rm(list=ls(all=t))
filename <- "DFM_InDepth20162017_ParentsStudents_NOPII" # !!!Update filename
functions_vers <- "functions_1.7.R" # !!!Update helper functions file
source (functions_vers)
## --------
## This is sdcMicro v5.6.0.
## For references, please have a look at citation('sdcMicro')
## Note: since version 5.0.0, the graphical user-interface is a shiny-app that can be started with sdcApp().
## Please submit suggestions and bugs at: https://github.com/sdcTools/sdcMicro/issues
## --------
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: sp
## Checking rgeos availability: TRUE
##
## Attaching package: 'raster'
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##
## select
## The following object is masked from 'package:sdcMicro':
##
## freq
## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/Usuario/Documents/R/win-library/3.6/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/Usuario/Documents/R/win-library/3.6/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## Overwritten PROJ_LIB was C:/Users/Usuario/Documents/R/win-library/3.6/rgdal/proj
## Loading required package: spatstat.data
## Loading required package: spatstat.geom
## spatstat.geom 2.1-0
##
## Attaching package: 'spatstat.geom'
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## area, rotate, shift
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##
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## spatstat.core 2.1-2
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## spatstat.linnet 2.1-1
##
## spatstat 2.1-0 (nickname: 'Comedic violence')
## For an introduction to spatstat, type 'beginner'
## rgeos version: 0.5-5, (SVN revision 640)
## GEOS runtime version: 3.8.0-CAPI-1.13.1
## Linking to sp version: 1.4-5
## Polygon checking: TRUE
##
## Spatial Point Pattern Analysis Code in S-Plus
##
## Version 2 - Spatial and Space-Time analysis
##
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## zoom
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## Loading required package: spam
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## Loading required package: grid
## Spam version 2.6-0 (2020-12-14) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
##
## Attaching package: 'spam'
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## See https://github.com/NCAR/Fields for
## an extensive vignette, other supplements and source code
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## tribble
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
# !!!Include any Direct PII variables
dropvars <- c("student_name",
"p_no_guardian_name",
"sap_househead_name",
"consent_signature",
"consent_signature_paper",
"hh_name1",
"hh_name2",
"hh_lastname1",
"hh_lastname2",
"hh_dni",
"nombres",
"i31",
"address",
"reference",
"i32",
"random_audio_hh",
"jefe_nom",
"jefe_priape",
"jefe_segape",
"bf1_fname",
"bf1_sname",
"bf1_flastname",
"bf1_slastname",
"bf2_fname",
"bf2_sname",
"bf2_flastname",
"bf2_slastname",
"bf3_fname",
"bf3_sname",
"bf3_flastname",
"bf3_slastname",
"DNI",
"devicephonenum",
"student_fullname",
"no_guardian_name",
"guard_name",
"guard_app",
"guard_apm",
"guard_nn",
"p4a",
"audio1_student",
"audio2_student",
"audio3_student",
"ss_phone",
"ss_photo")
mydata <- mydata[!names(mydata) %in% dropvars]
# !!!Replace vector in "variables" field below with relevant variable names
mydata <- encode_direct_PII_team (variables=c("id_encuestador"))
## [1] "Frequency table before encoding"
## id_encuestador. Seleccione el nombre del encuestador
## Missing-MINEDU
## 4111
## [1] "Frequency table after encoding"
## id_encuestador. Seleccione el nombre del encuestador
## 1
## 4111
# !!! Removed as it contains identifying information
dropvars <- c("DIGITA","i4")
mydata <- mydata[!names(mydata) %in% dropvars]
# !!!Include relevant variables, but check their population size first to confirm they are <100,000
locvars <- c("p_cod_mod2016_admin",
"cod_mod_2016",
"cod_mod_2015",
"cod_mod_app",
"codlocal",
"distrito",
"CODLOC",
"COD_MOD_2015",
"COD_MOD_2016",
"CODLOCAL_2016",
"COD_MOD_2017",
"CODLOCAL_2017",
"cod_mod2016_admin",
"cole2016_admin")
mydata <- encode_location (variables= locvars, missing=999999)
