DOL-ILAB SDC - DFM_InDepth20152016_StudentsParents_NOPII
Pablo Diego-Rosell, PhD
2021-10-13
- Setup filenames
- Setup data, functions and create dictionary for dataset review
- Direct PII: variables to be removed
- Direct PII-team: Encode field team names
- Small locations: Encode locations with pop <100,000 using random large numbers
- Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
- Indirect PII - Categorical: Recode, encode, or Top/bottom coding for extreme values
- Matching and crosstabulations: Run automated PII check
- Open-ends: review responses for any sensitive information, redact as necessary
- GPS data: Displace
- Save processed data in Stata and SPSS format
rm(list=ls(all=t))Setup filenames
filename <- "DFM_InDepth20152016_StudentsParents_NOPII" # !!!Update filename
functions_vers <- "functions_1.7.R" # !!!Update helper functions fileSetup data, functions and create dictionary for dataset review
source (functions_vers)Visually inspect variables in "dictionary.csv" and flag for risk, using the following flags:
# Direct PII: Respondent Names, Addresses, Identification Numbers, Phone Numbers
# Direct PII-team: Interviewer Names, other field team names
# Indirect PII-ordinal: Date of birth, Age, income, education, household composition.
# Indirect PII-categorical: Gender, education, ethnicity, nationality,
# occupation, employer, head of household, marital status
# GPS: Longitude, Latitude
# Small Location: Location (<100,000)
# Large Location (>100,000)
# Weight: weightVar
# Household ID: hhId,
# Open-ends: Review responses for any sensitive information, redact as necessary Direct PII: variables to be removed
# !!!Include any Direct PII variables
dropvars <- c("student_name",
"name_pad",
"num_telf",
"future_parent",
"school_parent",
"education_parent",
"pic_home",
"consent_signed",
"cto_padre_nom",
"cto_padre_app1",
"cto_padre_app2",
"p27a1",
"p27a2",
"p27a3",
"p27a4",
"p27a5",
"p27a6",
"p27a7",
"p27a8",
"p27a9",
"p27a10",
"p27d1",
"p27d2",
"p27d3",
"audio_video",
"nompad",
"app_pad",
"nommad",
"app_mad",
"address",
"dni",
"NUMERO_DOCUMENTO",
"guard_male_name",
"conf_guard_male_name",
"guard_male_surname",
"conf_guard_male_surname",
"guard_female_name",
"conf_guard_female_name",
"guard_female_surname",
"conf_guard_female_surname",
"nom_dist",
"nombres",
"fecha_nac_fixed",
"audio1_student",
"audio2_student",
"audio3_student")
mydata <- mydata[!names(mydata) %in% dropvars]Direct PII-team: Encode field team names
# !!!Replace vector in "variables" field below with relevant variable names
mydata <- mydata[!names(mydata) %in% "i5"]
mydata <- encode_direct_PII_team (variables="id_encuestador")## [1] "Frequency table before encoding"
## id_encuestador. ID del encuestador
## NONPII VERSION
## 39 2709
## [1] "Frequency table after encoding"
## id_encuestador. ID del encuestador
## 1 2
## 39 2709Small locations: Encode locations with pop <100,000 using random large numbers
# !!!Include relevant variables, but check their population size first to confirm they are <100,000
locvars <- c("cod_mod_app",
"Distrito",
"Provincia",
"prov",
"dist",
"cod_mod2",
"COD_MOD",
"cod_mod",
"school_fixed_primary",
"school_fixed_sec",
"cole2016_admin",
"cod_mod_2016",
"cod_mod_2015",
"p12",
"codlocal",
"s4p11b1_2015")
mydata <- encode_location (variables= locvars, missing=999999)## [1] "Frequency table before encoding"
## cod_mod_app. cod_mod
## 204800 204875 204909 205005 205047 205112 205120 205153 205682 205690 205773 205781 205815 207407 216341 220285 226704 232207 232223 232231 232249
## 5 1 1 2 2 3 3 1 1 1 2 1 1 1 1 2 1 2 1 2 1
## 232264 232504 232512 232538 232546 232553 232561 232579 232587 232595 232603 232611 232645 232728 232777 233130 233296 233361 233676 233718 233734
## 1 1 1 2 1 2 1 1 1 2 1 2 1 1 2 2 4 1 3 1 2
## 233882 233890 233908 233916 233924 233932 233940 233957 233965 233973 233981 233999 234021 234062 234096 234104 234112 234120 234138 234153 234161
## 1 1 4 3 2 2 3 1 2 2 2 2 2 2 2 3 1 2 3 3 2
## 234187 234211 234229 234237 234351 234369 234377 234385 234401 234419 234427 234443 234450 234500 234583 234674 234682 234781 234831 234856 236158
## 2 1 2 1 1 2 1 3 2 3 3 2 1 2 2 3 3 2 3 2 1
## 236349 236422 236448 236463 236471 236489 236653 236661 236927 287409 287425 287466 309286 309294 309377 309419 309682 310433 312090 312215 312306
## 2 4 1 5 1 3 1 8 4 2 3 1 1 6 1 1 1 3 2 1 2
## 312421 312744 312868 313080 313239 313395 313460 313890 313908 313965 313981 314070 314187 314211 314237 314245 314260 314278 314294 405258 405498
## 1 1 2 1 2 2 1 3 2 2 3 2 3 2 1 2 4 3 2 3 3
## 405704 405738 405746 405837 405852 405894 405902 405928 405936 406009 406066 406082 406116 406124 406140 406215 406223 406264 406413 406595 406629
## 3 2 2 1 1 2 2 1 2 2 3 3 4 2 1 2 2 2 1 1 3
## 406645 406975 406983 407007 407049 408245 408278 408286 408294 408328 408336 408393 408468 408476 408484 408492 408559 408567 408609 408666 408732
## 1 2 3 1 2 2 2 3 2 1 1 3 2 3 1 1 1 3 2 3 1
## 408773 408823 408856 408922 408955 408971 409003 409011 409029 409193 409227 409235 409243 409284 409292 409300 409318 409326 409359 409441 409565
## 1 2 1 2 3 2 2 3 3 2 3 1 2 2 3 3 2 1 2 2 2
## 409896 410464 410480 410514 410613 410670 410746 410779 410787 410803 473249 481283 486688 486928 489120 495069 495325 502922 504142 517581 517888
## 1 1 1 2 1 1 2 5 2 2 1 3 3 1 2 5 6 2 1 12 2
## 518084 518472 519496 519595 519678 525923 550392 551309 557587 579268 579276 579284 579292 579300 585885 587055 587147 587204 589804 591255 591602
## 10 4 2 2 6 3 2 3 3 2 1 1 3 6 2 6 1 3 3 8 4
## 592147 612051 612119 612291 612507 612689 612747 612770 612804 615013 616110 617787 617829 621391 623017 623041 637272 639542 639617 647388 647412
## 3 2 2 1 3 1 1 1 2 2 3 6 4 5 2 1 5 1 1 2 3
## 647446 647628 671628 672105 679829 680058 680082 680124 699603 712562 712711 712778 723031 730655 731273 735498 736116 776039 779041 783423 783621
## 6 1 1 4 1 2 1 4 7 1 1 2 1 1 2 3 1 1 1 3 1
## 783696 783704 783720 783787 783795 791319 791574 794438 796888 818674 844159 844183 891408 891812 895482 927871 930958 931055 931063 932236 932491
## 3 1 3 2 1 10 1 1 1 2 2 2 5 1 1 3 1 7 6 3 2
## 932608 932848 933226 933283 933291 933317 933531 933846 999999 1031574 1117704 1201649 1201870 1260942 1266428 1273655 1314376 1320647 1321322 1321330 1321355
## 3 3 2 3 1 3 1 3 97 2 2 4 2 2 3 1 2 3 3 4 5
## 1321421 1327279 1327287 1336072 1343573 1343581 1344639 1345024 1347269 1347293 1347301 1347434 1347459 1347921 1347939 1347970 1352269 1364868 1369248 1372507 1377209
## 4 1 1 1 1 3 4 2 1 2 1 1 2 4 2 7 1 3 1 3 1
## 1377233 1377415 1380021 1380120 1386226 1388610 1388644 1388651 1389261 1389279 1390095 1390467 1390517 1390665 1390673 1392083 1392091 1392109 1392117 1392125 1392141
## 1 3 6 1 2 1 1 3 4 1 1 2 4 5 4 1 4 2 1 5 5
## 1392174 1392224 1392240 1392257 1396191 1396209 1396225 1396852 1396878 1396886 1398783 1398932 1401934 1401942 1401959 1402536 1408426 1412634 1412873 1415983 1418615
## 5 2 2 3 4 2 8 6 1 2 1 1 2 2 5 3 2 3 2 3 1
## 1423003 1442185 1452705 1459791 1459809 1523802 1523810 1523828 1540988 1540996 1541192 1625532 1625557 1625573 1630631 1637263 1659101 1666130 <NA>
## 2 2 5 3 4 2 1 1 2 3 4 2 2 1 2 1 2 1 1732
## [1] "Frequency table after encoding"
## cod_mod_app. cod_mod
## 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659
## 4 1 3 3 4 1 12 2 1 1 4 1 2 4 1 8 2 1 5 2 1 3 2 2
## 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683
## 2 1 1 1 2 2 2 3 2 5 6 1 1 1 1 8 2 3 5 3 3 4 2 2
## 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707
## 3 2 1 1 2 1 5 4 4 1 1 1 3 1 1 2 2 1 1 2 1 3 3 1
## 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731
## 1 4 1 4 1 2 2 2 1 2 8 2 4 6 3 1 6 5 2 3 2 1 2 3
## 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755
## 1 1 5 2 3 1 1 2 2 1 2 3 3 6 1 3 1 6 2 3 2 3 3 1
## 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 777 778 779 780
## 2 1 1 1 3 2 3 2 2 1 2 1 2 1 2 1 2 6 2 1 2 7 2 1
## 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804
## 5 2 2 2 3 2 2 3 3 2 3 1 1 2 1 1 5 2 1 2 2 1 2 6
## 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828
## 2 1 1 2 1 2 6 5 3 1 3 2 3 3 2 2 3 1 2 2 1 1 2 3
## 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852
## 2 1 2 1 2 1 2 1 4 5 1 3 3 2 3 4 3 2 1 2 2 2 5 2
## 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876
## 1 2 3 1 1 3 1 2 1 3 1 3 2 3 5 2 1 1 2 2 2 1 10 2
## 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900
## 1 1 2 1 1 1 1 2 3 2 3 1 1 1 4 5 1 3 3 4 3 1 1 1
## 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924
## 1 3 2 2 2 4 1 2 3 2 2 2 2 1 2 3 1 1 1 2 3 1 1 1
## 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948
## 1 1 2 3 10 2 1 2 2 2 4 4 2 3 2 3 2 3 3 2 2 1 2 4
## 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972
## 1 1 3 3 1 3 1 2 2 7 1 3 1 2 1 1 1 2 3 1 2 3 1 3
## 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996
## 1 4 3 5 2 4 3 1 3 4 2 2 2 1 1 1 1 1 1 4 1 3 2 1
## 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020
## 1 3 1 3 1 1 3 2 3 4 2 3 2 2 2 1 1 2 2 1 2 1 2 2
## 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 999999 <NA>
## 2 3 2 2 6 6 7 3 3 3 1 97 1732
## [1] "Frequency table before encoding"
## Distrito. Distrito
## Missing-MINEDU <NA>
## 1016 1732
## [1] "Frequency table after encoding"
## Distrito. Distrito
## 620 <NA>
## 1016 1732
## [1] "Frequency table before encoding"
## Provincia. Provincia
## Missing-MINEDU <NA>
## 1016 1732
## [1] "Frequency table after encoding"
## Provincia. Provincia
## 721 <NA>
## 1016 1732
## [1] "Frequency table before encoding"
## prov. Provincia
## Missing-MINEDU <NA>
## 2709 39
## [1] "Frequency table after encoding"
## prov. Provincia
## 796 <NA>
## 2709 39
## [1] "Frequency table before encoding"
## dist. Distrito
## Missing-MINEDU <NA>
## 2709 39
## [1] "Frequency table after encoding"
## dist. Distrito
## 558 <NA>
## 2709 39
## [1] "Frequency table before encoding"
## cod_mod2. Código modular
## 204800 204875 204909 205005 205047 205112 205120 205153 205682 205690 205773 205781 205815 206334 207373 207407 216341 219741 220285 226704 232207
## 10 8 2 5 6 7 10 7 6 7 9 2 5 3 1 1 8 2 3 6 9
## 232223 232231 232249 232264 232504 232512 232538 232546 232553 232561 232579 232587 232595 232603 232611 232645 232728 232777 233130 233296 233361
## 9 6 2 7 3 6 3 5 5 4 3 7 5 7 2 1 3 6 4 5 4
## 233676 233718 233734 233825 233882 233890 233908 233916 233924 233932 233940 233957 233965 233973 233981 233999 234021 234062 234096 234104 234112
## 3 7 5 3 1 3 6 8 9 3 4 6 3 5 6 8 7 5 6 6 4
## 234120 234138 234153 234161 234187 234195 234203 234229 234237 234351 234369 234377 234385 234401 234419 234427 234443 234450 234500 234583 234674
## 4 8 6 8 5 1 4 5 6 1 9 5 8 6 8 7 6 3 7 3 9
## 234682 234781 234831 234856 236158 236349 236422 236448 236463 236471 236489 236653 236661 236927 287409 287425 287466 309286 309294 309377 309419
## 7 3 7 7 5 5 16 4 8 1 7 1 31 9 10 6 3 1 12 1 3
## 309435 309567 310433 310441 312090 312215 312306 312421 312744 312868 313080 313239 313395 313460 313890 313908 313965 313981 314070 314187 314211
## 1 4 3 1 2 6 10 5 2 2 1 2 9 1 3 8 6 6 2 5 4
## 314237 314245 314252 314260 314278 314294 405258 405498 405704 405738 405746 405852 405894 405902 405928 405936 406009 406066 406082 406116 406124
## 4 4 6 6 9 5 5 8 5 5 4 9 8 8 8 9 6 10 6 6 8
## 406140 406215 406223 406264 406413 406595 406629 406645 406975 406983 407007 407049 408211 408245 408278 408286 408294 408328 408336 408393 408468
## 3 5 6 4 7 8 7 5 6 10 4 7 1 7 4 4 3 5 1 4 8
## 408476 408484 408492 408559 408567 408609 408666 408732 408773 408823 408856 