#-------------------------------------------------------------------------------------------------------------------. # REGISTER CENSUS 2011 # Dataset FAMILY # # This R! routine adds variable and value labels. # It works with Register Census 2011 microdata, prepared by SORS in April 2014. # Your dataset might have a smaller set of variables. # # Version 1, December 2014 # R 3.1.2 # # Social Science Data Archives, University of Ljubljana # Sebastian Kocar # http://www.adp.fdv.uni-lj.si/ # Contact: arhiv.podatkov@fdv.uni-lj.si # # This R code is protected under the Creative Commons licence. #-------------------------------------------------------------------------------------------------------------------. #--- CONFIGURATION SECTION --- START --- # The following commands should contain the complete path and # name of the .txt file to be read (e.g. 'C:/Census2011_v1.txt') # and the .txt file to be saved with labels (e.g. 'C:/Census2011_labels.txt') # Change TXT_DATA_PATH to your filename. # Change TXT_DATA_PATH_SAVE to your filename. #--- CONFIGURATION SECTION --- END --- #-------------------------------------------------------------------------------------------------------------------. #----------------------------------------------Start data processing------------------------------------------------. #--- importing data data<-read.delim("TXT_DATA_PATH", header = TRUE, sep = "\t", dec=",") #--- adding labels for variables and variable values require(Hmisc) label(data$ID_DRUZ) <- "Family ID" label(data$TIP_DRUZ) <- "Type of family; 1 - Married couple, no children, 2 - Married couple with children, 3 - Mother with children, 4 - Father with children, 5 - Unmarried couple, no children, 6 - Unmarried couple with children, 7 - Grandparents with grandchildren, 8 - Grandparent with grandchildren, 9 - Siblings, 10 - Non-family member, 11 - Institutional household member, 12 - Other household member" label(data$DRUZ_OS) <- "Family size" label(data$VZP_DRUZ_IND) <- "Reconstituted family indicator; 1 - Reconstituted family - one of the biological parents lives i, 2 - Reconstituted family - one of the biological parents is not, 3 - Not reconstituted family, 4 - No data on reconstituted family available" label(data$TIP_VZP_DRUZ) <- "Type of reconstituted family; 1 - No common children, biological mother lives in a family, bio, 2 - No common children, biological father lives in a family, bio, 3 - At least one common child, biological father of non-common c, 4 - At least one common child, biological mother of non-common c, 5 - Biological father and/or mother of at least one child live i, 6 - Reconstituted family - one of the biological parents is not, 7 - Not reconstituted family, 8 - No data on reconstituted family available" label(data$ID_GO) <- "Household ID" label(data$TIP_GO) <- "Type of household; 1 - One-person, 2 - Multi-person non-family of relatives and non-relatives, 3 - Multi-person non-family of grandparents, grandchildren or si, 4 - Multi-person one-family, 5 - Multi-person one-family extended, 6 - Multi-person two or more-family, 7 - Multi-person two or more-family extended, 8 - Institutional household, 9 - Other household" label(data$GO_OS) <- "Household size" label(data$ST_DRUZ_GO) <- "Number of families in the household" label(data$STAR_M) <- "Age of mother / spouse / partner / grandmother" label(data$STAR_O) <- "Age of father / spouse / partner / grandfather" label(data$ST_OT_DRUZ) <- "Number of children in the family" label(data$STAR_OT_1) <- "Age of first child" label(data$STAR_OT_2) <- "Age of second child" label(data$STAR_OT_3) <- "Age