## [1] "Frequency table before encoding"
## p_cod_mod2016_admin.
## 207449 207795 207894 207985 208058 208348 208538 208546 208561 208587
## 1 2 1 2 4 2 3 3 7 1
## 208694 208736 209304 209387 209510 209528 209536 209908 209916 209924
## 1 5 3 4 1 1 6 19 7 2
## 209940 209965 209973 210260 215632 233056 236109 236117 236174 236224
## 6 9 3 1 8 2 3 8 11 2
## 236364 236778 245647 245654 245662 245670 245688 245696 245704 305656
## 3 11 2 1 17 6 10 5 1 7
## 314500 317131 317214 317289 317305 317313 317370 317453 317479 317560
## 1 3 2 1 1 3 1 2 2 5
## 317610 317941 318063 318089 318287 318352 318782 318824 318949 319004
## 1 1 4 4 1 3 1 1 1 1
## 319020 319061 319145 319160 319285 320655 322453 322479 322685 322974
## 1 1 2 2 4 1 3 15 1 1
## 323345 323378 325449 325464 325472 325480 325498 325506 325548 325555
## 3 14 6 13 16 1 1 5 1 2
## 325563 325589 325605 325613 325647 325662 325670 325704 327650 328039
## 10 6 2 2 9 1 7 9 1 2
## 328047 328260 328443 328468 328484 328518 328526 329029 329128 329151
## 3 2 1 1 1 4 5 3 1 1
## 329243 329573 329755 329805 330464 333666 333690 334649 334656 334664
## 1 14 10 4 16 6 3 4 2 8
## 334672 334680 334706 334714 334722 334730 334748 334847 334920 334987
## 12 1 8 2 5 3 9 2 1 8
## 335042 335091 335224 336495 336537 336545 336560 336586 336594 336610
## 4 14 1 2 4 3 8 5 2 3
## 336628 336636 337436 337568 337592 337733 337741 337766 338129 338228
## 15 5 13 5 4 2 3 2 1 2
## 338301 338343 338517 338640 338665 338848 339051 339317 339432 339499
## 3 2 6 5 3 1 7 1 1 1
## 339606 339804 340224 340281 340299 340315 340349 340372 340380 340398
## 5 1 8 1 2 10 7 1 6 1
## 340414 340422 340463 343566 405324 432773 432906 433227 433235 433276
## 3 2 3 2 1 3 1 4 1 6
## 433490 433540 433680 433821 433961 434019 434076 434159 434191 434282
## 6 4 4 8 5 4 2 3 3 3
## 434464 434480 434498 434506 434548 434597 434829 436170 436212 436287
## 3 4 4 2 3 2 5 1 8 1
## 436303 436360 436444 436451 436493 436543 436584 436634 436642 436725
## 2 5 5 6 4 2 1 4 1 3
## 436766 436782 437210 437228 437236 437244 437251 437269 437277 437285
## 5 1 7 28 17 8 2 2 12 10
## 437319 437335 437343 437350 437368 437400 437509 437525 437541 437707
## 12 4 12 6 2 4 2 1 2 3
## 437715 437723 437731 437749 437772 449868 466383 466730 468488 468611
## 7 2 3 1 1 7 2 15 2 2
## 469205 469700 481853 481903 482042 482091 488619 488635 493239 493544
## 2 8 6 9 1 6 9 11 1 12
## 495259 495812 496166 496844 497024 499699 500124 500348 501411 501502
## 3 4 12 3 2 25 1 19 2 3
## 501601 501676 501809 502435 502633 504993 505149 508903 510305 510800
## 10 6 4 2 19 5 2 5 1 1
## 513614 516674 519645 520486 521179 522318 522862 523423 523464 523621
## 2 3 4 4 4 1 2 4 3 2
## 523662 523761 526301 528380 534321 535823 536029 536128 536151 536326
## 1 5 4 4 1 4 3 8 1 16
## 546002 555847 555862 555946 556266 556472 556548 556571 565119 565143
## 19 1 10 5 2 2 2 12 3 2
## 565200 565234 565267 566141 566158 566414 566430 566455 566463 566471
## 2 8 2 20 2 4 14 4 3 12
## 567743 567750 567768 578260 578278 578286 578336 578351 578401 578443
## 1 9 1 1 2 10 1 9 8 2
## 578518 578526 578534 578542 579151 581710 581728 581736 581744 581777
## 11 9 23 8 12 2 2 14 3 2
## 581876 581892 581900 581991 582114 582122 582148 582163 582254 582304
## 5 2 6 3 1 1 1 3 3 4
## 582312 582387 582403 582411 582833 582866 582890 582932 582981 583013
## 5 13 11 9 12 4 15 5 13 10
## 583088 583328 583476 583534 583567 583591 583922 591131 591164 591198
## 7 4 6 1 3 25 4 1 2 8
## 598581 599159 599365 601492 603878 605469 605501 607143 607424 607556
## 9 2 12 8 9 17 3 1 1 5
## 607697 616185 628404 628602 628842 629261 629295 632299 632471 639112
## 1 9 2 2 3 2 1 3 1 3
## 639922 642801 642892 643692 643783 643817 644690 644880 647172 649129
## 5 3 3 4 2 7 2 3 11 1
## 649947 650002 650036 652081 656447 659623 659698 659722 659896 659953
## 5 1 9 1 1 1 3 5 12 4
## 662940 662957 663005 663013 663096 663112 663120 663138 663526 663534
## 2 1 3 1 6 10 3 10 1 4
## 663542 663559 663682 663971 664490 664508 664698 664722 664748 664920
## 6 10 4 10 1 1 14 1 17 2
## 665265 665489 691931 692434 693499 693622 693655 694547 694562 694570
## 1 9 5 3 12 13 4 2 3 10
## 694588 694596 694604 697557 703124 703215 703223 703231 703249 703736
## 11 2 10 3 3 11 12 1 10 2
## 703744 703751 704072 704312 704445 704460 704965 705053 705129 705160
## 9 16 1 2 3 7 1 8 5 1
## 705475 705772 725523 725770 725861 728055 728196 728717 730515 732321
## 1 1 1 7 4 2 4 9 2 1
## 732347 732495 735035 739367 743773 743815 743831 744540 744557 744573
## 1 6 2 1 11 5 12 3 6 2
## 751230 759399 759555 759613 762120 762468 762773 762856 762864 762880
## 1 5 1 10 1 3 9 12 1 16
## 762906 762914 763151 764035 764076 764134 764779 764936 765297 765305
## 9 4 6 1 2 1 6 6 13 10
## 765313 765321 765396 765412 765859 772970 773788 774026 774455 774679
## 2 1 5 4 8 1 5 16 4 2
## 774703 775312 775833 775874 777110 777144 777656 777680 777995 778027
## 10 8 2 2 3 2 23 14 8 1
## 778233 778738 778795 779041 780759 780767 780791 781278 781302 781351
## 11 11 9 1 1 1 2 5 9 4
## 781369 781385 781831 781930 782102 782664 785097 820407 821082 824003
## 21 1 3 1 6 9 11 4 9 1
## 824813 825752 826081 828962 832253 832279 832287 832303 832311 832337
## 3 1 1 1 1 7 2 5 7 1
## 834853 835058 846048 847087 855791 869198 869248 870931 871160 872127
## 3 7 6 2 2 10 1 4 1 1
## 872515 874198 874214 875476 879791 879817 883884 884510 884528 884544
## 12 1 10 1 13 1 1 5 2 12
## 884551 884593 884627 885517 900670 900704 900761 900910 900977 901033
## 9 2 1 1 1 1 8 10 3 11
## 901066 901082 901413 901587 915256 927814 928200 933598 1007160 1008440
## 1 2 1 1 2 11 1 8 1 20
## 1008929 1008960 1009844 1010040 1010149 1010180 1033729 1034016 1034685 1039676
## 2 15 6 2 8 1 2 3 1 1
## 1041516 1041557 1041631 1045111 1045434 1045715 1045798 1046226 1048990 1049493
## 1 2 15 5 3 1 10 1 3 1
## 1053628 1053669 1054154 1054196 1054238 1054352 1054394 1054436 1056902 1063106
## 14 13 10 15 11 5 3 2 8 8
## 1063148 1063221 1063304 1064989 1066026 1068238 1069954 1070036 1070077 1070390
## 10 10 7 3 7 7 3 6 10 5
## 1071919 1072040 1072727 1074301 1075779 1080068 1080258 1082874 1083633 1083674
## 8 1 2 5 1 7 6 1 2 2
## 1083716 1083815 1084508 1084987 1085851 1085976 1087295 1088400 1099654 1194265
## 4 10 11 1 2 2 3 5 1 11
## 1194380 1194810 1195189 1196526 1223023 1238229 1240720 1241082 1241454 1242908
## 18 4 10 3 8 5 9 1 1 1
## 1247832 1248392 1248509 1254192 1258334 1258649 1261742 1264340 1264670 1266840
## 2 11 1 1 1 3 3 1 3 2
## 1272822 1278662 1279124 1279363 1308907 1309392 1309574 1313444 1322593 1330315
## 4 1 14 1 4 2 8 3 3 1
## 1332220 1335637 1336072 1346675 1349448 1351410 1354091 1362318 1370345 1370378
## 6 1 1 1 1 2 1 1 2 1
## 1375211 1376870 1380740 1381078 1381110 1381144 1381342 1381375 1381599 1381862
## 2 1 14 1 1 2 4 1 8 1
## 1381896 1382829 1385251 1386168 1386226 1386234 1390137 1392893 1393453 1398148
## 2 4 1 13 2 10 1 1 1 6
## 1401801 1411438 1420694 1423615 1431667 1438027 1438035 1453232 1464668 1469675
## 1 12 3 10 1 2 2 1 1 1
## 1473511 1473644 1474600 1474964 1475011 1475201 1475284 1476258 1476464 1481514
## 1 1 2 12 9 12 10 1 7 1
## 1481720 1482975 1483627 1487339 1489822 1492255 1493964 1495365 1495407 1496314
## 1 1 1 1 1 1 1 17 5 1
## 1496355 1497007 1497056 1497551 1499748 1499961 1500354 1501188 1501451 1505494
## 2 16 10 1 1 1 4 2 8 14
## 1507094 1507250 1507276 1507532 1509108 1509496 1511351 1512789 1515360 1520279
## 12 12 1 12 1 2 13 1 1 1
## 1520287 1528520 1529981 1536994 1541879 1573328 1575323 1607944 1640556 1641521
## 4 1 1 1 1 1 1 1 9 10
## 1661271 1697234 1699933 1701002 <NA>
## 15 2 1 1 443
## [1] "Frequency table after encoding"
## p_cod_mod2016_admin.
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
## 2 2 9 9 1 1 2 4 1 9 13 10 9 2 5 6 3
## 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
## 2 10 5 7 1 1 2 1 19 4 1 4 11 1 25 4 8
## 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435
## 1 2 10 4 3 6 1 4 12 3 14 1 3 12 8 4 1
## 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452
## 2 1 7 6 6 4 1 4 12 11 6 10 15 1 3 13 11
## 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469
## 5 7 20 1 1 1 7 1 2 3 1 2 1 16 7 5 2
## 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486
## 1 1 16 10 1 8 1 7 1 10 2 12 6 16 2 3 8
## 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503
## 5 1 5 14 2 4 4 1 8 1 3 10 1 2 1 15 3
## 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520
## 4 5 1 9 8 4 1 9 11 2 6 6 4 2 1 6 2
## 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537
## 4 12 5 3 1 1 7 2 3 3 1 5 1 1 1 1 1
## 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554
## 1 6 1 2 3 1 1 10 14 4 10 3 1 1 4 1 2
## 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571
## 1 11 1 2 9 1 1 2 3 11 1 1 1 1 7 3 1
## 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588
## 2 1 1 5 9 2 2 9 3 1 10 15 9 10 1 1 4
## 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605
## 1 4 3 4 19 2 1 8 4 7 4 3 1 1 1 2 10
## 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622
## 12 1 13 14 7 2 4 1 10 6 3 2 1 3 2 4 3
## 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639
## 14 1 8 1 5 3 19 1 12 3 12 16 2 1 1 8 3
## 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 1 2 2 6 6 2 5 2 5 7 5 16 8 9 5 1 3
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673
## 3 12 8 3 1 16 1 5 10 5 3 15 6 4 12 1 8
## 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690
## 6 1 9 4 3 6 6 2 3 2 3 3 13 1 3 2 8
## 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707
## 1 19 5 25 1 2 6 2 28 1 1 4 2 5 3 1 4
## 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724
## 5 2 4 9 3 1 10 1 1 2 1 2 1 1 10 11 3
## 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741
## 8 4 3 2 1 1 1 3 11 1 2 1 10 1 1 6 1
## 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758
## 1 3 5 3 17 2 2 2 3 4 3 8 4 1 3 6 1
## 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775
## 1 2 1 1 2 7 1 5 8 1 2 1 12 1 5 9 10
## 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792
## 4 2 1 2 1 1 3 2 3 5 1 1 4 9 3 1 1
## 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809
## 15 1 1 23 2 12 17 13 2 7 5 1 2 1 17 2 1
## 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826
## 6 1 4 3 6 1 1 10 3 2 12 7 3 9 1 5 2
## 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843
## 8 3 8 9 13 12 1 17 2 1 6 15 1 1 8 8 1
## 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860
## 3 1 2 2 1 5 4 1 2 3 6 1 1 4 12 5 12
## 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877
## 12 8 8 11 2 9 1 2 1 1 2 1 12 3 1 2 2
## 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894
## 7 2 4 1 1 2 2 2 1 1 5 10 1 9 4 1 9
## 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911
## 3 4 2 2 5 5 11 11 2 9 4 1 5 6 5 1 1
## 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 7 4 1 2 10 2 4 2 1 1 10 7 12 12 1 23 3
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945
## 1 5 8 1 2 1 8 1 2 11 2 2 3 10 5 14 3
## 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962
## 2 1 1 7 1 3 1 2 4 5 3 20 1 2 11 8 21
## 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979
## 3 11 3 1 4 3 5 8 1 13 2 10 2 4 11 18 1
## 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996
## 8 5 1 5 9 10 4 4 1 1 10 1 2 3 9 1 4
## 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013
## 3 4 3 2 1 2 1 1 1 2 1 1 4 2 1 13 3
## 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030
## 4 15 6 2 2 9 1 13 14 6 4 3 1 2 3 8 1
## 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047
## 2 4 3 10 1 1 4 1 2 4 1 10 2 10 5 2 1
## 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064
## 3 3 10 2 2 14 1 1 2 3 14 9 2 12 1 2 5
## 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081
## 1 1 3 1 10 12 1 1 1 4 6 6 11 3 17 12 1
## 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098
## 2 2 9 1 10 2 3 3 11 2 2 2 2 6 1 16 2
## 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115
## 7 1 1 2 1 5 11 1 8 2 10 9 10 1 6 3 1
## 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132
## 7 5 2 2 14 7 1 2 8 14 12 3 1 13 1 3 1
## 1133 1134 1135 1136 1137 1138 <NA>
## 1 8 1 15 2 1 443
## [1] "Frequency table before encoding"
## cod_mod_2016.
## 99 207449 207795 207894 207985 208058 208348 208538 208546 208561
## 5 2 2 1 2 4 2 4 3 7
## 208587 208694 208736 209304 209387 209510 209528 209536 209908 209916
## 1 1 6 3 4 2 1 6 19 11
## 209924 209940 209965 209973 210260 215632 233056 236109 236117 236174
## 3 6 9 3 2 8 2 4 9 11
## 236224 236364 236778 245647 245654 245662 245670 245688 245696 245704
## 2 4 11 3 1 19 7 11 7 1
## 305656 314500 317131 317214 317289 317305 317313 317370 317453 317479
## 7 2 6 2 3 1 4 1 2 2
## 317560 317610 317941 318063 318089 318287 318352 318741 318782 318824
## 6 1 1 4 4 1 3 2 1 1
## 318949 319004 319020 319061 319145 319160 319285 320655 322453 322479
## 1 1 1 1 2 2 4 1 3 16
## 322685 322974 323345 323378 325449 325464 325472 325480 325498 325506
## 2 3 3 14 7 14 18 1 1 6
## 325548 325555 325563 325589 325605 325613 325647 325662 325670 325704
## 1 2 12 7 3 2 9 1 9 12
## 327650 328039 328047 328260 328401 328443 328468 328484 328518 328526
## 3 3 3 2 1 2 1 1 5 5
## 329029 329128 329151 329243 329573 329755 329805 330464 333666 333690
## 3 1 1 1 20 10 4 16 6 3
## 334094 334649 334656 334664 334672 334680 334706 334714 334722 334730
## 1 4 2 8 14 1 10 3 6 3
## 334748 334847 334920 334987 335042 335091 335224 336495 336537 336545
## 10 2 1 9 4 15 1 2 4 3
## 336560 336586 336594 336610 336628 336636 337436 337568 337592 337733
## 10 5 2 3 15 5 15 5 4 4
## 337741 337766 338129 338228 338301 338343 338517 338640 338665 338848
## 3 3 1 2 4 2 6 5 3 1
## 339051 339317 339432 339499 339606 339804 340224 340281 340299 340315
## 7 1 1 1 6 1 10 1 3 10
## 340349 340372 340380 340398 340414 340422 340448 340463 343566 405324
## 9 2 10 1 5 2 1 4 2 1
## 432773 432906 433227 433235 433276 433490 433540 433680 433821 433961
## 3 1 5 2 6 6 4 6 8 5
## 434019 434076 434159 434191 434282 434464 434480 434498 434506 434548
## 4 2 3 3 3 3 4 5 2 3
## 434597 434829 436170 436212 436287 436303 436360 436444 436451 436493
## 2 5 1 8 1 2 5 5 6 5
## 436543 436584 436634 436642 436725 436766 436782 437210 437228 437236
## 2 1 4 1 3 6 1 7 31 20
## 437244 437251 437269 437277 437285 437319 437335 437343 437350 437368
## 14 2 2 12 11 13 5 13 6 2
## 437400 437509 437525 437541 437707 437715 437723 437731 437749 437772
## 8 2 1 2 3 7 2 3 1 1
## 449868 466383 466730 468488 468611 469205 469700 481853 481903 482042
## 9 2 17 2 2 2 10 6 9 1
## 482091 488619 488635 493239 493544 493635 494732 495259 495812 496166
## 6 10 11 1 17 1 1 3 4 14
## 496844 497024 499699 500124 500348 500611 501411 501502 501601 501676
## 3 3 26 1 21 2 2 4 10 11
## 501809 502435 502633 504993 505149 508903 510305 510800 513614 516674
## 4 2 20 5 2 5 1 1 2 3
## 519645 520486 521179 522318 522862 523423 523464 523621 523662 523761
## 4 4 4 2 2 4 3 2 1 5
## 526301 527374 528380 534321 535823 536029 536128 536151 536326 546002
## 4 1 4 1 4 3 8 1 17 19
## 555847 555862 555946 556266 556357 556472 556548 556571 565119 565143
## 1 10 5 2 1 3 2 13 4 2
## 565200 565234 565267 566141 566158 566414 566430 566455 566463 566471
## 2 11 2 22 2 4 18 4 3 14
## 567743 567750 567768 578260 578278 578286 578336 578351 578401 578443
## 1 10 1 3 4 13 1 11 13 2
## 578518 578526 578534 578542 579151 581710 581728 581736 581744 581777
## 12 11 25 8 12 2 2 17 4 2
## 581876 581892 581900 581991 582114 582122 582148 582163 582254 582304
## 6 2 9 4 1 1 1 3 3 4
## 582312 582387 582403 582411 582833 582866 582890 582932 582981 583013
## 5 13 11 9 12 4 15 5 13 10
## 583088 583328 583476 583534 583567 583591 583922 591131 591164 591198
## 7 4 6 1 5 26 4 2 2 11
## 598581 599159 599365 601492 601708 603878 605469 605501 607143 607424
## 11 2 15 9 1 11 17 4 2 1
## 607556 607697 616185 628404 628602 628842 629261 629295 632299 632471
## 5 1 12 2 2 3 2 2 3 1
## 639112 639922 642801 642892 643692 643783 643817 644690 644880 647172
## 3 5 3 3 5 2 7 4 3 11
## 649129 649202 649947 650002 650036 652081 656447 659623 659698 659722
## 2 1 6 1 10 1 1 1 3 6
## 659896 659953 662940 662957 663005 663013 663096 663112 663120 663138
## 12 4 2 2 3 1 9 10 3 10
## 663526 663534 663542 663559 663682 663971 664292 664490 664508 664698
## 1 4 6 11 5 14 1 1 1 15
## 664706 664722 664748 664920 665265 665489 691931 692434 693499 693622
## 1 1 21 2 2 9 7 10 12 13
## 693655 694547 694562 694570 694588 694596 694604 697557 703124 703215
## 4 3 3 12 14 2 11 3 3 11
## 703223 703231 703249 703256 703736 703744 703751 704072 704312 704445
## 12 1 11 1 2 9 16 1 2 3
## 704460 704965 705053 705129 705160 705475 705772 725523 725770 725861
## 7 1 9 5 1 1 1 1 7 4
## 728055 728196 728717 730515 732321 732347 732495 735035 739367 743773
## 2 4 12 2 1 1 6 2 1 12
## 743815 743831 744540 744557 744573 751230 759399 759555 759613 762120
## 5 13 3 6 2 1 6 1 12 1
## 762468 762773 762856 762864 762880 762906 762914 763151 764035 764076
## 3 12 13 2 16 13 6 6 1 2
## 764134 764779 764936 765297 765305 765313 765321 765396 765412 765859
## 1 6 7 17 10 2 1 6 4 8
## 772970 773788 774026 774455 774679 774703 775312 775833 775874 777110
## 1 12 18 4 2 13 8 3 3 3
## 777144 777656 777680 777995 778027 778233 778738 778795 779041 779868
## 2 26 18 8 1 12 13 9 2 2
## 780759 780767 780791 781278 781302 781351 781369 781385 781831 781930
## 1 1 2 6 9 4 22 1 3 1
## 782102 782664 785097 820407 821082 824003 824813 825752 826081 828962
## 7 10 11 4 10 1 6 1 1 1
## 830620 832253 832279 832287 832303 832311 832337 834853 835058 846048
## 1 1 7 2 5 7 1 3 7 7
## 847087 855791 869198 869248 870931 871160 872127 872515 874198 874214
## 2 2 10 1 4 1 1 13 1 10
## 875476 879791 879817 883884 884510 884528 884544 884551 884593 884627
## 1 13 1 1 5 2 14 11 2 1
## 885517 900670 900704 900761 900910 900977 901033 901066 901082 901413
## 1 1 1 10 11 3 11 1 2 1
## 901587 915256 927814 928200 933598 1007160 1008440 1008929 1008960 1009844
## 1 2 12 1 9 1 21 2 16 6
## 1010040 1010149 1010180 1033729 1034016 1034685 1039676 1041516 1041557 1041631
## 2 10 1 2 4 1 1 1 2 18
## 1045111 1045277 1045434 1045715 1045798 1046226 1048990 1049493 1053628 1053669
## 7 1 4 1 10 1 3 1 14 14
## 1054154 1054196 1054238 1054352 1054394 1054436 1056902 1063106 1063148 1063221
## 12 15 12 5 4 2 9 9 12 10
## 1063304 1064989 1066026 1068238 1069954 1070036 1070077 1070390 1070481 1071919
## 9 3 8 8 4 6 12 6 1 14
## 1072040 1072727 1074301 1075779 1080068 1080258 1082031 1082874 1083633 1083674
## 2 2 5 1 7 7 2 1 2 2
## 1083716 1083815 1084508 1084987 1085851 1085976 1087295 1088400 1099654 1194265
## 4 11 13 1 3 2 4 5 1 11
## 1194380 1194810 1195189 1195577 1196526 1223023 1225549 1226422 1238229 1240720
## 18 4 11 1 3 9 1 1 5 9
## 1241082 1241454 1242908 1247832 1248392 1248509 1254192 1258334 1258649 1261742
## 1 1 1 3 11 1 1 1 3 3
## 1264340 1264670 1266840 1272822 1278662 1279124 1279363 1308907 1309392 1309574
## 1 4 2 4 1 14 1 4 3 8
## 1313444 1322593 1330315 1332220 1335637 1336072 1346675 1349448 1351410 1354091
## 3 3 1 6 1 2 1 1 2 1
## 1360957 1362318 1370345 1370378 1375211 1376870 1380740 1381078 1381110 1381144
## 1 1 2 1 2 1 14 1 1 2
## 1381342 1381375 1381599 1381862 1381896 1382829 1385251 1386168 1386226 1386234
## 4 1 8 1 2 4 1 14 2 11
## 1390137 1392893 1393453 1398148 1401801 1411438 1420694 1422666 1423615 1431667
## 1 1 2 9 1 16 3 2 12 1
## 1438027 1438035 1453232 1458850 1464668 1469675 1473511 1473644 1474600 1474964
## 2 4 1 1 1 1 1 1 2 15
## 1475011 1475201 1475284 1476258 1476464 1480086 1481514 1481720 1481795 1482975
## 10 12 11 1 7 1 1 1 1 1
## 1483627 1487339 1489822 1492149 1492255 1493964 1495365 1495407 1496314 1496355
## 1 1 1 1 1 1 18 5 1 2
## 1497007 1497056 1497551 1499748 1499961 1500354 1501188 1501451 1505494 1507094
## 16 11 1 1 1 4 2 9 14 13
## 1507250 1507276 1507532 1509108 1509496 1511351 1512789 1513159 1515360 1520279
## 12 1 13 1 2 13 3 1 1 1
## 1520287 1522721 1528520 1529981 1536994 1541879 1573328 1575323 1607944 1640556
## 4 1 1 1 1 1 1 1 1 10
## 1641521 1661271 1697234 1699933 1701002 <NA>
## 10 16 3 1 1 11
## [1] "Frequency table after encoding"
## cod_mod_2016.
## 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931
## 2 1 13 1 7 5 13 4 14 2 1 9 7 1 10 2 4
## 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948
## 2 1 2 9 3 7 10 5 1 2 11 1 2 9 2 8 16
## 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965
## 3 3 2 14 12 1 2 1 5 1 3 2 10 12 3 3 1
## 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982
## 11 3 2 1 6 6 2 31 12 3 10 3 8 10 4 1 6
## 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999
## 14 1 1 8 4 2 6 4 11 1 1 1 1 3 3 1 4
## 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016
## 1 1 2 1 3 1 12 5 1 9 1 3 4 2 1 2 7
## 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033
## 12 1 1 1 2 1 1 15 6 5 3 10 8 11 3 9 13
## 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050
## 2 16 3 2 2 1 4 2 2 2 4 13 1 4 1 3 6
## 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067
## 11 4 8 3 4 1 6 2 1 4 9 7 1 1 1 2 18
## 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084
## 14 2 10 2 3 1 1 5 3 9 1 1 15 12 2 2 2
## 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101
## 3 3 1 2 3 15 2 13 1 1 22 1 2 1 1 4 8
## 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118
## 5 1 15 6 17 2 14 4 10 2 1 1 1 18 1 1 2
## 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135
## 3 14 4 9 13 1 1 12 1 1 6 1 6 10 1 2 3
## 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 3 14 1 1 1 1 1 1 2 1 2 10 13 1 4 2 1
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169