408922 408955 408971 409003 409011 409029 409193 409227 409235 409243
## 7 5 5 3 8 6 8 3 4 3 8 7 4 5 6 10 9 5 8 7 6
## 409284 409292 409300 409318 409326 409359 409441 409565 409896 410464 410480 410514 410613 410670 410746 410779 410787 410803 473249 481283 486688
## 9 7 9 7 4 7 9 3 2 1 7 5 1 3 7 9 5 4 7 9 8
## 486928 489120 495069 495325 498782 499863 502922 504142 517581 517888 518084 518472 519496 519595 519678 525923 550392 551309 557587 579268 579276
## 2 8 14 16 1 3 2 13 25 8 22 10 6 6 6 21 7 3 9 9 1
## 579284 579292 579300 585885 587055 587147 587204 589200 589747 589804 591255 591602 592147 612051 612119 612291 612416 612507 612689 612747 612770
## 1 17 20 6 15 4 13 1 1 5 23 14 3 2 2 5 2 9 1 1 1
## 612804 615013 616110 617787 617829 621391 623017 623041 637272 639542 647388 647412 647446 647628 655746 671628 672105 678961 679829 680058 680082
## 4 4 6 17 6 15 4 2 9 2 10 7 16 4 1 4 4 2 2 8 1
## 680124 699603 712562 712711 712778 723031 730655 731273 735498 736116 775700 776039 783423 783597 783621 783696 783704 783720 783787 783795 791319
## 17 11 2 2 2 3 6 2 5 2 5 3 12 1 1 11 1 14 10 10 20
## 791574 794438 796888 818674 818708 844159 844183 891408 891812 895482 899351 927871 930958 931055 931063 932236 932434 932491 932608 932848 933226
## 4 1 2 6 3 2 4 14 1 1 3 10 1 15 18 12 2 4 8 7 6
## 933283 933291 933317 933531 933598 933846 1031574 1117704 1120005 1201649 1201870 1260942 1266428 1271840 1273655 1314376 1320647 1321322 1321330 1321355 1321421
## 10 1 6 1 2 6 4 10 1 14 6 2 6 1 2 2 10 6 11 9 11
## 1327279 1327287 1336072 1343573 1343581 1344639 1345024 1347269 1347293 1347301 1347434 1347459 1347921 1347939 1347970 1352269 1364868 1369248 1372507 1374438 1377209
## 2 9 3 9 11 13 7 1 13 8 1 7 19 11 14 1 7 14 7 1 11
## 1377233 1377415 1379361 1379544 1380021 1380120 1386226 1388610 1388644 1388651 1389261 1389279 1390095 1390467 1390517 1390582 1390665 1390673 1392083 1392091 1392109
## 1 10 1 4 18 4 10 2 5 11 9 5 2 11 6 1 17 16 7 6 10
## 1392117 1392125 1392141 1392174 1392216 1392224 1392240 1392257 1396191 1396209 1396225 1396852 1396878 1396886 1398783 1401934 1401942 1401959 1402536 1408426 1412634
## 7 13 16 14 2 7 3 8 19 11 27 17 4 8 1 11 8 17 7 2 5
## 1412873 1415983 1418615 1423003 1442185 1452705 1458348 1459791 1459809 1523802 1523810 1523828 1540988 1540996 1541192 1625532 1625557 1625573 1630631 1637263 1659101
## 3 4 4 12 5 7 9 9 12 9 8 6 7 10 10 7 8 8 3 4 9
## 1666130 1723469 <NA>
## 1 2 39
## [1] "Frequency table after encoding"
## cod_mod2. Código modular
## 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372
## 2 1 3 9 7 5 8 5 7 1 1 1 1 4 14 8 3 5 10 6 2 7 8 8 6 5 6 2 2 3 7 6 6
## 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405
## 8 18 5 19 3 10 6 13 8 4 7 10 6 4 9 4 1 8 4 1 6 16 2 17 5 4 7 2 2 20 9 5 5
## 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438
## 5 22 4 3 1 4 2 2 3 16 12 1 4 4 20 5 5 7 9 5 4 27 1 6 8 1 7 5 14 7 3 9 15
## 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471
## 3 1 10 8 2 16 4 3 6 6 14 12 8 17 9 17 4 2 9 11 5 6 6 2 6 8 11 6 15 1 11 8 5
## 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504
## 11 3 10 1 6 7 1 12 3 2 13 10 8 4 5 6 2 3 2 1 5 7 1 7 17 6 7 9 3 6 6 10 4
## 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537
## 7 8 5 9 9 10 3 6 9 9 3 9 3 1 6 5 7 6 3 2 8 5 4 3 8 14 5 6 1 4 5 9 6
## 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570
## 14 12 8 7 10 8 10 7 3 4 9 7 6 6 3 10 14 2 4 6 3 2 13 6 1 7 5 6 4 6 2 1 5
## 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603
## 7 4 6 9 1 4 7 9 6 2 5 1 1 4 7 4 8 3 3 3 7 6 10 1 8 11 4 10 9 2 2 9 7
## 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636
## 1 1 9 11 6 8 3 1 6 9 5 10 7 3 7 1 1 8 4 15 16 1 9 7 6 11 2 25 8 8 4 8 4
## 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669
## 2 7 2 6 9 8 9 2 5 1 18 8 4 1 13 1 6 2 1 6 2 14 1 8 6 6 5 1 7 2 7 3 7
## 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702
## 4 10 1 1 10 4 8 1 2 4 7 2 4 23 7 5 4 3 4 21 1 2 9 4 6 7 31 1 8 5 3 4 7
## 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735
## 7 3 6 10 3 16 1 2 9 6 1 3 9 10 3 5 9 4 11 12 9 5 11 7 8 7 14 2 5 19 1 3 1
## 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 <NA>
## 1 17 5 5 7 11 7 3 5 7 8 6 5 8 1 10 2 13 2 11 2 4 17 10 2 3 39
## [1] "Frequency table before encoding"
## COD_MOD.
## 204875 205005 205047 205120 205153 205773 206334 216341 220285 226704 232207 232223 232231 232249 232264 232504 232512 232538 232546 232553 232561
## 10 8 10 4 1 6 5 9 5 6 8 10 12 7 10 6 14 3 12 9 13
## 232579 232587 232595 232603 232611 232645 232728 232777 233296 233361 233676 233718 233734 233825 233890 233908 233916 233924 233940 233957 233965
## 10 15 7 12 4 4 10 14 6 12 5 17 15 12 17 7 17 16 3 6 7
## 233973 233981 233999 234021 234062 234070 234096 234104 234112 234120 234138 234153 234161 234187 234195 234203 234211 234229 234237 234369 234377
## 17 12 16 12 7 7 10 17 14 17 15 16 16 17 8 7 5 7 15 16 11
## 234385 234401 234419 234427 234443 234450 234500 234583 234674 234682 234781 234831 234856 287409 287417 287425 287466 312090 312207 312215 312306
## 10 10 15 14 18 8 14 5 17 2 4 15 17 17 2 9 3 5 1 6 13
## 312421 312744 312868 313239 313395 313460 313890 313908 313965 313981 314096 314187 314211 314237 314245 314252 314260 314278 314294 405258 405498
## 9 2 6 4 15 2 9 13 10 16 4 11 9 7 10 12 10 10 12 4 10
## 405704 405738 405746 405753 405852 405886 405894 405902 406082 406629 406645 406983 407007 407049 408245 408278 408286 408294 408328 408344 408393
## 1 7 1 2 3 1 12 15 16 3 3 17 5 17 6 9 3 1 4 9 13
## 408468 408476 408484 408492 408526 408559 408567 408583 408609 408666 408732 408773 408823 408856 408955 409003 409011 409029 409193 409292 409300
## 9 9 5 6 1 8 15 1 4 14 10 4 2 8 1 12 1 16 1 15 13
## 409326 409359 409441 409565 409896 410480 410514 410670 410738 410746 410779 410787 410803 473249 481283 499863 502922 504142 517888 550392 557587
## 11 12 2 5 2 9 8 6 3 5 9 5 7 9 9 12 3 2 13 3 10
## 587147 612291 612416 612689 612747 612804 615013 623017 623041 637215 647388 647412 671628 678904 678961 679829 680058 712562 712711 723031 731273
## 8 16 2 2 1 6 12 10 3 2 8 3 4 2 3 6 9 6 6 5 1
## 735498 736116 775700 783423 783597 796888 818674 818708 844159 844183 899351 932434 932491 932848 1117944 1201870 1266428 1412634 <NA>
## 6 4 11 16 2 4 7 6 6 5 4 5 8 1 2 17 4 10 1031
## [1] "Frequency table after encoding"
## COD_MOD.
## 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924
## 6 6 4 1 7 11 10 4 1 17 13 15 9 17 2 10 6 14 6 5 12 15 4 9 6 6 1 9 4 5 10 5 7
## 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957
## 3 4 7 10 7 6 5 5 5 14 2 16 15 7 13 10 4 9 2 1 8 13 17 15 8 3 16 3 6 4 2 12 8
## 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990
## 7 6 10 12 2 10 15 17 5 5 6 9 5 3 9 4 8 10 17 9 3 14 16 10 4 7 12 4 13 8 4 6 6
## 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023
## 4 9 3 17 12 2 10 16 10 9 16 8 2 2 14 2 15 10 13 16 13 12 8 3 1 7 3 18 2 1 5 2 14
## 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## 1 17 12 1 17 12 10 7 11 4 17 16 9 11 9 1 8 5 1 6 9 8 16 6 10 9 10 3 2 5 6 1 12
## 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089
## 7 2 10 17 5 4 7 9 17 11 10 15 12 10 8 17 12 9 2 14 15 7 1 4 1 3 6 12 12 6 16 3 3
## 1090 1091 1092 1093 1094 1095 1096 1097 1098 <NA>
## 17 16 12 1 2 5 15 3 15 1031
## [1] "Frequency table before encoding"
## cod_mod. cod_mod
## 204800 204875 204909 205005 205047 205112 205120 205153 205682 205690 205773 205781 205815 206334 216341 220285 226704 232207 232223 232231 232249
## 15 9 7 9 10 15 9 16 14 15 18 2 8 5 6 2 11 11 11 10 7
## 232264 232504 232512 232538 232546 232553 232561 232579 232587 232595 232603 232611 232645 232728 232777 233296 233361 233676 233718 233734 233825
## 10 7 15 4 8 7 15 7 7 8 11 2 4 9 11 6 12 2 10 12 4
## 233890 233908 233916 233924 233940 233957 233965 233973 233981 233999 234021 234062 234070 234096 234104 234112 234120 234138 234153 234161 234187
## 7 5 17 1 3 12 7 6 4 1 8 11 7 19 17 14 10 9 13 1 1
## 234195 234203 234211 234229 234237 234369 234377 234385 234401 234419 234427 234450 234500 234674 234682 234781 234831 234856 287409 287417 287425
## 1 3 2 3 3 2 8 2 6 1 9 5 3 1 8 2 3 17 4 2 3
## 287466 312090 312215 312306 312421 312744 312868 313395 313890 313908 313965 313981 314187 314211 314237 314245 314252 314260 314278 314294 405258
## 3 2 3 4 2 2 1 4 2 2 2 1 2 2 1 2 2 2 2 2 9
## 405498 405704 405738 405746 405753 405852 405894 405902 405928 405936 406009 406041 406066 406082 406116 406124 406140 406215 406223 406264 406413
## 10 7 14 8 2 13 12 16 14 16 16 14 16 16 11 13 3 16 13 5 16
## 406595 406629 406645 406975 406983 407007 407049 408245 408278 408286 408294 408328 408344 408393 408468 408476 408484 408492 408526 408559 408567
## 14 12 13 15 17 11 17 7 9 7 3 13 4 13 12 13 13 13 4 8 15
## 408583 408609 408666 408732 408773 408823 408856 408922 408955 408971 409003 409011 409029 409193 409227 409235 409243 409284 409292 409300 409318
## 7 8 11 1 6 8 16 17 9 13 5 11 1 3 16 12 10 15 10 12 16
## 409326 409359 409441 409565 409896 410480 410514 410670 410738 410746 410779 410787 410803 473249 481283 486688 499863 504142 517888 519496 519595
## 6 8 12 11 1 4 8 4 8 6 1 3 6 8 11 15 2 13 3 11 15
## 550392 551309 557587 585885 587147 592147 612291 612416 612689 623017 623041 637215 647388 647412 647628 671628 672105 678961 679829 680058 712562
## 12 9 5 10 2 4 16 2 1 2 1 2 16 5 11 7 6 3 6 10 6
## 712711 723031 730655 731273 731596 735498 775700 776039 783423 783597 796888 818674 818708 844159 844183 899351 930958 932434 932491 932848 1201870
## 1 2 9 11 4 3 9 7 11 2 1 7 4 1 5 1 15 5 9 13 17
## 1266428 1377209 1412634 <NA>
## 6 16 10 902
## [1] "Frequency table after encoding"
## cod_mod. cod_mod
## 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959
## 8 12 7 10 1 2 6 12 6 12 11 4 2 2 9 15 4 15 7 1 3 16 12 13 1 2 4 1 4 3 2 16 2
## 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 1 8 18 9 11 6 1 16 5 1 1 8 14 15 15 1 5 6 1 16 2 7 3 7 12 9 2 11 1 2 8 11 9
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025
## 8 11 16 11 8 2 8 9 9 14 2 12 4 9 7 3 5 11 4 4 8 16 3 10 2 1 3 10 15 2 3 9 5
## 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058
## 16 3 2 16 5 8 11 3 17 7 3 7 9 7 1 4 2 6 14 3 11 12 17 11 13 15 13 10 13 13 7 1 17
## 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
## 7 13 10 2 12 1 2 1 2 2 8 3 16 16 19 17 16 4 11 8 12 9 2 5 7 11 6 1 15 2 15 1 8
## 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124
## 14 7 11 4 6 13 2 17 6 8 3 10 11 7 11 13 10 3 16 13 7 3 13 6 6 4 8 2 4 9 14 2 10
## 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157
## 2 7 16 1 3 2 2 5 10 9 13 17 7 4 13 6 14 6 10 2 9 13 4 15 15 12 5 1 5 11 16 10 2
## 1158 1159 1160 <NA>
## 17 15 2 902
## [1] "Frequency table before encoding"
## school_fixed_primary. Seleccione la escuela primaria a donde realmente va el niño(a)
## 219741 312561 405217 408922 408971 409243 612291 647388 679829
## 2736 1 1 2 1 1 1 1 1 3
## [1] "Frequency table after encoding"
## school_fixed_primary. Seleccione la escuela primaria a donde realmente va el niño(a)
## 875 876 877 878 879 880 881 882 883 884
## 2736 1 1 3 1 1 1 2 1 1
## [1] "Frequency table before encoding"
## school_fixed_sec. Seleccione la escuela secundaria a donde realmente va el niño(a)
## 1253905 1345024 1347301 1347434 1347921 1379544 1380021 1392240 1395367 1401934 1402536 1452705 1540996 236109 309567 579300 589804 612507 616110 621391
## 2704 2 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 637272 680124 783720 791319 931055 931436 932608 933226 933317
