"                                 1321
2019 201901 "Australia" "Zimbabwe"                                   2137
2019 201901 "Australia" "New Caledonia"                           5153296
2019 201901 "Australia" "Burkina Faso"                              17046
2019 201901 "Australia" "Zambia"                                   189632
2019 201901 "Australia" "Cayman Isds"                                4391
2019 201901 "Australia" "Spain"                                 149509730
2019 201901 "Australia" "Marshall Isds"                              6860
2019 201901 "Australia" "Dominican Rep."                          2518931
2019 201901 "Australia" "Mali"                                      68096
2019 201901 "Australia" "Canada"                                162801943
2019 201901 "Australia" "Kazakhstan"                               474619
2019 201901 "Australia" "Colombia"                                6114289
2019 201901 "Australia" "Bulgaria"                                5803291
2019 201901 "Australia" "Mauritius"                                651966
2019 201901 "Australia" "Japan"                                1151845512
2019 201901 "Australia" "Paraguay"                                 443859
2019 201901 "Australia" "Viet Nam"                              390468713
2019 201901 "Australia" "China, Macao SAR"                        6119526
2019 201901 "Australia" "Ecuador"                                 2542077
2019 201901 "Australia" "Ethiopia"                                1054798
2019 201901 "Australia" "Lithuania"                               6207901
2019 201901 "Australia" "Bosnia Herzegovina"                      1142891
2019 201901 "Australia" "Iran"                                    1619303
2019 201901 "Australia" "France"                                349181319
2019 201901 "Australia" "Myanmar"                                 3635743
2019 201901 "Australia" "Qatar"                                  30589700
2019 201901 "Australia" "Trinidad and Tobago"                      699798
2019 201901 "Australia" "Namibia"                                  286874
2019 201901 "Australia" "Oman"                                    1920662
2019 201901 "Australia" "Bermuda"                                    6104
2019 201901 "Australia" "Algeria"                                68522824
2019 201901 "Australia" "Poland"                                 73089457
2019 201901 "Australia" "Burundi"                                  430214
2019 201901 "Australia" "Morocco"                                 2512228
2019 201901 "Australia" "Mozambique"                                81380
2019 201901 "Australia" "Bhutan"                                    26706
2019 201901 "Australia" "Pitcairn"                                   2016
2019 201901 "Australia" "Barbados"                                  69430
2019 201901 "Australia" "Venezuela"                                 75216
2019 201901 "Australia" "Belarus"                                12080033
2019 201901 "Australia" "Philippines"                            41483712
2019 201901 "Australia" "Uzbekistan"                                61587
2019 201901 "Australia" "United States Minor Outlying Islands"       6404
2019 201901 "Australia" "FS Micronesia"                               813
2019 201901 "Australia" "Czech Rep."                             68883229
2019 201901 "Australia" "Kenya"                                   3193627
2019 201901 "Australia" "Austria"                               103521547
2019 201901 "Australia" "Cyprus"                                  2720043
2019 201901 "Australia" "Saint Kitts and Nevis"                      4428
2019 201901 "Australia" "TFYR of Macedonia"                        323756
2019 201901 "Australia" "Germany"                               962855239
2019 201901 "Australia" "Guam"                                       1826
2019 201901 "Australia" "Madagascar"                              1347730
2019 201901 "Australia" "Bahamas"                                    1933
2019 201901 "Australia" "Belgium"                               113616405
2019 201901 "Australia" "Albania"                                  158647
2019 201901 "Australia" "Denmark"                                81303255
2019 201901 "Australia" "Mexico"                                199032058
2019 201901 "Australia" "Chile"                                  37709034
2019 201901 "Australia" "United States of America"             1914144484
end