of third child" label(data$STAR_OT_4) <- "Age of fourth child" label(data$STAR_OT_5) <- "Age of fifth child" label(data$STAR_OT_6) <- "Age of sixth child" label(data$STAR_OT_7) <- "Age of seventh child" label(data$STAR_OT_8) <- "Age of eighth child" label(data$STAR_OT_9) <- "Age of ninth child" label(data$STAR_OT_10) <- "Age of tenth child" label(data$STAR_OT_11) <- "Age of eleventh child" label(data$STAR_OT_12) <- "Age of twelfth child" label(data$STAR_OT_13) <- "Age of thirteenth child" label(data$STAR_MIN) <- "Age of youngest child" label(data$STAR_MAX) <- "Age of oldest child" label(data$ST_OT_DRUZ_0_5) <- "Number of children aged 0-5 years in the family" label(data$ST_OT_DRUZ_0_14) <- "Number of children aged 0-14 years in the family" label(data$ST_OT_DRUZ_0_17) <- "Number of children aged 0-17 years in the family" label(data$ST_OT_DRUZ_0_24) <- "Number of children aged 0-24 years in the family" label(data$TIP_DRUZ_OT_0_5) <- "Type of family by children aged 0-5 years; 1 - All children in family younger than x years, 2 - At least one child in family younger than x years, 3 - All children in family aged x years or more" label(data$TIP_DRUZ_OT_0_14) <- "Type of family by children aged 0-14 years; 1 - All children in family younger than x years, 2 - At least one child in family younger than x years, 3 - All children in family aged x years or more" label(data$TIP_DRUZ_OT_0_17) <- "Type of family by children aged 0-17 years; 1 - All children in family younger than x years, 2 - At least one child in family younger than x years, 3 - All children in family aged x years or more" label(data$TIP_DRUZ_OT_0_24) <- "Type of family by children aged 0-24 years; 1 - All children in family younger than x years, 2 - At least one child in family younger than x years, 3 - All children in family aged x years or more" label(data$ZS_M) <- "Marital status of mother; 1 - Single, 2 - Married, 3 - Widow/er, 4 - Divorced, 5 - Same gender partnership, 6 - Terminated IPS -death, 7 - Terminated IPS - other, 9 - Unknown" label(data$ZS_O) <- "Marital status of father; 1 - Single, 2 - Married, 3 - Widow/er, 4 - Divorced, 5 - Same gender partnership, 6 - Terminated IPS -death, 7 - Terminated IPS - other, 9 - Unknown" label(data$DRZ_M) <- "Citizenship of mother; 4 - Afghanistan, 8 - Albania, 9 - Kosovo, 12 - Algeria, 16 - American Samoa, 20 - Andorra, 24 - Angola, 31 - Azerbaijan, 32 - Argentina, 36 - Australia, 40 - Austria, 44 - Bahamas, 50 - Bangladesh, 51 - Armenia, 52 - Barbados, 56 - Belgium, 68 - Bolivia, Plurinational State of, 70 - Bosnia and Herzegovina, 72 - Botswana, 76 - Brazil, 84 - Belize, 86 - British Indian Ocean Territory, 90 - Solomon Islands, 96 - Brunei Darussalam, 100 - Bulgaria, 104 - Myanmar, 108 - Burundi, 112 - Belarus, 116 - Cambodia, 120 - Cameroon, 124 - Canada, 132 - Cape Verde, 136 - Cayman Islands, 140 - Central African Republic, 144 - Sri Lanka, 148 - Chad, 152 - Chile, 156 - China, 158 - Taiwan, province of China, 170 - Colombia, 174 - Comoros, 178 - Congo, 180 - Congo, the Democratic Republic of the, 188 - Costa Rica, 191 - Croatia, 192 - Cuba, 196 - Cyprus, 200 - Czechoslovakia, 203 - Czech Republic, 204 - Benin, 208 - Denmark, 212 - Dominica, 214 - Dominican Republic, 218 - Ecuador, 222 - El Salvador, 231 - Ethiopia, 232 - Eritrea, 233 - Estonia, 242 - Fiji, 246 - Finland, 250 - France, 254 - French Guiana, 258 - French Polynesia, 262 - Djibouti, 266 - Gabon, 268 - Georgia, 270 - Gambia, 275 - Palestinian Territory, occupied, 276 - Germany, 288 - Ghana, 300 - Greece, 320 - Guatemala, 324 - Guinea, 328 - Guyana, 