## 7 1 2 2 3 1 1 1 1
## [1] "Frequency table after encoding"
## school_fixed_sec. Seleccione la escuela secundaria a donde realmente va el niño(a)
## 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485
## 1 2 1 1 1 1 1 2 7 3 1 2 1 3 1 2704 1 1 2 1 2 1 1 1 1 1 1 1 1 1
## [1] "Frequency table before encoding"
## cole2016_admin.
## 55610 58185 59420 59509 59665 60433 62922 63530 64068 65421 66736 67161 68599 68603 68655 68679 68735 68900 68924 68938 68943 68957 68962 68976
## 1 1 1 1 1 1 1 1 1 2 1 1 12 3 3 2 1 3 1 4 2 8 2 3
## 68981 69103 69179 69235 69551 69669 69706 69810 69985 70007 70031 70074 70088 70111 70149 71115 71498 71592 71629 71733 71752 71766 71790 71926
## 8 2 3 2 2 1 4 1 16 2 1 2 2 1 5 2 1 3 1 15 4 2 1 2
## 71931 73119 73162 73181 73195 73280 73303 73322 73341 73398 73435 73459 73529 73534 73548 73553 73567 73572 73591 73609 73789 130308 142655 146154
## 1 12 10 2 8 2 6 8 4 19 4 2 3 14 3 6 6 7 4 6 11 14 7 5
## 147484 147686 147709 147714 148520 148600 148997 150122 150136 150202 150221 150259 150513 150532 150565 150570 150607 150612 150631 150645 150650 150754 150768 150773
## 4 4 3 2 5 10 2 4 4 7 5 15 6 21 6 6 6 5 3 16 6 9 7 12
## 150792 150834 150848 150966 150971 150985 151027 151070 151107 151188 151193 151206 151254 151598 151640 151664 151678 152060 152215 152239 152263 152282 152574 152588
## 17 4 14 15 16 4 7 3 9 9 7 1 8 1 10 3 1 1 14 15 4 4 4 5
## 152593 152606 152625 152668 152673 152734 152753 152786 153540 153818 153823 153837 153842 153861 153875 153880 153899 153903 153922 153941 153955 154021 154035 154064
## 6 10 3 12 9 15 6 17 1 24 6 1 5 2 22 5 4 6 1 4 15 14 7 3
## 154078 154083 154097 154120 154200 154238 154262 154549 155054 157010 157053 157072 157190 157213 157227 157345 157350 157374 157393 157406 157487 157492 157500 157538
## 3 6 3 25 2 1 6 2 1 3 15 5 6 1 8 6 1 31 10 6 14 3 6 1
## 157543 157595 157604 157618 157623 157656 157661 157680 157703 157717 157722 157736 157760 157779 157798 157802 157821 157835 157840 157864 157878 157915 157977 157982
## 6 8 22 3 7 3 9 7 5 17 14 10 1 5 4 8 6 15 18 6 3 6 13 10
## 158024 158057 158095 158104 158123 158161 158175 158180 158203 158217 158236 158241 158255 158335 158340 158359 158364 158378 158383 158401 158415 158444 158458 158477
## 3 5 4 16 6 13 11 8 9 8 21 11 6 1 15 19 4 4 13 22 6 1 8 6
## 158482 158496 158509 158547 158590 158608 158627 158632 158646 158665 158670 158707 158712 158745 158750 158788 158934 159207 159453 159491 159556 159702 159797 159815
## 17 16 15 2 16 4 5 1 1 4 4 2 3 4 2 24 2 1 6 5 14 2 5 17
## 164930 164968 165029 165072 165086 165091 165185 165190 165246 165326 165331 165345 165473 165543 165604 165637 165680 165699 165703 165717 165736 165741 165784 165798
## 1 7 8 4 3 16 6 8 7 4 4 2 8 2 2 17 8 7 4 5 8 26 7 4
## 165802 165840 165864 165915 165920 166038 166076 166104 166118 166316 166533 166590 166627 166632 166651 166774 166788 166830 166905 166948 167014 167170 167189 167194
## 5 4 11 3 2 1 16 7 11 2 1 4 7 6 9 8 2 4 3 2 20 20 4 5
## 167207 167212 167226 167231 167269 167311 167349 167354 167368 167410 167537 167561 167575 167580 167599 167603 167617 167636 167641 167679 167684 169126 169150 170196
## 8 19 22 21 7 6 5 4 4 2 24 5 16 7 5 14 9 6 6 1 23 1 13 1
## 170200 170219 170304 170318 170479 170484 170506 170610 170709 170832 170865 170907 170931 171134 343357 462430 462543 505991 508447 515508 517084 517102 520915 526465
## 6 3 5 8 8 5 14 2 1 4 5 5 2 5 11 9 2 13 3 7 1 7 1 14
## 526470 531928 534658 535506 538208 538227 538779 555306 556042 560162 562439 563151 571844 582376 585308 590263 601493 602242 603468 603581 603699 603717 603755 605066
## 16 14 7 1 9 1 10 7 1 2 1 5 7 4 7 8 18 5 11 6 16 1 3 3
## 605132 605146 609248 611760 748169 748739 999999 <NA>
## 3 8 27 1 2 1 200 39
## [1] "Frequency table after encoding"
## cole2016_admin.
## 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917
## 10 3 1 10 2 1 5 7 2 6 9 7 7 17 11 6 5 24 7 4 7 6 1 6
## 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941
## 5 1 4 6 4 12 1 6 8 5 3 3 1 2 1 7 20 4 17 6 2 9 2 24
## 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965
## 8 5 15 9 21 3 8 19 2 15 1 6 5 4 5 5 4 2 2 22 5 8 16 4
## 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989
## 4 9 4 9 16 1 15 16 3 15 13 6 1 2 1 14 1 6 1 9 4 4 4 2
## 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013
## 2 12 8 3 3 19 5 4 3 14 5 14 1 8 3 2 3 2 1 8 6 4 21 5
## 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037
## 6 1 6 6 7 19 10 5 1 14 2 8 4 17 3 5 4 13 5 3 5 1 5 15
## 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061
## 1 7 1 2 18 2 6 7 14 5 1 16 10 3 8 15 7 16 4 31 18 5 8 15
## 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085
## 2 8 11 6 3 4 6 14 6 22 7 1 13 8 3 6 2 6 4 1 6 7 5 6
## 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109
## 8 13 1 1 11 6 1 25 2 6 14 1 4 17 1 20 4 4 14 4 8 2 2 17
## 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134
## 1 2 3 9 1 2 7 5 3 14 1 5 1 15 6 4 15 1 2 1 22 2 13 1
## 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158
## 4 21 6 1 6 2 3 4 5 1 2 4 2 27 7 6 1 2 1 8 1 9 12 16
## 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182
## 4 7 1 8 3 8 14 16 5 8 5 2 24 3 6 22 7 1 6 9 3 2 6 11
## 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206
## 3 14 26 5 2 6 14 4 2 7 11 8 1 16 2 4 5 10 4 15 1 6 3 23
## 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230
## 4 8 9 8 2 17 1 1 1 8 3 3 7 7 4 3 3 11 1 1 12 16 4 6
## 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254
## 2 16 3 3 1 4 2 1 3 4 1 6 1 7 1 4 2 1 11 2 10 4 7 10
## 1255 1256 1257 1258 1259 1260 999999 <NA>
## 3 7 16 10 5 7 200 39
## [1] "Frequency table before encoding"
## cod_mod_2016.
## 204800 204875 204909 205005 205047 205112 205120 205153 205682 205690 205773 205781 205815 206334 207373 207407 216341 220285 226704 232207 232223
## 10 6 2 5 6 7 10 7 6 7 8 2 5 2 1 1 7 3 6 8 8
## 232231 232249 232264 232504 232512 232538 232546 232553 232561 232579 232587 232595 232603 232611 232645 232728 232777 233130 233296 233361 233676
## 6 1 7 3 6 3 5 5 4 3 7 5 7 2 1 3 6 4 5 4 3
## 233718 233734 233825 233882 233890 233908 233916 233924 233932 233940 233957 233965 233973 233981 233999 234021 234062 234096 234104 234112 234120
## 7 5 3 1 3 6 8 9 3 4 6 3 5 5 8 7 5 6 6 4 4
## 234138 234153 234161 234187 234195 234203 234211 234229 234237 234351 234369 234377 234385 234401 234419 234427 234443 234450 234500 234583 234674
## 7 6 8 5 1 3 1 4 6 1 9 5 8 6 8 7 6 3 6 3 9
## 234682 234781 234831 234856 236158 236349 236422 236448 236463 236471 236489 236653 236661 236927 287409 287425 287466 309286 309294 309377 309419
## 7 3 7 7 5 5 16 4 8 1 7 1 31 8 10 6 2 1 12 1 3
## 309435 309567 309682 310433 310441 312090 312215 312306 312421 312744 312868 313080 313239 313395 313460 313890 313908 313965 313981 314070 314187
## 1 4 1 3 1 2 5 10 4 2 2 1 2 9 1 4 8 6 6 2 6
## 314211 314237 314245 314252 314260 314278 314294 405258 405498 405704 405738 405746 405837 405852 405894 405902 405928 405936 406009 406066 406082
## 4 4 4 6 6 8 5 6 8 5 5 4 1 9 7 7 7 8 6 10 6
## 406116 406124 406140 406215 406223 406264 406413 406595 406629 406645 406975 406983 407007 407049 408211 408245 408278 408286 408294 408328 408336
## 6 6 1 5 5 3 6 7 8 4 5 10 4 6 1 7 4 4 3 5 1
## 408393 408468 408476 408484 408492 408559 408567 408609 408666 408732 408773 408823 408856 408922 408955 408971 409003 409011 409029 409193 409227
## 4 8 7 5 5 3 8 6 8 2 4 3 8 7 4 5 6 9 9 2 8
## 409235 409243 409284 409292 409300 409318 409326 409359 409441 409565 409896 410464 410480 410514 410613 410670 410746 410779 410787 410803 473249
## 7 5 9 7 9 6 4 7 9 3 1 1 6 5 1 3 6 9 5 4 5
## 481283 486688 486928 489120 495069 495325 498782 499863 502922 504142 517581 517888 518084 518472 519496 519595 519678 525923 550392 551309 557587
## 9 8 2 8 14 16 1 2 2 10 26 7 22 10 6 6 6 21 7 3 9
## 579268 579276 579284 579292 579300 585885 587055 587147 587204 589200 589747 589804 591255 591602 592147 612051 612119 612291 612416 612507 612689
## 9 1 1 17 20 6 15 4 13 1 1 5 23 14 4 2 2 5 1 9 1
## 612747 612770 612804 615013 616110 617787 617829 621391 623017 623041 637272 639542 639617 647388 647412 647446 647628 655746 671628 672105 678961
## 1 1 4 4 6 17 6 15 4 2 8 2 1 9 7 16 4 1 4 4 1
## 679829 680058 680082 680124 699603 712562 712711 712778 723031 730655 731273 735498 736116 775700 776039 779041 783423 783597 783621 783696 783704
## 2 9 1 17 12 1 2 2 2 5 2 4 2 5 3 1 12 1 1 11 1
## 783720 783787 783795 791319 791574 794438 796888 818674 818708 844159 844183 891408 891812 895482 927871 930958 931055 931063 932236 932434 932491
## 14 10 10 20 4 2 2 5 2 2 3 14 1 1 10 1 14 18 12 1 4
## 932608 932848 933226 933283 933291 933317 933531 933598 933846 999999 1031574 1117704 1120005 1201649 1201870 1260942 1266428 1271840 1273655 1314376 1320647
## 8 7 6 10 1 6 1 2 6 97 4 10 1 14 6 2 4 1 2 2 10
## 1321322 1321330 1321355 1321421 1327279 1327287 1336072 1343573 1343581 1344639 1345024 1347269 1347293 1347301 1347434 1347459 1347921 1347939 1347970 1352269 1364868
## 6 11 9 11 2 9 3 9 11 13 7 1 13 8 1 7 19 11 14 1 7
## 1369248 1372507 1374438 1377209 1377233 1377415 1379361 1379544 1380021 1380120 1386226 1388610 1388644 1388651 1389261 1389279 1390095 1390467 1390517 1390582 1390665
## 14 7 1 10 1 10 1 1 18 4 10 2 5 11 9 5 2 11 8 1 17
## 1390673 1392083 1392091 1392109 1392117 1392125 1392141 1392174 1392216 1392224 1392240 1392257 1396191 1396209 1396225 1396852 1396878 1396886 1398783 1398932 1401934
## 16 7 5 10 7 13 16 14 2 6 3 8 19 11 27 17 4 8 1 1 11
## 1401942 1401959 1402536 1408426 1412634 1412873 1415983 1418615 1423003 1442185 1452705 1458348 1459791 1459809 1523802 1523810 1523828 1540988 1540996 1541192 1625532