## 3 12 1 1 7 4 1 1 2 11 2 1 26 1 5 1 13
## 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186
## 5 7 15 6 1 18 2 1 10 2 7 2 3 3 9 1 2
## 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203
## 13 10 2 4 1 1 3 1 4 9 1 1 25 1 2 3 1
## 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220
## 4 11 4 11 10 13 14 8 1 3 1 2 3 2 2 14 1
## 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237
## 1 1 5 6 5 1 17 1 2 10 7 20 2 26 1 1 11
## 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254
## 1 1 1 15 11 16 12 1 9 14 2 1 8 12 1 2 4
## 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271
## 1 1 22 2 2 5 3 1 4 5 2 10 1 8 1 2 3
## 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288
## 19 2 1 3 1 4 2 1 4 12 15 5 15 1 4 8 6
## 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305
## 1 2 3 5 1 11 1 2 17 4 10 1 5 13 1 2 1
## 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322
## 8 4 4 2 6 1 21 1 3 1 1 2 12 4 14 4 2
## 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339
## 2 13 1 1 6 12 1 2 1 11 12 4 2 5 1 1 10
## 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356
## 1 1 20 2 3 7 7 4 2 12 4 2 2 1 12 5 2
## 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373
## 14 3 26 1 1 4 1 16 1 10 1 9 1 12 1 3 21
## 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390
## 4 11 10 6 18 4 3 1 9 2 1 2 2 1 4 3 18
## 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407
## 1 11 5 3 1 1 2 1 3 7 7 2 5 1 1 4 4
## 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
## 5 3 1 2 11 7 4 1 2 2 1 3 2 6 3 11 6
## 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441
## 4 5 1 6 13 6 7 3 14 3 17 4 13 7 5 14 17
## 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458
## 1 12 2 16 1 14 2 4 6 16 3 1 13 10 14 11 6
## 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475
## 3 3 11 2 9 2 4 10 2 2 4 9 4 5 1 1 10
## 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492
## 1 5 3 3 2 12 1 1 11 3 5 3 5 1 1 4 1
## 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509
## 11 6 2 1 1 5 16 4 3 3 6 4 3 3 2 6 1
## 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526
## 11 6 1 7 7 1 8 1 1 7 12 1 1 1 4 4 4
## 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543
## 3 9 1 9 1 1 10 2 3 5 6 1 2 2 13 2 3
## 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
## 20 3 1 9 7 10 2 4 4 2 12 21 2 2 4 10 1
## 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577
## 12 1 4 2 5 1 6 13 6 3 1 3 2 2 2 12 6
## 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594
## 5 1 18 5 5 4 4 8 7 4 1 9 3 3 6 9 10
## 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611
## 11 5 6 6 9 11 3 9 1 10 10 6 1 16 4 3 2
## 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628
## 1 12 1 9 1 1 1 2 1 1 3 2 1 1 4 2 4
## 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645
## 4 2 1 3 1 5 1 2 2 4 8 6 9 1 2 2 1
## 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662
## 1 1 11 11 11 3 1 3 1 1 2 2 11 10 13 1 2
## 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679
## 6 7 4 1 3 4 9 7 1 3 13 3 19 2 2 17 2
## 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
## 18 2 5 19 2 1 12 1 11 1 11 11 2 1 2 2 1
## 1697 1698 1699 <NA>
## 7 1 3 11
## [1] "Frequency table before encoding"
## cod_mod_2015.
## 205567 205997 206011 206037 206136 207795 207803 207845 207852 207894
## 3 6 1 3 2 5 1 1 1 1
## 207951 207985 207993 208058 208348 208389 208413 208538 208546 208553
## 2 6 1 12 5 2 2 3 7 2
## 208561 208579 208587 208694 208736 209304 209908 209916 209924 209940
## 13 1 2 1 8 1 14 7 1 5
## 209973 215632 215723 217554 235010 235333 236117 236174 236349 236778
## 1 17 1 1 2 2 7 10 2 9
## 245662 245704 315275 317040 317073 317131 317206 317214 317289 317313
## 12 2 1 3 1 13 3 6 17 7
## 317453 317479 317560 317578 317941 318063 318089 318204 318287 318303
## 4 4 16 1 6 10 6 1 4 1
## 318352 318576 318741 318782 318824 318931 318949 319004 319020 319061
## 3 1 11 3 2 2 5 1 2 4
## 319087 319145 319160 319202 319285 322453 322479 322644 322677 322685
## 1 14 2 2 4 5 10 1 2 14
## 322743 322768 322875 322958 322974 323253 323295 323378 323394 323451
## 1 2 2 1 7 6 2 23 3 2
## 325464 325472 325480 325498 325563 325670 325696 327551 327965 328039
## 10 13 2 1 10 4 1 1 1 13
## 328047 328062 328070 328229 328260 328344 328351 328369 328385 328401
## 6 12 1 2 5 1 2 1 9 6
## 328419 328443 328450 328468 328484 328518 328526 328567 328872 328963
## 5 5 3 1 2 7 12 1 1 1
## 329029 329045 329128 329573 330464 332213 333666 333690 334094 334649
## 4 1 1 14 16 1 12 12 7 6
## 334672 334748 334821 334847 334904 334912 334920 334961 334987 335026
## 9 3 1 7 3 3 2 1 15 1
## 335034 335042 335091 335158 335166 335182 335224 336511 336537 336560
## 1 16 29 2 1 2 2 1 1 11
## 336594 336628 336636 337436 337717 338129 338186 338228 338301 338343
## 1 9 5 12 1 2 1 3 9 8
## 338517 338525 338541 338566 338640 338665 338848 339036 339051 339077
## 16 2 8 1 10 6 3 2 12 1
## 339150 339291 339317 339333 339432 339507 339523 339606 339804 340224
## 1 1 3 1 2 2 1 16 3 3
## 340372 340380 398115 400705 404913 404947 405100 405183 405308 405324
## 1 9 1 1 1 3 1 1 1 2
## 406793 432906 433078 433227 433235 433276 433326 433367 433490 433540
## 3 1 2 10 4 11 2 1 16 14
## 433680 433706 433722 433821 433862 433961 434019 434076 434134 434159
## 14 1 1 15 1 9 12 3 4 6
## 434191 434258 434282 434381 434464 434480 434498 434506 434548 434563
## 12 1 5 1 11 14 10 6 10 2
## 434597 434720 434829 436154 436170 436196 436204 436212 436287 436295
## 14 2 12 1 1 2 2 13 5 5
## 436303 436345 436360 436428 436444 436451 436485 436493 436501 436543
## 2 2 8 3 7 12 3 15 1 4
## 436576 436584 436634 436642 436709 436725 436741 436766 436774 436782
## 3 2 12 1 2 5 1 11 2 1
## 436790 436824 437160 437210 437228 437236 437244 437251 437277 437319
## 1 1 1 7 12 2 6 2 6 10
## 437335 437343 437400 437715 437731 437772 449868 466342 466383 466508
## 8 11 6 1 1 1 5 3 4 1
## 466730 468488 468611 469700 472654 478404 481853 481903 482091 482109
## 16 9 2 6 1 2 3 8 16 1
## 486621 488619 488635 488676 488841 489104 492876 493338 493544 493635
## 1 6 10 1 1 1 1 3 15 1
## 493841 496166 496521 496844 497024 499699 500348 501411 501601 501676
## 5 12 2 13 8 10 22 5 8 7
## 501700 501908 501957 502336 502534 502633 505149 508903 510305 510602
## 1 3 2 2 1 12 1 14 1 6
## 510800 512020 513614 516674 516872 518340 523464 523662 523761 524264
## 13 2 4 7 3 1 13 3 10 1
## 525857 526301 526376 526400 527572 528380 535666 536326 541011 542357
## 3 11 1 1 5 6 1 13 2 1
## 542720 543306 543645 546002 551804 555599 555862 555946 556290 556357
## 1 1 1 19 1 1 8 2 2 1
## 556472 556548 556555 556571 565119 565234 566141 566166 566430 566448
## 3 5 4 11 3 9 17 2 9 1
## 566463 566471 566489 567750 578286 578336 578351 578401 578492 578518
## 3 10 2 6 11 1 8 10 2 11
## 578526 578534 578542 579151 581728 581736 581777 581876 581884 581900
## 7 12 4 11 2 11 2 3 1 8
## 581991 582254 582262 582387 582403 582411 582833 582890 582932 582981
## 5 1 1 12 8 8 11 10 2 10
## 583013 583104 583534 583567 583591 583922 584946 587279 590133 591198
## 11 1 2 4 17 12 7 1 2 9
## 594895 598581 599365 601492 601708 603878 605469 607424 607556 616185
## 1 7 14 8 5 13 15 2 2 10
## 628370 628404 628602 629261 629295 632299 632356 639112 639732 642801
## 1 3 4 5 8 4 1 6 3 1
## 642892 642926 643817 644880 646646 646711 647172 647792 649913 649947
## 1 1 8 2 5 1 10 4 1 9
## 650036 652081 656447 659623 659664 659698 659706 659714 659722 659896
## 7 3 1 2 1 4 1 3 4 9
## 659953 662841 663096 663112 663138 663534 663542 663559 663682 663971
## 4 2 10 7 7 3 5 10 12 14
## 664284 664490 664722 664748 664920 665265 665372 665398 665422 665489
## 1 2 1 15 1 1 2 2 8 7
## 689679 690008 691782 691808 691931 692434 692442 693382 693465 693499
## 2 1 4 3 1 9 1 1 3 10
## 693622 693630 693655 694224 694315 694422 694463 694547 694570 694588
## 13 1 3 2 3 1 3 3 8 10
## 695288 697557 703124 703215 703223 703231 703249 703256 703736 703751
## 1 6 6 8 11 1 7 2 3 16
## 704072 704312 704445 704965 705053 705129 705160 705376 725523 728055
## 1 3 11 1 8 11 3 1 2 3
## 728196 728717 732347 732461 732495 743773 743807 743815 743831 744540
## 4 11 1 1 6 13 2 1 12 6
## 744557 759399 759613 762120 762468 762500 762757 762773 762856 762880
## 11 15 11 3 8 2 1 9 12 14
## 762906 763151 763177 764035 764076 764084 764779 764910 765297 765305
## 4 11 2 3 7 2 15 1 12 8
## 765321 765396 765859 772913 772970 773788 774026 774679 774703 774737
## 2 2 20 1 4 8 16 1 10 1
## 775312 776138 776161 776229 777110 777144 777656 777680 777995 778076
## 14 2 1 1 6 5 19 8 8 3
## 778233 778738 778761 778795 779041 780700 780759 780767 780791 781096
## 9 6 1 6 1 1 1 1 8 2
## 781278 781302 781351 781369 781385 781427 781831 781930 782078 782102
## 7 8 9 14 7 1 5 1 1 5
## 782664 785097 817916 820407 821082 824003 824813 825752 826081 826263
## 8 9 2 4 16 2 15 1 2 2
## 828210 829325 831313 832253 832303 832311 834853 834994 835058 846048
## 1 3 1 1 9 11 6 2 12 15
## 847087 855247 855791 869032 869198 870345 870931 872127 872515 874198
## 3 1 6 1 8 2 4 2 11 3
## 874214 879791 882993 884544 884551 884585 884593 884635 900704 900761
## 8 9 2 15 6 1 2 2 2 9
## 900795 900852 900910 901033 901066 915256 922872 923748 927814 928820
## 2 3 9 8 1 5 1 1 11 1
## 933598 1007160 1007491 1008440 1008960 1010040 1010149 1033729 1041391 1041516
## 7 1 3 14 16 3 7 6 3 1
## 1041631 1045079 1045111 1045277 1045798 1048990 1053628 1053669 1054154 1054196
## 16 3 12 2 8 3 12 11 9 12
## 1054238 1054279 1054352 1056902 1062942 1063023 1063106 1063148 1063221 1063304
## 12 1 4 5 1 1 7 8 8 8
## 1064989 1066026 1068238 1069954 1070036 1070077 1070390 1071257 1071919 1072040
## 4 5 6 1 5 10 11 1 12 2
## 1075779 1080068 1080258 1082031 1083815 1084508 1084987 1085919 1088400 1098102
## 2 15 10 2 10 10 4 2 14 1
## 1099654 1147610 1185644 1194265 1194380 1195189 1195221 1195478 1195841 1195874
## 1 1 1 10 16 13 1 2 1 2
## 1196047 1196526 1210137 1223023 1227461 1229558 1238229 1238708 1240357 1240720
## 2 9 1 6 1 2 14 3 1 9
## 1241082 1241678 1242908 1248350 1248392 1248509 1258649 1261742 1265214 1266840
## 2 1 2 1 9 2 7 7 1 2
## 1268150 1273150 1273275 1279124 1309392 1309574 1313444 1321256 1335637 1349349
## 2 1 1 12 1 8 4 6 1 1
## 1351410 1375211 1376854 1380740 1381078 1381110 1381144 1381219 1381342 1381375
## 3 2 7 13 1 2 2 1 10 1
## 1381599 1381862 1382829 1383199 1386168 1386234 1386283 1390442 1391481 1393453
## 6 1 5 3 12 11 6 1 1 2
## 1398148 1411438 1438027 1438035 1473511 1473644 1474600 1474964 1475011 1475045
## 8 14 2 3 1 1 6 15 10 1
## 1475201 1475250 1475284 1475755 1476258 1476464 1477264 1484443 1486018 1493964
## 11 2 13 2 3 6 1 1 2 3
## 1495365 1495407 1497007 1497056 1497601 1501451 1505494 1507094 1507250 1507276
## 14 4 14 9 1 6 13 12 15 9
## 1507318 1507532 1509496 1511351 1512789 1513951 1520279 1520287 1531359 1574557
## 1 11 4 11 3 1 2 4 2 2
## 1607944 1640556 1641521 1661271
## 2 9 8 13
## [1] "Frequency table after encoding"
## cod_mod_2015.
## 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394
## 1 6 2 10 1 10 1 10 10 2 9 1 2 14 1 12 2
## 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411
## 5 3 3 2 4 16 9 12 2 2 1 1 2 2 11 1 13
## 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428
## 16 2 10 4 12 10 3 2 12 13 16 1 9 1 3 2 4
## 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445
## 14 5 2 1 11 6 2 2 8 8 2 8 1 1 1 6 5
## 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462
## 2 14 1 1 16 10 3 9 2 1 5 9 3 15 11 1 11
## 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479
## 1 2 2 1 3 1 5 6 13 3 1 1 2 9 13 1 11
## 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 1 6 6 1 1 5 3 6 3 3 4 22 2 2 11 7 8
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513
## 11 12 3 1 3 15 6 10 2 1 1 3 1 2 1 2 8
## 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530
## 1 2 9 9 17 8 4 1 10 2 16 2 2 10 10 1 1
## 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547
## 4 8 11 3 13 9 7 5 10 13 2 8 1 1 9 7 5
## 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564
## 8 6 5 1 12 1 2 14 1 3 11 12 1 10 1 2 3
## 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581
## 2 12 1 1 3 8 4 14 1 8 2 2 1 1 2 4 5
## 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598
## 2 3 14 1 1 5 1 1 12 10 13 5 2 7 1 11 2
## 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615
## 2 1 19 8 7 1 2 6 9 1 1 5 3 2 4 10 6
## 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632
## 12 1 3 2 8 7 6 1 1 10 1 12 1 14 12 1 1
## 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649
## 2 1 2 2 2 6 13 14 1 8 4 14 1 9 3 12 2
## 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666
## 1 1 5 1 7 5 1 2 1 2 6 14 5 17 1 1 2
## 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683
## 8 3 10 5 9 7 14 7 16 3 20 8 3 1 4 3 1
## 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700
## 5 9 6 15 2 7 3 7 1 5 9 2 15 2 7 1 15
## 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717
## 12 1 4 3 7 3 16 7 12 14 10 12 1 7 16 1 12
## 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734
## 14 11 8 7 1 8 10 1 8 1 10 6 23 2 1 1 1
## 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751
## 12 7 1 5 1 1 17 2 2 2 12 1 2 8 1 1 8
## 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 1 2 11 2 1 10 3 12 6 1 3 3 2 9 1 3 13
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785
## 1 14 15 1 11 1 3 6 1 1 1 1 13 4 11 4 6
## 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802
## 2 1 1 3 12 1 1 1 1 1 2 14 1 2 11 3 4
## 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819
## 1 11 7 1 3 1 1 9 3 2 2 3 2 11 5 6 8
## 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
## 1 5 15 10 6 1 1 8 1 6 12 1 5 3 1 1 1
## 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853
## 14 6 3 11 15 4 5 11 2 9 1 12 2 1 9 2 2
## 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870
## 2 2 1 5 3 17 2 1 12 1 2 4 2 6 1 2 2
## 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887
## 2 6 2 3 2 1 9 12 1 3 9 2 1 4 2 1 10
## 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904
## 11 2 9 2 8 1 12 9 13 4 8 7 3 1 6 11 2
## 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921
## 1 6 8 8 2 1 11 1 10 1 1 1 8 15 1 9 1
## 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938
## 11 13 2 2 4 2 2 4 8 2 10 1 3 3 9 11 4
## 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955
## 1 1 13 8 12 1 6 1 3 1 6 13 6 2 4 2 12
## 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972
## 3 14 1 8 4 1 4 3 1 8 3 1 2 3 4 3 1
## 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989
## 7 2 13 15 2 1 3 1 1 7 5 13 1 1 9 7 1
## 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006
## 3 10 5 7 6 1 1 7 1 1 8 8 7 8 3 3 10
## 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023
## 5 5 16 8 1 8 6 16 10 7 1 2 1 4 1 1 2
## 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 3 1 14 1 2 12 3 1 1 1 2 16 7 1 6 7 6
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057
## 1 2 2 13 3 2 2 3 16 12 1 1 4 3 1 1 1
## 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074
## 2 15 6 6 2 3 29 2 1 9 11 1 2 1 1 1 11
## 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
## 1 2 2 15 11 2 1 19 1 3 3 11 4 4 1 11 1
## 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108
## 4 5 2 12 12 6 6 2 1 1 1 1 8 9 1 16 3
## 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125
## 1 2 15 2 16 7 4 4 10 14 4 6 2 3 1 1 14
## 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141
## 5 1 3 6 10 15 2 11 1 10 2 2 9 1 1 1
## [1] "Frequency table before encoding"
## cod_mod_app.
## 207449 207795 207894 207985 208058 208348 208538 208546 208561 208587
## 1 2 1 2 4 2 3 3 7 1
## 208694 208736 209304 209387 209510 209528 209536 209908 209916 209924
## 1 5 3 4 1 1 6 19 7 2
## 209940 209965 209973 210260 215632 233056 236109 236117 236174 236224
## 6 9 3 1 8 2 3 8 11 2
## 236364 236778 245647 245654 245662 245670 245688 245696 245704 305656
## 3 11 2 1 17 6 10 5 1 7
## 314500 317131 317214 317289 317305 317313 317370 317453 317479 317560
## 1 3 2 1 1 3 1 2 2 5
## 317610 317941 318063 318089 318287 318352 318782 318824 318949 319004
## 1 1 4 4 1 3 1 1 1 1
## 319020 319061 319145 319160 319285 320655 322453 322479 322685 322974
## 1 1 2 2 4 1 3 15 1 1
## 323345 323378 325449 325464 325472 325480 325498 325506 325548 325555
## 3 14 6 13 16 1 1 5 1 2
## 325563 325589 325605 325613 325647 325662 325670 325704 327650 328039
## 10 6 2 2 9 1 6 9 1 2
## 328047 328260 328443 328468 328484 328518 328526 329029 329128 329151
## 3 2 1 1 1 4 5 3 1 1
## 329243 329573 329755 329805 330464 333666 333690 334649 334656 334664
## 1 14 10 4 16 6 3 4 2 8
## 334672 334680 334706 334714 334722 334730 334748 334847 334920 334987
## 12 1 8 1 5 3 9 2 1 8
## 335042 335091 335224 336495 336537 336545 336560 336586 336594 336610
## 4 14 1 2 4 3 8 5 2 3
## 336628 336636 337436 337568 337592 337733 337741 337766 338129 338228
## 14 5 13 5 4 2 3 2 1 2
## 338301 338343 338517 338640 338665 338848 339051 339317 339432 339499
## 3 2 6 5 3 1 7 1 1 1
## 339606 339804 340224 340281 340299 340315 340349 340372 340380 340398
## 5 1 8 1 2 10 7 1 6 1
## 340414 340422 340463 343566 405324 432773 432906 433227 433235 433276
## 3 2 3 2 1 3 1 4 1 6
## 433490 433540 433680 433821 433961 434019 434076 434159 434191 434282
## 6 4 4 8 5 4 2 3 3 3
## 434464 434480 434498 434506 434548 434597 434829 436170 436212 436287
## 3 4 4 2 3 2 5 1 8 1
## 436303 436360 436444 436451 436493 436543 436584 436634 436642 436725
## 2 5 5 6 4 2 1 4 1 3
## 436766 436782 437210 437228 437236 437244 437251 437269 437277 437285
## 5 1 7 28 17 8 2 2 12 10
## 437319 437335 437343 437350 437368 437400 437509 437525 437541 437707
## 12 4 12 6 2 4 2 1 2 3
## 437715 437723 437731 437749 437772 449868 466383 466730 468488 468611
## 7 2 3 1 1 7 2 15 2 2
## 469205 469700 481853 481903 482042 482091 488619 488635 493239 493544
## 2 8 6 9 1 5 9 11 1 12
## 495259 495812 496166 496844 497024 499699 500124 500348 501411 501502
## 3 4 12 3 2 25 1 19 2 3
## 501601 501676 501809 502435 502633 504993 505149 508903 510305 510800
## 10 6 4 2 19 5 2 5 1 1
## 513614 516674 519645 520486 521179 522318 522862 523423 523464 523621
## 2 3 4 4 4 1 2 4 3 2
## 523662 523761 526301 528380 534321 535823 536029 536128 536151 536326
## 1 5 4 4 1 4 3 8 1 16
## 546002 555847 555862 555946 556266 556472 556548 556571 565119 565143
## 19 1 10 5 2 2 2 12 3 2
## 565200 565234 565267 566141 566158 566414 566430 566455 566463 566471
## 2 8 2 20 2 4 14 4 3 12
## 567743 567750 567768 578260 578278 578286 578336 578351 578401 578443
## 1 9 1 1 2 10 1 9 8 2
## 578518 578526 578534 578542 579151 581710 581728 581736 581744 581777
## 11 9 23 8 12 2 2 14 3 2
## 581876 581892 581900 581991 582114 582122 582148 582163 582254 582304
## 5 2 6 3 1 1 1 3 3 4
## 582312 582387 582403 582411 582833 582866 582890 582932 582981 583013
## 5 13 11 9 12 4 15 5 13 10
## 583088 583328 583476 583534 583567 583591 583922 591131 591164 591198
## 7 4 6 1 3 25 4 1 2 8
## 598581 599159 599365 601492 603878 605469 605501 607143 607424 607556
## 9 2 12 8 9 17 3 1 1 5
## 607697 616185 628404 628602 628842 629261 629295 632299 632471 639112
## 1 9 2 2 3 2 1 3 1 3
## 639922 642801 642892 643692 643783 643817 644690 644880 647172 649129
## 5 3 3 4 2 7 2 3 11 1
## 649947 650002 650036 652081 656447 659623 659698 659722 659896 659953
## 5 1 9 1 1 1 3 5 12 4
## 662940 662957 663005 663013 663096 663112 663120 663138 663526 663534
## 2 1 3 1 6 10 3 10 1 4
## 663542 663559 663682 663971 664490 664508 664698 664722 664748 664920
## 6 10 4 10 1 1 14 1 17 2
## 665265 665489 691931 692434 693499 693622 693655 694547 694562 694570
## 1 9 5 3 12 13 4 2 3 10
## 694588 694596 694604 697557 703124 703215 703223 703231 703249 703736
## 11 2 10 3 3 11 12 1 10 2
## 703744 703751 704072 704312 704445 704460 704965 705053 705129 705160
## 9 16 1 2 3 7 1 8 5 1
## 705475 705772 725523 725770 725861 728055 728196 728717 730515 732321
## 1 1 1 7 4 2 4 9 2 1
## 732347 732495 735035 739367 743773 743815 743831 744540 744557 744573
## 1 6 2 1 11 5 12 3 6 2
## 751230 759399 759555 759613 762120 762468 762773 762856 762864 762880
## 1 5 1 10 1 3 9 12 1 16
## 762906 762914 763151 764035 764076 764134 764779 764936 765297 765305
## 6 4 6 1 2 1 6 6 13 10
## 765313 765321 765396 765412 765859 772970 773788 774026 774455 774679
## 2 1 5 4 7 1 5 16 4 2
## 774703 775312 775833 775874 777110 777144 777656 777680 777995 778027
## 10 8 2 2 3 2 23 14 8 1
## 778233 778738 778795 779041 780759 780767 780791 781278 781302 781351
## 11 11 8 1 1 1 2 5 9 4
## 781369 781385 781831 781930 782102 782664 785097 820407 821082 824003
## 21 1 3 1 6 9 11 4 9 1
## 824813 825752 826081 828962 832253 832279 832287 832303 832311 832337
## 3 1 1 1 1 7 2 5 7 1
## 834853 835058 846048 847087 855791 869198 869248 870931 871160 872127
## 3 7 6 2 2 10 1 4 1 1
## 872515 874198 874214 875476 879791 879817 883884 884510 884528 884544
## 12 1 10 1 13 1 1 5 2 11
## 884551 884593 884627 885517 900670 900704 900761 900910 900977 901033
## 9 2 1 1 1 1 8 10 3 11
## 901066 901082 901413 901587 915256 927814 928200 933598 1007160 1008440
## 1 2 1 1 2 11 1 8 1 20
## 1008929 1008960 1009844 1010040 1010149 1010180 1033729 1034016 1034685 1039676
## 2 15 6 2 8 1 2 3 1 1
## 1041516 1041557 1041631 1045111 1045434 1045715 1045798 1046226 1048990 1049493
## 1 2 15 5 3 1 10 1 3 1
## 1053628 1053669 1054154 1054196 1054238 1054352 1054394 1054436 1056902 1063106
## 14 13 10 15 11 5 3 2 8 8
## 1063148 1063221 1063304 1064989 1066026 1068238 1069954 1070036 1070077 1070390
## 10 10 7 3 7 6 3 6 10 5
## 1071919 1072040 1072727 1074301 1075779 1080068 1080258 1082874 1083633 1083674
## 8 1 2 5 1 7 6 1 2 2
## 1083716 1083815 1084508 1084987 1085851 1085976 1087295 1088400 1099654 1194265
## 4 10 11 1 2 2 3 5 1 11
## 1194380 1194810 1195189 1196526 1223023 1238229 1240720 1241082 1241454 1242908
## 18 4 10 3 8 5 9 1 1 1
## 1247832 1248392 1248509 1254192 1258334 1258649 1261742 1264340 1264670 1266840
## 2 11 1 1 1 3 3 1 3 2
## 1272822 1278662 1279124 1279363 1308907 1309392 1309574 1313444 1322593 1330315
## 4 1 14 1 4 2 8 3 3 1
## 1332220 1335637 1336072 1346675 1349448 1351410 1354091 1362318 1370345 1370378
## 6 1 1 1 1 2 1 1 2 1
## 1375211 1376870 1380740 1381078 1381110 1381144 1381342 1381375 1381599 1381862
## 2 1 14 1 1 2 4 1 8 1
## 1381896 1382829 1385251 1386168 1386226 1386234 1390137 1392893 1393453 1398148
## 2 4 1 12 2 9 1 1 1 6
## 1401801 1411438 1420694 1423615 1431667 1438027 1438035 1453232 1464668 1469675
## 1 11 3 10 1 2 2 1 1 1
## 1473511 1473644 1474600 1474964 1475011 1475201 1475284 1476258 1476464 1481514
## 1 1 2 12 9 12 10 1 7 1
## 1481720 1482975 1483627 1487339 1489822 1492255 1493964 1495365 1495407 1496314
## 1 1 1 1 1 1 1 17 5 1
## 1496355 1497007 1497056 1497551 1499748 1499961 1500354 1501188 1501451 1505494
## 2 16 10 1 1 1 4 2 8 14
## 1507094 1507250 1507276 1507532 1509108 1509496 1511351 1512789 1515360 1520279
## 12 12 1 12 1 2 13 1 1 1
## 1520287 1528520 1529981 1536994 1541879 1573328 1575323 1607944 1640556 1641521
## 4 1 1 1 1 1 1 1 9 10
## 1661271 1697234 1699933 1701002 <NA>
## 15 2 1 1 457
## [1] "Frequency table after encoding"
## cod_mod_app.
## 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411
## 1 8 4 1 17 3 2 1 1 5 3 1 1 7 10 9 1
## 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428
## 6 1 4 3 5 1 1 9 9 1 3 8 1 4 20 1 7
## 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445
## 5 5 7 3 17 1 8 3 2 2 8 1 14 4 2 3 6
## 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462
## 15 1 12 5 8 2 1 9 16 3 3 3 6 2 1 1 2
## 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479
## 2 10 1 11 1 6 2 5 8 1 2 1 6 13 1 25 7
## 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 2 1 5 1 5 1 10 4 2 1 2 7 2 2 5 9 2
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513
## 2 3 16 8 14 7 1 1 13 2 1 8 5 4 6 2 1
## 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530
## 2 11 12 16 1 7 11 11 4 4 11 1 1 2 1 4 2
## 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547
## 4 12 9 3 3 5 1 2 1 2 3 4 3 5 1 25 2
## 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564
## 1 11 12 5 1 11 14 5 1 3 3 3 2 1 1 5 3
## 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581
## 3 9 3 3 1 1 20 1 2 12 1 8 1 8 14 2 2
## 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598
## 11 16 2 3 5 9 2 15 1 3 1 1 10 1 9 1 4
## 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615
## 2 10 3 7 12 11 4 3 4 6 1 2 2 4 1 10 10
## 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632
## 2 8 2 10 2 2 10 1 1 5 8 1 1 1 4 1 3
## 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649
## 2 1 1 12 9 11 1 7 2 1 3 5 2 2 11 1 10
## 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666
## 1 7 1 10 8 5 13 7 6 1 10 2 1 4 7 1 1
## 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683
## 1 7 6 4 10 1 2 1 1 8 2 1 6 2 9 10 19
## 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700
## 2 15 8 2 3 3 9 3 1 3 12 1 1 5 7 8 2
## 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717
## 1 1 3 1 15 23 19 4 1 1 2 10 1 12 1 4 11
## 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734
## 14 2 8 1 4 1 2 1 4 1 1 1 6 6 4 3 9
## 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751
## 1 12 10 12 3 4 4 2 6 9 1 1 1 12 4 9 8
## 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 6 8 2 1 3 2 1 2 13 16 1 2 10 5 2 3 4
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785
## 9 1 4 10 4 3 2 2 13 9 11 14 2 4 8 3 1
## 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802
## 11 3 4 5 17 2 1 3 2 3 1 2 8 4 5 1 6
## 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819
## 1 6 8 1 1 2 10 1 3 3 9 10 1 11 1 3 4
## 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
## 1 2 6 2 1 1 1 11 12 2 19 4 6 1 1 9 1
## 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853
## 12 17 1 6 7 1 2 11 1 9 3 11 2 6 1 2 4
## 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870
## 15 9 2 1 13 4 2 1 2 12 1 14 3 1 1 2 3
## 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887
## 1 1 3 3 13 1 1 2 5 10 1 3 3 1 1 3 1
## 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904
## 9 7 6 2 8 2 2 5 1 5 1 3 13 3 1 2 28
## 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921
## 10 7 1 2 2 10 8 3 1 4 1 1 2 23 3 6 3
## 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938
## 5 4 7 1 5 1 3 2 8 2 13 1 1 1 1 4 1
## 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955
## 6 1 1 1 14 1 5 19 4 10 1 12 2 1 1 4 5
## 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972
## 3 3 14 1 3 1 9 4 4 6 3 2 2 5 3 2 3
## 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989
## 4 1 3 5 1 10 18 1 2 1 1 15 9 1 1 3 6
## 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006
## 3 1 2 2 1 8 5 6 2 1 2 2 4 2 1 3 3
## 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023
## 1 1 1 1 5 3 2 1 4 1 10 2 12 1 6 9 1
## 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 10 4 5 15 3 4 1 8 1 1 14 5 12 2 1 1 10
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057