332 - Haiti, 336 - Holy See (Vatican City State), 340 - Honduras, 344 - Hong Kong, 348 - Hungary, 352 - Iceland, 356 - India, 360 - Indonesia, 364 - Iran, Islamic Republic of, 368 - Iraq, 372 - Ireland, 376 - Israel, 380 - Italy, 384 - Cote D´Ivoire, 388 - Jamaica, 392 - Japan, 398 - Kazakhstan, 400 - Jordan, 404 - Kenya, 408 - Korea, Democratic People´s Republic of, 410 - Korea, Republic of, 414 - Kuwait, 417 - Kyrgyzstan, 418 - Lao People´s Democratic Republic, 422 - Lebanon, 428 - Latvia, 430 - Liberia, 434 - Libya, 438 - Liechtenstein, 440 - Lithuania, 442 - Luxembourg, 450 - Madagascar, 454 - Malawi, 458 - Malaysia, 466 - Mali, 470 - Malta, 480 - Mauritius, 484 - Mexico, 492 - Monaco, 496 - Mongolia, 498 - Moldova, Republic of, 499 - Montenegro, 500 - Montserrat, 504 - Morocco, 508 - Mozambique, 512 - Oman, 516 - Namibia, 524 - Nepal, 528 - Netherlands, 530 - Netherlands Antilles, 533 - Aruba, 554 - New Zealand, 558 - Nicaragua, 566 - Nigeria, 578 - Norway, 586 - Pakistan, 591 - Panama, 598 - Papua New Guinea, 600 - Paraguay, 604 - Peru, 608 - Philippines, 616 - Poland, 620 - Portugal, 624 - Guinea-Bissau, 626 - Timor-Leste, 630 - Puerto Rico, 634 - Qatar, 642 - Romania, 643 - Russian federation, 646 - Rwanda, 659 - Saint Kitts and Nevis, 662 - Saint Lucia, 682 - Saudi Arabia, 686 - Senegal, 688 - Serbia, 694 - Sierra Leone, 702 - Singapore, 703 - Slovakia, 704 - Viet Nam, 705 - Slovenia, 706 - Somalia, 710 - South Africa, 716 - Zimbabwe, 724 - Spain, 729 - Sudan, 732 - Western Sahara, 736 - Sudan, 740 - Suriname, 752 - Sweden, 756 - Switzerland, 760 - Syrian Arab republic, 762 - Tajikistan, 764 - Thailand, 768 - Togo, 780 - Trinidad and Tobago, 784 - United Arab Emirates, 788 - Tunisia, 792 - Turkey, 795 - Turkmenistan, 800 - Uganda, 804 - Ukraine, 807 - Macedonia, the Former Yugoslav Republic of, 810 - USSR, 818 - Egypt, 826 - United Kingdom, 834 - Tanzania, United Republic of, 840 - United States, 854 - Burkina Faso, 858 - Uruguay, 860 - Uzbekistan, 862 - Venezuela, Bolivarian Republic of, 876 - Wallis and Futuna, 887 - Yemen, 890 - Yugoslavia, 891 - Serbia and Montenegro, 894 - Zambia, 901 - Kosovo, 995 - Palestinian Territory, Occupied, 998 - Abroad, 999 - Unknown country" label(data$DRZ_O) <- "Citizenship of father; 4 - Afghanistan, 8 - Albania, 9 - Kosovo, 12 - Algeria, 16 - American Samoa, 20 - Andorra, 24 - Angola, 31 - Azerbaijan, 32 - Argentina, 36 - Australia, 40 - Austria, 44 - Bahamas, 50 - Bangladesh, 51 - Armenia, 52 - Barbados, 56 - Belgium, 68 - Bolivia, Plurinational State of, 70 - Bosnia and Herzegovina, 72 - Botswana, 76 - Brazil, 84 - Belize, 86 - British Indian Ocean Territory, 90 - Solomon Islands, 96 - Brunei Darussalam, 100 - Bulgaria, 104 - Myanmar, 108 - Burundi, 112 - Belarus, 116 - Cambodia, 120 - Cameroon, 124 - Canada, 132 - Cape Verde, 136 - Cayman Islands, 140 - Central African Republic, 144 - Sri Lanka, 148 - Chad, 152 - Chile, 156 - China, 158 - Taiwan, province of China, 170 - Colombia, 174 - Comoros, 178 - Congo, 180 - Congo, the Democratic Republic of the, 188 - Costa Rica, 191 - Croatia, 192 - Cuba, 196 - Cyprus, 200 - Czechoslovakia, 203 - Czech Republic, 204 - Benin, 208 - Denmark, 212 - Dominica, 214 - Dominican Republic, 218 - Ecuador, 222 - El Salvador, 231 - Ethiopia, 232 - Eritrea, 233 - Estonia, 242 - Fiji, 246 - Finland, 250 - France, 254 - French Guiana, 258 - French Polynesia, 262 - Djibouti, 266 - Gabon, 268 - Georgia, 270 - Gambia, 275 - Palestinian Territory, occupied, 276 - Germany, 288 - Ghana, 300 - Greece, 320 - Guatemala, 324 - Guinea, 328 - Guyana, 332 - Haiti, 