## 8 17 7 2 5 3 5 4 12 4 7 9 10 12 9 8 6 7 10 10 7
## 1625557 1625573 1630631 1637263 1659101 1666130 1723469
## 8 8 3 4 9 1 2
## [1] "Frequency table after encoding"
## cod_mod_2016.
## 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527
## 1 7 3 2 6 6 6 10 3 5 4 3 15 4 9 3 1 8 12 8 4 8 1 11
## 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551
## 2 2 1 17 1 7 7 8 3 1 5 9 11 4 1 8 8 6 4 8 12 5 3 9
## 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575
## 1 9 7 16 4 3 7 7 4 11 5 10 2 3 2 8 5 6 8 16 6 2 8 1
## 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 595 596 597 598 599 600
## 9 7 4 3 5 6 8 3 5 2 9 19 7 4 9 2 1 1 10 10 9 14 5 5
## 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
## 7 2 4 8 7 6 6 1 6 4 6 6 8 12 8 2 8 2 17 5 4 4 4 5
## 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648
## 1 8 14 1 20 7 1 7 4 5 1 10 5 26 6 8 6 10 16 6 7 1 16 6
## 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 5 17 2 11 2 5 9 2 17 13 1 3 1 14 1 22 7 13 5 1 6 5 1 10
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696
## 5 8 9 1 9 3 7 9 1 2 4 2 2 4 6 1 4 4 10 2 11 2 4 5
## 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
## 6 7 5 4 6 8 7 1 6 8 4 2 3 13 9 7 10 2 9 4 2 6 3 4
## 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744
## 17 1 3 11 2 4 1 18 2 1 4 14 5 5 1 9 1 6 4 11 6 6 5 10
## 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 1 5 2 4 5 9 10 6 2 5 3 6 7 23 5 14 13 7 1 20 7 7 1 5
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792
## 4 2 1 2 3 1 1 1 5 6 1 7 5 8 6 8 5 6 3 1 10 9 19 7
## 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 1 3 4 31 7 6 4 10 4 10 6 9 8 4 2 7 10 5 1 3 8 4 27 7
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840
## 4 8 2 9 1 8 10 3 10 7 8 9 6 14 14 2 1 8 6 6 10 5 1 1
## 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
## 8 2 1 12 4 5 6 11 6 4 1 17 14 7 7 3 15 1 1 18 6 6 1 4
## 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888
## 9 6 1 2 3 3 1 3 7 8 12 9 6 1 10 6 7 6 5 2 5 4 2 16
## 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
## 21 6 5 3 1 2 1 3 1 6 7 7 7 9 10 4 6 2 1 7 1 6 4 7
## 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 999999
## 7 9 1 8 1 8 3 5 14 7 12 3 5 4 2 10 2 11 97
## [1] "Frequency table before encoding"
## cod_mod_2015.
## 204800 204875 204909 205005 205047 205112 205120 205153 205682 205690 205773 205781 205815 206334 216341 220285 226704 232207 232223 232231 232249
## 13 10 6 9 10 15 10 17 14 14 14 5 8 5 9 5 17 15 14 15 7
## 232264 232504 232512 232538 232546 232553 232561 232579 232587 232595 232603 232611 232645 232728 232777 233296 233361 233676 233718 233734 233825
## 15 10 17 4 16 12 13 10 15 8 16 5 4 10 14 6 12 5 17 15 14
## 233890 233908 233916 233924 233940 233957 233965 233973 233981 233999 234021 234062 234070 234096 234104 234112 234120 234138 234153 234161 234187
## 17 7 17 16 3 15 7 17 12 16 15 15 15 15 17 14 17 16 17 16 17
## 234195 234203 234211 234229 234237 234369 234377 234385 234401 234419 234427 234443 234450 234500 234583 234674 234682 234781 234831 234856 287409
## 8 8 5 7 15 16 11 10 10 15 14 18 8 14 5 17 9 7 15 17 17
## 287417 287425 287466 312090 312207 312215 312306 312421 312744 312868 313239 313395 313460 313890 313908 313965 313981 314096 314187 314211 314237
## 2 9 3 5 1 6 14 9 2 6 4 15 2 9 13 10 16 4 11 9 7
## 314245 314252 314260 314278 314294 405258 405498 405704 405738 405746 405753 405852 405886 405894 405902 405928 405936 406009 406041 406066 406082
## 10 12 10 10 12 10 10 7 13 8 2 14 1 13 16 12 16 16 14 16 16
## 406116 406124 406140 406215 406223 406264 406413 406595 406629 406645 406975 406983 407007 407049 408245 408278 408286 408294 408328 408344 408393
## 11 9 3 16 12 4 15 12 15 15 14 17 13 17 13 9 6 4 16 9 13
## 408468 408476 408484 408492 408526 408559 408567 408583 408609 408666 408732 408773 408823 408856 408922 408955 408971 409003 409011 409029 409193
## 17 16 9 11 5 8 15 6 12 14 10 11 9 14 15 10 13 14 15 16 5
## 409227 409235 409243 409284 409292 409300 409318 409326 409359 409441 409565 409896 410480 410514 410670 410738 410746 410779 410787 410803 473249
## 16 12 9 15 16 16 13 13 16 13 13 3 12 8 9 11 9 9 8 13 12
## 481283 486688 499863 502922 504142 517888 519496 519595 550392 551309 557587 585885 587147 592147 612291 612416 612689 612747 612804 615013 623017
## 15 15 12 3 14 13 11 14 16 9 16 10 8 4 16 2 2 1 6 12 10
## 623041 637215 647388 647412 647628 671628 672105 678904 678961 679829 680058 712562 712711 723031 730655 731273 731596 735498 736116 775700 776039
## 3 2 14 8 11 8 6 2 3 6 16 6 6 5 8 12 4 6 4 11 6
## 783423 783597 796888 818674 818708 844159 844183 899351 930958 932434 932491 932848 1117944 1201870 1266428 1377209 1412634 <NA>
## 17 2 4 11 9 6 5 4 15 6 9 14 2 17 5 15 10 95
## [1] "Frequency table after encoding"
## cod_mod_2015.
## 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455
## 10 15 12 15 14 16 14 13 12 13 2 16 4 4 13 11 7 5 2 12 17 8 4 15 9 14 5 8 9 8 10 15 4
## 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488
## 17 15 14 6 15 10 10 11 6 17 13 15 8 4 17 6 13 13 6 15 11 4 3 5 10 10 8 12 5 14 15 1 6
## 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521
## 13 10 1 2 7 15 16 16 14 14 16 10 13 15 9 15 5 6 15 9 8 7 8 10 7 10 8 14 6 13 12 17 11
## 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554
## 9 9 10 16 12 16 4 11 15 13 9 3 3 17 15 15 9 9 9 3 16 8 15 6 2 2 17 17 12 14 14 12 16
## 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587
## 9 10 12 9 10 13 17 16 6 17 15 12 12 8 6 8 14 1 16 14 6 5 16 15 12 4 17 13 11 12 9 6 2
## 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620
## 16 15 5 8 5 17 16 17 5 17 15 16 7 15 6 9 5 3 5 13 16 16 14 14 4 17 2 11 6 13 16 17 14
## 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653
## 9 9 9 16 7 10 2 9 17 9 7 13 11 16 15 12 8 14 4 2 15 14 14 11 15 16 4 16 10 16 16 15 17
## 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 <NA>
## 3 18 11 6 2 17 14 16 10 10 14 5 5 10 3 10 12 95
## [1] "Frequency table before encoding"
## p12. Seleccione la escuela a la que le gustarÃa asistir. Si la escuela no está en la
## 1031574 1117704 1270214 1274398 1320647 1321421 1327287 1339472 1341585 1343573 1343581 1344639 1345024 1347301 1347921 1347939 1347970 1364868 1364900 1369248
## 2266 1 3 1 1 4 4 1 1 1 3 3 4 2 2 1 6 8 4 2 1
## 1370378 1371095 1372507 1374438 1379320 1380021 1380120 1386432 1389279 1390095 1390467 1390517 1392083 1392091 1392117 1392125 1392174 1392224 1392232 1392240 1392257
## 1 1 3 2 1 1 1 1 2 1 1 3 2 3 1 4 3 9 1 3 3
## 1393313 1396191 1396209 1396225 1396852 1396886 1398932 1401934 1401942 1401959 1402536 1412873 1415983 1423003 1452705 1458348 1470582 1523802 1523810 1523828 1540988
## 1 1 4 9 3 6 1 6 2 10 3 1 1 3 5 2 1 4 5 3 7
## 1540996 1625532 1625557 1625573 1637263 1659101 207407 233056 233130 236158 236174 236422 236430 236463 236646 236653 236661 236927 309286 309294 309419
## 2 1 4 6 1 4 2 2 3 3 1 1 1 2 3 1 9 1 1 9 3
## 309567 309641 309716 310433 310441 477828 489096 495069 495325 517581 518084 518241 518472 519678 525923 579243 579292 579300 579409 587055 587204
## 3 5 1 1 1 1 2 3 7 10 6 1 3 2 14 1 5 6 1 2 13
## 589804 591164 591255 591602 612051 612507 617787 617829 621391 637272 647446 680082 680124 699603 712778 730515 783696 783704 783720 783787 783795
## 1 1 3 1 2 10 6 7 7 15 6 1 11 3 1 1 6 1 1 5 3
## 785097 791319 891408 891788 894915 927814 927871 929638 930859 931063 931436 932236 932608 933283 933317 933556 933598 933846 934141
## 1 19 7 1 1 1 2 1 1 6 1 3 3 3 4 2 1 5 2
## [1] "Frequency table after encoding"
## p12. Seleccione la escuela a la que le gustarÃa asistir. Si la escuela no está en la
## 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453
## 3 3 9 2 3 6 10 10 1 2 3 2 2 3 2 2 1 2 2 3 1 1 3 1 1 2 5 5 1 1 1 1 1
## 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486
## 4 1 6 1 7 1 2 1 3 1 1 3 5 1 1 7 1 1 6 1 6 1 5 9 1 1 4 1 2 3 1 2 5
## 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519
## 7 3 3 4 8 1 7 15 3 1 4 3 1 6 6 1 2 3 3 1 4 1 4 19 4 1 3 6 1 1 1 10 1
## 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552
## 1 1 2 1 1 1 1 1 1 14 6 3 6 3 4 2266 2 2 4 1 3 3 1 9 4 3 6 2 9 3 5 1 1
## 553 554 555 556 557 558 559 560 561 562 563 564 565
## 2 3 1 1 13 11 3 1 3 2 7 1 1
## [1] "Frequency table before encoding"
## codlocal. codlocal
## 55610 57968 58185 59420 59509 59665 60433 62922 63530 64068 65421 66736 67161 68599 68603 68655 68679 68735 68900 68924 68938 68943 68957 68962
## 1 1 1 1 1 1 2 1 1 1 2 1 1 12 3 3 2 1 3 1 4 2 8 2
## 68976 68981 69103 69179 69235 69551 69669 69706 69810 69985 70007 70031 70074 70088 70111 70149 71115 71498 71592 71629 71733 71752 71766 71790
## 3 8 2 3 2 2 1 4 1 16 2 1 2 2 1 5 2 1 3 1 15 4 2 1
## 71926 71931 73119 73162 73181 73195 73280 73303 73322 73341 73398 73435 73459 73529 73534 73548 73553 73567 73572 73591 73609 73789 130308 142655
## 2 1 12 10 3 8 2 6 10 4 19 4 2 3 14 3 6 6 7 4 6 11 14 7
## 146154 147484 147686 147709 147714 148520 148600 148997 150122 150136 150202 150221 150259 150513 150532 150565 150570 150607 150612 150631 150645 150650 150754 150768
## 5 4 4 3 2 6 10 2 4 4 7 5 15 7 21 6 6 6 5 3 16 6 9 7
## 150773 150792 150834 150848 150966 150971 150985 151027 151070 151107 151188 151193 151206 151254 151598 151640 151664 151678 152060 152215 152239 152263 152282 152574
## 12 17 4 14 15 16 4 7 3 9 9 7 1 8 1 10 3 1 1 14 15 4 4 4
## 152588 152593 152606 152625 152668 152673 152734 152753 152786 153540 153818 153823 153837 153842 153861 153875 153880 153899 153903 153922 153941 153955 154021 154035
## 5 7 10 3 12 9 15 6 17 1 24 6 1 5 2 22 5 4 6 1 4 15 14 7
## 154064 154078 154083 154097 154120 154200 154238 154262 154549 155054 157010 157053 157072 157190 157213 157227 157345 157350 157374 157393 157406 157487 157492 157500
## 3 3 6 3 26 2 1 6 2 1 3 15 5 6 1 8 6 1 31 10 6 14 3 6
## 157538 157543 157595 157604 157618 157623 157656 157661 157680 157703 157717 157722 157736 157760 157779 157798 157802 157821 157835 157840 157864 157878 157915 157977
## 1 6 8 22 3 7 3 9 7 5 17 14 10 1 5 4 8 6 15 18 6 3 6 13
## 157982 158024 158057 158095 158104 158123 158161 158175 158180 158203 158217 158236 158241 158255 158335 158340 158359 158364 158378 158383 158401 158415 158444 158458
## 10 3 5 4 16 6 13 11 8 9 9 21 11 6 1 15 19 4 4 13 22 6 1 8
## 158477 158482 158496 158509 158547 158590 158608 158627 158632 158646 158651 158665 158670 158707 158712 158745 158750 158788 158934 159207 159453 159491 159556 