## 3 3 1 2 2 4 1 4 2 2 4 1 5 10 2 2 12
## 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074
## 10 3 10 11 5 5 17 1 2 1 7 12 10 21 1 8 7
## 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
## 1 8 4 8 16 1 1 9 4 1 4 2 1 2 16 1 4
## 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108
## 2 6 5 8 2 3 6 1 1 6 5 2 2 1 2 1 5
## 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125
## 14 6 1 11 3 1 6 1 1 10 2 1 1 1 1 1 1
## 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142
## 12 2 2 1 3 14 2 5 2 3 1 3 1 1 4 1 6
## 1143 1144 1145 1146 1147 1148 <NA>
## 2 2 12 4 12 5 457
## [1] "Frequency table before encoding"
## codlocal.
## 139511 139648 139766 139785 139846 139870 139889 139907 139912 139926 139945 139988
## 1 2 2 9 7 15 14 2 1 2 4 7
## 140034 140048 140053 140091 140109 140114 140171 140190 140246 140251 140265 140270
## 17 10 1 1 4 12 16 6 1 2 4 2
## 140289 140294 140500 141081 141811 142491 142518 142523 142537 142561 142603 143400
## 1 4 2 1 1 12 12 3 11 20 6 1
## 143457 143462 143693 143711 143725 143730 143754 144522 144541 144560 144579 144584
## 8 9 4 3 13 11 3 6 1 10 1 2
## 144598 144602 144621 144635 144683 144697 144701 144777 144782 144800 144819 144824
## 1 7 2 3 5 6 15 5 1 8 1 4
## 144980 145140 145381 146130 146187 146192 146253 146286 146328 146333 146352 146385
## 1 15 1 10 2 4 12 2 2 3 9 12
## 146432 146446 148129 148134 148313 148346 148577 148600 148681 148964 148978 148983
## 2 4 12 2 12 2 1 2 11 2 1 11
## 148997 149020 149044 149063 149077 149261 288030 288054 288073 288092 288110 288148
## 9 11 2 10 1 13 1 2 2 2 3 12
## 288153 288167 288172 288191 288214 288228 288233 288247 288266 288271 288327 288332
## 5 1 1 2 3 4 2 1 6 3 10 7
## 288346 288370 288394 288431 288445 288450 288469 288474 288488 288493 288520 288539
## 11 1 1 11 3 1 3 4 2 5 3 2
## 288544 288676 288860 288935 289652 291278 291283 291297 291301 291315 291396 291570
## 4 2 18 1 1 11 4 9 3 4 1 1
## 291631 291706 291711 291730 291792 291810 291834 291848 291853 291914 291933 291947
## 1 15 5 6 1 5 1 9 5 6 7 1
## 291985 291990 292013 292046 292089 292094 292126 292150 292188 292193 292254 292268
## 10 12 9 1 1 4 7 10 5 2 10 3
## 292273 292329 292348 292367 292452 292471 292490 292517 292541 292579 292640 293201
## 2 3 2 1 10 1 9 12 7 3 13 1
## 294979 295059 295097 295115 295139 295158 295573 295605 295648 295653 295747 295752
## 14 1 7 16 8 6 2 1 2 6 8 2
## 296129 296761 296780 296799 296803 296822 296841 296855 296879 296884 296898 296902
## 1 1 2 5 3 5 6 2 3 3 1 2
## 296921 296978 296983 296997 297077 297416 298053 298067 298086 298091 298114 298133
## 4 9 5 2 2 1 5 1 1 3 13 2
## 298736 298741 298779 298784 298798 298802 298816 298821 298878 298897 298915 298920
## 5 14 1 2 13 3 2 5 4 3 16 9
## 298939 298944 299024 299043 299062 299076 299081 299793 300249 300715 300739 300763
## 3 3 7 2 3 4 2 1 1 1 8 23
## 301258 301277 301282 301296 301338 301376 301404 301442 301456 301461 301550 301574
## 3 3 1 5 5 18 6 2 3 2 2 6
## 301611 301625 301649 301668 301673 301687 301692 301705 301710 301729 301748 301753
## 2 24 3 16 4 1 11 2 2 7 2 1
## 301772 301786 301791 301809 301814 301828 301833 301847 301852 301866 301871 301885
## 12 5 6 19 1 7 9 2 12 1 8 7
## 301890 301908 301913 301927 301932 302984 303035 303733 303747 304266 304761 304799
## 1 3 2 3 31 1 1 1 5 3 1 4
## 304860 304884 304898 304916 304935 304940 304978 304983 304997 305020 305044 305077
## 3 9 14 12 10 2 6 9 1 5 6 1
## 305082 305096 305119 305124 305572 305789 305826 305831 305845 305850 305893 305911
## 5 4 1 14 1 13 6 1 5 2 4 2
## 305925 305930 305949 305954 305973 305992 306005 306166 306661 306675 306717 306736
## 12 3 2 1 2 2 4 1 11 9 6 2
## 307383 307415 307458 307477 307514 308684 308759 308797 308801 308820 308839 308900
## 1 9 12 10 9 4 1 3 1 1 16 5
## 308919 308938 308943 308962 309773 310045 310050 310069 310093 310875 310899 310903
## 5 4 19 1 1 1 3 4 2 3 6 4
## 310960 310979 310984 310998 311002 311035 311064 311097 311101 311120 311144 311158
## 4 3 2 4 1 14 2 12 1 2 9 13
## 311163 311200 311219 311224 311238 311785 313478 313591 313652 313708 313732 313765
## 1 6 3 4 3 1 11 2 2 13 11 9
## 313845 313850 313930 314005 314034 314048 314067 314091 314114 314152 314171 314213
## 3 1 12 1 2 1 6 2 4 1 1 12
## 314227 314289 314519 314604 314637 314958 315000 315019 315043 315062 315284 315302
## 2 13 18 10 1 1 7 3 2 9 4 1
## 315316 315321 315401 315910 315948 315953 315972 316641 316679 318230 318334 318348
## 4 1 7 3 15 5 10 20 7 10 1 10
## 319017 319084 319102 319116 319121 319159 319244 319263 319282 319300 319319 319324
## 3 10 15 6 6 11 12 22 7 16 2 1
## 319338 319343 319362 319376 319381 319404 320087 320105 320426 320596 320600 320619
## 1 11 4 1 5 2 1 15 13 17 4 14
## 320638 320643 320662 320695 320704 320756 320775 320822 320841 320855 320860 320879
## 2 1 3 6 4 10 6 6 2 4 7 2
## 321143 321195 321416 321869 321874 321888 321893 322982 323000 324014 324405 324429
## 1 1 2 5 3 3 7 11 1 12 9 33
## 324472 324491 324504 324523 324537 324542 324561 324575 324599 324617 324641 324655
## 12 11 1 7 2 12 12 13 2 12 5 10
## 324660 324679 324684 324698 324702 324716 324735 324740 324759 324764 324778 324797
## 1 2 1 2 23 3 1 26 1 10 2 11
## 324815 324844 324877 324882 324900 324919 324943 324957 325004 325117 325141 325155
## 14 13 10 11 3 7 12 2 1 11 2 6
## 325202 325235 325259 325264 325297 325301 325315 325396 325400 325419 325438 325508
## 2 1 14 3 3 6 2 12 11 5 26 12
## 326720 326843 327772 328984 329304 329337 329484 329549 329610 329629 329667 329672
## 1 16 1 1 2 6 8 11 12 10 5 6
## 329686 329728 329733 329747 329752 329771 329790 329813 329889 329931 329950 329969
## 9 2 1 3 3 4 14 3 5 11 1 10
## 329974 329993 330722 330920 332151 332165 332170 332844 332943 332976 332995 333037
## 4 1 1 1 4 14 1 3 11 8 6 1
## 333056 333061 333075 333099 333103 333117 333122 333136 333221 333235 333240 333297
## 2 7 5 10 17 10 2 3 2 5 4 8
## 333320 333377 333382 333396 333400 333424 333438 333443 333457 333462 333481 333513
## 13 6 4 2 11 10 3 4 3 11 5 20
## 333527 333551 333565 333570 333589 333594 333650 333674 333688 334715 335885 336111
## 2 8 1 11 4 7 5 3 3 1 1 2
## 336125 336371 336903 337422 337436 337441 337455 337460 338681 338695 338704 338718
## 2 1 1 4 4 3 4 6 12 17 4 14
## 338723 338761 338817 338841 338855 338884 340189 340231 340269 340274 340288 340325
## 1 2 22 17 1 4 2 2 12 2 4 7
## 341117 342843 342862 342923 342956 342999 343734 343753 343767 343772 343786 343791
## 1 14 3 3 4 2 2 27 21 21 6 2
## 343809 343814 343828 343833 343871 343885 343908 343913 343951 343994 344007 344026
## 2 8 7 2 11 9 11 10 14 1 2 4
## 344045 344088 344111 344125 344130 344154 344168 344413 345403 345832 346493 346544
## 4 7 12 15 4 1 1 1 1 1 3 14
## 346563 346582 346624 346638 346643 346657 346695 346704 346718 346723 346756 346775
## 6 3 12 12 11 1 23 3 1 17 18 13
## 346817 346822 346836 346841 346855 346860 346879 346884 346997 347195 348411 504760
## 20 1 16 7 2 9 26 12 1 1 5 1
## 506194 507301 508447 513788 517946 519691 524032 525442 526328 526880 534347 539298
## 1 2 2 1 1 2 1 1 5 1 1 4
## 541692 545322 545497 549103 549264 552893 570745 571311 572396 573032 574705 580283
## 1 1 1 1 1 1 3 2 12 1 1 6
## 585780 589698 591333 593539 593657 593940 594044 594138 594435 594459 594464 594478
## 1 1 1 1 14 3 1 4 2 2 2 1
## 594690 595171 595623 608461 611717 612236 627799 686699 687118 687383 687528 687585
## 1 4 1 1 5 1 1 4 1 18 2 12
## 687887 694208 702604 705235 708535 709163 710133 711905 711967 713546 713773 715055
## 14 2 1 1 1 4 1 1 1 10 2 1
## 718426 720660 720820 721527 721773 722112 723611 724253 725870 783493 <NA>
## 4 4 1 1 10 3 1 1 13 1 26
## [1] "Frequency table after encoding"
## codlocal.
## 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773
## 1 6 11 3 6 1 1 2 16 3 2 10 13 1 1 1 11
## 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790
## 6 5 6 15 3 1 8 13 6 2 12 2 13 2 1 8 11
## 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807
## 4 3 6 11 1 3 20 2 1 5 3 6 3 1 5 1 1
## 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824
## 23 1 4 11 1 2 1 4 12 1 3 14 10 4 1 9 2
## 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841
## 2 2 5 12 4 6 4 10 3 6 11 18 2 2 1 11 6
## 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858
## 1 8 1 11 5 4 1 1 18 2 4 1 1 4 8 12 1
## 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875
## 1 3 3 2 4 14 1 3 3 10 5 1 9 1 5 1 4
## 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892
## 12 7 8 16 17 2 3 13 1 2 2 1 1 1 12 4 3
## 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909
## 11 1 1 6 9 14 12 10 6 2 8 3 13 8 1 4 10
## 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926
## 22 4 2 2 12 12 9 15 9 2 1 1 1 12 2 12 1
## 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943
## 14 10 15 12 13 3 4 7 12 2 14 5 1 3 6 1 1
## 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 1 10 3 7 2 1 9 5 15 7 7 11 2 2 2 1 1
## 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977
## 1 9 2 7 3 3 21 14 14 6 6 9 17 2 18 11 1
## 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994
## 10 1 3 3 3 12 1 7 4 1 3 2 8 12 3 26 2
## 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011
## 1 6 2 5 33 3 4 18 4 4 3 1 4 5 1 2 2
## 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028
## 1 1 13 9 1 2 17 5 1 23 1 6 1 1 12 3 4
## 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045
## 2 4 3 4 2 3 1 2 4 1 13 1 1 2 1 3 4
## 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062
## 10 4 3 1 20 4 17 6 14 14 2 1 22 7 11 1 1
## 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079
## 1 2 12 9 1 2 10 5 2 2 1 2 1 4 4 11 1
## 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096
## 10 3 19 2 1 4 1 21 1 11 1 13 3 2 7 2 11
## 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113
## 3 2 4 6 2 1 2 14 2 4 3 2 1 5 24 2 13
## 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130
## 1 1 9 4 5 1 1 6 12 10 1 4 2 4 1 4 11
## 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
## 13 1 10 5 16 3 3 1 2 1 10 14 9 2 11 10 3
## 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164
## 6 5 2 5 1 1 1 13 2 5 2 26 6 11 10 5 3
## 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181
## 11 8 2 2 4 3 1 10 1 9 23 1 9 1 5 1 2
## 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198
## 1 2 14 3 2 20 1 1 4 1 7 2 5 5 2 3 1
## 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215
## 1 4 1 12 16 3 4 14 12 1 1 1 4 6 12 7 3
## 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
## 2 10 1 4 10 2 3 5 4 1 6 6 1 3 15 1 26
## 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249
## 19 2 1 18 2 1 3 2 2 6 2 2 10 1 3 1 2
## 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266
## 1 16 5 9 12 6 1 1 4 27 7 7 1 1 5 1 9
## 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283
## 5 2 4 1 14 2 4 1 2 5 1 1 1 7 1 2 9
## 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300
## 1 1 11 3 1 12 7 1 1 9 5 2 4 10 2 12 1
## 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317
## 5 3 12 1 5 6 1 7 4 8 2 4 12 1 3 17 1
## 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334
## 2 1 7 3 1 12 1 2 3 3 4 1 1 12 11 4 1
## 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351
## 17 5 1 6 13 1 1 1 2 1 12 11 2 4 4 3 4
## 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368
## 2 2 1 2 12 7 5 3 6 3 10 16 1 1 4 11 1
## 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385
## 9 1 9 14 1 12 15 10 2 1 10 3 6 31 11 9 7
## 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402
## 1 6 4 2 1 5 7 10 1 2 2 11 8 12 14 2 2
## 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419
## 1 2 1 3 6 7 2 10 1 2 5 1 6 12 2 1 2
## 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436
## 7 9 6 2 4 7 5 12 1 5 4 1 11 3 20 11 7
## 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453
## 7 1 3 2 2 3 2 2 4 2 3 15 4 1 2 10 3
## 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470
## 9 1 2 12 11 16 16 11 14 3 12 2 2 15 2 3 1
## 1471 1472 1473 1474 <NA>
## 13 2 7 13 26
## [1] "Frequency table before encoding"
## distrito. Distrito del hogar
## Callao Bellavista Carmen de la Legua Reynoso
## 217 20 5
## La Perla Ventanilla Mi Perú
## 30 150 3
## Cusco San Jerónimo San Sebastian
## 54 5 28
## Santiago Wanchaq Lima Cercado
## 63 19 123
## Ancón Ate Barranco
## 45 257 9
## Breña Carabayllo Chaclacayo
## 17 74 19
## Chorrillos Cieneguilla Comas
## 113 35 279
## El Agustino Independencia Jesús MarÃa
## 150 71 7
## La Molina La Victoria Lince
## 10 72 4
## Los Olivos Lurigancho - Chosica Lurin
## 56 142 39
## Magdalena del Mar Pueblo Libre Miraflores
## 13 19 4
## Pachacamac Pucusana Puente Piedra
## 32 1 161
## Punta Hermosa Punta Negra RÃmac
## 9 1 104
## San Bartolo San Borja San Isidro
## 1 17 2
## San Juan de Lurigancho San Juan de Miraflores San Luis
## 465 155 9
## San MartÃn de Porres San Miguel Santa Anita
## 305 31 82
## Santa MarÃa del Mar Santa Rosa Santiago de Surco
## 1 1 48
## Surquillo Villa El Salvador Villa MarÃa del Triunfo
## 31 245 257
## <NA>
## 1
## [1] "Frequency table after encoding"
## distrito. Distrito del hogar
## 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652
## 113 48 28 63 54 5 74 217 31 10 31 150 19 155 1 1 17
## 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669
## 9 45 9 4 1 39 56 71 257 465 19 305 3 123 245 4 17
## 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686
## 82 72 7 32 104 150 1 13 257 30 9 5 161 279 20 19 1
## 687 688 689 <NA>
## 2 142 35 1
## [1] "Frequency table before encoding"
## CODLOC. SAP2015: Codigo de Local
## 65591 139785 139827 139832 139870 139889 139894 139912 139931 139988 140034 140048
## 1 8 1 2 11 12 1 3 2 6 13 12
## 140053 140091 140114 140190 140213 142518 142523 142537 142561 143438 143725 143730
## 2 1 10 7 1 13 1 12 2 1 12 8
## 144522 144536 144541 144560 144579 144598 144602 144659 144758 144782 144800 144819
## 7 1 1 10 1 3 6 1 2 1 6 2
## 145140 145244 146130 146154 146253 146347 146352 146366 146385 146578 148049 148054
## 8 1 10 2 11 6 8 3 13 6 1 6
## 148068 148129 148313 148370 148412 148474 148544 148558 148681 148983 148997 149020
## 1 11 14 1 3 1 3 1 11 10 8 10
## 149063 149261 287912 287974 288054 288110 288134 288148 288191 288214 288266 288271
## 12 14 1 1 4 2 1 11 4 1 8 3
## 288327 288346 288370 288431 288539 288860 291278 291297 291607 291768 291834 291914
## 13 8 1 4 2 17 12 4 4 1 1 1
## 291947 291990 292046 292126 292193 292329 292428 292490 292579 292640 294979 295059
## 3 13 2 1 3 4 2 8 1 14 15 2
## 295083 295101 295115 295139 296761 296780 296997 298741 298784 298798 298864 298883
## 2 1 18 2 2 1 2 15 1 12 1 2
## 298897 298920 299081 300739 301263 301282 301324 301376 301418 301423 301475 301507
## 14 5 2 9 1 3 3 15 1 1 2 1
## 301531 301593 301625 301649 301668 301692 301705 301734 301767 301772 301786 304761
## 1 2 15 2 17 11 1 1 2 5 7 2
## 304775 304780 304879 304884 304898 304921 304983 305077 305119 305789 305794 305925
## 1 1 1 9 14 7 10 3 2 10 1 10
## 305930 305949 305954 305987 305992 306656 307383 307401 307415 307458 307477 307590
## 1 2 1 2 3 1 2 1 1 12 9 1
## 308603 308679 308684 308735 308801 308820 308839 308863 308877 308943 310922 310979
## 1 2 2 1 3 2 16 3 1 3 2 3
## 310998 311002 311035 311064 311097 311115 311144 311158 311163 311177 311182 313478
## 4 1 11 2 3 1 10 12 1 2 1 10
## 313708 313727 313732 313765 313789 313845 313850 313925 313930 314005 314034 314171
## 10 1 9 8 1 1 3 2 15 1 2 2
## 314227 314289 314519 314604 315062 315321 315905 315948 315972 316655 316660 318230
## 1 15 19 11 9 4 2 12 9 1 1 10
## 318292 318334 318348 318640 319084 319102 319116 319121 319178 319239 319244 319263
## 1 1 11 1 3 12 5 5 1 1 12 15
## 319296 319300 319343 320105 320619 320638 320662 320704 320723 320756 321416 321874
## 1 14 11 16 8 2 3 3 1 8 2 7
## 321893 322982 324014 324429 324434 324472 324486 324491 324504 324523 324537 324542
## 6 2 11 17 2 13 2 9 2 1 1 9
## 324561 324603 324617 324641 324655 324660 324684 324702 324716 324735 324764 324778
## 5 1 13 5 9 2 2 14 2 1 4 1
## 324797 324815 324844 324863 324877 324943 324957 325004 325117 325141 325179 325184
## 11 14 5 1 5 3 5 2 10 3 2 1
## 325259 325320 325396 325400 325419 325438 325508 326843 329304 329610 329629 329653
## 10 3 4 8 2 11 13 16 3 8 8 2
## 329667 329686 329733 329785 329931 329945 329950 332151 332165 332924 332943 332957
## 1 9 4 1 11 2 1 3 14 1 10 2
## 332962 333056 333061 333099 333103 333117 333122 333160 333202 333259 333264 333297
## 1 2 1 9 9 13 5 2 1 3 1 3
## 333301 333320 333339 333377 333424 333438 333462 333513 333532 336111 337511 338681
## 1 15 1 3 5 1 6 2 2 2 2 12
## 338695 338723 338761 338841 338855 338884 339685 340231 340269 340274 340288 342843
## 16 1 2 15 3 1 2 1 12 1 3 10
## 342904 342923 343767 343772 343786 343828 343833 343847 343871 343885 343913 343951
## 1 2 16 6 5 5 3 2 11 1 11 12
## 343994 344007 344012 344031 344111 344125 344154 346493 346515 346520 346544 346558
## 3 5 2 1 12 17 1 4 1 1 11 5
## 346577 346582 346600 346624 346638 346643 346662 346723 346737 346742 346756 346775
## 1 1 1 11 10 14 1 17 1 1 16 14
## 346822 348411 365311 365368 366018 366117 367466 494054 494940 495746 499848 506194
## 4 1 1 1 1 1 1 1 1 7 1 2
## 519691 580283 593539 593638 593657 593940 593983 594435 595213 595425 647759 687118
## 1 3 2 1 14 3 1 2 1 3 2 3
## 687528 687585 687887 720839 722112 725870 725889 <NA>
## 2 14 14 1 1 12 1 2063
## [1] "Frequency table after encoding"
## CODLOC. SAP2015: Codigo de Local
## 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792
## 3 13 4 10 3 2 5 13 1 5 2 2 3 1 5 3 12
## 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809
## 11 18 1 1 2 1 6 10 1 12 1 3 1 4 1 3 15
## 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826
## 1 10 3 4 2 1 14 3 2 2 2 2 3 11 4 2 12
## 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843
## 2 5 1 16 1 8 1 1 15 1 2 2 1 10 3 7 15
## 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860
## 2 1 10 3 1 2 1 2 10 1 2 2 11 1 1 1 1
## 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877
## 1 2 8 9 6 1 11 12 8 1 15 3 2 8 1 1 1
## 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894
## 14 2 2 2 1 3 8 12 1 13 4 4 3 17 1 1 14
## 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911
## 4 9 11 14 10 2 2 3 1 3 3 3 2 1 5 6 14
## 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 10 2 12 1 1 1 2 12 9 5 2 1 1 2 1 16 3
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945
## 1 1 2 12 1 8 14 2 3 10 5 1 9 10 2 15 1
## 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962
## 9 1 16 1 1 13 1 6 2 1 19 7 1 4 2 11 9
## 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979
## 1 4 3 5 13 1 9 1 1 7 1 16 15 16 2 1 1
## 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996
## 1 1 2 2 11 2 4 8 1 10 6 10 8 12 14 11 7
## 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013
## 4 3 11 2 1 10 12 1 8 1 2 3 5 2 2 11 15
## 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030
## 1 2 13 3 17 2 5 2 1 17 1 2 1 1 1 13 2
## 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047
## 1 1 1 15 2 11 8 1 10 2 17 2 1 5 3 12 9
## 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064
## 1 1 1 5 6 2 3 2 1 10 10 11 9 5 11 3 1
## 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081
## 1 1 1 1 6 1 1 1 3 3 3 1 1 2 2 3 6
## 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098
## 2 9 12 5 2 1 1 1 8 1 12 1 11 5 3 1 14
## 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115
## 4 15 12 9 1 2 7 1 1 11 3 12 3 11 1 1 3
## 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132
## 11 1 1 1 1 1 3 10 1 1 1 1 1 8 2 3 6
## 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149
## 11 14 2 14 4 7 2 2 14 1 12 14 2 14 16 1 2
## 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166
## 12 1 1 8 2 13 2 8 2 9 1 14 1 2 13 1 17
## <NA>
## 2063
## [1] "Frequency table before encoding"
## COD_MOD_2015. SAP2015: codigo modular
## 205567 205997 206011 206037 206136 207795 207803 207845 207852 207894
## 3 6 1 3 2 5 1 1 1 1
## 207951 207985 207993 208058 208348 208389 208413 208538 208546 208553
## 2 6 1 10 5 2 2 3 7 2
## 208561 208579 208587 208694 208736 209304 209916 209924 209940 209973
## 12 1 2 1 7 1 7 1 5 1
## 215632 215723 217554 235010 235333 236117 236174 236349 236778 245662
## 16 1 1 2 2 7 10 2 9 12
## 245704 315275 317040 317073 317131 317206 317214 317289 317313 317453
## 2 1 3 1 11 3 6 15 7 3
## 317479 317560 317578 317941 318063 318089 318204 318287 318303 318352
## 3 16 1 2 10 6 1 4 1 1
## 318576 318741 318782 318824 318931 318949 319004 319020 319061 319087
## 1 11 3 2 2 4 1 2 3 1
## 319145 319160 319202 319285 322453 322479 322644 322677 322685 322743
## 13 2 2 4 5 9 1 2 14 1
## 322768 322875 322958 322974 323253 323295 323378 323394 323451 325464
## 2 2 1 7 6 2 14 3 2 10
## 325472 325480 325498 325563 325670 325696 327551 327965 328039 328047
## 13 2 1 10 4 1 1 1 10 6
## 328062 328070 328229 328260 328344 328351 328369 328385 328401 328419
## 9 1 2 3 1 2 1 6 6 5
## 328443 328450 328468 328518 328526 328567 328872 328963 329029 329045
## 5 1 1 4 12 1 1 1 4 1
## 329128 329573 330464 332213 333666 334094 334649 334672 334748 334821
## 1 14 16 1 12 7 5 9 3 1
## 334847 334904 334912 334920 334961 334987 335026 335034 335042 335091
## 7 2 3 2 1 14 1 1 16 11
## 335158 335166 335182 335224 336511 336537 336560 336594 336628 336636
## 2 1 2 1 1 1 11 1 9 5
## 337436 337717 338129 338186 338228 338301 338343 338517 338525 338541
## 12 1 2 1 3 9 8 14 2 3
## 338566 338640 338665 338848 339036 339051 339077 339150 339291 339317
## 1 10 6 3 2 12 1 1 1 3
## 339333 339432 339507 339523 339606 339804 340224 340372 340380 398115
## 1 1 2 1 16 3 2 1 9 1
## 400705 404913 404947 405100 405183 405308 405324 406793 432906 433078
## 1 1 3 1 1 1 2 3 1 2
## 433227 433235 433276 433326 433367 433490 433540 433680 433706 433722
## 9 4 9 2 1 8 14 13 1 1
## 433821 433862 433961 434019 434076 434134 434159 434191 434258 434282
## 14 1 9 10 3 2 6 12 1 3
## 434381 434464 434480 434498 434506 434548 434563 434597 434720 434829
## 1 9 14 8 2 7 2 10 2 9
## 436154 436170 436196 436204 436212 436287 436295 436303 436345 436360
## 1 1 2 2 12 4 3 2 2 5
## 436428 436444 436451 436485 436493 436501 436543 436576 436584 436634
## 3 2 9 3 10 1 3 2 1 12
## 436642 436709 436725 436741 436766 436774 436782 436790 436824 437160
## 1 2 5 1 7 2 1 1 1 1
## 437210 437228 437236 437244 437251 437277 437319 437335 437343 437400
## 5 12 2 6 2 6 10 8 11 6
## 437715 437731 437772 449868 466342 466383 466508 466730 468488 468611
## 1 1 1 5 3 4 1 16 9 2
## 469700 472654 478404 481903 482091 482109 486621 488619 488635 488676
## 6 1 2 7 16 1 1 5 10 1
## 488841 489104 492876 493338 493544 493635 493841 496166 496521 496844
## 1 1 1 3 15 1 5 11 1 13
## 497024 499699 500348 501411 501601 501676 501700 501908 501957 502336
## 7 10 16 2 8 7 1 3 2 2
## 502534 502633 505149 508903 510305 510602 510800 512020 513614 516674
## 1 12 1 11 1 5 5 2 4 4
## 516872 518340 523464 523662 523761 524264 525857 526301 526376 526400
## 3 1 12 3 10 1 3 11 1 1
## 527572 528380 535666 536326 541011 542357 542720 543645 546002 551804
## 4 1 1 13 2 1 1 1 17 1
## 555599 555862 555946 556290 556357 556472 556548 556555 556571 565119
## 1 7 2 2 1 3 1 2 11 3
## 565234 566141 566166 566430 566448 566463 566471 566489 567750 578286
## 9 12 2 9 1 3 10 2 1 9
## 578336 578351 578401 578492 578518 578526 578534 578542 579151 581728
## 1 8 10 2 11 7 7 4 11 2
## 581736 581777 581876 581884 581900 581991 582254 582262 582387 582403
## 11 2 3 1 7 4 1 1 10 8
## 582411 582833 582890 582932 582981 583013 583104 583534 583567 583591
## 7 11 10 2 10 11 1 2 4 16
## 583922 584946 587279 590133 591198 594895 598581 599365 601492 601708
## 10 7 1 2 9 1 7 14 8 4
## 603878 605469 607424 607556 616185 628370 628404 628602 629261 629295
## 13 15 1 2 10 1 1 1 2 8
## 632299 632356 639112 639732 642801 642926 643817 644880 646646 646711
## 4 1 5 3 1 1 8 2 4 1
## 647172 647792 649913 649947 