336 - Holy See (Vatican City State), 340 - Honduras, 344 - Hong Kong, 348 - Hungary, 352 - Iceland, 356 - India, 360 - Indonesia, 364 - Iran, Islamic Republic of, 368 - Iraq, 372 - Ireland, 376 - Israel, 380 - Italy, 384 - Cote D´Ivoire, 388 - Jamaica, 392 - Japan, 398 - Kazakhstan, 400 - Jordan, 404 - Kenya, 408 - Korea, Democratic People´s Republic of, 410 - Korea, Republic of, 414 - Kuwait, 417 - Kyrgyzstan, 418 - Lao People´s Democratic Republic, 422 - Lebanon, 428 - Latvia, 430 - Liberia, 434 - Libya, 438 - Liechtenstein, 440 - Lithuania, 442 - Luxembourg, 450 - Madagascar, 454 - Malawi, 458 - Malaysia, 466 - Mali, 470 - Malta, 480 - Mauritius, 484 - Mexico, 492 - Monaco, 496 - Mongolia, 498 - Moldova, Republic of, 499 - Montenegro, 500 - Montserrat, 504 - Morocco, 508 - Mozambique, 512 - Oman, 516 - Namibia, 524 - Nepal, 528 - Netherlands, 530 - Netherlands Antilles, 533 - Aruba, 554 - New Zealand, 558 - Nicaragua, 566 - Nigeria, 578 - Norway, 586 - Pakistan, 591 - Panama, 598 - Papua New Guinea, 600 - Paraguay, 604 - Peru, 608 - Philippines, 616 - Poland, 620 - Portugal, 624 - Guinea-Bissau, 626 - Timor-Leste, 630 - Puerto Rico, 634 - Qatar, 642 - Romania, 643 - Russian federation, 646 - Rwanda, 659 - Saint Kitts and Nevis, 662 - Saint Lucia, 682 - Saudi Arabia, 686 - Senegal, 688 - Serbia, 694 - Sierra Leone, 702 - Singapore, 703 - Slovakia, 704 - Viet Nam, 705 - Slovenia, 706 - Somalia, 710 - South Africa, 716 - Zimbabwe, 724 - Spain, 729 - Sudan, 732 - Western Sahara, 736 - Sudan, 740 - Suriname, 752 - Sweden, 756 - Switzerland, 760 - Syrian Arab republic, 762 - Tajikistan, 764 - Thailand, 768 - Togo, 780 - Trinidad and Tobago, 784 - United Arab Emirates, 788 - Tunisia, 792 - Turkey, 795 - Turkmenistan, 800 - Uganda, 804 - Ukraine, 807 - Macedonia, the Former Yugoslav Republic of, 810 - USSR, 818 - Egypt, 826 - United Kingdom, 834 - Tanzania, United Republic of, 840 - United States, 854 - Burkina Faso, 858 - Uruguay, 860 - Uzbekistan, 862 - Venezuela, Bolivarian Republic of, 876 - Wallis and Futuna, 887 - Yemen, 890 - Yugoslavia, 891 - Serbia and Montenegro, 894 - Zambia, 901 - Kosovo, 995 - Palestinian Territory, Occupied, 998 - Abroad, 999 - Unknown country" label(data$PREB_M) <- "Type of resident - mother; 1 - Citizen of the RS with permanent residence, 2 - Citizen of the RS with temporary residence, 3 - Foreigner with permanent residence, 4 - Foreigner with temporary residence, 5 - Citizen of the RS with usual residence abroad, 6 - Person with registered residence in Slovenia not included in" label(data$PREB_O) <- "Type of resident - father; 1 - Citizen of the RS with permanent residence, 2 - Citizen of the RS with temporary residence, 3 - Foreigner with permanent residence, 4 - Foreigner with temporary residence, 5 - Citizen of the RS with usual residence abroad, 6 - Person with registered residence in Slovenia not included in" label(data$DRZ_HOM_ZP) <- "Citizenship of spouse / partners; 1 - Husband/male partner and wife/female partner are citizens of, 2 - Husband/male partner is a citizen of the RS, wife/female par, 3 - Husband/male partner is a foreign citizen, wife/female partn, 4 - Husband/male partner and wife/female partner are foreign cit" label(data$AKT_M) <- "Current activity status of father; 1 - Employed, 2 - Self employed, 3 - Farmer / contributing family worker, 4 - Unemployed, never worked before, 5 - Unemployed, previously in employment, 6 - Person aged 0-14 years, 7 - Pupil, 8 - Student, 9 - Pension recipient, 10 - Social transfers recipient, 11 - Other inactive person" label(data$AKT_O) <- "Current