159702
## 6 17 16 15 2 16 4 5 1 1 1 4 4 2 3 4 2 24 2 1 6 5 14 2
## 159797 159815 164930 164968 165029 165072 165086 165091 165185 165190 165246 165326 165331 165345 165473 165543 165604 165637 165680 165699 165703 165717 165736 165741
## 5 17 1 7 8 4 3 16 6 8 7 4 4 2 8 2 2 17 8 7 4 5 8 26
## 165784 165798 165802 165840 165864 165915 165920 166038 166076 166104 166118 166316 166533 166590 166627 166632 166651 166774 166788 166830 166905 166948 167014 167170
## 7 4 5 4 12 3 2 1 16 7 11 2 1 4 7 6 9 8 2 4 4 2 20 20
## 167189 167194 167207 167212 167226 167231 167269 167311 167349 167354 167368 167410 167537 167561 167575 167580 167599 167603 167617 167636 167641 167679 167684 169126
## 4 5 8 19 22 21 7 6 5 4 4 2 24 5 16 7 5 14 9 6 6 1 23 1
## 169150 170196 170200 170219 170304 170318 170375 170479 170484 170506 170610 170709 170832 170865 170907 170931 171134 340231 340293 343357 462430 462543 505991 508447
## 13 1 6 3 5 8 1 8 5 14 2 1 4 5 5 2 5 1 1 11 9 2 14 3
## 515508 517084 517102 520915 526465 526470 531928 534658 535506 538208 538227 538779 555306 556042 560162 562439 563151 571844 582376 585308 590263 601493 602242 603468
## 7 1 7 1 14 16 14 7 1 9 1 10 7 1 2 1 5 7 4 7 10 18 5 11
## 603581 603699 603717 603755 605066 605132 605146 609248 611760 748169 748739 999999
## 8 16 1 3 3 3 8 27 1 2 1 218
## [1] "Frequency table after encoding"
## codlocal. codlocal
## 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329
## 21 3 7 7 2 12 3 15 1 8 2 26 7 4 16 5 2 16 5 19 15 6 5 6
## 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353
## 17 6 24 4 2 8 3 5 6 9 1 22 12 5 6 7 1 3 1 8 7 15 8 4
## 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
## 1 2 20 1 6 4 1 15 4 1 2 10 2 7 2 5 6 7 2 1 3 2 9 2
## 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
## 18 4 8 4 6 4 1 4 7 16 1 2 31 10 3 21 4 14 5 10 2 19 7 5
## 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425
## 7 4 5 21 11 3 1 6 6 3 1 2 14 14 3 10 20 3 4 4 14 14 2 1
## 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449
## 15 2 11 3 1 3 3 3 16 1 8 1 5 4 17 1 4 14 1 9 14 3 8 4
## 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473
## 4 8 15 15 26 11 6 5 10 1 4 15 2 7 6 19 2 6 14 7 7 10 6 1
## 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 495 496 497 498
## 4 8 1 5 12 23 17 2 9 22 2 6 3 5 5 13 1 3 2 4 8 1 5 4
## 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522
## 7 1 8 4 7 2 9 1 17 1 13 1 1 4 6 16 4 17 5 2 2 6 6 3
## 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546
## 1 3 4 22 1 2 3 2 5 1 3 2 16 5 24 5 6 1 9 12 6 2 7 16
## 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570
## 3 4 1 4 7 2 14 1 8 6 9 4 4 2 3 13 14 17 7 6 9 1 1 5
## 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594
## 5 10 4 15 1 9 8 4 2 5 3 7 1 6 4 7 2 1 2 5 7 6 11 8
## 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618
## 9 5 4 4 4 7 16 12 2 1 14 5 1 27 10 1 8 1 18 6 1 1 22 6
## 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642
## 1 1 7 3 9 1 5 13 14 1 2 1 8 8 16 1 4 1 1 7 3 4 3 2
## 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666
## 10 2 8 1 15 3 10 6 1 6 16 4 6 11 3 6 16 8 11 6 7 3 1 2
## 667 668 669 670 671 672 673 674 675 676 677 999999
## 2 14 6 1 5 1 6 24 8 3 4 218
## [1] "Frequency table before encoding"
## s4p11b1_2015. Codigo modular de la escuela a la que iria en secundaria (urbano)
## 145499 305888 318414 330562 340288 <NA>
## 3 2 2 1 1 2739
## [1] "Frequency table after encoding"
## s4p11b1_2015. Codigo modular de la escuela a la que iria en secundaria (urbano)
## 654 655 656 657 658 <NA>
## 3 1 2 2 1 2739# !!! Removed as it contains identifying information
dropvars <- c("nombre_colegio",
"school2014_name",
"school2014_name1",
"school2013_name",
"school2013_name1")
mydata <- mydata[!names(mydata) %in% dropvars]Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
# Recode education attainment of adults to reduce risk of re-identification
break_edu <- c(10,12,13)
labels_edu <- c("1ro-2do de secundaria"=1,
"3ro de secundaria"=2,
"4to-5to de secundaria"=3)
mydata <- ordinal_recode (variable="p29_1a2", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p29_1a2. Hermano(a)
## 1ro de secundaria 2do de secundaria 3ro de secundaria 4to de secundaria 5to de secundaria <NA>
## 19 37 53 36 15 2588
## recoded
## [10,12) [12,13) [13,1e+06)
## 10 19 0 0
## 11 37 0 0
## 12 0 53 0
## 13 0 0 36
## 14 0 0 15
## [1] "Frequency table after encoding"
## p29_1a2. Hermano(a)
## 1ro-2do de secundaria 3ro de secundaria 4to-5to de secundaria <NA>
## 56 53 51 2588
## [1] "Inspect value labels and relabel as necessary"
## 1ro-2do de secundaria 3ro de secundaria 4to-5to de secundaria
## 1 2 3break_edu <- c(10,12)
labels_edu <- c("1ro-2do de secundaria"=1,
"3ro de secundaria or more"=2)
mydata <- ordinal_recode (variable="p29_1a3", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p29_1a3. Hermano(a)
## 1ro de secundaria 2do de secundaria 3ro de secundaria 4to de secundaria 5to de secundaria <NA>
## 11 12 14 8 8 2695
## recoded
## [10,12) [12,1e+06)
## 10 11 0
## 11 12 0
## 12 0 14
## 13 0 8
## 14 0 8
## [1] "Frequency table after encoding"
## p29_1a3. Hermano(a)
## 1ro-2do de secundaria 3ro de secundaria or more <NA>
## 23 30 2695
## [1] "Inspect value labels and relabel as necessary"
## 1ro-2do de secundaria 3ro de secundaria or more
## 1 2break_edu <- c(-98,0,2,3)
labels_edu <- c("No se"=1,
"Inicial or Primaria"=2,
"Secundaria"=3,
"Superior no universitaria or more"=4)
mydata <- ordinal_recode (variable="p5a1", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p5a1. Hermano(a)
## Inicial Primaria Secundaria Superior no universitaria Superior universitaria o mas
## 18 265 406 34 6
## <NA>
## 2019
## recoded
## [-98,0) [0,2) [2,3) [3,1e+06)
## 0 0 18 0 0
## 1 0 265 0 0
## 2 0 0 406 0
## 3 0 0 0 34
## 4 0 0 0 6
## [1] "Frequency table after encoding"
## p5a1. Hermano(a)
## Inicial or Primaria Secundaria Superior no universitaria or more <NA>
## 283 406 40 2019
## [1] "Inspect value labels and relabel as necessary"
## No se Inicial or Primaria Secundaria Superior no universitaria or more
## 1 2 3 4break_edu <- c(-98,0,1,2,3)
labels_edu <- c("No se"=1,
"Inicial"=2,
"Primaria"=3,
"Secundaria"=4,
"Superior no universitaria or more"=5)
mydata <- ordinal_recode (variable="p5a2", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p5a2. Hermano(a)
## Inicial Primaria Secundaria Superior no universitaria Superior universitaria o mas
## 60 291 218 19 2
## <NA>
## 2158
## recoded
## [-98,0) [0,1) [1,2) [2,3) [3,1e+06)
## 0 0 60 0 0 0
## 1 0 0 291 0 0
## 2 0 0 0 218 0
## 3 0 0 0 0 19
## 4 0 0 0 0 2
## [1] "Frequency table after encoding"
## p5a2. Hermano(a)
## Inicial Primaria Secundaria Superior no universitaria or more
## 60 291 218 21
## <NA>
## 2158
## [1] "Inspect value labels and relabel as necessary"
## No se Inicial Primaria Secundaria
## 1 2 3 4
## Superior no universitaria or more
## 5break_edu <- c(-98,0,1,2)
labels_edu <- c("No se"=1,
"Inicial"=2,
"Primaria"=3,
"Secundaria or more"=4)
mydata <- ordinal_recode (variable="p5a3", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p5a3. Hermano(a)
## Inicial Primaria Secundaria Superior no universitaria <NA>
## 65 222 66 7 2388
## recoded
## [-98,0) [0,1) [1,2) [2,1e+06)
## 0 0 65 0 0
## 1 0 0 222 0
## 2 0 0 0 66
## 3 0 0 0 7
## [1] "Frequency table after encoding"
## p5a3. Hermano(a)
## Inicial Primaria Secundaria or more <NA>
## 65 222 73 2388
## [1] "Inspect value labels and relabel as necessary"
## No se Inicial Primaria Secundaria or more
## 1 2 3 4break_edu <- c(-98,0,1,2)
labels_edu <- c("No se"=1,
"Inicial"=2,
"Primaria"=3,
"Secundaria or more"=4)
mydata <- ordinal_recode (variable="p5a4", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p5a4. Hermano(a)
## Inicial Primaria Secundaria Superior no universitaria <NA>
## 55 87 29 2 2575
## recoded
## [-98,0) [0,1) [1,2) [2,1e+06)
## 0 0 55 0 0
## 1 0 0 87 0
## 2 0 0 0 29
## 3 0 0 0 2
## [1] "Frequency table after encoding"
## p5a4. Hermano(a)
## Inicial Primaria Secundaria or more <NA>
## 55 87 31 2575
## [1] "Inspect value labels and relabel as necessary"
## No se Inicial Primaria Secundaria or more
## 1 2 3 4break_edu <- c(4,6,7,8,9)
labels_edu <- c("1ro-2do de primaria"=1,
"3ro de primaria"=2,
"4t0 de primaria"=3,
"5to de primaria"=4,
"6to de primaria"=5)
mydata <- ordinal_recode (variable="p28a1", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p28a1. Hermano(a)
## 1ro de primaria 2do de primaria 3ro de primaria 4to de primaria 5to de primaria 6to de primaria <NA>
## 14 26 43 52 44 39 2530
## recoded
## [4,6) [6,7) [7,8) [8,9) [9,1e+06)
## 4 14 0 0 0 0
## 5 26 0 0 0 0
## 6 0 43 0 0 0
## 7 0 0 52 0 0
## 8 0 0 0 44 0
## 9 0 0 0 0 39
## [1] "Frequency table after encoding"
## p28a1. Hermano(a)
## 1ro-2do de primaria 3ro de primaria 4t0 de primaria 5to de primaria 6to de primaria <NA>
## 40 43 52 44 39 2530
## [1] "Inspect value labels and relabel as necessary"
## 1ro-2do de primaria 3ro de primaria 4t0 de primaria 5to de primaria 6to de primaria
## 1 2 3 4 5break_edu <- c(4,5,6,7,8)
labels_edu <- c("1ro de primaria"=1,
"2do de primaria"=2,
"3r0 de primaria"=3,
"4to de primaria"=4,
"5to-6to de primaria"=5)
mydata <- ordinal_recode (variable="p28a2", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p28a2. Hermano(a)
## 1ro de primaria 2do de primaria 3ro de primaria 4to de primaria 5to de primaria 6to de primaria <NA>
## 37 40 51 57 37 24 2502
## recoded
## [4,5) [5,6) [6,7) [7,8) [8,1e+06)
## 4 37 0 0 0 0
## 5 0 40 0 0 0
## 6 0 0 51 0 0
## 7 0 0 0 57 0
## 8 0 0 0 0 37
## 9 0 0 0 0 24
## [1] "Frequency table after encoding"
## p28a2. Hermano(a)
## 1ro de primaria 2do de primaria 3r0 de primaria 4to de primaria 5to-6to de primaria <NA>
## 37 40 51 57 61 2502
## [1] "Inspect value labels and relabel as necessary"
## 1ro de primaria 2do de primaria 3r0 de primaria 4to de primaria 5to-6to de primaria
## 1 2 3 4 5break_edu <- c(4,5,6,7,8)
labels_edu <- c("1ro de primaria"=1,
"2do de primaria"=2,
"3r0 de primaria"=3,
"4to de primaria"=4,
"5to-6to de primaria"=5)
mydata <- ordinal_recode (variable="p28a3", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p28a3. Hermano(a)