650036 652081 656447 659623 659664 659698
## 10 4 1 4 7 3 1 2 1 3
## 659706 659714 659722 659896 659953 662841 663096 663112 663138 663534
## 1 3 4 9 4 2 10 7 7 3
## 663542 663559 663682 663971 664284 664722 664748 664920 665265 665372
## 5 10 12 14 1 1 14 1 1 2
## 665398 665422 665489 689679 690008 691782 691808 691931 692434 692442
## 2 8 4 2 1 4 3 1 9 1
## 693382 693465 693499 693622 693630 693655 694224 694315 694422 694463
## 1 3 10 13 1 3 2 3 1 3
## 694547 694570 694588 695288 697557 703124 703215 703223 703231 703249
## 3 8 10 1 6 6 8 11 1 7
## 703256 703736 703751 704072 704445 705053 705129 705160 705376 725523
## 2 3 16 1 9 8 9 3 1 2
## 728055 728196 728717 732347 732461 732495 743773 743807 743815 743831
## 3 4 11 1 1 4 13 2 1 11
## 744540 744557 759399 759613 762120 762468 762500 762757 762773 762856
## 6 10 15 11 2 6 2 1 9 11
## 762880 762906 763151 763177 764035 764076 764084 764779 764910 765297
## 14 4 9 2 3 5 2 14 1 11
## 765305 765321 765396 765859 772913 772970 773788 774026 774679 774703
## 8 2 2 20 1 4 8 16 1 10
## 774737 775312 776138 776161 776229 777110 777144 777656 777680 777995
## 1 14 2 1 1 3 5 14 6 8
## 778076 778233 778738 778761 778795 779041 780700 780759 780767 780791
## 3 7 5 1 6 1 1 1 1 8
## 781096 781278 781302 781351 781369 781385 781427 781831 781930 782078
## 2 5 5 7 14 7 1 5 1 1
## 782102 782664 785097 817916 821082 824003 824813 825752 826081 826263
## 5 8 9 2 13 2 12 1 2 2
## 828210 829325 831313 832253 832303 832311 834853 834994 835058 846048
## 1 3 1 1 7 8 4 2 10 15
## 847087 855247 855791 869032 869198 872127 872515 874198 874214 879791
## 3 1 5 1 8 2 11 3 8 8
## 882993 884544 884551 884585 884593 884635 900704 900761 900795 900852
## 2 15 6 1 2 2 2 9 2 3
## 900910 901033 901066 915256 922872 923748 927814 928820 933598 1007160
## 9 8 1 5 1 1 11 1 7 1
## 1007491 1008440 1008960 1010040 1010149 1033729 1041391 1041516 1041631 1045079
## 3 11 16 3 7 6 3 1 16 3
## 1045111 1045798 1048990 1053628 1053669 1054154 1054196 1054238 1054279 1054352
## 10 7 3 12 11 8 12 12 1 3
## 1056902 1062942 1063023 1063106 1063148 1063221 1063304 1064989 1066026 1068238
## 5 1 1 7 8 8 8 4 4 6
## 1069954 1070036 1070077 1070390 1071257 1071919 1072040 1075779 1080068 1080258
## 1 5 10 10 1 12 2 2 15 10
## 1082031 1083815 1084508 1084987 1085919 1088400 1098102 1099654 1147610 1185644
## 2 10 10 4 2 11 1 1 1 1
## 1194265 1194380 1195189 1195221 1195478 1195841 1195874 1196047 1196526 1210137
## 10 16 13 1 2 1 2 2 9 1
## 1223023 1227461 1229558 1238229 1238708 1240357 1240720 1241082 1241678 1242908
## 6 1 2 13 3 1 9 2 1 2
## 1248350 1248392 1248509 1258649 1261742 1265214 1266840 1268150 1273150 1273275
## 1 9 2 7 5 1 2 2 1 1
## 1279124 1309392 1309574 1313444 1321256 1335637 1349349 1351410 1375211 1376854
## 12 1 5 1 6 1 1 3 2 7
## 1380740 1381078 1381110 1381144 1381219 1381342 1381375 1381599 1381862 1382829
## 13 1 2 2 1 10 1 6 1 5
## 1383199 1386168 1386234 1386283 1390442 1391481 1393453 1398148 1411438 1438027
## 3 12 11 6 1 1 2 8 14 2
## 1438035 1473511 1473644 1474600 1474964 1475011 1475045 1475201 1475250 1475284
## 1 1 1 3 15 10 1 11 2 13
## 1475755 1476258 1476464 1477264 1484443 1486018 1493964 1495365 1495407 1497007
## 2 3 1 1 1 2 3 12 4 14
## 1497056 1497601 1501451 1505494 1507094 1507250 1507276 1507318 1507532 1509496
## 9 1 6 13 12 15 7 1 11 3
## 1511351 1512789 1513951 1520279 1520287 1531359 1574557 1607944 1640556 1641521
## 11 3 1 2 4 2 2 2 9 8
## 1661271 <NA>
## 13 378
## [1] "Frequency table after encoding"
## COD_MOD_2015. SAP2015: codigo modular
## 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630
## 1 2 1 7 7 4 1 2 1 1 1 2 3 1 5 1 10
## 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647
## 9 2 4 1 4 1 5 1 1 2 2 1 9 1 4 10 2
## 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664
## 1 2 3 1 16 7 10 2 12 4 11 3 1 2 1 13 2
## 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681
## 10 10 7 5 10 10 10 1 4 3 2 11 3 5 1 1 2
## 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698
## 10 5 1 2 1 3 1 7 7 2 1 3 5 1 4 9 1
## 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715
## 3 5 1 1 4 5 1 1 3 10 3 2 11 6 1 14 2
## 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732
## 3 1 1 9 14 1 9 12 1 2 5 11 3 1 1 1 2
## 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749
## 2 1 2 1 11 3 3 1 6 1 2 6 1 11 7 2 2
## 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766
## 2 1 14 2 6 5 1 1 3 4 6 4 10 2 3 7 3
## 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783
## 10 14 1 16 6 1 1 1 2 9 9 9 9 1 1 4 3
## 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 1 8 2 2 2 3 11 7 1 3 2 11 5 14 1 1 16
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817
## 14 10 3 13 1 4 6 3 12 1 1 8 1 1 2 12 8
## 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834
## 6 1 2 10 1 15 10 1 10 1 1 2 8 2 11 1 1
## 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851
## 11 1 15 2 5 2 12 3 9 1 8 2 4 1 1 1 20
## 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868
## 7 13 4 14 1 2 1 9 3 13 1 8 1 1 11 3 1
## 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885
## 7 11 1 6 7 10 10 2 7 5 3 1 4 2 2 9 7
## 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902
## 2 10 7 3 4 2 10 7 9 1 3 11 1 6 1 3 5
## 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919
## 8 5 4 5 1 12 8 2 1 1 1 11 1 2 1 1 12
## 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936
## 12 5 1 4 1 3 13 1 16 8 1 5 1 3 2 3 11
## 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953
## 1 4 2 1 1 10 1 3 2 1 1 3 3 3 1 9 2
## 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970
## 6 10 8 11 2 7 3 1 2 1 1 10 9 11 15 3 15
## 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987
## 1 7 8 2 2 1 5 1 8 1 12 6 2 9 10 2 2
## 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004
## 1 6 16 6 1 11 6 11 3 13 1 10 3 1 1 1 1
## 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021
## 3 9 1 1 1 1 1 12 4 1 3 9 17 4 3 5 12
## 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038
## 16 1 14 6 12 2 15 2 14 2 2 10 3 4 1 12 2
## 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055
## 5 1 11 4 7 1 5 5 3 7 1 2 7 10 8 2 7
## 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 1 1 8 2 16 10 10 1 8 1 16 1 5 2 5 11 9
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089
## 16 1 2 2 6 1 7 8 1 8 1 12 1 1 1 1 7
## 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106
## 4 6 8 1 14 1 10 1 2 2 6 1 6 3 1 9 2
## 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123
## 1 1 4 7 2 13 1 10 3 6 7 2 1 1 1 2 1
## 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140
## 1 1 10 14 13 4 1 1 5 1 1 2 13 16 1 7 8
## 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157
## 7 6 2 2 8 3 1 3 3 8 4 2 11 9 9 1 13
## 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174
## 3 3 11 3 10 9 15 7 1 7 13 2 6 2 9 1 2
## 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191
## 12 1 16 1 2 1 11 2 3 5 2 11 3 7 2 12 2
## 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208
## 15 8 6 16 2 6 1 11 14 15 2 6 1 9 14 16 12
## 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225
## 8 11 1 13 14 4 6 1 11 1 1 1 1 8 1 15 2
## 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242
## 12 2 3 8 1 1 2 8 3 3 14 5 10 1 9 9 14
## 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
## 1 3 14 1 2 1 1 2 1 2 1 16 2 6 3 1 9
## 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276
## 13 2 5 1 2 8 9 1 2 1 1 1 1 1 1 2 1
## 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293
## 1 10 2 1 1 5 1 1 12 1 2 2 3 8 11 1 1
## 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310
## 1 2 2 10 4 3 2 8 2 9 5 7 5 4 2 2 3
## 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327
## 13 9 4 13 14 2 10 8 1 2 10 1 2 3 2 1 7
## 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 2 2 4 1 7 12 1 2 2 1 4 3 3 9 12 1 1
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361
## 2 1 2 3 1 2 3 2 3 2 2 7 1 12 3 4 3
## 1362 1363 1364 <NA>
## 2 12 1 378
## [1] "Frequency table before encoding"
## COD_MOD_2016.
## 207449 207795 207985 208058 208348 208538 208546 208561 208587 208736
## 2 2 2 4 2 4 2 7 1 3
## 209304 209387 209510 209528 209536 209908 209916 209924 209940 209965
## 2 4 2 1 5 18 11 3 4 10
## 209973 210260 215632 233056 236109 236117 236174 236224 236364 236778
## 3 2 8 2 5 9 11 2 4 11
## 245647 245654 245662 245670 245688 245696 305656 314500 317131 317214
## 3 1 18 6 11 7 7 2 5 2
## 317289 317305 317313 317370 317453 317479 317560 317610 318063 318089
## 2 1 4 1 2 2 6 1 3 3
## 318287 318352 318741 318949 319020 319061 319145 319160 319285 320655
## 1 3 2 1 1 1 2 1 3 1
## 322453 322479 322685 322958 322974 323345 323378 325449 325464 325472
## 3 12 2 1 3 3 13 7 13 14
## 325480 325498 325506 325548 325555 325563 325589 325605 325613 325647
## 1 1 6 1 2 12 7 2 2 9
## 325662 325670 325704 327650 328039 328047 328260 328443 328484 328518
## 1 6 13 3 3 3 2 2 1 5
## 328526 329029 329128 329151 329243 329573 329755 329805 330464 333666
## 5 3 1 1 1 19 10 3 15 2
## 333690 334094 334649 334656 334664 334672 334680 334706 334714 334722
## 3 1 4 2 8 12 1 10 3 6
## 334730 334748 334771 334847 334920 334987 335042 335091 335224 336495
## 3 9 1 2 1 7 4 15 1 2
## 336537 336545 336560 336586 336594 336610 336628 336636 337212 337436
## 3 3 10 5 2 3 15 5 1 15
## 337568 337592 337733 337741 337766 338228 338301 338343 338517 338640
## 5 4 4 3 3 2 4 2 6 3
## 338665 338848 339051 339317 339432 339499 339606 339804 340224 340281
## 3 1 6 1 1 1 5 1 8 1
## 340299 340315 340349 340372 340380 340398 340414 340422 340448 340463
## 3 10 9 2 10 1 5 2 1 4
## 343566 395392 421396 432773 433227 433235 433276 433490 433540 433680
## 2 1 1 3 3 2 4 5 4 6
## 433821 433961 434019 434076 434159 434191 434282 434464 434480 434498
## 5 5 3 2 3 3 2 3 2 3
## 434506 434548 434597 434688 434829 436170 436212 436287 436303 436360
## 2 3 2 1 5 1 7 1 2 4
## 436444 436451 436493 436543 436584 436634 436642 436725 436766 437210
## 5 5 5 2 1 2 1 3 6 5
## 437228 437236 437244 437251 437269 437277 437285 437319 437335 437343
## 28 18 13 1 2 10 10 13 3 13
## 437350 437368 437400 437442 437475 437509 437525 437541 437707 437715
## 6 2 8 1 1 2 1 2 3 5
## 437723 437731 437749 437772 449868 466730 468488 469205 469700 481853
## 2 3 1 2 11 17 2 2 10 3
## 481903 482042 482091 488619 488635 493239 493544 495259 495812 496166
## 8 1 6 10 9 1 16 3 4 13
## 496521 496844 497024 499699 500124 500348 500611 501411 501502 501601
## 1 3 3 24 1 18 2 1 4 10
## 501676 501809 502435 502633 504993 505149 508903 510305 510800 513614
## 11 4 2 18 5 1 5 1 1 2
## 516674 519645 520486 521179 522318 522862 523423 523464 523621 523662
## 3 4 4 4 2 2 4 3 2 1
## 523761 526301 528380 534321 535823 536029 536128 536151 536326 546002
## 4 3 4 2 4 3 8 1 15 15
## 548834 555847 555862 555946 556266 556357 556472 556548 556571 565119
## 1 1 8 4 2 1 2 2 12 1
## 565143 565200 565234 565267 566141 566158 566414 566430 566455 566463
## 1 2 10 2 21 3 4 18 4 3
## 566471 567743 567750 567768 578260 578278 578286 578351 578401 578443
## 14 1 8 1 3 4 10 11 13 2
## 578518 578526 578534 578542 579151 581710 581736 581744 581876 581892
## 12 10 19 6 12 2 17 4 5 2
## 581900 581991 582114 582122 582148 582163 582254 582304 582312 582387
## 6 3 1 1 1 3 3 4 5 11
## 582403 582411 582833 582866 582890 582932 582981 583013 583088 583328
## 11 8 11 5 14 3 13 10 7 4
## 583476 583567 583591 583922 591131 591164 591198 598581 599159 599365
## 6 4 23 3 2 2 11 12 2 14
## 601492 603878 605469 605501 607143 607424 607556 607697 616185 628404
## 9 11 16 4 3 1 5 1 12 2
## 628602 628842 629261 629295 632299 632471 639112 639922 642801 642892
## 2 3 2 2 2 1 3 5 3 2
## 643262 643692 643783 643817 644690 644880 647040 647065 647172 649129
## 1 4 2 3 4 3 1 3 11 2
## 649202 649947 650002 650036 652081 657783 659698 659722 659896 659953
## 1 6 1 8 1 1 2 5 12 3
## 662940 662957 663005 663013 663096 663112 663120 663138 663526 663534
## 2 2 3 1 9 6 3 10 1 2
## 663542 663559 663682 663971 664292 664490 664508 664698 664706 664748
## 5 11 5 15 2 1 1 15 1 19
## 664912 664920 665190 665265 665471 665489 691931 692434 693499 693622
## 2 2 1 2 1 7 6 9 9 13
## 693655 694547 694562 694570 694588 694596 694604 697557 703124 703215
## 3 1 3 12 13 2 9 3 3 11
## 703223 703231 703249 703256 703736 703744 703751 704312 704445 704460
## 12 1 11 1 2 9 16 2 3 7
## 704965 705053 705129 705160 705475 705772 725770 725861 728055 728196
## 1 9 4 1 1 1 7 3 1 3
## 728717 730515 732321 732347 732354 732495 735035 743773 743815 743831
## 12 2 2 1 1 4 2 10 4 12
## 744540 744557 744573 751230 759399 759555 759613 762468 762773 762781
## 3 6 2 1 5 1 12 3 12 1
## 762856 762864 762880 762906 762914 763151 764076 764134 764779 764936
## 10 2 16 12 6 6 2 1 6 7
## 765297 765305 765313 765396 765412 765859 773788 774026 774455 774679
## 16 10 2 4 4 6 11 15 4 1
## 774703 775312 775833 775874 777110 777144 777300 777656 777680 777714
## 12 7 3 2 3 2 1 24 17 1
## 777995 778027 778233 778738 778795 779041 779868 780759 780791 781278
## 8 1 10 11 7 2 2 1 1 5
## 781302 781351 781369 781385 781831 782078 782102 782664 785097 820407
## 7 3 21 1 1 1 7 10 11 4
## 821082 824003 824813 825752 828962 832253 832279 832287 832303 832311
## 7 1 6 1 1 2 7 2 4 3
## 832337 832642 833913 834853 835058 846048 847087 855791 869198 869248
## 1 1 1 1 6 6 1 2 7 1
## 870931 871160 872515 874214 875443 875476 879791 879817 883884 884510
## 2 2 12 10 2 1 10 1 1 5
## 884528 884544 884551 884593 884627 884981 885517 900670 900761 900910
## 2 14 11 2 1 1 1 1 11 10
## 900977 901033 901082 901355 901413 901587 927814 928200 933598 1007491
## 3 11 2 1 1 1 12 1 9 1
## 1008440 1008929 1008960 1009844 1010040 1010149 1010180 1033729 1034016 1034685
## 20 2 13 6 2 10 1 2 4 1
## 1039676 1041557 1041631 1045111 1045277 1045434 1045715 1045798 1046226 1048990
## 1 2 18 6 1 4 1 7 1 2
## 1049493 1053628 1053669 1054154 1054196 1054238 1054352 1054394 1054436 1056746
## 1 13 14 11 14 12 4 4 2 2
## 1056902 1057637 1063106 1063148 1063221 1063304 1063346 1064989 1066026 1068238
## 9 1 7 11 10 7 1 3 8 6
## 1069954 1070036 1070077 1070390 1070481 1071919 1072040 1072727 1074301 1080068
## 4 5 12 4 1 13 2 2 5 7
## 1080258 1080613 1082031 1082874 1083633 1083674 1083716 1083815 1084508 1085851
## 3 1 2 1 2 2 4 11 10 3
## 1085976 1087295 1088400 1089044 1097567 1097781 1098243 1099597 1099654 1159011
## 2 4 5 1 1 1 1 1 1 1
## 1194265 1194380 1194810 1195114 1195189 1195577 1196203 1196526 1199009 1212877
## 9 18 4 1 8 1 1 3 1 1
## 1223023 1225549 1226422 1238229 1240183 1240720 1241082 1241454 1242908 1244102
## 8 1 1 5 1 9 1 1 1 1
## 1247832 1248392 1254192 1255074 1258334 1258649 1259134 1261742 1264340 1264670
## 3 10 1 1 1 3 1 3 1 4
## 1265743 1266840 1267079 1272822 1278662 1279124 1279363 1306612 1308907 1309392
## 1 1 1 4 1 11 1 2 4 2
## 1309574 1313444 1322593 1330315 1332220 1335546 1336072 1346675 1349448 1351410
## 5 3 3 2 6 1 2 1 1 2
## 1354091 1357615 1360957 1362318 1370345 1370378 1372655 1375211 1376854 1376870
## 1 1 1 1 2 1 1 2 1 1
## 1380740 1381078 1381342 1381599 1381896 1382654 1382829 1383413 1385251 1386168
## 13 1 4 6 2 1 5 1 1 12
## 1386226 1386234 1390137 1392810 1392893 1393453 1398148 1401801 1402064 1411438
## 2 10 1 1 1 1 9 1 1 15
## 1412790 1420694 1422666 1423615 1431667 1438027 1438035 1453232 1457530 1458850
## 1 3 2 12 1 1 6 1 1 1
## 1464668 1469675 1474964 1475011 1475201 1475284 1475532 1475607 1476464 1480086
## 1 1 16 9 12 11 1 1 6 1
## 1481514 1481720 1481795 1482975 1483627 1485218 1485762 1487008 1487040 1487339
## 1 1 1 1 1 1 1 1 1 1
## 1488469 1489822 1492149 1492255 1495365 1495407 1496314 1496355 1497007 1497056
## 1 1 1 1 15 3 1 2 15 12
## 1497551 1497825 1499300 1499748 1499961 1500354 1501188 1501451 1502517 1505494
## 2 1 1 1 1 4 2 7 1 14
## 1507045 1507094 1507250 1507276 1507532 1509108 1509181 1509421 1509496 1511351
## 1 13 12 1 12 1 1 1 2 13
## 1512789 1513159 1515360 1519149 1520279 1520287 1522721 1526466 1528520 1529981
## 1 1 1 1 1 3 1 1 1 1
## 1535103 1541879 1573328 1575323 1585652 1607944 1636489 1637602 1638816 1640556
## 1 1 1 1 1 1 1 1 1 10
## 1641521 1660448 1661271 1669647 1697234 1699933 1701002 1703487 1718055 1720275
## 10 1 16 1 2 1 1 1 1 2
## 1730803 <NA>
## 1 291
## [1] "Frequency table after encoding"
## COD_MOD_2016.
## 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000
## 1 1 4 18 3 5 4 19 4 1 2 5 2 2 2 1 1
## 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017
## 15 7 4 2 1 2 5 6 10 1 1 10 3 1 9 1 11
## 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034
## 13 1 5 21 4 4 4 1 18 1 2 1 6 8 2 2 1
## 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051
## 1 1 18 10 3 4 3 1 10 6 1 1 5 3 1 1 2
## 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068
## 1 1 1 4 1 4 2 1 3 3 3 1 4 1 3 1 2
## 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085
## 2 4 10 12 2 6 3 2 18 12 1 1 1 3 11 10 10
## 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102
## 2 3 2 12 13 1 6 10 2 4 1 12 2 17 3 1 13
## 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
## 2 23 1 1 1 4 1 1 1 1 2 5 2 5 7 3 16
## 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
## 12 1 3 9 3 3 7 6 1 1 7 1 14 13 13 16 2
## 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153
## 1 1 7 1 2 3 16 1 8 4 3 1 3 12 8 7 1
## 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170
## 3 1 3 2 2 12 7 6 3 7 2 5 11 1 2 2 10
## 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187
## 3 1 2 4 4 5 10 4 3 2 3 3 4 3 1 2 15
## 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204
## 1 2 6 2 13 9 4 2 2 2 6 8 9 1 1 2 9
## 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221
## 1 2 1 1 4 4 3 3 10 2 2 2 3 7 2 2 12
## 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238
## 4 1 12 1 2 2 1 1 3 7 1 10 13 7 3 2 2
## 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255
## 2 1 5 12 1 1 1 14 1 2 14 5 2 12 4 4 1
## 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272
## 1 1 2 1 13 3 1 1 1 1 1 7 1 6 1 3 2
## 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289
## 2 12 2 15 3 3 1 3 1 4 12 1 1 7 1 1 2
## 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306
## 2 1 9 3 2 1 11 3 10 5 5 10 2 2 1 13 11
## 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323
## 1 1 4 4 2 8 11 12 1 5 1 4 1 1 15 16 5
## 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340
## 1 2 2 16 1 2 1 15 10 12 2 4 6 1 4 11 3
## 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357
## 6 2 5 13 2 3 4 2 11 4 17 11 1 1 12 1 2
## 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374
## 1 2 13 2 9 6 4 3 1 3 1 2 2 7 1 11 1
## 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391
## 1 1 1 2 3 1 12 3 4 9 5 4 5 1 1 1 1
## 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
## 2 2 11 4 9 1 3 1 9 1 1 2 3 18 5 2 1
## 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425
## 4 2 2 4 1 11 5 1 1 10 5 2 1 18 2 11 1
## 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442
## 2 12 14 4 7 3 1 4 4 1 3 1 1 2 6 1 8
## 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459
## 5 10 1 5 1 1 24 11 7 1 1 12 5 4 1 2 1
## 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476
## 13 3 1 1 6 1 1 7 3 1 2 3 1 1 1 10 1
## 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493
## 3 13 13 3 16 17 8 3 10 1 1 1 3 6 1 4 8
## 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510
## 3 6 1 1 1 11 1 1 10 3 1 2 2 1 1 7 1
## 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527
## 1 3 1 7 1 5 1 8 1 8 5 2 2 10 2 16 8
## 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544
## 1 3 5 24 1 2 1 1 2 5 7 11 9 1 1 2 4
## 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561
## 2 6 3 1 3 8 1 1 1 4 1 1 3 4 4 4 2
## 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578
## 15 10 1 4 2 9 11 11 1 1 1 2 2 11 6 1 3
## 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595
## 2 1 9 5 1 2 1 2 1 7 10 20 2 1 1 1 1
## 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612
## 2 1 12 10 1 7 1 5 12 5 4 1 5 2 3 2 6
## 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629
## 6 4 12 11 2 1 1 4 4 14 1 3 3 2 1 10 2
## 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646
## 3 1 11 12 2 6 1 1 10 6 6 1 6 12 1 3 2
## 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663
## 1 1 1 1 12 5 5 1 9 14 3 2 3 2 2 13 11
## 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
## 5 3 1 1 11 8 1 1 1 3 10 6 1 3 1 6 15
## 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697
## 2 15 11 3 1 1 1 2 9 1 6 10 3 2 3 21 1
## 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714
## 5 2 2 2 1 1 1 1 3 10 5 2 28 1 9 15 11
## 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731
## 3 2 1 18 1 9 2 2 4 14 4 12 2 1 1 6 2
## 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748
## 2 3 2 2 1 1 6 4 4 3 3 1 3 1 1 15 3
## 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765
## 1 3 3 2 3 2 2 19 1 5 7 8 1 13 11 7 9
## 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782
## 3 6 15 1 2 1 14 15 1 6 1 10 11 1 1 13 1
## 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 <NA>
## 2 3 19 1 3 10 2 18 1 1 2 12 291
## [1] "Frequency table before encoding"
## CODLOCAL_2016. Codigo de local
## 102471 139511 139648 139766 139785 139846 139870 139889 139907 139926 139945 139988
## 1 1 2 2 5 7 14 12 2 2 5 6
## 140034 140048 140109 140114 140171 140190 140213 140246 140251 140265 140270 140289
## 15 10 3 12 12 5 1 1 2 4 2 1
## 140294 140500 141081 141255 142491 142518 142523 142537 142561 142603 143198 143400
## 4 2 1 1 9 11 3 11 19 5 1 1
## 143457 143462 143693 143711 143725 143730 143754 143768 144522 144560 144584 144602
## 8 10 4 2 13 11 3 1 5 10 1 7
## 144621 144635 144683 144697 144701 144744 144777 144800 144819 144824 144942 144980
## 2 1 3 4 15 1 5 6 1 4 1 1
## 145140 145381 146130 146187 146192 146253 146286 146328 146333 146352 146385 146432
## 12 1 10 2 5 11 2 2 3 9 12 2
## 146446 148129 148134 148313 148346 148577 148600 148681 148964 148978 148983 148997
## 4 12 2 12 2 1 2 11 2 1 11 9
## 149020 149044 149063 149077 149261 171799 255039 257986 278615 288030 288073 288092
## 11 2 10 1 12 1 1 1 1 1 2 2
## 288110 288148 288153 288167 288191 288214 288228 288233 288247 288266 288271 288327
## 3 13 5 1 1 3 4 2 1 11 2 10
## 288332 288346 288394 288431 288445 288450 288469 288474 288488 288493 288520 288539
## 6 11 1 10 3 1 3 4 2 5 3 2
## 288544 288676 288860 289652 291278 291283 291297 291301 291315 291396 291570 291631
## 3 2 17 1 11 5 6 3 3 1 1 1
## 291706 291711 291730 291792 291810 291848 291853 291914 291933 291985 291990 292013
## 12 5 6 1 4 8 5 4 6 7 10 9
## 292089 292094 292126 292150 292188 292193 292254 292268 292273 292329 292348 292367
## 1 4 6 10 5 2 11 2 2 3 2 1
## 292452 292471 292490 292517 292541 292579 292640 293041 293201 294979 295059 295097
## 8 1 10 12 6 2 12 1 1 14 1 7
## 295115 295139 295158 295573 295605 295648 295653 295747 295752 296129 296214 296761
## 15 3 6 2 1 2 6 8 3 1 1 2
## 296780 296799 296803 296822 296841 296855 296879 296884 296898 296902 296921 296978
## 2 5 3 3 6 2 3 2 1 2 3 7
## 296983 296997 297077 297355 297416 297464 298053 298067 298086 298091 298114 298133
## 3 2 2 1 1 1 5 1 1 3 12 2
## 298736 298741 298779 298784 298798 298802 298816 298821 298878 298897 298915 298920
## 5 13 1 1 12 3 2 6 4 2 12 9
## 298939 298944 299024 299043 299062 299076 299081 299793 299910 300249 300715 300739
## 2 3 7 2 3 4 2 1 2 2 1 8
## 300763 301258 301277 301282 301296 301338 301376 301404 301442 301456 301461 301550
## 19 3 3 1 4 5 15 6 2 3 2 2
## 301574 301611 301625 301649 301668 301673 301687 301692 301705 301710 301729 301748
## 6 2 23 3 16 2 1 9 1 2 3 3
## 301753 301772 301786 301791 301809 301814 301828 301833 301847 301852 301866 301871
## 1 10 4 3 18 1 7 9 2 10 1 8
## 301885 301890 301908 301913 301927 301932 302984 303035 303653 303733 303747 304266
## 7 1 3 2 3 28 1 1 1 1 5 3
## 304718 304799 304860 304884 304898 304916 304935 304940 304978 304983 304997 305020
## 2 4 3 7 15 11 10 2 6 7 1 5
## 305039 305044 305077 305082 305096 305124 305572 305789 305826 305831 305845 305850
## 1 5 1 5 4 12 1 13 5 1 2 1
## 305893 305911 305925 305930 305954 305973 305992 306005 306166 306661 306675 