      Comment

      • #18
        Starting with the data in #17 you can create a new data set that contains two variables: the names of all the countries, and a variable that identifies each country as an Importer of an Exporter.
        Code:
        * Example generated by -dataex-. To install: ssc install dataex
clear
input int Year long Month str24 Importers str44 Exporters double Trade
2019 201901 "Australia" "Serbia"                                  2706433
2019 201901 "Australia" "El Salvador"                              341597
2019 201901 "Australia" "Timor-Leste"                              335021
2019 201901 "Australia" "Cabo Verde"                                 5757
2019 201901 "Australia" "Vanuatu"                                   64854
2019 201901 "Australia" "Tunisia"                                 3109798
2019 201901 "Australia" "Liberia"                                    1345
2019 201901 "Australia" "Malta"                                   1691151
2019 201901 "Australia" "Libya"                                  61504797
2019 201901 "Australia" "Suriname"                                   1077
2019 201901 "Australia" "Kuwait"                                 10274753
2019 201901 "Australia" "Cambodia"                               17118371
2019 201901 "Australia" "New Zealand"                           429702886
2019 201901 "Australia" "Brazil"                                 69890417
2019 201901 "Australia" "Uganda"                                   550144
2019 201901 "Australia" "Slovenia"                               11298070
2019 201901 "Australia" "Niger"                                     10315
2019 201901 "Australia" "Comoros"                                    5600
2019 201901 "Australia" "Cuba"                                    1176378
2019 201901 "Australia" "Congo"                                    125747
2019 201901 "Australia" "Rep. of Moldova"                          171228
2019 201901 "Australia" "Nauru"                                      1880
2019 201901 "Australia" "Italy"                                 479991405
2019 201901 "Australia" "Somalia"                                    5766
2019 201901 "Australia" "Gambia"                                     1991
2019 201901 "Australia" "Sri Lanka"                              17304844
2019 201901 "Australia" "Ireland"                               122058289
2019 201901 "Australia" "Maldives"                                 139068
2019 201901 "Australia" "Seychelles"                               132510
2019 201901 "Australia" "Curaçao"                                   1869
2019 201901 "Australia" "Haiti"                                    206319
2019 201901 "Australia" "French Polynesia"                          12329
2019 201901 "Australia" "Lao People's Dem. Rep."                   490843
2019 201901 "Australia" "Brunei Darussalam"                      97923810
2019 201901 "Australia" "Indonesia"                             259529276
2019 201901 "Australia" "Luxembourg"                              1933694
2019 201901 "Australia" "Br. Virgin Isds"                          439710
2019 201901 "Australia" "Nigeria"                                86426658
2019 201901 "Australia" "Syria"                                    211276
2019 201901 "Australia" "Azerbaijan"                                 1321
2019 201901 "Australia" "Zimbabwe"                                   2137
2019 201901 "Australia" "New Caledonia"                           5153296
2019 201901 "Australia" "Burkina Faso"                              17046
2019 201901 "Australia" "Zambia"                                   189632
2019 201901 "Australia" "Cayman Isds"                                4391
2019 201901 "Australia" "Spain"                                 149509730
2019 201901 "Australia" "Marshall Isds"                              6860
2019 201901 "Australia" "Dominican Rep."                          2518931
2019 201901 "Australia" "Mali"                                      68096
2019 201901 "Australia" "Canada"                                162801943
2019 201901 "Australia" "Kazakhstan"                               474619
2019 201901 "Australia" "Colombia"                                6114289
2019 201901 "Australia" "Bulgaria"                                5803291
2019 201901 "Australia" "Mauritius"                                651966
2019 201901 "Australia" "Japan"                                1151845512
2019 201901 "Australia" "Paraguay"                                 443859
2019 201901 "Australia" "Viet Nam"                              390468713
2019 201901 "Australia" "China, Macao SAR"                        6119526
2019 201901 "Australia" "Ecuador"                                 2542077
2019 201901 "Australia" "Ethiopia"                                1054798
2019 201901 "Australia" "Lithuania"                               6207901
2019 201901 "Australia" "Bosnia Herzegovina"                      1142891
2019 201901 "Australia" "Iran"                                    1619303
2019 201901 "Australia" "France"                                349181319
2019 201901 "Australia" "Myanmar"                                 3635743
2019 201901 "Australia" "Qatar"                                  30589700
2019 201901 "Australia" "Trinidad and Tobago"                      699798
2019 201901 "Australia" "Namibia"                                  286874
2019 201901 "Australia" "Oman"                                    1920662
2019 201901 "Australia" "Bermuda"                                    6104
2019 201901 "Australia" "Algeria"                                68522824
2019 201901 "Australia" "Poland"                                 73089457
2019 201901 "Australia" "Burundi"                                  430214
2019 201901 "Australia" "Morocco"                                 2512228
2019 201901 "Australia" "Mozambique"                                81380
2019 201901 "Australia" "Bhutan"                                    26706
2019 201901 "Australia" "Pitcairn"                                   2016
2019 201901 "Australia" "Barbados"                                  69430
2019 201901 "Australia" "Venezuela"                                 75216
2019 201901 "Australia" "Belarus"                                12080033
2019 201901 "Australia" "Philippines"                            41483712
2019 201901 "Australia" "Uzbekistan"                                61587
2019 201901 "Australia" "United States Minor Outlying Islands"       6404
2019 201901 "Australia" "FS Micronesia"                               813
2019 201901 "Australia" "Czech Rep."                             68883229
2019 201901 "Australia" "Kenya"                                   3193627
2019 201901 "Australia" "Austria"                               103521547
2019 201901 "Australia" "Cyprus"                                  2720043
2019 201901 "Australia" "Saint Kitts and Nevis"                      4428
2019 201901 "Australia" "TFYR of Macedonia"                        323756
2019 201901 "Australia" "Germany"                               962855239
2019 201901 "Australia" "Guam"                                       1826
2019 201901 "Australia" "Madagascar"                              1347730
2019 201901 "Australia" "Bahamas"                                    1933
2019 201901 "Australia" "Belgium"                               113616405
2019 201901 "Australia" "Albania"                                  158647
2019 201901 "Australia" "Denmark"                                81303255
2019 201901 "Australia" "Mexico"                                199032058
2019 201901 "Australia" "Chile"                                  37709034
2019 201901 "Australia" "United States of America"             1914144484
end

keep Importers Exporters
gen long obs_no = _n
rename (Importers Exporters) country=
reshape long country, i(obs_no) j(group) string
drop obs_no
duplicates drop
isid country, sort
save imp_exp, replace
        Next, you can bring that variable into the data from #13
        Code:
        // HERE READ IN THE DATA FROM #13; I'M NOT REPEATING THE DATAEX HERE
merge m:1 country using imp_exp, keep(match master)
list country if _merge == 1 // FIND ANY COUNTRIES NOT FOUND IN imp_exp
        If there are any countries in that data that did not appear in your imp_exp file, that code will show you a list of them and you can try to find out their correct status.

        That's all you should do here. The idea of putting the importers and exporters into separate columns when there is no connection between particular importers and is dangerous: you would be associated unrelated information in the same observations (rows) of the dataset. That's a recipe for disaster in Stata. Don't even think about doing it! Anything you need to do that treats the importers and exporters differently can now be done by using the variable group in an -if- condition, or in a -by- prefix.

        Comment

        • #19
          Dear Clyde, regarding your kind response, when i run this codes I got this error "variable country does not uniquely identify observations in the using data". Also I ran label variable country "country"
          label variable group "group" from #18. But still I have this problem.
          r(459);

          Could you please guide me?

          Comment

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