## 1ro de primaria 2do de primaria 3ro de primaria 4to de primaria 5to de primaria 6to de primaria <NA>
## 42 44 37 33 28 7 2557
## recoded
## [4,5) [5,6) [6,7) [7,8) [8,1e+06)
## 4 42 0 0 0 0
## 5 0 44 0 0 0
## 6 0 0 37 0 0
## 7 0 0 0 33 0
## 8 0 0 0 0 28
## 9 0 0 0 0 7
## [1] "Frequency table after encoding"
## p28a3. Hermano(a)
## 1ro de primaria 2do de primaria 3r0 de primaria 4to de primaria 5to-6to de primaria <NA>
## 42 44 37 33 35 2557
## [1] "Inspect value labels and relabel as necessary"
## 1ro de primaria 2do de primaria 3r0 de primaria 4to de primaria 5to-6to de primaria
## 1 2 3 4 5break_edu <- c(4,6)
labels_edu <- c("1ro or 2do de primaria"=1,
"3ro de primaria or more"=2)
mydata <- ordinal_recode (variable="p28a4", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p28a4. Hermano(a)
## 1ro de primaria 2do de primaria 3ro de primaria 4to de primaria 5to de primaria 6to de primaria <NA>
## 18 14 14 12 2 6 2682
## recoded
## [4,6) [6,1e+06)
## 4 18 0
## 5 14 0
## 6 0 14
## 7 0 12
## 8 0 2
## 9 0 6
## [1] "Frequency table after encoding"
## p28a4. Hermano(a)
## 1ro or 2do de primaria 3ro de primaria or more <NA>
## 32 34 2682
## [1] "Inspect value labels and relabel as necessary"
## 1ro or 2do de primaria 3ro de primaria or more
## 1 2break_edu <- c(-98,-1,0,1,2,3)
labels_edu <- c("No se"=1,
"sin nivel"=2,
"Inicial"=3,
"Primaria completa"=4,
"Secundaria completa"=5,
"Superior tecnica incompleta/completa or Superior universitaria completa/incompleta"=6)
mydata <- ordinal_recode (variable="p6_1", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p6_1. Padre
## Sin nivel Inicial Primaria completa Secundaria completa Superior tecnica incompleta
## 89 88 544 133 3
## Superior tecnica completa Superior universitaria completa <NA>
## 4 1 1886
## recoded
## [-98,-1) [-1,0) [0,1) [1,2) [2,3) [3,1e+06)
## -1 0 89 0 0 0 0
## 0 0 0 88 0 0 0
## 1 0 0 0 544 0 0
## 2 0 0 0 0 133 0
## 3 0 0 0 0 0 3
## 4 0 0 0 0 0 4
## 6 0 0 0 0 0 1
## [1] "Frequency table after encoding"
## p6_1. Padre
## sin nivel Inicial
## 89 88
## Primaria completa Secundaria completa
## 544 133
## Superior tecnica incompleta/completa or Superior universitaria completa/incompleta <NA>
## 8 1886
## [1] "Inspect value labels and relabel as necessary"
## No se sin nivel
## 1 2
## Inicial Primaria completa
## 3 4
## Secundaria completa Superior tecnica incompleta/completa or Superior universitaria completa/incompleta
## 5 6break_edu <- c(-98,-1,0,1,2,3)
labels_edu <- c("No se"=1,
"sin nivel"=2,
"Inicial"=3,
"Primaria completa"=4,
"Secundaria completa"=5,
"Superior tecnica incompleta/completa or Superior universitaria completa/incompleta"=6)
mydata <- ordinal_recode (variable="p6_2", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p6_2. Madre
## Sin nivel Inicial Primaria completa Secundaria completa Superior tecnica incompleta Superior tecnica completa
## 283 134 471 53 1 4
## <NA>
## 1802
## recoded
## [-98,-1) [-1,0) [0,1) [1,2) [2,3) [3,1e+06)
## -1 0 283 0 0 0 0
## 0 0 0 134 0 0 0
## 1 0 0 0 471 0 0
## 2 0 0 0 0 53 0
## 3 0 0 0 0 0 1
## 4 0 0 0 0 0 4
## [1] "Frequency table after encoding"
## p6_2. Madre
## sin nivel Inicial
## 283 134
## Primaria completa Secundaria completa
## 471 53
## Superior tecnica incompleta/completa or Superior universitaria completa/incompleta <NA>
## 5 1802
## [1] "Inspect value labels and relabel as necessary"
## No se sin nivel
## 1 2
## Inicial Primaria completa
## 3 4
## Secundaria completa Superior tecnica incompleta/completa or Superior universitaria completa/incompleta
## 5 6break_edu <- c(-98,-1,2,3)
labels_edu <- c("No se"=1,
"Primaria completa or less"=2,
"Secundaria completa"=3,
"Superior tecnica incompleta or more"=4)
mydata <- ordinal_recode (variable="p6a1", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p6a1. Hermano(a)
## Sin nivel Inicial Primaria completa Secundaria completa
## 2 2 55 65
## Superior tecnica incompleta Superior tecnica completa Superior universitaria incompleta <NA>
## 7 3 2 2612
## recoded
## [-98,-1) [-1,2) [2,3) [3,1e+06)
## -1 0 2 0 0
## 0 0 2 0 0
## 1 0 55 0 0
## 2 0 0 65 0
## 3 0 0 0 7
## 4 0 0 0 3
## 5 0 0 0 2
## [1] "Frequency table after encoding"
## p6a1. Hermano(a)
## Primaria completa or less Secundaria completa Superior tecnica incompleta or more <NA>
## 59 65 12 2612
## [1] "Inspect value labels and relabel as necessary"
## No se Primaria completa or less Secundaria completa Superior tecnica incompleta or more
## 1 2 3 4break_edu <- c(-98,-1,2)
labels_edu <- c("No se"=1,
"Primaria completa or less"=2,
"Secundaria completa or more"=3)
mydata <- ordinal_recode (variable="p6a2", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p6a2. Hermano(a)
## Sin nivel Inicial Primaria completa Secundaria completa Superior tecnica incompleta <NA>
## 1 2 22 22 2 2699
## recoded
## [-98,-1) [-1,2) [2,1e+06)
## -1 0 1 0
## 0 0 2 0
## 1 0 22 0
## 2 0 0 22
## 3 0 0 2
## [1] "Frequency table after encoding"
## p6a2. Hermano(a)
## Primaria completa or less Secundaria completa or more <NA>
## 25 24 2699
## [1] "Inspect value labels and relabel as necessary"
## No se Primaria completa or less Secundaria completa or more
## 1 2 3break_edu <- c(-98,-1,2)
labels_edu <- c("No se"=1,
"Primaria completa or less"=2,
"Secundaria completa or more"=3)
mydata <- ordinal_recode (variable="p6a3", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p6a3. Hermano(a)
## Sin nivel Inicial Primaria completa Secundaria completa Superior tecnica incompleta <NA>
## 1 1 14 10 1 2721
## recoded
## [-98,-1) [-1,2) [2,1e+06)
## -1 0 1 0
## 0 0 1 0
## 1 0 14 0
## 2 0 0 10
## 3 0 0 1
## [1] "Frequency table after encoding"
## p6a3. Hermano(a)
## Primaria completa or less Secundaria completa or more <NA>
## 16 11 2721
## [1] "Inspect value labels and relabel as necessary"
## No se Primaria completa or less Secundaria completa or more
## 1 2 3break_edu <- c(-98,-1,1)
labels_edu <- c("No se"=1,
"Inicial or less"=2,
"Primaria completa or more"=3)
mydata <- ordinal_recode (variable="p6b1", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## p6b1. Abuelo(a)
## Sin nivel Inicial Primaria completa Secundaria completa <NA>
## 54 4 38 1 2651
## recoded
## [-98,-1) [-1,1) [1,1e+06)
## -1 0 54 0
## 0 0 4 0
## 1 0 0 38
## 2 0 0 1
## [1] "Frequency table after encoding"
## p6b1. Abuelo(a)
## Inicial or less Primaria completa or more <NA>
## 58 39 2651
## [1] "Inspect value labels and relabel as necessary"
## No se Inicial or less Primaria completa or more
## 1 2 3# Top code household composition variables with large and unusual numbers
mydata <- top_recode ("p1", break_point=10, missing=c(888, 999999)) ## [1] "Frequency table before encoding"
## p1. Cuantas personas viven en total en el hogar?
## 1 2 3 4 5 6 7 8 9 10 12 16 <NA>
## 2 24 98 185 239 201 140 68 31 24 3 1 1732
## [1] "Frequency table after encoding"
## p1. Cuantas personas viven en total en el hogar?
## 1 2 3 4 5 6 7 8 9 10 or more <NA>
## 2 24 98 185 239 201 140 68 31 28 1732
mydata <- top_recode ("p2c", break_point=6, missing=c(888, 999999))## [1] "Frequency table before encoding"
## p2c. Con cuantos hermanos o hermanas vive?
## 0 1 2 3 4 5 6 7 9 10 <NA>
## 126 204 246 209 120 67 27 14 2 1 1732
## [1] "Frequency table after encoding"
## p2c. Con cuantos hermanos o hermanas vive?
## 0 1 2 3 4 5 6 or more <NA>
## 126 204 246 209 120 67 44 1732
mydata <- top_recode ("p2d", break_point=2, missing=c(888, 999999))## [1] "Frequency table before encoding"
## p2d. Con cuantos abuelos o abuelas vive?
## 0 1 2 3 <NA>
## 912 77 26 1 1732
## [1] "Frequency table after encoding"
## p2d. Con cuantos abuelos o abuelas vive?
## 0 1 2 or more <NA>
## 912 77 27 1732
mydata <- top_recode ("p2e", break_point=1, missing=c(888, 999999))## [1] "Frequency table before encoding"
## p2e. Con cuantos tios o tias vive?
## 0 1 2 <NA>
## 997 13 6 1732
## [1] "Frequency table after encoding"
## p2e. Con cuantos tios o tias vive?
## 0 1 or more <NA>
## 997 19 1732
mydata <- top_recode ("p2f", break_point=1, missing=c(888, 999999))## [1] "Frequency table before encoding"
## p2f. Con cuantos sobrinos vive?
## 0 1 2 3 <NA>
## 978 29 7 2 1732
## [1] "Frequency table after encoding"
## p2f. Con cuantos sobrinos vive?
## 0 1 or more <NA>
## 978 38 1732
mydata <- top_recode ("p2g", break_point=1, missing=c(888, 999999))## [1] "Frequency table before encoding"
## p2g. Con cuantos otros miembros del hogar vive el/la nino/a?
## 0 1 2 3 5 <NA>
## 944 58 7 6 1 1732
## [1] "Frequency table after encoding"
## p2g. Con cuantos otros miembros del hogar vive el/la nino/a?
## 0 1 or more <NA>
## 944 72 1732
mydata <- top_recode ("sc_ave_3a", break_point=200, missing=c(888, 999999))## [1] "Frequency table before encoding"
## sc_ave_3a. Men Height (BT)
## 51 55 56 60 65 70 75 80 85 86 89 90 98 100 105 109 110 112 115 118 120 123 125 128 130 135 137 140 145 150 151 152 153
## 2 1 2 7 2 4 1 10 1 1 1 3 1 38 2 1 3 1 1 1 21 2 4 1 13 6 2 24 1 109 1 1 1
## 155 156 157 158 159 160 161 162 163 165 168 170 174 175 176 178 180 181 185 186 187 190 195 200 204 210 215 230 250 258 260 270 272
## 5 4 1 2 1 148 1 4 2 30 3 124 1 11 1 1 108 1 1 1 1 16 2 65 1 2 1 1 4 1 1 1 2
## <NA>
## 1931
## [1] "Frequency table after encoding"
## sc_ave_3a. Men Height (BT)
## 51 55 56 60 65 70 75 80 85 86 89 90 98 100
## 2 1 2 7 2 4 1 10 1 1 1 3 1 38
## 105 109 110 112 115 118 120 123 125 128 130 135 137 140
## 2 1 3 1 1 1 21 2 4 1 13 6 2 24
## 145 150 151 152 153 155 156 157 158 159 160 161 162 163
## 1 109 1 1 1 5 4 1 2 1 148 1 4 2
## 165 168 170 174 175 176 178 180 181 185 186 187 190 195
## 30 3 124 1 11 1 1 108 1 1 1 1 16 2
## 200 or more <NA>
## 79 1931
mydata <- top_recode ("sc_ave_3b", break_point=175, missing=c(888, 999999))## [1] "Frequency table before encoding"
## sc_ave_3b. Women Height (BT)
## 51 52 54 55 56 58 60 64 70 74 76 80 85 89 90 95 100 104 105 110 114 115 117 118 120 123 125 126 127 128 129 130 134
## 7 1 1 3 4 1 6 1 5 1 1 10 2 1 10 1 46 1 1 17 1 1 1 1 31 3 7 1 1 1 1 31 1
## 135 136 137 139 140 142 143 144 145 147 149 150 151 152 153 154 155 156 157 158 159 160 162 163 164 165 166 168 169 170 175 177 180
## 4 1 1 1 65 1 1 1 34 2 2 204 3 1 2 2 44 5 1 3 1 92 1 2 1 15 1 1 1 43 1 1 24
## 181 185 189 190 193 198 200 202 205 230 245 249 250 272 <NA>
## 2 2 1 12 1 1 20 1 1 1 1 1 4 2 1935
## [1] "Frequency table after encoding"
## sc_ave_3b. Women Height (BT)
## 51 52 54 55 56 58 60 64 70 74 76 80 85 89
## 7 1 1 3 4 1 6 1 5 1 1 10 2 1
## 90 95 100 104 105 110 114 115 117 118 120 123 125 126
## 10 1 46 1 1 17 1 1 1 1 31 3 7 1
## 127 128 129 130 134 135 136 137 139 140 142 143 144 145
## 1 1 1 31 1 4 1 1 1 65 1 1 1 34
## 147 149 150 151 152 153 154 155 156 157 158 159 160 162
## 2 2 204 3 1 2 2 44 5 1 3 1 92 1
## 163 164 165 166 168 169 170 175 or more <NA>
## 2 1 15 1 1 1 43 76 1935
# Top code high income to the 99.5 percentile
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7_1)[na.exclude(mydata$p7_1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7_1", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7_1. Padre