306717
## 2 2 10 2 1 2 2 4 1 11 9 6
## 306736 307038 307383 307415 307458 307477 307514 308684 308759 308797 308839 308863
## 2 1 1 9 12 9 8 3 1 2 13 1
## 308900 308919 308938 308943 308962 309773 310045 310050 310069 310093 310875 310899
## 7 4 4 18 1 1 1 3 4 2 3 6
## 310903 310960 310979 310984 310998 311035 311064 311097 311101 311120 311144 311158
## 4 4 3 2 3 14 2 8 1 2 9 13
## 311163 311200 311219 311224 311238 311549 311785 311870 312394 313478 313591 313652
## 1 6 3 4 3 1 1 1 1 10 2 2
## 313708 313732 313765 313845 313850 313930 314034 314048 314067 314091 314114 314152
## 11 11 5 3 1 12 1 1 6 2 3 1
## 314213 314227 314289 314519 314557 314604 314637 314958 315000 315019 315043 315062
## 10 2 13 18 1 10 1 1 7 3 2 9
## 315284 315302 315316 315321 315401 315910 315934 315948 315953 315972 316641 316679
## 3 1 3 1 6 3 1 15 3 8 16 7
## 318230 318348 319017 319084 319102 319116 319121 319159 319244 319263 319282 319300
## 10 9 1 8 14 4 5 11 12 21 6 15
## 319319 319324 319338 319343 319362 319376 319381 319404 320087 320105 320426 320596
## 2 1 1 12 4 1 3 2 1 14 10 17
## 320600 320619 320643 320662 320695 320704 320756 320775 320822 320841 320855 320860
## 4 13 1 1 4 2 10 5 6 2 4 5
## 320879 321063 321143 321195 321303 321416 321435 321869 321874 321888 321893 322128
## 2 2 1 1 1 2 1 5 3 3 6 1
## 322473 322982 323000 324014 324405 324429 324472 324491 324523 324537 324542 324561
## 1 8 1 12 7 32 12 10 7 2 12 10
## 324575 324599 324617 324622 324641 324655 324660 324679 324698 324702 324716 324740
## 10 2 12 1 4 11 1 2 2 21 2 24
## 324759 324764 324778 324797 324815 324844 324877 324882 324900 324919 324943 324957
## 1 8 2 10 13 11 8 10 3 6 11 2
## 325117 325141 325155 325202 325235 325259 325264 325297 325301 325315 325396 325400
## 11 1 4 2 1 16 2 3 6 2 15 11
## 325419 325438 325481 325508 326720 326843 327220 327772 328984 329337 329484 329549
## 4 20 1 10 1 16 1 1 1 5 7 8
## 329610 329629 329667 329672 329686 329691 329728 329733 329747 329752 329771 329790
## 11 10 5 6 9 1 1 1 2 2 4 13
## 329813 329889 329931 329950 329969 329974 329993 330722 330920 332132 332151 332165
## 3 3 9 1 10 4 1 2 1 1 1 15
## 332170 332448 332844 332943 332976 332995 333037 333056 333061 333075 333099 333103
## 1 1 3 12 7 6 1 2 5 5 10 18
## 333117 333136 333221 333235 333240 333297 333320 333377 333382 333396 333400 333424
## 7 3 2 4 3 5 14 5 3 2 8 10
## 333438 333443 333457 333462 333481 333513 333527 333551 333565 333570 333589 333594
## 3 4 3 11 5 18 2 8 2 10 4 7
## 333650 333674 333688 333749 334225 334715 335885 336111 336125 336229 336371 336903
## 5 3 3 1 1 1 1 2 2 2 1 1
## 337002 337422 337436 337441 337455 337460 338025 338681 338695 338704 338718 338723
## 1 4 4 3 4 6 1 12 16 4 10 1
## 338761 338775 338817 338841 338855 338884 340189 340231 340269 340274 340288 340325
## 2 1 21 16 1 4 2 2 12 1 3 7
## 341117 342843 342862 342923 342956 342999 343734 343753 343767 343772 343786 343791
## 1 13 3 3 3 2 2 23 18 18 3 2
## 343809 343814 343828 343871 343885 343908 343913 343951 344007 344026 344045 344088
## 3 6 6 11 8 9 10 14 2 4 4 6
## 344111 344125 344130 344168 344413 344903 345403 345832 346087 346493 346544 346563
## 12 16 3 1 1 1 1 1 1 3 13 2
## 346582 346624 346638 346643 346657 346681 346695 346704 346718 346723 346756 346775
## 3 12 12 11 1 3 22 3 1 16 15 12
## 346817 346836 346841 346855 346860 346879 346884 346997 347143 347195 347303 347807
## 18 15 7 2 9 24 13 1 1 1 1 1
## 348411 470067 480702 495746 504760 507301 507947 508447 513788 517946 519691 523971
## 3 1 1 1 2 2 1 2 1 1 2 1
## 524032 524414 525442 526328 526880 534347 534390 539298 541692 545322 545497 549103
## 1 1 1 5 1 1 1 4 1 1 1 1
## 549221 549264 552893 553185 555716 570745 571311 572396 573032 580283 585780 588830
## 1 1 1 1 1 3 2 12 1 7 1 1
## 589698 591333 593657 593940 594044 594138 594459 594464 594478 594690 594954 595171
## 1 1 13 1 1 4 2 1 1 1 1 4
## 595623 608461 611717 612236 623621 625682 627799 670066 686699 686717 687383 687528
## 1 1 4 1 1 1 1 1 4 1 16 1
## 687585 687793 687887 694208 697848 697872 698876 702171 702604 705235 705315 708535
## 11 1 11 2 1 1 1 1 1 1 1 2
## 709163 710133 710699 711905 711967 712090 712981 713546 713773 714683 715055 718426
## 4 1 1 1 1 1 1 10 2 1 1 4
## 720052 720387 720660 720820 721527 721773 722112 723611 724253 725870 742695 748391
## 1 1 4 1 1 7 3 1 1 13 1 1
## 783493 <NA>
## 1 297
## [1] "Frequency table after encoding"
## CODLOCAL_2016. Codigo de local
## 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822
## 3 4 9 1 1 6 2 5 1 10 1 3 1 2 1 1 1
## 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839
## 6 6 1 2 1 10 1 5 1 12 1 1 1 32 1 8 2
## 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856
## 1 10 8 22 7 4 3 1 19 4 5 1 12 3 24 1 1
## 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873
## 2 2 14 2 1 2 6 6 1 13 11 1 12 12 13 2 15
## 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890
## 2 6 11 3 11 8 3 1 1 12 4 12 3 19 11 3 1
## 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907
## 15 10 4 1 1 12 1 2 4 18 10 1 10 6 10 12 18
## 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924
## 3 5 3 10 1 10 4 11 1 1 8 2 12 3 2 1 18
## 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941
## 10 1 1 2 1 6 2 4 1 7 1 9 3 12 2 2 2
## 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958
## 1 12 5 2 10 2 1 13 7 2 3 1 2 15 5 9 5
## 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975
## 1 12 2 16 1 1 4 4 1 2 2 5 2 3 10 11 2
## 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 1 9 7 6 9 6 6 12 2 3 1 3 4 5 1 8 1
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009
## 1 17 4 5 1 15 3 18 2 13 2 2 2 4 8 9 13
## 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026
## 7 9 8 3 12 21 6 2 2 7 5 2 2 10 4 1 2
## 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043
## 4 2 5 4 3 9 6 11 10 18 1 4 21 13 3 3 1
## 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060
## 3 1 12 13 4 7 3 7 3 17 12 1 14 4 10 5 5
## 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077
## 23 1 3 4 5 9 11 10 2 1 7 9 10 10 5 3 3
## 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094
## 3 11 2 1 6 1 2 7 1 10 1 3 2 10 6 2 4
## 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111
## 2 15 14 2 16 1 12 2 1 5 3 6 2 1 2 4 2
## 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128
## 3 2 1 4 1 6 6 1 15 12 1 1 7 5 2 5 1
## 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145
## 5 1 1 4 16 1 13 12 3 11 1 2 4 2 2 2 16
## 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162
## 21 2 2 1 1 1 4 20 2 18 1 4 1 11 1 1 1
## 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179
## 11 1 1 5 2 1 2 2 10 1 1 3 12 3 1 1 15
## 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196
## 1 1 14 1 1 3 1 3 3 1 1 3 5 1 2 11 1
## 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213
## 2 2 2 2 3 28 5 1 10 10 1 1 6 2 5 1 1
## 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230
## 12 8 1 8 3 7 14 4 2 1 1 1 1 6 2 7 1
## 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247
## 1 4 1 5 1 4 6 3 3 1 3 9 18 10 10 11 3
## 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
## 1 2 1 5 2 9 11 6 3 14 11 3 3 2 2 24 2
## 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281
## 2 15 2 12 4 6 5 3 1 11 1 4 1 1 8 1 11
## 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298
## 2 3 3 2 1 6 1 1 13 14 1 3 2 1 1 6 2
## 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315
## 3 5 1 1 12 2 7 7 1 2 1 16 1 1 2 8 4
## 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332
## 3 1 3 11 1 3 11 1 1 2 13 3 1 9 2 2 1
## 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349
## 1 1 1 1 8 4 1 4 10 10 1 3 1 3 1 3 3
## 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366
## 7 2 15 13 2 12 11 1 1 10 5 1 4 1 2 3 3
## 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383
## 12 4 5 1 2 4 2 2 3 13 16 5 1 1 12 1 7
## 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400
## 10 3 1 8 6 1 1 2 1 11 3 1 1 3 2 5 10
## 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417
## 1 15 18 1 11 8 1 1 9 1 11 3 1 9 4 1 1
## 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434
## 2 1 12 1 3 5 2 1 9 1 2 13 1 2 7 1 3
## 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451
## 1 1 1 3 1 1 6 2 7 15 1 3 8 7 1 11 1
## 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468
## 1 2 1 8 1 1 8 3 1 4 16 1 1 1 9 16 10
## 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485
## 1 11 1 12 1 2 5 2 2 2 13 2 2 10 4 1 4
## 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502
## 7 1 12 2 6 1 1 1 1 2 3 6 4 1 1 23 1
## 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519
## 4 10 2 1 4 4 2 3 1 16 3 2 3 5 4 3 1
## 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
## 1 1 1 1 1 4 2 1 1 12 1 3 2 4 1 10 2
## 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553
## 6 6 2 1 1 1 1 13 7 2 4 4 11 7 12 5 2
## 1554 1555 1556 1557 1558 1559 1560 1561 1562 <NA>
## 4 1 3 2 2 3 2 1 1 297
## [1] "Frequency table before encoding"
## COD_MOD_2017. COD_MOD
## 207449 209304 209387 209510 209528 209536 209908 209916 209924 209940
## 2 5 5 5 2 8 17 8 5 3
## 209965 209973 210260 233056 236109 236117 236174 236224 236364 236778
## 13 3 2 2 4 9 9 2 4 9
## 239764 245647 245654 245662 245670 245688 245696 263095 273607 286625
## 1 3 1 12 9 15 9 1 1 1
## 305656 314500 322479 323345 325449 325464 325472 325480 325498 325506
## 4 2 9 2 7 8 13 4 7 7
## 325548 325555 325563 325589 325605 325613 325647 325662 325670 325696
## 3 4 8 7 3 2 11 1 8 1
## 325704 325712 327650 329151 329243 329342 329573 329755 329805 329813
## 17 1 2 1 1 1 13 11 11 1
## 330464 334649 334656 334664 334672 334680 334706 334714 334722 334730
## 11 6 3 12 10 1 13 3 7 3
## 334748 335091 336495 336537 336545 336560 336578 336586 336594 336602
## 9 1 5 4 4 7 3 6 4 1
## 336610 336628 336636 337212 337436 337568 337592 337717 337733 337741
## 4 17 4 1 13 6 4 1 6 4
## 337766 340224 340281 340299 340315 340349 340364 340372 340380 340398
## 4 7 1 5 14 10 1 6 7 1
## 340414 340422 340430 340448 340463 343566 356725 372680 427690 432773
## 4 5 1 3 4 6 1 1 1 3
## 433490 434282 436642 436667 437210 437228 437236 437244 437251 437269
## 1 1 3 1 4 32 15 15 2 5
## 437277 437285 437319 437335 437343 437350 437368 437400 437442 437475
## 11 12 12 3 11 8 3 8 1 1
## 437509 437525 437541 437707 437715 437723 437731 437749 437772 449819
## 4 1 3 4 10 4 7 2 2 4
## 449868 452565 466383 466730 469205 469700 481853 481903 482042 488619
## 9 1 1 14 4 6 2 5 3 10
## 488635 492876 493239 493544 493635 493841 495259 495424 495812 496166
## 9 1 3 10 1 1 3 1 12 10
## 497081 497198 498824 499699 500124 500348 500611 501411 501502 501601
## 1 1 3 20 1 11 4 6 3 9
## 501676 501700 501809 502047 502435 502484 502633 504993 505149 519645
## 7 1 2 2 3 1 9 6 1 5
## 520486 521179 522318 522862 523423 523621 523761 534321 535823 536029
## 21 4 2 1 5 2 1 1 6 4
## 536128 536151 536326 542357 546002 555862 555946 556266 556290 556472
## 9 2 14 1 9 3 5 1 1 7
## 556571 565143 565200 565234 565267 566141 566158 566166 566414 566430
## 10 1 5 7 2 18 3 1 2 17
## 566455 566463 566471 566489 567743 567750 567768 578260 578278 578286
## 5 1 15 1 2 7 1 2 4 8
## 578294 578310 578336 578351 578401 578443 578518 578526 578534 578542
## 1 1 1 5 6 3 9 11 18 11
## 579151 581710 581736 581744 581876 581892 581900 581991 582114 582148
## 9 6 13 7 6 2 6 1 2 1
## 582163 582254 582304 582312 582379 582387 582403 582411 582833 582866
## 8 2 4 10 1 5 10 5 6 4
## 582890 582932 582981 583013 583088 583328 583476 583534 583567 583591
## 13 3 9 7 8 6 3 1 6 15
## 591131 591164 591198 598581 599159 599365 601492 603878 605469 605501
## 2 2 7 11 1 8 10 7 12 4
## 607143 607556 607697 616185 628842 632471 639922 642801 642892 642926
## 2 8 1 10 7 2 9 3 3 2
## 643692 643783 643817 643874 644690 644880 647065 647172 649129 649897
## 5 3 4 1 4 4 3 9 3 1
## 650002 650036 656447 659698 659722 659896 659953 662940 662957 663005
## 1 5 1 1 4 10 2 2 6 3
## 663013 663096 663112 663120 663138 663153 663526 663534 663542 663559
## 1 6 6 1 6 1 1 6 5 10
## 663971 664292 664458 664698 664706 664748 664912 664920 665190 665265
## 10 2 1 21 1 15 1 1 1 2
## 665471 665489 667428 679027 690008 691931 692434 693499 693622 693655
## 1 7 1 1 4 16 10 6 11 3
## 694547 694562 694570 694588 694596 694604 703215 703223 703231 703249
## 1 3 8 7 2 8 7 10 2 8
## 703256 703744 703751 704460 705053 705160 705459 705475 705772 725770
## 2 11 13 9 8 1 1 1 5 10
## 725861 728196 728717 730515 732321 732339 732347 732354 732495 735035
## 3 2 12 2 5 1 2 1 4 1
## 743773 743799 743815 743831 744573 745448 751230 759555 759613 762104
## 7 1 4 9 2 1 1 1 8 1
## 762773 762781 762856 762864 762880 762906 762914 763169 763201 764134
## 9 1 7 2 10 14 7 3 1 1
## 764936 765297 765305 765313 765321 765396 765412 773788 773846 774026
## 12 9 9 2 1 10 6 6 3 9
## 774455 774679 774703 775833 775874 777334 777656 777680 777714 777995
## 3 3 8 3 4 2 16 23 1 5
## 778233 778738 778795 779041 779868 781278 781302 781369 782045 782078
## 8 10 7 3 3 4 6 18 1 1
## 782102 782664 785097 831305 832253 832279 832287 832337 833913 869198
## 5 7 8 1 1 10 3 4 1 5
## 869248 870931 872515 873679 874214 874230 874305 875443 875476 879791
## 1 1 11 1 6 1 1 1 1 9
## 879817 883884 884510 884528 884544 884551 884593 884601 884627 884981
## 1 1 5 4 11 12 3 1 1 1
## 897728 900647 900761 900910 900977 901033 901082 901413 901587 922054
## 1 1 6 10 5 9 1 1 1 1
## 927814 930537 933598 1006956 1007160 1007491 1008440 1008929 1008960 1009844
## 11 1 6 1 2 2 15 4 8 8
## 1010040 1010149 1010180 1034016 1034685 1039676 1041557 1041631 1045434 1045715
## 1 6 1 4 1 1 2 12 4 1
## 1045756 1045798 1048990 1049493 1053628 1053669 1054154 1054196 1054238 1054311
## 1 4 1 1 9 10 8 10 6 1
## 1054352 1054394 1054436 1054956 1055284 1056746 1056902 1057637 1063106 1063148
## 3 3 2 1 1 2 8 1 6 6
## 1063221 1063262 1063304 1063346 1064989 1066026 1068238 1069954 1070036 1070077
## 3 1 6 3 2 6 4 4 6 8
## 1070119 1070150 1070192 1071919 1072040 1072727 1074301 1075944 1080258 1082031
## 3 1 1 7 2 2 4 1 1 2
## 1082874 1083633 1083674 1083716 1083815 1083864 1084508 1085851 1085976 1087295
## 1 1 3 6 8 1 11 4 2 3
## 1089598 1095801 1097567 1097781 1127158 1141225 1159011 1194265 1194380 1194810
## 2 1 1 1 1 1 1 8 11 5
## 1195189 1195577 1199009 1221555 1223023 1225549 1226422 1238229 1240183 1240720
## 5 1 1 1 9 1 1 1 1 8
## 1241454 1242437 1242569 1242908 1244102 1247832 1248392 1255074 1257773 1258334
## 1 2 1 2 1 4 10 1 1 1
## 1259134 1264670 1266840 1267079 1267921 1272822 1278662 1279124 1306612 1308907
## 1 4 1 1 1 5 1 9 3 4
## 1309392 1309574 1311984 1312362 1316025 1316645 1320100 1322593 1324839 1330315
## 2 2 1 1 1 1 2 3 1 2
## 1332048 1332220 1335546 1336072 1342732 1345206 1346675 1349448 1354091 1354729
## 1 7 1 2 1 1 1 1 1 2
## 1357615 1362318 1363522 1370345 1372655 1375211 1376854 1376870 1380393 1380740
## 1 1 1 2 1 2 1 1 1 9
## 1381144 1381342 1381599 1381821 1381896 1382829 1383173 1385251 1386168 1386226
## 2 1 4 1 2 1 2 1 10 2
## 1386234 1392893 1393453 1398148 1399922 1411438 1412790 1420694 1422666 1423615
## 6 1 1 6 1 11 1 4 2 16
## 1426592 1431667 1438035 1453232 1457530 1458850 1464668 1469667 1469675 1472836
## 1 1 4 1 1 1 1 1 1 1
## 1474964 1475011 1475201 1475250 1475284 1475532 1476464 1476985 1477199 1480086
## 10 8 9 1 8 1 3 1 1 1
## 1481795 1482355 1482975 1483627 1484039 1485218 1487040 1487339 1487552 1488469
## 1 2 2 2 1 1 1 1 1 1
## 1489822 1492149 1495365 1495407 1496256 1496355 1497007 1497056 1497551 1497825
## 5 1 10 1 1 2 13 9 2 1
## 1499748 1500354 1501188 1501451 1502103 1505494 1507094 1507250 1507532 1508662
## 1 4 1 7 1 11 10 7 8 1
## 1509181 1509421 1510361 1511351 1512680 1512789 1513159 1513928 1519149 1520287
## 1 1 1 10 1 1 1 1 1 2
## 1522721 1529692 1529981 1534809 1535103 1535392 1537240 1539717 1549021 1569573
## 1 1 1 1 1 1 1 1 1 1
## 1573328 1575323 1577485 1585652 1595347 1636489 1638972 1639277 1640556 1641521
## 1 1 1 1 3 1 1 1 6 10
## 1660448 1661271 1662030 1664044 1664390 1669647 1697234 1699107 1699933 1701002
## 1 13 1 1 1 1 2 1 1 1
## 1703487 1718055 1720275 1732023 1734300 1748730 1748987 1749266 1754530 1754639
## 1 1 2 1 1 1 3 1 1 1
## <NA>
## 951
## [1] "Frequency table after encoding"
## COD_MOD_2017. COD_MOD
## 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855
## 1 1 7 10 3 1 1 2 1 1 12 6 12 1 8 4 1
## 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872
## 7 7 8 2 1 8 4 1 1 1 2 1 4 9 1 1 3
## 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889
## 1 3 1 5 1 1 1 8 1 1 2 2 21 8 9 7 2
## 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906
## 1 2 9 3 15 3 4 4 4 10 1 1 5 5 1 8 7
## 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923
## 8 1 7 10 17 9 1 1 9 1 1 1 1 1 11 5 2
## 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940
## 2 15 4 7 10 1 1 1 5 4 10 1 3 1 9 1 1
## 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957
## 3 5 1 1 9 1 1 1 2 2 10 6 1 1 1 1 5
## 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974
## 4 7 3 7 9 6 10 11 1 10 6 6 4 3 1 5 1
## 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991
## 3 5 1 1 2 1 1 9 10 2 8 3 1 3 3 1 4
## 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 9 9 1 10 3 7 9 3 2 2 1 1 9 1 9 12 3
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025
## 11 1 2 1 8 8 5 6 7 6 1 2 1 5 1 4 3
## 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042
## 1 8 1 1 6 2 10 1 1 4 10 7 1 1 3 5 10
## 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059
## 4 1 1 1 2 8 6 1 1 3 5 1 8 1 11 7 5
## 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076
## 3 9 1 2 2 1 1 1 3 1 17 7 3 1 7 4 1
## 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093
## 5 3 1 6 6 4 4 1 5 3 13 5 11 9 1 1 1
## 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110
## 1 5 1 13 13 2 1 3 4 2 2 4 3 2 1 6 9
## 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127
## 12 17 3 1 20 8 18 1 10 1 7 3 1 4 3 10 4
## 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144
## 4 1 1 1 32 10 1 1 2 1 5 1 1 2 12 8 7
## 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161
## 1 1 1 4 1 2 2 1 1 2 1 16 6 6 23 14 1
## 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178
## 1 4 1 7 8 21 1 2 1 1 16 1 6 4 1 4 1
## 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195
## 1 5 10 11 6 1 1 5 1 4 7 14 5 15 2 4 6
## 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212
## 5 9 1 6 3 1 1 1 1 9 4 2 3 11 13 6 1
## 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229
## 4 1 2 1 1 1 1 1 1 8 6 1 2 4 2 1 3
## 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246
## 6 1 1 4 6 1 11 1 1 1 7 1 12 1 6 1 1
## 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263
## 1 5 11 3 1 4 2 2 3 1 6 1 9 4 4 4 1
## 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
## 9 2 2 1 4 14 1 5 7 1 9 1 3 12 1 13 10
## 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297
## 2 2 9 1 1 15 6 12 1 15 1 4 1 3 4 6 1
## 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314
## 1 1 2 2 4 7 7 7 2 8 10 1 2 5 6 1 1
## 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331
## 10 1 1 3 1 1 3 3 4 3 11 1 3 2 1 1 4
## 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348
## 1 2 1 1 13 10 15 3 2 5 5 1 1 8 6 9 1
## 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365
## 13 4 2 1 11 10 8 1 1 9 7 8 10 1 4 5 15
## 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382
## 1 2 4 1 1 1 1 1 12 1 2 13 1 1 1 1 6
## 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399
## 7 2 11 1 11 1 4 8 2 10 9 2 2 2 1 1 13
## 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416
## 2 10 2 1 1 3 5 9 1 1 4 1 1 1 12 1 2
## 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433
## 1 18 1 4 11 11 1 1 10 1 5 3 1 8 1 4 3
## 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450
## 1 1 1 2 7 6 2 1 4 3 2 13 4 8 11 2 8
## 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467
## 2 8 10 1 11 2 1 8 1 9 2 6 1 10 1 1 6
## 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484
## 3 1 1 7 1 1 9 1 1 9 1 4 4 6 18 3 1
## 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501
## 7 1 2 1 4 6 6 1 14 6 6 1 1 3 2 8 17
## 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518
## 8 6 1 1 2 8 1 11 2 8 1 3 1 1 7 3 2
## 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535
## 10 1 9 1 3 1 2 3 1 10 11 3 3 2 6 2 10
## 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 <NA>
## 2 1 1 1 16 2 3 1 6 2 1 9 1 951
## [1] "Frequency table before encoding"
## CODLOCAL_2017. Codigo de local
## 42401 44570 52857 59806 68429 121262 139785 139870 139889 139907 139945 139988
## 1 1 1 1 1 1 5 13 10 2 4 7
## 140005 140034 140048 140091 140114 140133 140171 140190 140213 140246 140251 140265
## 1 14 7 1 10 3 12 4 1 1 3 5
## 140270 140289 140294 140500 141081 142491 142518 142523 142537 142561 142603 142735
## 5 2 6 2 1 6 6 5 9 17 8 1
## 143400 143462 143725 143730 143754 143768 143914 144522 144560 144602 144621 144683
## 5 13 9 8 3 1 1 1 6 5 1 2
## 144697 144701 144744 144777 144800 144824 144942 144980 145140 145381 146130 146187
## 3 21 2 1 4 4 1 1 9 1 7 2
## 146192 146253 146286 146328 146333 146352 146385 146432 146446 148129 148134 148313
## 4 8 2 2 3 9 9 2 4 10 2 11
## 148346 148600 148681 148964 148978 148983 148997 149020 149044 149063 149261 219254
## 2 2 9 2 1 9 6 7 1 6 10 1
## 220097 221049 223185 230713 275495 278615 287988 288054 288092 288110 288148 288167
## 1 1 1 1 1 1 1 1 3 4 6 2
## 288186 288214 288266 288271 288327 288346 288370 288431 288445 288450 288469 288474
## 1 3 7 1 7 5 1 9 4 1 4 4
## 288488 288493 288520 288539 288544 288676 288860 289336 289652 290410 291278 291283
## 5 6 3 4 4 2 14 1 1 1 10 5
## 291297 291301 291612 291631 291706 291711 291768 291848 291914 291947 291985 291990
## 4 3 1 1 8 4 4 6 4 1 4 7
## 292013 292070 292089 292094 292126 292150 292188 292254 292273 292305 292310 292329
## 10 1 2 4 16 14 10 9 2 1 1 2
## 292348 292367 292452 292490 292517 292579 292640 292678 293041 294979 295059 295097
## 3 1 5 6 9 2 8 1 1 11 4 4
## 295115 295139 295158 295648 295653 295747 295752 296129 296172 296214 296737 296761
## 11 1 7 1 7 12 3 5 1 1 3 1
## 296841 296855 296879 296884 296898 296902 296921 296978 296983 296997 297077 297176
## 8 1 8 4 1 3 3 6 1 2 2 1
## 297275 297464 298053 298067 298091 298114 298133 298736 298741 298779 298784 298798
## 1 1 4 1 5 9 3 6 11 3 1 8
## 298816 298878 298915 298920 298939 299024 299043 299062 299076 299081 299118 299793
## 1 3 9 8 2 7 2 2 6 2 1 1
## 299910 300249 300490 300739 300763 301376 301611 301625 301630 301649 301668 301692
## 2 1 1 5 9 10 3 18 1 7 13 9
## 301705 301710 301748 301753 301767 301772 301791 301809 301814 301828 301833 301847
## 3 2 3 2 1 7 3 10 1 10 11 6
## 301852 301866 301871 301885 301890 301908 301913 301927 301932 302229 302408 302955
## 11 2 9 10 4 3 2 4 32 1 1 1
## 302984 303035 303653 303733 303747 304266 304718 304860 304884 304898 304916 304935
## 1 1 1 2 9 3 1 2 6 10 6 3
## 304940 304959 304983 305020 305039 305082 305096 305124 305572 305789 305831 305850
## 4 1 6 4 3 6 5 10 1 12 1 1
## 305906 305911 305925 305930 305973 305992 306005 306166 306661 306675 306736 307038
## 1 5 7 1 2 1 6 1 15 10 2 1
## 307383 307415 307458 307477 307514 307528 308684 308759 308839 308863 308900 308919
## 2 6 12 10 5 1 2 1 8 2 9 5
## 308938 308943 308962 308981 309688 310045 310050 310069 310093 310903 310960 310979
## 4 12 1 1 1 1 3 4 2 2 5 1
## 310998 311016 311035 311064 311097 311101 311120 311144 311158 311163 311182 311200
## 1 1 15 3 6 1 2 6 11 2 1 8
## 311219 311224 311238 311870 312394 313478 313591 313652 313708 313732 313765 313845
## 7 4 3 1 1 10 2 5 5 10 4 2
## 313930 314067 314152 314213 314227 314289 314487 314519 314557 314604 314637 314736
## 7 7 1 10 6 10 1 12 1 7 1 1
## 314958 315000 315043 315062 315302 315340 315401 315948 315972 316641 316660 316679
## 1 8 2 8 1 4 9 13 3 13 1 9
## 318230 318348 318414 319084 319102 319116 319121 319159 319178 319244 319263 319300
## 10 8 1 5 10 3 4 8 1 6 18 13
## 319319 319324 319343 319357 319362 319376 319404 319574 319932 320087 320105 320186
## 2 1 9 1 3 1 5 1 1 1 8 1
## 320426 320596 320619 320662 320704 320756 320775 320841 320855 320860 320879 321063