activity status of mother; 1 - Employed, 2 - Self employed, 3 - Farmer / contributing family worker, 4 - Unemployed, never worked before, 5 - Unemployed, previously in employment, 6 - Person aged 0-14 years, 7 - Pupil, 8 - Student, 9 - Pension recipient, 10 - Social transfers recipient, 11 - Other inactive person" label(data$AKT_ZP) <- "Current activity status of spouse / partners; 1 - Husband/male partner and wife/female partner are employed, 2 - Husband/male partner is employed, wife/female partner is une, 3 - Husband/male partner is employed, wife/female partner is ina, 4 - Husband/male partner and wife/female partner are unemployed, 5 - Husband/male partner is unemployed, wife/female partner is i, 6 - Husband/male partner and wife/female partner are inactive" label(data$IZB_M) <- "Educational attainment of mother; 0 - No education, 11001 - First stage of basic education/Incomplete first stage of bas, 11002 - First stage of basic education/Complete first stage of basic, 11003 - Fulfilled basic education requirement/Incomplete second stag, 12001 - Second stage of basic education/Basic education, 13001 - Short-term vocational upper secondary education/Short-term v, 14001 - Vocational upper secondary education/Vocational upper second, 15001 - Technical upper secondary education/Technical upper secondar, 15002 - General upper secondary education/General upper secondary ed, 16101 - Higher vocational education/Higher vocational education, 16102 - Short-term higher education (former)/Short-term higher educa, 16201 - Specialization after short-term higher education (former)/Sp, 16202 - Professional higher education (former)/Professional higher e, 16203 - Professional higher education (first Bologna cycle)/Professi, 16204 - Academic higher education (first Bologna cycle)/Academic hig, 17001 - Specialization after professional higher education (former)/, 17002 - Academic higher education (former)/Academic higher education, 17003 - Master´s education (second Bologna cycle)/Master (second Bo, 18101 - Specialization after academic higher education (former)/Spec, 18102 - Education leading to "magisterij" of science (former)/"Magis, 18201 - Education leading to doctorate of science (former)/Doctorate, 18202 - Education leading to doctorate of science (third Bologna cyc" label(data$IZB_O) <- "Educational attainment of father; 0 - No education, 11001 - First stage of basic education/Incomplete first stage of bas, 11002 - First stage of basic education/Complete first stage of basic, 11003 - Fulfilled basic education requirement/Incomplete second stag, 12001 - Second stage of basic education/Basic education, 13001 - Short-term vocational upper secondary education/Short-term v, 14001 - Vocational upper secondary education/Vocational upper second, 15001 - Technical upper secondary education/Technical upper secondar, 15002 - General upper secondary education/General upper secondary ed, 16101 - Higher vocational education/Higher vocational education, 16102 - Short-term higher education (former)/Short-term higher educa, 16201 - Specialization after short-term higher education (former)/Sp, 16202 - Professional higher education (former)/Professional higher e, 16203 - Professional higher education (first Bologna cycle)/Professi, 16204 - Academic higher education (first Bologna cycle)/Academic hig, 17001 - Specialization after professional higher education (former)/, 17002 - Academic higher education (former)/Academic higher education, 17003 - Master´s education (second Bologna cycle)/Master (second Bo, 18101 - Specialization after academic higher