## 0 4 10 30 50 60 70 72 75 80 90 100 105 120 125 130 140 150 160 175 180 200 210 220 240 250 255 280 300 325 350 400 500
## 2 1 1 1 4 3 3 1 2 1 2 12 1 9 5 1 1 17 1 1 7 23 3 1 4 24 2 1 21 1 8 15 7
## 600 700 750 800 810 900 960 1000 1200 1300 1500 1600 1900 2000 3000 <NA>
## 6 2 1 3 1 4 1 7 5 1 2 1 1 1 1 2525
## [1] "Frequency table after encoding"
## p7_1. Padre
## 0 4 10 30 50 60 70 72 75 80 90 100
## 2 1 1 1 4 3 3 1 2 1 2 12
## 105 120 125 130 140 150 160 175 180 200 210 220
## 1 9 5 1 1 17 1 1 7 23 3 1
## 240 250 255 280 300 325 350 400 500 600 700 750
## 4 24 2 1 21 1 8 15 7 6 2 1
## 800 810 900 960 1000 1200 1300 1500 1600 1900 1988 or more <NA>
## 3 1 4 1 7 5 1 2 1 1 2 2525
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7_2)[na.exclude(mydata$p7_2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7_2", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7_2. Madre
## 0 20 25 30 35 50 65 75 80 100 125 150 160 187 200 210 225 400 900 1000 <NA>
## 2 1 1 1 1 3 1 3 2 7 2 6 2 1 7 1 1 1 1 1 2703
## [1] "Frequency table after encoding"
## p7_2. Madre
## 0 20 25 30 35 50 65 75 80 100 125 150 160 187
## 2 1 1 1 1 3 1 3 2 7 2 6 2 1
## 200 210 225 400 900 978 or more <NA>
## 7 1 1 1 1 1 2703
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7c1)[na.exclude(mydata$p7c1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7c1", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7c1. Tio(a)
## 200 800 <NA>
## 1 1 2746
## [1] "Frequency table after encoding"
## p7c1. Tio(a)
## 200 797 or more <NA>
## 1 1 2746
percentile_99.5 <- floor(quantile(na.exclude(mydata$p49)[na.exclude(mydata$p49)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p49", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p49. Cuanto gasta cada mes en la educacion de todos sus hijos que viven en este hoga
## 0 5 10 12 15 20 25 28 30 35 40 45 46 50 55 60 62 70 80 90 100 120 135 140 150 160 170 180 200 210 220 230 250
## 13 5 14 1 4 25 5 1 27 2 18 2 1 86 1 17 1 14 23 5 129 15 1 2 62 3 1 7 155 1 1 1 38
## 280 300 350 380 400 450 500 520 600 650 700 800 900 1000 1200 1500 2000 3000 <NA>
## 2 103 10 1 41 2 72 1 22 1 6 10 4 39 1 5 12 3 1732
## [1] "Frequency table after encoding"
## p49. Cuanto gasta cada mes en la educacion de todos sus hijos que viven en este hoga
## 0 5 10 12 15 20 25 28 30 35 40 45
## 13 5 14 1 4 25 5 1 27 2 18 2
## 46 50 55 60 62 70 80 90 100 120 135 140
## 1 86 1 17 1 14 23 5 129 15 1 2
## 150 160 170 180 200 210 220 230 250 280 300 350
## 62 3 1 7 155 1 1 1 38 2 103 10
## 380 400 450 500 520 600 650 700 800 900 1000 1200
## 1 41 2 72 1 22 1 6 10 4 39 1
## 1500 2000 or more <NA>
## 5 15 1732
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7a1)[na.exclude(mydata$p7a1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7a1", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7a1. Hermano(a)
## 0 15 20 50 60 100 120 125 150 180 200 210 250 270 300 350 400 500 600 900 1000 1200 1350 1500 <NA>
## 1 2 2 5 2 3 2 1 6 1 11 1 4 1 6 1 2 3 1 3 2 1 1 1 2685
## [1] "Frequency table after encoding"
## p7a1. Hermano(a)
## 0 15 20 50 60 100 120 125 150 180 200 210
## 1 2 2 5 2 3 2 1 6 1 11 1
## 250 270 300 350 400 500 600 900 1000 1200 1350 1453 or more
## 4 1 6 1 2 3 1 3 2 1 1 1
## <NA>
## 2685
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7a2)[na.exclude(mydata$p7a2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7a2", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7a2. Hermano(a)
## 0 15 50 75 125 150 160 200 250 300 350 500 800 1000 <NA>
## 3 2 2 1 1 3 1 3 2 1 1 1 1 1 2725
## [1] "Frequency table after encoding"
## p7a2. Hermano(a)
## 0 15 50 75 125 150 160 200 250 300 350 500 800 978 or more
## 3 2 2 1 1 3 1 3 2 1 1 1 1 1
## <NA>
## 2725
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7a3)[na.exclude(mydata$p7a3)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7a3", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7a3. Hermano(a)
## 0 75 90 150 200 500 800 1000 <NA>
## 1 1 1 1 1 1 1 1 2740
## [1] "Frequency table after encoding"
## p7a3. Hermano(a)
## 0 75 90 150 200 500 800 993 or more <NA>
## 1 1 1 1 1 1 1 1 2740
percentile_99.5 <- floor(quantile(na.exclude(mydata$p7a4)[na.exclude(mydata$p7a4)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p7a4", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## p7a4. Hermano(a)
## 0 200 <NA>
## 2 1 2745
## [1] "Frequency table after encoding"
## p7a4. Hermano(a)
## 0 198 or more <NA>
## 2 1 2745
# Remove as it constains identifying education
mydata <- mydata[!names(mydata) %in% "birthdate"]Indirect PII - Categorical: Recode, encode, or Top/bottom coding for extreme values
# !!!Include relevant variables in list below (Indirect PII - Categorical, and Ordinal if not processed yet)
indirect_PII <- c("sexo",
"i14",
"telf_yesno",
"gender_nino",
"dropout_reasons_1",
"dropout_reasons_2",
"dropout_reasons_3",
"dropout_reasons_4",
"dropout_reasons_5",
"dropout_reasons_6",
"dropout_reasons_7",
"dropout_reasons_8",
"dropout_reasons_9",
"dropout_reasons_10",
"dropout_reasons_11",
"dropout_reasons_12",
"dropout_reasons_13",
"dropout_reasons_14",
"dropout_reasons_99",
"p2a",
"p2b",
"p3a1",
"p3a2",
"p3a3",
"p3a4",
"p3a5",
"p3a6",
"p3a7",
"p3a8",
"p3a9",
"p3a10",
"p3b1",
"p3b2",
"p3b3",
"p3c1",
"p3c2",
"p3d1",
"p3d2",
"p3d3",
"p26a1",
"p26a2",
"p26a3",
"p26a4",
"p26a5",
"p26a6",
"p26a7",
"p26a8",
"p26a9",
"p26a10",
"p26c1",
"p26c2",
"p26d1",
"p26d2",
"p26d3",
"p4_1",
"p4_2",
"p4a1",
"p4a2",
"p4a3",
"p4a4",
"p4a5",
"p4a6",
"p4a7",
"p4a8",
"p4a9",
"p4a10",
"p4b1",
"p4b2",
"p4b3",
"p4c1",
"p4c2",
"p4d1",
"p4d2",
"p4d3",
"p5_aa1",
"p5_aa2",
"p5_aa3",
"p5_aa4",
"p5_aa5",
"p5_aa6",
"p5_aa7",
"p5_aa8",
"p5_aa9",
"p10_1",
"p42",
"p44b_1",
"p44b_2",
"p44b_3",
"p44b_4",
"p44b_5",
"p44b_6",
"p44b_7",
"p44b_99",
"nivel",
"p1a_1",
"p1a_2",
"p1a_3",
"dout_reasons_2",
"dout_reasons_3",
"dout_reasons_4",
"dout_reasons_5",
"dout_reasons_6",
"dout_reasons_7",
"dout_reasons_8",
"dout_reasons_9",
"dout_reasons_10",
"dout_reasons_11",
"dout_reasons_12",
"dout_reasons_13",
"dout_reasons_14",
"p16",
"gender",
"hazardous_work",
"worst_forms",
"child_labor",
"juntos_dist_hogar",
"juntos1",
"juntos2",
"juntos3",
"juntos4",
"juntos_rnu",
"juntos_dist",
"juntos",
"juntos_ind",
"pobn",
"pobx",
"D_distjuntos",
"school_fixed_level",
"D_liveswithmother",
"D_liveswithfather",
"p12c",
"dout_reasons",
"dout_reasons_1",
"dout_decision",
"genero",
"p22a",
"p22b",
"act_sd_4",
"act_sd_4a",
"act_sd_4b",
"act_sd_4c",
"act_sd_5",
"act_sd_5a",
"act_sd_5b",
"act_sd_5c",
"act_sd_6",
"act_sd_6a",
"act_sd_6b",
"act_sd_6c",
"act_sd_7",
"act_sd_7a",
"act_sd_7b",
"act_sd_7c",
"act_sd_8",
"act_sd_8a",
"act_sd_8b",
"act_sd_8c",
"act_sd_9",
"act_sd_9a",
"act_sd_9b",
"act_sd_9c",
"act_sd_10",
"act_sd_10a",
"act_sd_10b",
"act_sd_10c",
"act_sd_11",
"act_sd_11a",
"act_sd_11b",
"act_sd_11c",
"act_sd_12",
"act_sd_12a",
"act_sd_12b",
"act_sd_12c",
"act_sd_13",
"act_sd_13a",
"act_sd_13b",
"act_sd_13c",
"act_sd_14",
"act_sd_14a",
"act_sd_14b",
"act_sd_14c",
"act_sd_15",
"act_sd_15a",
"act_sd_15b",
"act_sd_15c",
"act_sd_16",
"act_sd_16a",
"act_sd_16b",
"act_sd_16c",
"act_sd_17",
"act_sd_17a",
"act_sd_17b",
"act_sd_17c",
"act_sd_18",
"act_sd_18a",
"act_sd_18b",
"act_sd_18c",
"act_sd_19",
"act_sd_19a",
"act_sd_19b",
"act_sd_19c",
"act_sd_20",
"act_sd_20a",
"act_sd_20b",
"act_sd_20c",
"act_sd_21",
"act_sd_21a",
"act_sd_21b",
"act_sd_21c",
"act_sd_22",
"act_sd_22a",
"act_sd_23",
"act_sd_23a",
"act_sd_24",
"act_sd_1",
"act_sd_2",
"act_sd_3",
"act_wed_4",
"act_wed_4a",
"act_wed_4b",
"act_wed_4c",
"act_wed_5",
"act_wed_5a",
"act_wed_5b",
"act_wed_5c",
"act_wed_6",
"act_wed_6a",
"act_wed_6b",
"act_wed_6c",
"act_wed_7",
"act_wed_7a",
"act_wed_7b",
"act_wed_7c",
"act_wed_8",
"act_wed_8a",
"act_wed_8b",
"act_wed_8c",
"act_wed_9",
"act_wed_9a",
"act_wed_9b",
"act_wed_9c",
"act_wed_10",
"act_wed_10a",
"act_wed_10b",
"act_wed_10c",
"act_wed_11",
"act_wed_11a",
"act_wed_11b",
"act_wed_11c",
"act_wed_12",
"act_wed_12a",
"act_wed_12b",
"act_wed_12c",
"act_wed_13",
"act_wed_13a",
"act_wed_13b",
"act_wed_13c",
"act_wed_14",
"act_wed_14a",
"act_wed_14b",
"act_wed_14c",
"act_wed_15",
"act_wed_15a",
"act_wed_15b",
"act_wed_15c",
"act_wed_16",
"act_wed_16a",
"act_wed_16b",
"act_wed_16c",
"act_wed_17",
"act_wed_17a",
"act_wed_17b",
"act_wed_17c",
"act_wed_18",
"act_wed_18a",
"act_wed_18b",
"act_wed_18c",
"act_wed_19",
"act_wed_19a",
"act_wed_19b",
"act_wed_19c",
"act_wed_20",
"act_wed_20a",
"act_wed_20b",
"act_wed_20c",
"act_wed_21",
"act_wed_21a",
"act_wed_21b",
"act_wed_21c",
"act_wed_22",
"act_wed_22a",
"act_wed_22b",
"act_wed_22c",
"act_wed_23",
"act_wed_23a",
"act_wed_24",
"act_wed_1",
"act_wed_2",
"act_wed_3",
"p25a1",
"p25a2",
"p25a3",
"p25b",
"p25c",
"p25d",
"p25e",
"p25_1a",
"p25_1b",
"p25_1c",
"p25_1d",
"p25_1e",
"p25_1f",
"p25_2g",
"p25_3h",
"p25_4i",
"p25_5j",
"p25_6k",
"p25_7l",
"p25_8m",
"p25_9n",
"p25_10o",
"p25_11p",
"p25_12q",
"p25_13r",
"p25_14s",
"p25_14t",
"p25_2a",
"p25_2b",
"p25_2c",
"p25_2d",
"p25_2e",
"p25_2f",
"p25_2g1",
"p25_2h",
"p25_2i",
"p27a",
"p27b",
"p27c",
"p27d",
"p27e",
"switcher_2016",
"switcher_2015",
"switcher_2014",
"asissted_2014",
"same_school2014",
"same_school2013",
"asissted_2013",
"s5p15a_2015",
"s5p12a_2015",
"s5p12c_2015",
"s5p16_2015",
"s6p22a_2015",
"s6p22b_2015",
"s6p25p25a1_2015",
"s6p25p25a2_2015",
"s6p25p25a3_2015",
"s6p25p25b_2015",
"s6p25p25c_2015",
"s6p25p25d_2015",
"s6p25p25e_2015",
"s6p25_1p25_1a_2015",
"s6p25_1p25_1b_2015",
"s6p25_1p25_1c_2015",
"s6p25_1p25_1d_2015",
"s6p25_1p25_1e_2015",
"s6p25_1p25_1f_2015",
"s6p25_1p25_2g_2015",
"s6p25_1p25_3h_2015",
"s6p25_1p25_4i_2015",
"s6p25_1p25_5j_2015",
"s6p25_1p25_6k_2015",
"s6p25_1p25_7l_2015",
"s6p25_1p25_8m_2015",
"s6p25_1p25_9n_2015",
"s6p25_1p25_10o_2015",
"s6p25_1p25_11p_2015",
"s6p25_1p25_12q_2015",
"s6p25_1p25_13r_2015",
"s6p25_1p25_14s_2015",
"s6p25_1p25_14t_2015",
"s6p25_2p25_2a_2015",
"s6p25_2p25_2b_2015",
"s6p25_2p25_2c_2015",
"s6p25_2p25_2d_2015",
"s6p25_2p25_2e_2015",
"s6p25_2p25_2f_2015",
"s6p25_2p25_2g_2015",
"s6p25_2p25_2h_2015",
"s6p25_2p25_2i_2015",
"s7p27a_2015",
"s7p27b_2015",
"s7p27c_2015",
"s7p27d_2015",
"s7p27e_2015",
"info4a_2015",
"info4b_2015",
"hazardous_work_2015",
"worst_forms_2015",
"child_labor_2015")
capture_tables (indirect_PII)