## 5 13 15 2 6 6 1 4 7 4 6 1
## 321143 321303 321416 321435 321789 321888 321893 322128 322982 324014 324429 324472
## 1 1 2 1 1 4 4 1 7 8 24 9
## 324486 324491 324523 324537 324542 324561 324575 324599 324617 324622 324641 324655
## 2 11 8 2 7 11 7 2 10 1 2 6
## 324660 324679 324702 324740 324764 324778 324797 324815 324844 324877 324882 324900
## 2 2 15 16 7 2 10 7 10 6 7 5
## 324924 324938 324943 325117 325198 325202 325235 325240 325259 325264 325297 325301
## 3 1 9 9 3 3 1 1 17 3 4 7
## 325315 325396 325400 325419 325438 325481 325508 326720 326838 326843 327772 329549
## 1 14 8 5 18 1 7 1 1 10 1 5
## 329610 329629 329672 329686 329728 329747 329752 329771 329785 329813 329865 329889
## 6 9 3 8 1 6 4 2 1 3 3 1
## 329931 329945 329950 329969 329974 329993 330920 332132 332151 332165 332170 332245
## 8 1 7 11 21 3 1 1 1 11 3 1
## 332448 332844 332938 332943 333061 333075 333099 333103 333136 333221 333235 333259
## 1 1 1 11 10 5 6 17 4 4 1 1
## 333320 333377 333396 333400 333424 333443 333462 333513 333527 333551 333565 333570
## 11 6 2 6 10 12 12 15 3 8 5 12
## 333589 333594 333674 333749 334857 335183 335885 336111 336125 336229 336253 336371
## 5 12 4 1 2 2 1 3 4 3 1 1
## 336903 337002 337422 337436 337441 337455 337460 337511 338025 338681 338695 338704
## 1 1 3 6 4 6 4 1 1 9 12 4
## 338718 338723 338761 338775 338817 338841 338855 338860 338884 340170 340189 340231
## 8 1 1 1 15 9 1 1 4 2 4 3
## 340269 340274 340288 341117 342329 342843 342862 342956 342999 343734 343753 343767
## 8 3 3 1 1 8 2 11 4 2 15 11
## 343772 343786 343791 343809 343814 343828 343833 343871 343885 343908 343913 343951
## 9 2 3 2 6 8 1 9 5 8 6 10
## 344026 344045 344088 344111 344125 344286 344823 344984 345139 345403 345460 345832
## 3 5 3 9 10 1 1 1 2 1 1 1
## 346087 346544 346558 346577 346624 346638 346643 346681 346695 346718 346723 346737
## 1 7 1 1 8 8 7 3 13 3 10 1
## 346756 346775 346817 346836 346841 346855 346860 346879 346884 346997 347911 348411
## 9 11 11 9 7 3 11 20 17 1 1 3
## 352752 382320 400215 435719 461968 468998 477238 483456 487628 495746 504760 507301
## 1 1 1 1 1 1 1 1 1 1 2 6
## 507947 508447 513222 513788 516051 517946 523971 524032 524414 526880 527634 534347
## 1 2 1 1 1 1 1 1 1 1 1 1
## 534390 539298 541692 545497 549264 552893 554967 567941 570745 570873 571311 572396
## 1 6 1 1 1 1 1 1 4 2 2 16
## 573032 580283 585780 588830 589698 589702 591333 593657 593940 594044 594119 594138
## 1 4 1 1 1 1 1 9 2 1 1 1
## 594459 594464 595171 595623 595656 602001 602020 611717 612236 618403 623452 623621
## 2 1 4 1 1 1 1 2 2 1 1 1
## 626144 664857 686623 686699 686717 687383 687528 687585 687887 694208 694374 696146
## 1 3 1 5 1 13 1 8 9 2 1 1
## 697424 697848 697872 698876 702171 702543 705315 708535 709163 710133 710699 711967
## 1 2 1 1 1 2 1 2 4 1 1 1
## 712090 712981 713546 713773 714348 714485 715055 717078 717304 718582 720387 720660
## 1 1 13 1 1 1 1 1 1 1 1 4
## 721773 723611 724253 725851 725870 742695 744072 746368 746599 783493 <NA>
## 2 1 1 1 10 1 1 1 1 1 966
## [1] "Frequency table after encoding"
## CODLOCAL_2017. Codigo de local
## 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732
## 5 1 6 1 3 1 6 1 1 2 8 5 1 1 3 6 3
## 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749
## 1 11 9 13 1 1 6 1 1 1 3 9 1 9 6 1 1
## 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766
## 1 1 3 2 1 4 5 1 1 15 2 1 9 1 8 1 3
## 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783
## 1 5 2 1 1 1 11 9 1 9 21 4 4 1 2 2 5
## 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 3 1 1 7 1 10 7 10 1 6 11 3 9 1 11 1 4
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817
## 9 5 7 2 16 9 4 1 8 7 14 4 1 2 2 16 2
## 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834
## 1 1 11 1 2 1 4 13 3 3 12 1 1 6 1 6 10
## 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851
## 2 1 1 2 2 6 9 1 1 9 1 4 1 10 9 11 2
## 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868
## 10 1 2 2 1 18 1 7 1 1 1 7 4 4 12 3 1
## 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885
## 1 8 9 1 8 2 1 2 1 3 3 5 3 1 7 10 17
## 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902
## 5 1 2 5 4 4 14 11 4 12 6 1 5 1 1 4 6
## 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919
## 3 4 7 1 9 2 1 14 13 3 3 1 13 11 10 4 10
## 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936
## 1 6 1 2 1 12 8 11 10 17 3 2 1 1 2 2 1
## 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953
## 10 1 1 1 1 3 4 8 3 1 1 4 1 9 11 2 1
## 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970
## 1 2 1 1 4 1 1 1 10 8 21 5 2 1 3 1 9
## 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987
## 1 1 9 1 3 2 1 7 3 2 1 4 13 1 1 1 2
## 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004
## 2 1 3 1 8 3 1 4 2 7 12 1 12 1 10 8 1
## 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021
## 6 1 11 9 1 1 13 2 2 1 5 7 10 2 4 18 15
## 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038
## 1 1 6 1 9 15 6 2 1 10 5 4 3 2 1 6 2
## 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055
## 10 6 15 1 1 4 11 3 2 7 1 1 1 10 8 4 4
## 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 1 11 1 32 2 6 1 1 1 2 9 8 1 1 1 1 2
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089
## 4 10 5 1 3 1 4 1 14 8 3 1 3 1 9 1 17
## 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106
## 4 4 1 1 3 5 1 6 1 1 1 1 15 9 4 5 1
## 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123
## 5 1 3 1 1 1 9 1 2 10 6 1 1 2 4 11 1
## 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140
## 11 1 3 10 1 1 24 12 2 3 1 3 4 6 6 5 1
## 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157
## 1 3 6 15 1 5 1 10 1 2 4 1 7 5 8 1 8
## 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174
## 8 4 8 2 2 1 2 1 3 8 1 6 3 6 5 5 3
## 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191
## 1 5 1 2 4 1 1 7 1 7 10 7 3 5 1 10 3
## 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208
## 2 5 16 7 1 1 1 3 11 17 1 2 6 6 2 6 1
## 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225
## 5 12 9 1 2 1 3 1 1 1 1 12 10 1 1 7 1
## 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242
## 1 2 9 1 1 9 1 1 3 1 15 4 3 7 10 4 1
## 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
## 4 3 6 4 3 6 10 2 1 1 6 1 4 3 1 20 5
## 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276
## 1 1 4 8 7 1 4 3 4 1 10 4 4 7 13 9 2
## 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293
## 1 1 1 2 1 13 6 1 7 6 3 2 7 1 1 2 2
## 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310
## 3 7 5 10 8 1 1 1 6 1 4 1 3 4 8 3 6
## 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327
## 1 13 7 2 1 9 2 1 1 1 1 1 10 5 1 1 1
## 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 8 5 9 1 9 8 2 8 5 6 8 8 1 10 7 2 7
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361
## 5 1 7 1 4 2 2 2 3 1 1 1 4 1 18 2 2
## 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
## 1 1 8 10 8 8 1 4 3 1 1 9 7 2 12 6 6
## 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395
## 6 8 9 1 2 1 1 11 9 2 2 10 6 1 4 1 1
## 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 <NA>
## 13 1 1 2 1 1 1 1 2 1 12 4 11 7 966
## [1] "Frequency table before encoding"
## cod_mod2016_admin.
## 207449 207795 207894 207985 208058 208348 208538 208546 208561 208587
## 2 2 1 2 4 2 4 3 7 1
## 208694 208736 209304 209387 209510 209528 209536 209908 209916 209924
## 1 6 3 4 2 1 6 18 11 3
## 209940 209965 209973 210260 215632 233056 236109 236117 236174 236224
## 5 9 3 2 8 2 4 9 11 2
## 236364 236778 245647 245662 245670 245688 245696 245704 305656 314500
## 4 11 3 19 7 11 7 1 7 2
## 317131 317214 317289 317305 317313 317370 317453 317479 317560 317610
## 5 2 3 1 4 1 2 2 4 1
## 317941 318063 318089 318287 318352 318741 318782 318824 318949 319004
## 1 4 4 1 3 3 1 1 1 1
## 319020 319061 319145 319160 319285 320655 322453 322479 322685 322974
## 1 1 2 2 4 1 3 16 3 3
## 323345 323378 325449 325464 325472 325480 325498 325506 325548 325555
## 3 14 7 14 18 1 1 6 1 2
## 325563 325589 325605 325613 325647 325662 325670 325704 327650 328039
## 12 7 2 2 9 1 8 12 3 3
## 328047 328260 328401 328443 328468 328484 328518 328526 329029 329128
## 3 2 1 2 1 1 5 5 3 1
## 329151 329243 329573 329755 329805 330464 333666 333690 334094 334649
## 1 1 20 10 3 15 6 3 1 4
## 334656 334664 334672 334680 334706 334714 334722 334730 334748 334847
## 1 8 13 1 10 3 6 3 10 1
## 334920 334987 335042 335091 335224 336495 336537 336545 336560 336586
## 1 9 4 15 1 2 4 3 10 5
## 336594 336610 336628 336636 337436 337568 337592 337733 337741 337766
## 2 3 15 5 15 5 4 4 3 3
## 338228 338301 338343 338517 338640 338665 338848 339051 339317 339432
## 2 4 1 6 5 3 1 6 1 1
## 339499 339606 339804 340224 340281 340299 340315 340349 340372 340380
## 1 6 1 10 1 3 10 9 2 10
## 340398 340414 340422 340448 340463 343566 405324 432773 432906 433227
## 1 5 2 1 4 2 1 3 1 5
## 433235 433276 433490 433540 433680 433821 433961 434019 434076 434191
## 2 6 6 4 6 8 4 4 2 3
## 434282 434464 434480 434498 434506 434548 434597 434829 436170 436212
## 3 3 4 5 2 3 2 4 1 8
## 436287 436303 436360 436444 436451 436493 436543 436584 436634 436642
## 1 2 5 5 6 5 2 1 4 2
## 436725 436766 436782 437210 437228 437236 437244 437251 437269 437277
## 3 6 1 7 31 20 13 2 2 12
## 437285 437319 437335 437343 437350 437368 437400 437509 437525 437541
## 10 13 6 13 6 2 7 2 1 2
## 437707 437715 437723 437731 437749 437772 449868 466383 466730 468488
## 3 7 2 3 1 1 10 2 18 2
## 468611 469205 469700 481853 481903 482042 482091 488619 488635 493239
## 2 2 8 6 8 1 6 10 11 1
## 493544 495259 495812 496166 496844 497024 499699 500124 500348 500611
## 15 3 4 13 3 3 26 1 19 3
## 501411 501502 501601 501676 501809 501908 502435 502633 504993 505149
## 2 3 10 11 4 1 2 20 5 2
## 508903 510305 510800 513614 516674 519645 520486 521179 522318 522862
## 5 1 1 2 3 3 4 4 2 2
## 523423 523464 523621 523662 523761 526301 528380 534321 535823 536029
## 4 3 2 1 5 4 4 1 4 3
## 536128 536151 536326 546002 555847 555862 555946 556266 556357 556472
## 7 1 16 18 1 9 5 2 1 3
## 556548 556571 565119 565143 565200 565234 565267 566141 566158 566414
## 2 13 4 2 2 12 2 22 2 4
## 566430 566455 566463 566471 567743 567750 567768 578260 578278 578286
## 16 4 2 14 1 9 1 3 4 13
## 578336 578351 578401 578443 578518 578526 578534 578542 579151 581710
## 1 11 13 2 11 11 22 8 12 2
## 581728 581736 581744 581777 581876 581892 581900 581991 582114 582122
## 2 17 4 1 5 2 8 4 1 1
## 582148 582163 582254 582304 582312 582387 582403 582411 582833 582866
## 1 3 3 4 5 12 11 8 12 4
## 582890 582932 582981 583013 583088 583328 583476 583534 583567 583591
## 15 5 13 10 7 4 5 1 6 26
## 583922 591131 591164 591198 598581 599159 599365 601492 601708 603878
## 3 2 2 11 11 2 15 9 1 11
## 605469 605501 607143 607424 607556 616185 628404 628602 628842 629261
## 17 4 2 1 5 12 2 2 3 2
## 629295 632299 632471 639112 639922 642801 642892 643692 643783 643817
## 2 3 1 3 5 3 3 5 2 7
## 644690 644880 647172 649129 649202 649947 650002 650036 652081 656447
## 4 3 10 2 1 6 1 10 1 1
## 659698 659722 659896 659953 662940 662957 663005 663013 663096 663112
## 3 6 12 4 2 2 3 1 9 9
## 663120 663138 663526 663534 663542 663559 663682 663971 664292 664490
## 3 10 1 4 7 11 5 12 2 1
## 664508 664698 664706 664722 664748 664920 665265 665489 691931 692434
## 1 15 1 1 20 1 2 9 3 8
## 693499 693622 693655 694547 694562 694570 694588 694596 694604 697557
## 12 14 4 2 3 12 13 2 11 3
## 703124 703215 703223 703231 703249 703256 703736 703744 703751 704072
## 3 11 12 1 11 1 2 9 16 1
## 704312 704445 704460 704965 705053 705129 705160 705475 705772 725523
## 2 3 7 1 9 5 1 1 1 1
## 725770 725861 728055 728196 728717 730515 732321 732347 732495 735035
## 7 4 2 4 12 2 1 1 7 2
## 739367 743773 743815 743831 744540 744557 744573 751230 759399 759555
## 1 12 5 13 3 6 2 1 6 1
## 759613 762120 762468 762773 762856 762864 762880 762906 762914 763151
## 12 1 3 12 13 2 16 12 5 6
## 764035 764076 764134 764779 764936 765297 765305 765313 765321 765396
## 1 2 1 6 7 17 10 2 1 6
## 765412 765859 772970 773788 774026 774455 774679 774703 775312 775833
## 4 7 1 12 15 4 2 12 8 3
## 775874 777110 777144 777656 777680 777995 778233 778738 778795 779041
## 3 3 2 25 18 8 13 13 9 2
## 779868 780759 780767 780791 781278 781302 781351 781369 781385 781831
## 2 1 1 2 6 9 4 22 1 3
## 781930 782102 782664 785097 820407 821082 824003 824813 825752 828962
## 1 7 10 11 4 10 1 6 1 1
## 832253 832279 832287 832303 832311 832337 834853 835058 846048 847087
## 1 7 2 4 5 1 3 7 7 2
## 855791 869198 869248 870931 871160 872127 872515 874198 874214 875476
## 2 9 1 4 1 1 13 1 10 1
## 879791 879817 883884 884510 884528 884544 884551 884593 884627 885517
## 12 1 1 5 2 14 10 2 1 1
## 900670 900761 900795 900910 900977 901033 901082 901413 901587 915256
## 1 10 1 11 3 11 2 1 1 2
## 927814 928200 933598 1007160 1008440 1008929 1008960 1009844 1010040 1010149
## 12 1 9 1 21 2 15 6 2 10
## 1010180 1033729 1034016 1034685 1039676 1041516 1041557 1041631 1045111 1045277
## 1 2 4 1 1 1 2 17 4 1
## 1045434 1045715 1045798 1046226 1048990 1049493 1053628 1053669 1054154 1054196
## 4 1 10 1 3 1 14 14 12 15
## 1054238 1054352 1054394 1054436 1056902 1063023 1063106 1063148 1063221 1063304
## 12 4 4 2 9 1 9 12 10 9
## 1064989 1066026 1068238 1069954 1070036 1070077 1070390 1070481 1071919 1072040
## 3 7 6 4 7 12 5 1 14 2
## 1072727 1074301 1075779 1080068 1080258 1082031 1082874 1083633 1083674 1083716
## 2 5 1 7 7 2 1 2 2 4
## 1083815 1084508 1084987 1085851 1085976 1087295 1088400 1099654 1194265 1194380
## 11 13 1 3 2 4 5 1 10 19
## 1194810 1195189 1195577 1196526 1223023 1225549 1238229 1240720 1241082 1241454
## 4 11 1 4 9 1 5 9 1 1
## 1242908 1247832 1248392 1248509 1254192 1258334 1258649 1261742 1264340 1264670
## 1 3 11 1 1 1 3 3 1 4
## 1266840 1272822 1278662 1279124 1279363 1308907 1309392 1309574 1313444 1322593
## 2 4 1 13 1 4 3 8 3 3
## 1330315 1332220 1335637 1336072 1346675 1349448 1351410 1354091 1360957 1362318
## 1 6 1 2 1 1 2 1 1 1
## 1370345 1370378 1375211 1376870 1380740 1381078 1381110 1381144 1381342 1381375
## 2 1 2 1 14 1 1 2 4 1
## 1381599 1381862 1381896 1382829 1385251 1386168 1386226 1386234 1390137 1392893
## 8 1 2 3 1 14 2 11 1 1
## 1393453 1398148 1401801 1411438 1420694 1422666 1423615 1431667 1438027 1438035
## 1 8 1 16 3 2 12 1 2 4
## 1453232 1458850 1464668 1469675 1473644 1474600 1474964 1475011 1475201 1475284
## 1 1 1 1 1 2 15 10 12 11
## 1476258 1476464 1480086 1481514 1481720 1481795 1482975 1483627 1487339 1489822
## 2 6 1 1 1 1 1 1 1 1
## 1492255 1493964 1495365 1495407 1496314 1496355 1497007 1497056 1497551 1499748
## 1 1 18 5 1 2 16 11 1 1
## 1499961 1500354 1501188 1501451 1505494 1507094 1507250 1507276 1507532 1509108
## 1 4 2 9 14 13 12 1 12 1
## 1509496 1511351 1512789 1513159 1515360 1520279 1520287 1522721 1528520 1529981
## 2 13 3 1 1 1 3 1 1 1
## 1536994 1541879 1573328 1575323 1607944 1640556 1641521 1661271 1697234 1699933
## 1 1 1 1 1 10 10 16 3 1
## 1701002 <NA>
## 1 107
## [1] "Frequency table after encoding"
## cod_mod2016_admin.
## 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
## 3 1 8 7 11 3 8 8 11 1 7 13 5 10 13 14 1
## 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913
## 7 2 1 1 3 1 4 1 1 9 11 2 2 1 1 9 4
## 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930
## 11 5 1 2 10 20 10 4 2 8 10 5 1 1 7 12 1
## 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947
## 1 2 10 22 1 6 2 7 1 2 2 1 1 2 14 16 9
## 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964
## 7 1 12 10 6 4 2 13 1 2 10 12 2 3 1 11 1
## 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981
## 1 2 2 2 1 5 6 3 3 12 14 1 1 1 4 1 5
## 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998
## 12 1 2 2 4 9 1 2 2 14 2 1 1 3 1 5 1
## 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015
## 1 7 2 2 2 12 2 8 1 3 1 3 9 8 7 2 3
## 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032
## 4 4 5 2 18 4 1 11 4 1 2 1 3 4 12 2 4
## 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
## 2 1 5 3 13 12 1 1 17 3 4 1 2 1 14 4 5
## 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066
## 5 2 15 2 3 9 9 4 15 2 2 4 4 4 1 3 2
## 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083
## 13 4 4 1 7 5 3 1 15 6 1 2 15 1 1 1 1
## 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100
## 11 2 3 3 3 3 3 4 3 4 5 12 5 3 1 10 3
## 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117
## 12 3 3 6 10 13 2 2 1 9 1 16 3 3 3 1 3
## 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134
## 1 4 12 3 4 3 1 2 1 1 13 4 3 10 1 1 11
## 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151
## 22 2 2 5 2 11 13 4 5 3 2 4 18 5 2 1 1
## 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
## 2 2 5 3 9 6 1 4 16 1 1 4 1 3 1 1 1
## 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185
## 5 7 1 9 3 1 11 12 1 4 10 4 2 3 7 4 8
## 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202
## 4 3 1 1 1 6 1 1 8 1 5 2 12 6 2 1 12
## 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219
## 2 5 1 12 5 7 3 5 9 1 11 4 2 7 1 3 14
## 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236
## 15 4 1 4 2 11 2 1 19 5 1 17 2 3 10 1 3
## 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253
## 2 3 17 3 10 2 2 12 12 2 2 6 1 16 1 6 1
## 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270
## 6 9 4 12 7 4 16 7 4 15 2 9 12 4 4 2 2
## 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
## 1 13 6 10 4 1 4 20 6 16 8 2 1 1 18 1 8
## 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304
## 19 4 6 3 2 11 2 13 11 25 1 2 1 7 1 1 13
## 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321
## 1 4 2 3 14 7 18 13 8 2 14 3 2 10 3 3 3
## 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338
## 2 14 6 1 1 1 12 1 1 2 12 3 1 1 11 3 6
## 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355
## 1 1 4 4 3 1 1 2 1 1 1 1 13 1 3 5 1
## 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372
## 2 2 2 5 2 15 11 3 2 10 14 2 10 1 1 2 10
## 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389
## 1 1 6 12 2 1 1 1 4 1 4 1 11 8 1 6 2
## 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406
## 1 7 3 3 2 1 11 1 10 13 1 3 3 4 1 10 2
## 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423
## 6 15 1 10 15 13 12 6 9 6 5 1 2 1 20 14 20
## 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
## 1 1 1 9 2 2 1 1 3 1 11 19 12 1 2 2 3
## 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457
## 4 1 12 1 6 12 2 2 5 6 1 3 11 1 3 5 3
## 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474
## 2 1 5 2 1 4 5 11 2 2 11 7 11 26 11 13 1
## 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491
## 12 3 3 2 4 2 1 8 2 1 1 1 1 3 1 4 1
## 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508
## 8 2 6 2 1 1 5 1 3 3 9 7 2 7 5 4 2
## 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525
## 1 2 6 1 6 15 18 1 6 2 1 17 1 1 3 1 1
## 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542
## 3 4 4 3 4 1 15 1 7 2 4 6 8 1 6 3 2
## 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559
## 2 1 1 6 3 3 4 2 1 11 9 4 5 31 9 3 1
## 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576
## 6 9 1 5 10 7 1 2 2 1 1 12 1 12 10 16 7
## 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593
## 9 4 7 1 1 1 9 4 3 1 2 26 1 1 8 3 11
## 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610
## 2 1 1 1 9 1 3 2 2 21 1 5 2 10 4 10 5
## 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627
## 4 4 2 18 2 10 4 1 1 13 1 4 1 13 9 1 13
## 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644
## 10 2 4 22 3 3 7 3 10 2 1 1 3 12 1 15 1
## 1645 1646 1647 1648 1649 1650 <NA>
## 2 1 2 1 2 16 107
## [1] "Frequency table before encoding"
## cole2016_admin.
## 1007160 1008440 1008929 1008960 1009844 1010040 1010149 1010180 1033729
## 107 1 21 2 15 6 2 10 1 2
## 1034016 1034685 1039676 1041516 1041557 1041631 1045111 1045277 1045434 1045715
## 4 1 1 1 2 17 4 1 4 1
## 1045798 1046226 1048990 1049493 1053628 1053669 1054154 1054196 1054238 1054352
## 10 1 3 1 14 14 12 15 12 4
## 1054394 1054436 1056902 1063023 1063106 1063148 1063221 1063304 1064989 1066026
## 4 2 9 1 9 12 10 9 3 7
## 1068238 1069954 1070036 1070077 1070390 1070481 1071919 1072040 1072727 1074301
## 6 4 7 12 5 1 14 2 2 5
## 1075779 1080068 1080258 1082031 1082874 1083633 1083674 1083716 1083815 1084508
## 1 7 7 2 1 2 2 4 11 13
## 1084987 1085851 1085976 1087295 1088400 1099654 1194265 1194380 1194810 1195189
## 1 3 2 4 5 1 10 19 4 11
## 1195577 1196526 1223023 1225549 1238229 1240720 1241082 1241454 1242908 1247832
## 1 4 9 1 5 9 1 1 1 3
## 1248392 1248509 1254192 1258334 1258649 1261742 1264340 1264670 1266840 1272822
## 11 1 1 1 3 3 1 4 2 4
## 1278662 1279124 1279363 1308907 1309392 1309574 1313444 1322593 1330315 1332220
## 1 13 1 4 3 8 3 3 1 6
## 1335637 1336072 1346675 1349448 1351410 1354091 1360957 1362318 1370345 1370378
## 1 2 1 1 2 1 1 1 2 1
## 1375211 1376870 1380740 1381078 1381110 1381144 1381342 1381375 1381599 1381862
## 2 1 14 1 1 2 4 1 8 1
## 1381896 1382829 1385251 1386168 1386226 1386234 1390137 1392893 1393453 1398148
## 2 3 1 14 2 11 1 1 1 8
## 1401801 1411438 1420694 1422666 1423615 1431667 1438027 1438035 1453232 1458850
## 1 16 3 2 12 1 2 4 1 1
## 1464668 1469675 1473644 1474600 1474964 1475011 1475201 1475284 1476258 1476464
## 1 1 1 2 15 10 12 11 2 6
## 1480086 1481514 1481720 1481795 1482975 1483627 1487339 1489822 1492255 1493964
## 1 1 1 1 1 1 1 1 1 1
## 1495365 1495407 1496314 1496355 1497007 1497056 1497551 1499748 1499961 1500354
## 18 5 1 2 16 11 1 1 1 4
## 1501188 1501451 1505494 1507094 1507250 1507276 1507532 1509108 1509496 1511351
## 2 9 14 13 12 1 12 1 2 13
## 1512789 1513159 1515360 1520279 1520287 1522721 1528520 1529981 1536994 1541879
## 3 1 1 1 3 1 1 1 1 1
## 1573328 1575323 1607944 1640556 1641521 1661271 1697234 1699933 1701002 207449
## 1 1 1 10 10 16 3 1 1 2
## 207795 207894 207985 208058 208348 208538 208546 208561 208587 208694
## 2 1 2 4 2 4 3 7 1 1
## 208736 209304 209387 209510 209528 209536 209908 209916 209924 209940
## 6 3 4 2 1 6 18 11 3 5
## 209965 209973 210260 215632 233056 236109 236117 236174 236224 236364
## 9 3 2 8 2 4 9 11 2 4
## 236778 245647 245662 245670 245688 245696 245704 305656 314500 317131
## 11 3 19 7 11 7 1 7 2 5
## 317214 317289 317305 317313 317370 317453 317479 317560 317610 317941
## 2 3 1 4 1 2 2 4 1 1
## 318063 318089 318287 318352 318741 318782 318824 318949 319004 319020
## 4 4 1 3 3 1 1 1 1 1
## 319061 319145 319160 319285 320655 322453 322479 322685 322974 323345
## 1 2 2 4 1 3 16 3 3 3
## 323378 325449 325464 325472 325480 325498 325506 325548 325555 325563
## 14 7 14 18 1 1 6 1 2 12
## 325589 325605 325613 325647 325662 325670 325704 327650 328039 328047
## 7 2 2 9 1 8 12 3 3 3
## 328260 328401 328443 328468 328484 328518 328526 329029 329128 329151
## 2 1 2 1 1 5 5 3 1 1
## 329243 329573 329755 329805 330464 333666 333690 334094 334649 334656
## 1 20 10 3 15 6 3 1 4 1
## 334664 334672 334680 334706 334714 334722 334730 334748 334847 334920
## 8 13 1 10 3 6 3 10 1 1
## 334987 335042 335091 335224 336495 336537 336545 336560 336586 336594
## 9 4 15 1 2 4 3 10 5 2
## 336610 336628 336636 337436 337568 337592 337733 337741 337766 338228
## 3 15 5 15 5 4 4 3 3 2
## 338301 338343 338517 338640 338665 338848 339051 339317 339432 339499
## 4 1 6 5 3 1 6 1 1 1
## 339606 339804 340224 340281 340299 340315 340349 340372 340380 340398
## 6 1 10 1 3 10 9 2 10 1
## 340414 340422 340448 340463 343566 405324 432773 432906 433227 433235
## 5 2 1 4 2 1 3 1 5 2
## 433276 433490 433540 433680 433821 433961 434019 434076 434191 434282
## 6 6 4 6 8 4 4 2 3 3
## 434464 434480 434498 434506 434548 434597 434829 436170 436212 436287
## 3 4 5 2 3 2 4 1 8 1
## 436303 436360 436444 436451 436493 436543 436584 436634 436642 436725
## 2 5 5 6 5 2 1 4 2 3
## 436766 436782 437210 437228 437236 437244 437251 437269 437277 437285
## 6 1 7 31 20 13 2 2 12 10
## 437319 437335 437343 437350 437368 437400 437509 437525 437541 437707
## 13 6 13 6 2 7 2 1 2 3
## 437715 437723 437731 437749 437772 449868 466383 466730 468488 468611
## 7 2 3 1 1 10 2 18 2 2
## 469205 469700 481853 481903 482042 482091 488619 488635 493239 493544
## 2 8 6 8 1 6 10 11 1 15
## 495259 495812 496166 496844 497024 499699 500124 500348 500611 501411
## 3 4 13 3 3 26 1 19 3 2
## 501502 501601 501676 501809 501908 502435 502633 504993 505149 508903
## 3 10 11 4 1 2 20 5 2 5
## 510305 510800 513614 516674 519645 520486 521179 522318 522862 523423
## 1 1 2 3 3 4 4 2 2 4
## 523464 523621 523662 523761 526301 528380 534321 535823 536029 536128
## 3 2 1 5 4 4 1 4 3 7
## 536151 536326 546002 555847 555862 555946 556266 556357 556472 556548
## 1 16 18 1 9 5 2 1 3 2
## 556571 565119 565143 565200 565234 565267 566141 566158 566414 566430
## 13 4 2 2 12 2 22 2 4 16
## 566455 566463 566471 567743 567750 567768 578260 578278 578286 578336
## 4 2 14 1 9 1 3 4 13 1
## 578351 578401 578443 578518 578526 578534 578542 579151 581710 581728
## 11 13 2 11 11 22 8 12 2 2
## 581736 581744 581777 581876 581892 581900 581991 582114 582122 582148
## 17 4 1 5 2 8 4 1 1 1
## 582163 582254 582304 582312 582387 582403 582411 582833 582866 582890
## 3 3 4 5 12 11 8 12 4 15
## 582932 582981 583013 583088 583328 583476 583534 583567 583591 583922
## 5 13 10 7 4 5 1 6 26 3
## 591131 591164 591198 598581 599159 599365 601492 601708 603878 605469
## 2 2 11 11 2 15 9 1 11 17
## 605501 607143 607424 607556 616185 628404 628602 628842 629261 629295
## 4 2 1 5 12 2 2 3 2 2
## 632299 632471 639112 639922 642801 642892 643692 643783 643817 644690
## 3 1 3 5 3 3 5 2 7 4
## 644880 647172 649129 649202 649947 650002 650036 652081 656447 659698
## 3 10 2 1 6 1 10 1 1 3
## 659722 659896 659953 662940 662957 663005 663013 663096 663112 663120
## 6 12 4 2 2 3 1 9 9 3
## 663138 663526 663534 663542 663559 663682 663971 664292 664490 664508
## 10 1 4 7 11 5 12 2 1 1
## 664698 664706 664722 664748 664920 665265 665489 691931 692434 693499
## 15 1 1 20 1 2 9 3 8 12
## 693622 693655 694547 694562 694570 694588 694596 694604 697557 703124
## 14 4 2 3 12 13 2 11 3 3
## 703215 703223 703231 703249 703256 703736 703744 703751 704072 704312
## 11 12 1 11 1 2 9 16 1 2
## 704445 704460 704965 705053 705129 705160 705475 705772 725523 725770
## 3 7 1 9 5 1 1 1 1 7
## 725861 728055 728196 728717 730515 732321 732347 732495 735035 739367
## 4 2 4 12 2 1 1 7 2 1
## 743773 743815 743831 744540 744557 744573 751230 759399 759555 759613
## 12 5 13 3 6 2 1 6 1 12
## 762120 762468 762773 762856 762864 762880 762906 762914 763151 764035
## 1 3 12 13 2 16 12 5 6 1
## 764076 764134 764779 764936 765297 765305 765313 765321 765396 765412
## 2 1 6 7 17 10 2 1 6 4
## 765859 772970 773788 774026 774455 774679 774703 775312 775833 775874
## 7 1 12 15 4 2 12 8 3 3
## 777110 777144 777656 777680 777995 778233 778738 778795 779041 779868
## 3 2 25 18 8 13 13 9 2 2
## 780759 780767 780791 781278 781302 781351 781369 781385 781831 781930
## 1 1 2 6 9 4 22 1 3 1
## 782102 782664 785097 820407 821082 824003 824813 825752 828962 832253
## 7 10 11 4 10 1 6 1 1 1
## 832279 832287 832303 832311 832337 834853 835058 846048 847087 855791
## 7 2 4 5 1 3 7 7 2 2
## 869198 869248 870931 871160 872127 872515 874198 874214 875476 879791
## 9 1 4 1 1 13 1 10 1 12
## 879817 883884 884510 884528 884544 884551 884593 884627 885517 900670
## 1 1 5 2 14 10 2 1 1 1
## 900761 900795 900910 900977 901033 901082 901413 901587 915256 927814
## 10 1 11 3 11 2 1 1 2 12
## 928200 933598
## 1 9
## [1] "Frequency table after encoding"
## cole2016_admin.
## 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 5 1 1 3 10 5 1 2 9 3 12 1 1 1 3 5 5
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849