education (former)/Spec, 18102 - Education leading to "magisterij" of science (former)/"Magis, 18201 - Education leading to doctorate of science (former)/Doctorate, 18202 - Education leading to doctorate of science (third Bologna cyc" label(data$IZB_ZP) <- "Educational attainment of spouse / partners; 1 - Husband/male partner and wife/female partner have basic educ, 2 - Husband/male partner has basic education or less, wife/femal, 3 - Husband/male partner has basic education or less, wife/femal, 4 - Husband/male partner and wife/female partner have upper seco, 5 - Husband/male partner has tertiary education, wife/female par, 6 - Husband/male partner and wife/female partner have tertiary e" label(data$PRIS_M) <- "Immigrant background of mother; 1 - First generation, 2 - Second generation, both of parents first generation immigran, 3 - Second generation, only father first generation immigrant, 4 - Second generation, only mother first generation immigrant, 5 - Third generation, both of parents second generation immigran, 6 - Third generation, only father second generation immigrant, 7 - Third generation, only mother second generation immigrant, 8 - No immigrant background, father and mother not immigrants, 9 - No immigrant background, father is not immigrant, no data on, 10 - No immigrant background, mother is not immigrant, no data on, 11 - No data on immigrant background of parents" label(data$PRIS_O) <- "Immigrant background of father; 1 - First generation, 2 - Second generation, both of parents first generation immigran, 3 - Second generation, only father first generation immigrant, 4 - Second generation, only mother first generation immigrant, 5 - Third generation, both of parents second generation immigran, 6 - Third generation, only father second generation immigrant, 7 - Third generation, only mother second generation immigrant, 8 - No immigrant background, father and mother not immigrants, 9 - No immigrant background, father is not immigrant, no data on, 10 - No immigrant background, mother is not immigrant, no data on, 11 - No data on immigrant background of parents" label(data$PRIS_ZP_2) <- "Immigrant background of spouse / partners; 1 - Husband/male partner and wife/female partner are first gener, 2 - Husband/male partner is a first generation immigrant, wife/f, 3 - Husband/male partner is a first generation immigrant, wife/f, 4 - Husband/male partner and wife/female partner are second gene, 5 - Husband/male partner is a second generation immigrant, wife/, 6 - Husband/male partner and wife/female partner are third gener, 7 - Husband/male partner is a first generation immigrant, wife/f, 8 - Husband/male partner is a second generation immigrant, wife/, 9 - Husband/male partner is a third generation immigrant, wife/f, 10 - Husband/male partner and wife/female partner have no immigra, 11 - No data on immigrant background" label(data$ROJ_IND) <- "Birth indicator; 1 - Have not given birth, 2 - Have given birth" label(data$ROJ_ST) <- "Number of live-born children" label(data$MATI) <- "Status of biological mother; 1 - Mother lives in the same family, 2 - Mother lives in the same household, 3 - Mother lives in an other household in Slovenia, 4 - Mother is not the resident of Slovenia, 5 - No data" label(data$OCE) <- "Status of biological father; 1 - Father lives in the same family, 2 - Father lives in the same household, 3 - Father lives in an other household in Slovenia, 4 - Father is not the resident of Slovenia, 5 - No data" label(data$STARSI) <- "Status of spouse; 1 - Parents live in the same family, 2 - Parents live in the