# Recode those with very specific values.
break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Otros"=2,
"Trabajo remunerado"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"Otros"=5)
mydata <- ordinal_recode (variable="p4_1", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4_1. Padre
## Estudia y tiene un trabajo remunerado Trabajo remunerado Quehaceres del hogar o trabajo no remunerado
## 4 220 648
## Infante pre-escolar (menor a 2 anos) <NA>
## 6 1870
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 2 0 4 0 0 0
## 3 0 0 220 0 0
## 4 0 0 0 648 0
## 5 0 0 0 0 6
## [1] "Frequency table after encoding"
## p4_1. Padre
## Otros Trabajo remunerado Quehaceres del hogar o trabajo no remunerado
## 10 220 648
## <NA>
## 1870
## [1] "Inspect value labels and relabel as necessary"
## Otros Otros Trabajo remunerado
## 1 2 3
## Quehaceres del hogar o trabajo no remunerado Otros
## 4 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Otros"=2,
"Trabajo remunerado"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"Otros"=5)
mydata <- ordinal_recode (variable="p4_2", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4_2. Madre
## Estudia Estudia y tiene un trabajo remunerado Trabajo remunerado
## 2 1 44
## Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor a 2 anos) <NA>
## 903 9 1789
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 2 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 44 0 0
## 4 0 0 0 903 0
## 5 0 0 0 0 9
## [1] "Frequency table after encoding"
## p4_2. Madre
## Otros Trabajo remunerado Quehaceres del hogar o trabajo no remunerado
## 12 44 903
## <NA>
## 1789
## [1] "Inspect value labels and relabel as necessary"
## Otros Otros Trabajo remunerado
## 1 2 3
## Quehaceres del hogar o trabajo no remunerado Otros
## 4 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Estudia"=1,
"Otros"=2,
"Trabajo remunerado"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"Otros"=5)
mydata <- ordinal_recode (variable="p4a1", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4a1. Hermano(a)
## Estudia Estudia y tiene un trabajo remunerado Trabajo remunerado
## 719 12 52
## Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor a 2 anos) <NA>
## 84 23 1858
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 719 0 0 0 0
## 2 0 12 0 0 0
## 3 0 0 52 0 0
## 4 0 0 0 84 0
## 5 0 0 0 0 23
## [1] "Frequency table after encoding"
## p4a1. Hermano(a)
## Estudia Otros Trabajo remunerado
## 719 35 52
## Quehaceres del hogar o trabajo no remunerado <NA>
## 84 1858
## [1] "Inspect value labels and relabel as necessary"
## Estudia Otros Trabajo remunerado
## 1 2 3
## Quehaceres del hogar o trabajo no remunerado Otros
## 4 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Estudia"=1,
"Otros"=2,
"Otros"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"Infante pre-escolar (menor de 2 anos)"=5)
mydata <- ordinal_recode (variable="p4a2", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4a2. Hermano(a)
## Estudia Estudia y tiene un trabajo remunerado Trabajo remunerado
## 584 9 15
## Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor a 2 anos) <NA>
## 34 44 2062
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 584 0 0 0 0
## 2 0 9 0 0 0
## 3 0 0 15 0 0
## 4 0 0 0 34 0
## 5 0 0 0 0 44
## [1] "Frequency table after encoding"
## p4a2. Hermano(a)
## Estudia Otros Quehaceres del hogar o trabajo no remunerado
## 584 24 34
## Infante pre-escolar (menor de 2 anos) <NA>
## 44 2062
## [1] "Inspect value labels and relabel as necessary"
## Estudia Otros Otros
## 1 2 3
## Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor de 2 anos)
## 4 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Estudia"=1,
"Otros"=2,
"Otros"=3,
"Otros"=4,
"Infante pre-escolar (menor de 2 anos)"=5)
mydata <- ordinal_recode (variable="p4a3", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4a3. Hermano(a)
## Estudia Estudia y tiene un trabajo remunerado Trabajo remunerado
## 361 1 7
## Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor a 2 anos) <NA>
## 20 51 2308
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 361 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 7 0 0
## 4 0 0 0 20 0
## 5 0 0 0 0 51
## [1] "Frequency table after encoding"
## p4a3. Hermano(a)
## Estudia Otros Infante pre-escolar (menor de 2 anos) <NA>
## 361 28 51 2308
## [1] "Inspect value labels and relabel as necessary"
## Estudia Otros Otros Otros
## 1 2 3 4
## Infante pre-escolar (menor de 2 anos)
## 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Estudia"=1,
"Otros"=2,
"Otros"=3,
"Otros"=4,
"Infante pre-escolar (menor de 2 anos)"=5)
mydata <- ordinal_recode (variable="p4a4", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4a4. Hermano(a)
## Estudia Estudia y tiene un trabajo remunerado Trabajo remunerado
## 176 1 2
## Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor a 2 anos) <NA>
## 9 43 2517
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 176 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 2 0 0
## 4 0 0 0 9 0
## 5 0 0 0 0 43
## [1] "Frequency table after encoding"
## p4a4. Hermano(a)
## Estudia Otros Infante pre-escolar (menor de 2 anos) <NA>
## 176 12 43 2517
## [1] "Inspect value labels and relabel as necessary"
## Estudia Otros Otros Otros
## 1 2 3 4
## Infante pre-escolar (menor de 2 anos)
## 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Estudia"=1,
"Otros"=2,
"Otros"=3,
"Otros"=4,
"Infante pre-escolar (menor de 2 anos)"=5)
mydata <- ordinal_recode (variable="p4a5", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4a5. Hermano(a)
## Estudia Quehaceres del hogar o trabajo no remunerado Infante pre-escolar (menor a 2 anos)
## 71 4 36
## <NA>
## 2637
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 71 0 0 0 0
## 4 0 0 0 4 0
## 5 0 0 0 0 36
## [1] "Frequency table after encoding"
## p4a5. Hermano(a)
## Estudia Otros Infante pre-escolar (menor de 2 anos) <NA>
## 71 4 36 2637
## [1] "Inspect value labels and relabel as necessary"
## Estudia Otros Otros Otros
## 1 2 3 4
## Infante pre-escolar (menor de 2 anos)
## 5break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Otros"=2,
"Otros"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"Otros"=5)
mydata <- ordinal_recode (variable="p4b1", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4b1. Abuelo(a)
## Estudia Quehaceres del hogar o trabajo no remunerado <NA>
## 3 101 2644
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 3 0 0 0 0
## 4 0 0 0 101 0
## [1] "Frequency table after encoding"
## p4b1. Abuelo(a)
## Otros Quehaceres del hogar o trabajo no remunerado <NA>
## 3 101 2644
## [1] "Inspect value labels and relabel as necessary"
## Otros Otros Otros
## 1 2 3
## Quehaceres del hogar o trabajo no remunerado Otros
## 4 5Matching and crosstabulations: Run automated PII check
# selected categorical key variables: gender, occupation/education and age
selectedKeyVars = c('sexo', 'grado2015') ##!!! Replace with candidate categorical demo vars
selectedKeyVars2= c('i14','p4_1')
# creating the sdcMicro object with the assigned variables
sdcInitial <- createSdcObj(dat = mydata, keyVars = selectedKeyVars)
sdcInitial## The input dataset consists of 2748 rows and 1641 variables.
## --> Categorical key variables: sexo, grado2015
## ----------------------------------------------------------------------## Information on categorical key variables:
##
## Reported is the number, mean size and size of the smallest category >0 for recoded variables.
## In parenthesis, the same statistics are shown for the unmodified data.
## Note: NA (missings) are counted as seperate categories!## Key Variable Number of categories Mean size Size of smallest (>0)
## sexo 3 (3) 1354.500 (1354.500) 1319 (1319)
## grado2015 3 (3) 1354.500 (1354.500) 1167 (1167)## ----------------------------------------------------------------------## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 0 (0.000%)
## - 3-anonymity: 0 (0.000%)
## - 5-anonymity: 0 (0.000%)
##
## ----------------------------------------------------------------------sdcInitial2 <- createSdcObj(dat = mydata, keyVars = selectedKeyVars2)
sdcInitial2## The input dataset consists of 2748 rows and 1641 variables.
## --> Categorical key variables: i14, p4_1
## ----------------------------------------------------------------------## Information on categorical key variables:
##
## Reported is the number, mean size and size of the smallest category >0 for recoded variables.
## In parenthesis, the same statistics are shown for the unmodified data.
## Note: NA (missings) are counted as seperate categories!## Key Variable Number of categories Mean size Size of smallest (>0)
## i14 3 (3) 508.000 (508.000) 370 (370)
## p4_1 5 (5) 219.500 (219.500) 4 (4)## ----------------------------------------------------------------------## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 0 (0.000%)
## - 3-anonymity: 0 (0.000%)
## - 5-anonymity: 0 (0.000%)
##
## ----------------------------------------------------------------------Show values of key variable of records that violate k-anonymity
mydata <- labelDataset(mydata)
notAnon <- sdcInitial@risk$individual[,2] < 2 # for 2-anonymity
mydata[notAnon,selectedKeyVars]## # A tibble: 0 x 2
## # ... with 2 variables: sexo <dbl>, grado2015 <dbl>sdcFinal <- localSuppression(sdcInitial)
notAnon2 <- sdcInitial2@risk$individual[,2] < 2 # for 2-anonymity
mydata[notAnon2,selectedKeyVars2]## # A tibble: 0 x 2
## # ... with 2 variables: i14 <dbl+lbl>, p4_1 <dbl+lbl>sdcFinal2 <- localSuppression(sdcInitial2)Open-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("dropout_reasons_otro",
"rp_finance_2a",
"p15a_prop",
"p44c",
"p51a",
"centro_poblado",
"referencia",
"school_fixed",
"p11b",
"p13c1",
"rs_finance_2a",
"q48",
"act_sd_4o",
"act_sd_5o",
"act_sd_6o",
"act_sd_7o",
"act_sd_8o",
"act_sd_9o",
"act_sd_10o",
"act_sd_11o",
"act_sd_12o",
"act_sd_13o",
"act_sd_14o",
"act_sd_15o",
"act_sd_16o",
"act_sd_17o",
"act_sd_18o",
"act_sd_19o",
"act_sd_20o",
"act_sd_21o",
"act_sd_22o",
"act_sd_23o",
"act_sd_24o",
"act_sd_1o",
"act_sd_2o",
"act_sd_3o",
"act_wed_4o",
"act_wed_5o",
"act_wed_6o",
"act_wed_7o",
"act_wed_8o",
"act_wed_9o",
"act_wed_10o",
"act_wed_11o",
"act_wed_12o",
"act_wed_13o",
"act_wed_14o",
"act_wed_15o",
"act_wed_16o",
"act_wed_17o",
"act_wed_18o",
"act_wed_19o",
"act_wed_20o",
"act_wed_21o",
"act_wed_22o",
"act_wed_23o",
"act_wed_24o",
"act_wed_1o",
"act_wed_2o",
"act_wed_3o",
"switcher_2016_otro",
"switcher_2015_otro",
"switcher_2014_otro",
"p35a1",
"s4p11b_2015",
"s4p13c1_2015",
"s5p18_2015")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata <- mydata[!names(mydata) %in% "dropout_reasons_otro"]
mydata <- mydata[!names(mydata) %in% "rp_finance_2a"]
mydata <- mydata[!names(mydata) %in% "p15a_prop"]
mydata <- mydata[!names(mydata) %in% "p44c"]
mydata <- mydata[!names(mydata) %in% "p51a"]
mydata <- mydata[!names(mydata) %in% "centro_poblado"]
mydata <- mydata[!names(mydata) %in% "referencia"]
mydata <- mydata[!names(mydata) %in% "school_fixed"]
mydata <- mydata[!names(mydata) %in% "p11b"]
mydata <- mydata[!names(mydata) %in% "p13c1"]
mydata <- mydata[!names(mydata) %in% "rs_finance_2a"]
mydata <- mydata[!names(mydata) %in% "q48"]
mydata <- mydata[!names(mydata) %in% "act_sd_4o"]
mydata <- mydata[!names(mydata) %in% "act_sd_5o"]
mydata <- mydata[!names(mydata) %in% "act_sd_6o"]
mydata <- mydata[!names(mydata) %in% "act_sd_7o"]
mydata <- mydata[!names(mydata) %in% "act_sd_8o"]
mydata <- mydata[!names(mydata) %in% "act_sd_9o"]
mydata <- mydata[!names(mydata) %in% "act_sd_10o"]
mydata <- mydata[!names(mydata) %in% "act_sd_11o"]
mydata <- mydata[!names(mydata) %in% "act_sd_12o"]
mydata <- mydata[!names(mydata) %in% "act_sd_13o"]
mydata <- mydata[!names(mydata) %in% "act_sd_14o"]
mydata <- mydata[!names(mydata) %in% "act_sd_15o"]
mydata <- mydata[!names(mydata) %in% "act_sd_16o"]
mydata <- mydata[!names(mydata) %in% "act_sd_17o"]
mydata <- mydata[!names(mydata) %in% "act_sd_18o"]
mydata <- mydata[!names(mydata) %in% "act_sd_19o"]
mydata <- mydata[!names(mydata) %in% "act_sd_20o"]
mydata <- mydata[!names(mydata) %in% "act_sd_21o"]
mydata <- mydata[!names(mydata) %in% "act_sd_22o"]
mydata <- mydata[!names(mydata) %in% "act_sd_23o"]
mydata <- mydata[!names(mydata) %in% "act_sd_24o"]
mydata <- mydata[!names(mydata) %in% "act_sd_1o"]
mydata <- mydata[!names(mydata) %in% "act_sd_2o"]
mydata <- mydata[!names(mydata) %in% "act_sd_3o"]
mydata <- mydata[!names(mydata) %in% "act_wed_4o"]
mydata <- mydata[!names(mydata) %in% "act_wed_5o"]
mydata <- mydata[!names(mydata) %in% "act_wed_6o"]
mydata <- mydata[!names(mydata) %in% "act_wed_7o"]
mydata <- mydata[!names(mydata) %in% "act_wed_8o"]
mydata <- mydata[!names(mydata) %in% "act_wed_9o"]
mydata <- mydata[!names(mydata) %in% "act_wed_10o"]
mydata <- mydata[!names(mydata) %in% "act_wed_11o"]
mydata <- mydata[!names(mydata) %in% "act_wed_12o"]
mydata <- mydata[!names(mydata) %in% "act_wed_13o"]
mydata <- mydata[!names(mydata) %in% "act_wed_14o"]
mydata <- mydata[!names(mydata) %in% "act_wed_15o"]
mydata <- mydata[!names(mydata) %in% "act_wed_16o"]
mydata <- mydata[!names(mydata) %in% "act_wed_17o"]
mydata <- mydata[!names(mydata) %in% "act_wed_18o"]
mydata <- mydata[!names(mydata) %in% "act_wed_19o"]
mydata <- mydata[!names(mydata) %in% "act_wed_20o"]
mydata <- mydata[!names(mydata) %in% "act_wed_21o"]
mydata <- mydata[!names(mydata) %in% "act_wed_22o"]
mydata <- mydata[!names(mydata) %in% "act_wed_23o"]
mydata <- mydata[!names(mydata) %in% "act_wed_24o"]
mydata <- mydata[!names(mydata) %in% "act_wed_1o"]
mydata <- mydata[!names(mydata) %in% "act_wed_2o"]
mydata <- mydata[!names(mydata) %in% "act_wed_3o"]
mydata <- mydata[!names(mydata) %in% "switcher_2016_otro"]
mydata <- mydata[!names(mydata) %in% "switcher_2015_otro"]
mydata <- mydata[!names(mydata) %in% "switcher_2014_otro"]
mydata <- mydata[!names(mydata) %in% "p35a1"]
mydata <- mydata[!names(mydata) %in% "s4p11b_2015"]
mydata <- mydata[!names(mydata) %in% "s4p13c1_2015"]
mydata <- mydata[!names(mydata) %in% "s5p18_2015"]GPS data: Displace
mydata <- mydata[!names(mydata) %in% "geo_pointsaltitude"]
mydata <- mydata[!names(mydata) %in% "geo_points1"]Save processed data in Stata and SPSS format
haven::write_dta(mydata, paste0(filename, "_PU.dta"))
mydata <- haven::read_dta(paste0(filename, "_PU.dta"))
colnames(mydata) <- gsub('^_', '', colnames(mydata))
mydata[is.na(mydata)] <- NA
names(mydata)[names(mydata) == "ANEXO"] <- "ANEXO1"
names(mydata)[names(mydata) == "COD_MOD"] <- "cod_mod_spss"
haven::write_sav(mydata, paste0(filename, "_PU.sav"))
# Add report title dynamically
title_var <- paste0("DOL-ILAB SDC - ", filename)