same household, 3 - One parent lives in the same family, the other one in the sa, 4 - Mother lives in the same family or in the same household, fa, 5 - Father lives in the same family or in the same household, mo, 6 - Parents live in an other household in Slovenia, 7 - Mother lives in the same family or in the same household, fa, 8 - Father lives in the same family or in the same household, mo, 9 - One parent lives in an other household in Slovenia, the othe, 10 - Parents are not the residents of Slovenia, 11 - Mother lives in an other household in Slovenia, no data on f, 12 - Father lives in an other household in Slovenia, no data on m, 13 - One parent is not the resident of Slovenia, no data on the o, 14 - No data" label(data$HS_MID_NAST) <- "Type of accommodation; 1 - Social work center without conventional dwellings, 2 - Residence for pupils and students, 3 - Postgraduate´s house, 4 - Special social care institute, 5 - Community, 6 - Prison, 7 - Religious institution, 8 - Elderly home, 9 - Elderly home with private households, 10 - Residence for students and religious institution, 11 - Elderly home, religious institution and private households, 12 - Elderly home and private households, 13 - Residence for students, religious institution and private ho, 14 - Worker dormitory in non-residential building, 15 - Worker dormitory with short-term settlement, 16 - Worker dormitory for communities, 17 - Worker dormitory in one-dwelling building, 18 - Worker dormitory in multi-dwelling building, 19 - Residence for students, worker dormitory and conventional dw, 20 - Religious institution, residence for students and private ho, 21 - Special social care institute with private households, 22 - Roma settlement, 23 - Social work center with conventional dwellings, 24 - Special accommodation center, 25 - Residence for students in building with conventional dwellin, 26 - Residence for pupils and students without institutional hous, 27 - Religious institution without institutional household, 28 - Special social care institute without institutional househol, 29 - Hotel and other tourist accommodation, 30 - Collective living quarter as a part of the building, 31 - Collective living quarter with 7 or less residents" label(data$ID_KOHEZIJSKA) <- "Usual residence - cohesion region - LOOK UP RPE_POPIS_2011 CODEBOOK" label(data$ID_STATISTICNA) <- "Usual residence - statistical region - LOOK UP RPE_POPIS_2011 CODEBOOK" label(data$ID_OBCINA) <- "Usual residence - municipality - LOOK UP RPE_POPIS_2011 CODEBOOK" label(data$NA_ID) <- "Usual residence - settlement - LOOK UP RPE_POPIS_2011 CODEBOOK" label(data$TIP_NASELJA) <- "Type of settlement / area" label(data$VEL_NAS) <- "Size of locality; 1 - 1,000,000 inhabitants or more, 2 - From 500,000 to 999,999 inhabitants, 3 - From 200,000 to 499,999 inhabitants, 4 - From 100,000 to 199,999 inhabitants, 5 - From 50,000 to 99,999 inhabitants, 6 - From 20,000 to 49,999 inhabitants, 7 - From 10,000 to 19,999 inhabitants, 8 - From 5,000 to 9,999 inhabitants, 9 - From 2,000 to 4,999 inhabitants, 10 - From 1,000 to 1,999 inhabitants, 11 - From 500 to 999 inhabitants, 12 - From 200 to 499 inhabitants, 13 - From 100 to 199 inhabitants, 14 - From 50 to 99 inhabitants, 15 - From 25 to 49 inhabitants, 16 - From 1 to 24 inhabitants" #----------------------------------------------End data processing------------------------------------------------. #--- displaying all labels attach(data) describe(data) #--- saving txt dataset with labels write.table(data, "TXT_DATA_PATH_SAVE", quote = FALSE, sep = "\t", dec=",", na = "NA")