## 5 1 18 13 9 3 1 1 12 11 10 4 18 4 4 1 4
## 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866
## 4 3 2 4 2 4 10 6 8 15 1 6 1 1 2 1 2
## 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883
## 1 1 3 1 4 3 1 12 6 10 16 12 17 1 2 9 3
## 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900
## 13 16 1 7 2 1 2 1 2 3 1 12 7 6 26 2 8
## 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917
## 9 10 19 11 2 1 1 7 6 4 11 2 1 1 4 1 1
## 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934
## 1 1 2 1 10 2 11 3 1 12 2 1 5 14 1 1 14
## 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951
## 2 12 3 12 2 1 1 9 1 1 11 1 2 4 1 2 5
## 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968
## 1 1 1 4 6 4 4 4 11 1 1 1 7 4 3 9 11
## 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985
## 4 2 6 9 2 3 3 1 3 10 13 8 10 1 4 2 1
## 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002
## 4 1 25 2 7 2 4 12 2 1 4 7 12 7 11 1 2
## 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019
## 12 4 3 1 3 5 7 1 1 4 15 11 6 3 12 1 1
## 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036
## 3 13 1 1 1 2 1 5 5 13 16 1 1 4 4 4 3
## 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053
## 7 10 16 5 1 7 1 14 19 3 1 5 2 14 2 14 9
## 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070
## 1 7 3 1 2 3 4 1 5 18 6 5 6 3 3 13 4
## 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087
## 1 4 20 3 11 2 1 3 10 3 5 6 1 13 10 1 4
## 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## 4 1 1 2 7 1 16 2 1 1 12 1 1 2 8 9 1
## 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121
## 2 1 3 1 4 1 3 12 21 6 1 1 2 1 1 2 1
## 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138
## 1 3 2 2 1 2 12 5 5 2 13 2 3 3 7 1 1
## 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155
## 7 14 22 3 31 4 3 6 3 1 2 6 11 12 12 2 2
## 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172
## 4 3 2 6 5 2 7 2 1 8 3 18 2 11 3 12 12
## 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
## 1 3 1 2 2 15 2 6 13 1 12 3 6 1 1 1 2
## 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206
## 1 2 1 2 1 22 1 15 7 14 13 2 7 4 3 4 1
## 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223
## 2 9 10 2 4 1 12 10 1 3 2 10 3 3 9 1 1
## 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240
## 5 2 3 3 7 12 1 1 2 2 12 2 8 3 1 3 11
## 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257
## 4 1 2 3 10 4 4 1 2 6 18 3 2 1 1 4 11
## 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274
## 2 1 2 2 2 1 3 1 14 15 4 1 13 5 1 2 10
## 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291
## 2 3 3 6 9 1 4 12 1 2 6 14 1 1 9 1 8
## 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308
## 1 2 2 2 1 1 3 2 12 1 3 1 4 20 1 1 4
## 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325
## 6 9 8 1 2 1 5 1 1 3 8 2 5 1 6 3 4
## 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342
## 3 1 1 5 10 15 5 1 1 1 1 13 5 1 4 1 3
## 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359
## 5 1 11 10 8 20 6 3 3 1 2 4 12 3 19 2 10
## 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
## 9 4 3 3 2 2 4 1 5 2 9 3 2 10 1 9 7
## 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393
## 107 5 4 4 4 4 1 9 2 13 2 22 13 9 6 2 3
## 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410
## 11 1 4 15 2 18 13 5 1 3 1 11 1 1 6 1 1
## 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427
## 13 2 4 10 4 1 2 1 1 1 7 2 2 11 3 6 6
## 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444
## 3 1 3 13 1 11 5 15 1 26 2 12 3 3 4 4 3
## 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461
## 2 8 4 2 2 17 11 2 16 2 1 1 6 4 2 3 4
## 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478
## 1 16 2 10 3 1 2 1 1 1 1 1 9 16 1 3 1
## 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495
## 4 4 10 9 12 10 1 5 17 9 9 10 3 1 1 7 5
## 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512
## 7 5 2 2 11 2 1 11 2 5 14 2 3 10 3 4 8
## 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529
## 20 12 15 2 1 17 1 1 2 1 2 1 12 10 8 14 1
## 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546
## 2 3 1 15 2 8 10 2 2 13 4 2 1 11 6 1 1
## 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563
## 7 1 7 1 4 3 11 7 1 2 2 1 6 3 2 4 5
## 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580
## 2 3 1 4 1 5 8 11 15 13 2 2 11 1 1 7 6
## 1581 1582 1583 1584 1585 1586 1587
## 1 15 1 8 2 2 1
# !!! Removed as it contains identifying information
dropvars <- c("p_sc_info_311",
"p_sc_info_321",
"p_sc_info_331",
"p_sc_info_341",
"p_sc_info_351",
"p_sc_info_361",
"p_sc_info_371",
"p_sc_info_381",
"p_sc_info_391",
"NOMESC",
"nombre_colegio",
"prompt_cole_name",
"cole2016_correct",
"cole2016_new",
"cole2016",
"pref19b",
"school2015_name",
"school2015_name1",
"school2015_name1_extra",
"school2014_name",
"school2014_name1",
"school2014_name1_extra",
"school2013_name",
"school2013_name1",
"school2013_name1_extra",
"school2012_name",
"school2012_name1",
"school2012_name1_extra",
"school2011_name",
"school2011_name1",
"school2011_name1_extra",
"school2010_name",
"school2010_name1",
"school2010_name1_extra",
"hs_gps_where",
"ss_gps_where")
mydata <- mydata[!names(mydata) %in% dropvars]
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
mydata <- top_recode ("hh_ageinyears", break_point=59, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## hh_ageinyears.
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 8 11 19 7 19 8 6 10 16 12 20 42 49 83 112 113 140
## 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## 158 139 160 190 186 190 172 161 163 146 154 128 114 98 111 89 91
## 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
## 70 54 66 49 49 36 34 27 13 17 14 13 13 6 11 7 6
## 69 70 71 72 73 74 75 76 77 78 79 80 81 83 84 87 <NA>
## 5 5 12 2 7 3 2 2 1 2 3 2 1 5 3 1 445
## [1] "Frequency table after encoding"
## hh_ageinyears. 59
## 18 19 20 21 22 23 24
## 8 11 19 7 19 8 6
## 25 26 27 28 29 30 31
## 10 16 12 20 42 49 83
## 32 33 34 35 36 37 38
## 112 113 140 158 139 160 190
## 39 40 41 42 43 44 45
## 186 190 172 161 163 146 154
## 46 47 48 49 50 51 52
## 128 114 98 111 89 91 70
## 53 54 55 56 57 58 59 or more
## 54 66 49 49 36 34 183
## <NA>
## 445
mydata <- top_recode ("age_hh", break_point=59, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## age_hh. Age (round) at survey - Household Head
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 8 11 18 8 19 8 6 10 16 12 20 42 44 81 115 109 143
## 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## 161 140 160 184 189 190 169 166 158 147 151 131 118 96 113 85 96
## 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
## 69 52 68 48 49 39 33 28 13 17 14 13 13 6 10 8 6
## 69 70 71 72 73 74 75 76 77 78 79 80 81 83 84 87 <NA>
## 4 6 12 2 7 3 2 2 1 2 3 2 1 5 3 1 445
## [1] "Frequency table after encoding"
## age_hh. Age (round) at survey - Household Head
## 18 19 20 21 22 23 24
## 8 11 18 8 19 8 6
## 25 26 27 28 29 30 31
## 10 16 12 20 42 44 81
## 32 33 34 35 36 37 38
## 115 109 143 161 140 160 184
## 39 40 41 42 43 44 45
## 189 190 169 166 158 147 151
## 46 47 48 49 50 51 52
## 131 118 96 113 85 96 69
## 53 54 55 56 57 58 59 or more
## 52 68 48 49 39 33 184
## <NA>
## 445
mydata$age <- trunc(mydata$age)
mydata <- top_recode ("age", break_point=59, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## age.
## 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## 10 67 110 69 53 68 32 33 15 23 7 20 8 6 10 16 12
## 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
## 20 42 49 83 114 111 140 158 145 154 190 186 192 170 161 163 147
## 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
## 153 128 114 98 111 89 91 72 53 65 49 49 36 34 27 13 17
## 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
## 14 13 13 7 10 7 6 5 5 12 2 7 3 2 2 1 2
## 79 80 81 83 84 87 <NA>
## 3 2 1 5 3 1 2
## [1] "Frequency table after encoding"
## age. 59
## 11 12 13 14 15 16 17
## 10 67 110 69 53 68 32
## 18 19 20 21 22 23 24
## 33 15 23 7 20 8 6
## 25 26 27 28 29 30 31
## 10 16 12 20 42 49 83
## 32 33 34 35 36 37 38
## 114 111 140 158 145 154 190
## 39 40 41 42 43 44 45
## 186 192 170 161 163 147 153
## 46 47 48 49 50 51 52
## 128 114 98 111 89 91 72
## 53 54 55 56 57 58 59 or more
## 53 65 49 49 36 34 183
## <NA>
## 2
mydata <- mydata[!names(mydata) %in% "hh_birthdate"]
# Remove as it constains identifying education
mydata <- mydata[!names(mydata) %in% "i15"]
mydata <- mydata[!names(mydata) %in% "i16"]
mydata <- mydata[!names(mydata) %in% "birthdate"]
# Recode education attainment of adults to reduce risk of re-identification
break_edu <- c(-98,1,3,4,5,6,7,8,9)
labels_edu <- c("No se"=1,
"Pri Incomp or less"=2,
"Pri Comp"=3,
"Sec Incomp"=4,
"Sec Comp"=5,
"Tec Incomp"=6,
"Tec Comp"=7,
"Uni Incomp"=8,
"Uni Comp"=9)
mydata <- ordinal_recode (variable="K_MaxEducLevel_hhpartner", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## K_MaxEducLevel_hhpartner. Max Educ Level (Attended/Attending) - Partner
## Sin nivel Pri Incomp Pri Comp Sec Incomp Sec Comp Tec Incomp Tec Comp
## 17 121 225 438 955 113 230
## Uni Incomp Uni Comp <NA>
## 57 93 1862
## recoded
## [-98,1) [1,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,9) [9,1e+06)
## 1 0 17 0 0 0 0 0 0 0
## 2 0 121 0 0 0 0 0 0 0
## 3 0 0 225 0 0 0 0 0 0
## 4 0 0 0 438 0 0 0 0 0
## 5 0 0 0 0 955 0 0 0 0
## 6 0 0 0 0 0 113 0 0 0
## 7 0 0 0 0 0 0 230 0 0
## 8 0 0 0 0 0 0 0 57 0
## 9 0 0 0 0 0 0 0 0 93
## [1] "Frequency table after encoding"
## K_MaxEducLevel_hhpartner. Max Educ Level (Attended/Attending) - Partner
## Pri Incomp or less Pri Comp Sec Incomp Sec Comp
## 138 225 438 955
## Tec Incomp Tec Comp Uni Incomp Uni Comp
## 113 230 57 93
## <NA>
## 1862
## [1] "Inspect value labels and relabel as necessary"
## No se Pri Incomp or less Pri Comp Sec Incomp
## 1 2 3 4
## Sec Comp Tec Incomp Tec Comp Uni Incomp
## 5 6 7 8
## Uni Comp
## 9
break_edu <- c(-98,1,2,3,4,5,6,7,8)
labels_edu <- c("No se"=1,
"Sin nivel"=2,
"Pri Incomp"=3,
"Pri Comp"=4,
"Sec Incomp"=5,
"Sec Comp"=6,
"Tec Incomp"=7,
"Tec Comp"=8,
"Uni Incomp/Comp"=9)
mydata <- ordinal_recode (variable="K_MaxEducLevel_a2", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## K_MaxEducLevel_a2. Max Educ Level (Attended/Attending) - Sibling 2
## Sin nivel Pri Incomp Pri Comp Sec Incomp Sec Comp Tec Incomp Tec Comp
## 76 535 355 164 235 34 34
## Uni Incomp Uni Comp <NA>
## 23 8 2647
## recoded
## [-98,1) [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,1e+06)
## 1 0 76 0 0 0 0 0 0 0
## 2 0 0 535 0 0 0 0 0 0
## 3 0 0 0 355 0 0 0 0 0
## 4 0 0 0 0 164 0 0 0 0
## 5 0 0 0 0 0 235 0 0 0
## 6 0 0 0 0 0 0 34 0 0
## 7 0 0 0 0 0 0 0 34 0
## 8 0 0 0 0 0 0 0 0 23
## 9 0 0 0 0 0 0 0 0 8
## [1] "Frequency table after encoding"
## K_MaxEducLevel_a2. Max Educ Level (Attended/Attending) - Sibling 2
## Sin nivel Pri Incomp Pri Comp Sec Incomp Sec Comp
## 76 535 355 164 235
## Tec Incomp Tec Comp Uni Incomp/Comp <NA>
## 34 34 31 2647
## [1] "Inspect value labels and relabel as necessary"
## No se Sin nivel Pri Incomp Pri Comp Sec Incomp
## 1 2 3 4 5
## Sec Comp Tec Incomp Tec Comp Uni Incomp/Comp
## 6 7 8 9
break_edu <- c(-98,1,2,3,4,5,6)
labels_edu <- c("No se"=1,
"Sin nivel"=2,
"Pri Incomp"=3,
"Pri Comp"=4,
"Sec Incomp"=5,
"Sec Comp"=6,
"Tec Incomp/Comp or Uni Incomp/Comp"=7)
mydata <- ordinal_recode (variable="K_MaxEducLevel_a3", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## K_MaxEducLevel_a3. Max Educ Level (Attended/Attending) - Sibling 3
## Sin nivel Pri Incomp Pri Comp Sec Incomp Sec Comp Tec Incomp Tec Comp
## 37 246 133 49 74 7 7
## Uni Incomp Uni Comp <NA>
## 7 2 3549
## recoded
## [-98,1) [1,2) [2,3) [3,4) [4,5) [5,6) [6,1e+06)
## 1 0 37 0 0 0 0 0
## 2 0 0 246 0 0 0 0
## 3 0 0 0 133 0 0 0
## 4 0 0 0 0 49 0 0
## 5 0 0 0 0 0 74 0
## 6 0 0 0 0 0 0 7
## 7 0 0 0 0 0 0 7
## 8 0 0 0 0 0 0 7
## 9 0 0 0 0 0 0 2
## [1] "Frequency table after encoding"
## K_MaxEducLevel_a3. Max Educ Level (Attended/Attending) - Sibling 3
## Sin nivel Pri Incomp
## 37 246
## Pri Comp Sec Incomp
## 133 49
## Sec Comp Tec Incomp/Comp or Uni Incomp/Comp
## 74 23
## <NA>
## 3549
## [1] "Inspect value labels and relabel as necessary"
## No se Sin nivel
## 1 2
## Pri Incomp Pri Comp
## 3 4
## Sec Incomp Sec Comp
## 5 6
## Tec Incomp/Comp or Uni Incomp/Comp
## 7
break_edu <- c(-98,1,3,4,6)
labels_edu <- c("No se"=1,
"Pri Incomp or less"=2,
"Pri Comp"=3,
"Sec Incomp/Comp"=4,
"Tec Incomp/Comp or Uni Incomp/Comp"=5)
mydata <- ordinal_recode (variable="K_MaxEducLevel_a4", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## K_MaxEducLevel_a4. Max Educ Level (Attended/Attending) - Sibling 4
## Sin nivel Pri Incomp Pri Comp Sec Incomp Sec Comp Tec Incomp Tec Comp
## 7 90 43 21 22 4 1
## Uni Incomp <NA>
## 1 3922
## recoded
## [-98,1) [1,3) [3,4) [4,6) [6,1e+06)
## 1 0 7 0 0 0
## 2 0 90 0 0 0
## 3 0 0 43 0 0
## 4 0 0 0 21 0
## 5 0 0 0 22 0
## 6 0 0 0 0 4
## 7 0 0 0 0 1
## 8 0 0 0 0 1
## [1] "Frequency table after encoding"
## K_MaxEducLevel_a4. Max Educ Level (Attended/Attending) - Sibling 4
## Pri Incomp or less Pri Comp
## 97 43
## Sec Incomp/Comp Tec Incomp/Comp or Uni Incomp/Comp
## 43 6
## <NA>
## 3922
## [1] "Inspect value labels and relabel as necessary"
## No se Pri Incomp or less
## 1 2
## Pri Comp Sec Incomp/Comp
## 3 4
## Tec Incomp/Comp or Uni Incomp/Comp
## 5
break_edu <- c(-98,1,4)
labels_edu <- c("No se"=1,
"Pri Incomp/Comp or less"=2,
"Sec Incomp/Comp or more"=3)
mydata <- ordinal_recode (variable="K_MaxEducLevel_a5", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## K_MaxEducLevel_a5. Max Educ Level (Attended/Attending) - Sibling 5
## Sin nivel Pri Incomp Pri Comp Sec Incomp Sec Comp Tec Comp <NA>
## 4 25 15 7 9 2 4049
## recoded
## [-98,1) [1,4) [4,1e+06)
## 1 0 4 0
## 2 0 25 0
## 3 0 15 0
## 4 0 0 7
## 5 0 0 9
## 7 0 0 2
## [1] "Frequency table after encoding"
## K_MaxEducLevel_a5. Max Educ Level (Attended/Attending) - Sibling 5
## Pri Incomp/Comp or less Sec Incomp/Comp or more <NA>
## 44 18 4049
## [1] "Inspect value labels and relabel as necessary"
## No se Pri Incomp/Comp or less Sec Incomp/Comp or more
## 1 2 3
break_edu <- c(-11,-10,-9,-8,-7,1,2,3,4,5,6)
labels_edu <- c("Missing - Zenekon"=1,
"Missing - IPA"=2,
"No indica"=3,
"No se puede leer"=4,
"Error"=5,
"No termino secundaria"=6,
"Termino secundaria"=7,
"Termino carrera tecnica"=8,
"Termino carrera universitaria"=9,
"No se"=10,
"Otro"=11)
mydata <- ordinal_recode (variable="mom_edu", break_points=break_edu, missing=999999, value_labels=labels_edu)
## [1] "Frequency table before encoding"
## mom_edu. P3. Educ. de la madre
## Missing-MINEDU No termino secundaria
## 58 1169
## Termino secundaria Termino carrera tecnica
## 1239 340
## Termino carrera universitaria No se
## 335 515
## No tengo este familiar <NA>
## 9 446
## recoded
## [-11,-10) [-10,-9) [-9,-8) [-8,-7) [-7,1) [1,2) [2,3) [3,4) [4,5) [5,6)
## -99999 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 1169 0 0 0 0
## 2 0 0 0 0 0 0 1239 0 0 0
## 3 0 0 0 0 0 0 0 340 0 0
## 4 0 0 0 0 0 0 0 0 335 0
## 5 0 0 0 0 0 0 0 0 0 515
## 6 0 0 0 0 0 0 0 0 0 0
## recoded
## [6,1e+06)
## -99999 0
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 9
## [1] "Frequency table after encoding"
## mom_edu. P3. Educ. de la madre
## No termino secundaria Termino secundaria
## 1169 1239
## Termino carrera tecnica Termino carrera universitaria
## 340 335
## No se Otro
## 515 9
## <NA>
## 504
## [1] "Inspect value labels and relabel as necessary"
## Missing - Zenekon Missing - IPA
## 1 2
## No indica No se puede leer
## 3 4
## Error No termino secundaria
## 5 6
## Termino secundaria Termino carrera tecnica
## 7 8
## Termino carrera universitaria No se
## 9 10
## Otro
## 11
# 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. ¿Cuántas personas viven en total en el hogar?
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 <NA>
## 1 119 408 931 1011 572 278 143 86 51 22 17 10 9 6 2 445
## [1] "Frequency table after encoding"
## p1. ¿Cuántas personas viven en total en el hogar?
## 1 2 3 4 5 6 7
## 1 119 408 931 1011 572 278
## 8 9 10 or more <NA>
## 143 86 117 445
mydata <- top_recode ("p2c", break_point=5, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p2c. Hermanos o hermanas de ${student_name}
## 0 1 2 3 4 5 6 7 8 <NA>
## 474 1122 1177 575 208 69 24 14 3 445
## [1] "Frequency table after encoding"
## p2c. Hermanos o hermanas de ${student_name}
## 0 1 2 3 4 5 or more <NA>
## 474 1122 1177 575 208 110 445
mydata <- top_recode ("p2d", break_point=3, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p2d. Abuelos o abuelas vive de ${student_name}
## 0 1 2 3 <NA>
## 3114 362 186 4 445
## [1] "Frequency table after encoding"
## p2d. Abuelos o abuelas vive de ${student_name}
## 0 1 2 3 or more <NA>
## 3114 362 186 4 445
mydata <- top_recode ("p2e", break_point=4, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p2e. TÃos o tÃas de ${student_name}
## 0 1 2 3 4 5 6 <NA>
## 3221 239 135 41 16 8 6 445
## [1] "Frequency table after encoding"
## p2e. TÃos o tÃas de ${student_name}
## 0 1 2 3 4 or more <NA>
## 3221 239 135 41 30 445
mydata <- top_recode ("p2f", break_point=3, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p2f. Sobrinos de ${student_name}
## 0 1 2 3 4 5 6 <NA>
## 3376 173 73 26 14 3 1 445
## [1] "Frequency table after encoding"
## p2f. Sobrinos de ${student_name}
## 0 1 2 3 or more <NA>
## 3376 173 73 44 445
mydata <- top_recode ("p2g", break_point=4, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p2g. Otros familiares o miembros que vivan en el hogar
## 0 1 2 3 4 5 6 7 9 <NA>
## 3100 417 78 46 11 7 3 3 1 445
## [1] "Frequency table after encoding"
## p2g. Otros familiares o miembros que vivan en el hogar
## 0 1 2 3 4 or more <NA>
## 3100 417 78 46 25 445
# Top code number of rooms of the house
mydata <- top_recode ("p9", break_point=7, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p9. ¿Cuántas habitaciones tiene esta vivienda sin incluir cocina, baños, pasillos ni
## 1 2 3 4 5 6 7 8 9 10 11 12 15 <NA>
## 613 1305 955 479 176 91 22 13 4 2 1 4 1 445
## [1] "Frequency table after encoding"
## p9. ¿Cuántas habitaciones tiene esta vivienda sin incluir cocina, baños, pasillos ni
## 1 2 3 4 5 6 7 or more <NA>
## 613 1305 955 479 176 91 47 445
# Top code number of siblings studying in the same school
mydata <- top_recode ("p7a1", break_point=2, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p7a1. ¿Cuántos hermanos mayores tienes que estudian en tu escuela?
## 1 2 3 4 5 6 <NA>
## 538 85 22 1 2 1 3462
## [1] "Frequency table after encoding"
## p7a1. ¿Cuántos hermanos mayores tienes que estudian en tu escuela?
## 1 2 or more <NA>
## 538 111 3462
mydata <- top_recode ("p7b1", break_point=3, missing=c(888, 999999))
## [1] "Frequency table before encoding"
## p7b1. ¿Cuántos hermanos menores tienes que estudian en tu escuela?
## 1 2 3 4 5 <NA>
## 940 279 45 5 2 2840
## [1] "Frequency table after encoding"
## p7b1. ¿Cuántos hermanos menores tienes que estudian en tu escuela?
## 1 2 3 or more <NA>
## 940 279 52 2840
# Top code high income to the 99.5 percentile
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_hh)[na.exclude(mydata$inc_hh)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_hh", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_hh. Monthly Labor Income (PEN) - Household Head
## 0 15 40 50 60 80 97 100 120 150 160 180 200 240
## 4 1 1 1 2 4 3 9 3 7 1 2 26 4
## 250 280 300 308 320 332 350 360 380 390 400 425 440 450
## 8 2 39 1 1 1 7 7 1 1 66 2 1 7
## 460 480 488 500 520 540 550 560 570 600 608 650 700 708
## 3 7 1 110 1 1 2 1 1 106 2 6 60 1
## 720 750 758 760 800 808 850 858 860 880 900 920 950 960
## 6 33 1 1 166 1 303 1 5 2 70 2 11 2
## 987 1000 1008 1050 1070 1100 1135 1139 1145 1200 1250 1290 1300 1350
## 1 171 1 2 2 17 1 1 1 134 2 1 32 1
## 1400 1450 1460 1500 1600 1700 1800 1900 1950 2000 2050 2100 2200 2300
## 29 2 1 111 27 9 32 1 1 34 1 1 3 2
## 2350 2400 2500 2600 2700 2800 3000 3400 3500 3600 4000 4500 5000 6000
## 1 8 18 1 1 6 12 1 4 1 3 1 2 1
## 15000 <NA>
## 1 2311
## [1] "Frequency table after encoding"
## inc_hh. Monthly Labor Income (PEN) - Household Head
## 0 15 40 50 60 80
## 4 1 1 1 2 4
## 97 100 120 150 160 180
## 3 9 3 7 1 2
## 200 240 250 280 300 308
## 26 4 8 2 39 1
## 320 332 350 360 380 390
## 1 1 7 7 1 1
## 400 425 440 450 460 480
## 66 2 1 7 3 7
## 488 500 520 540 550 560
## 1 110 1 1 2 1
## 570 600 608 650 700 708
## 1 106 2 6 60 1
## 720 750 758 760 800 808
## 6 33 1 1 166 1
## 850 858 860 880 900 920
## 303 1 5 2 70 2
## 950 960 987 1000 1008 1050
## 11 2 1 171 1 2
## 1070 1100 1135 1139 1145 1200
## 2 17 1 1 1 134
## 1250 1290 1300 1350 1400 1450
## 2 1 32 1 29 2
## 1460 1500 1600 1700 1800 1900
## 1 111 27 9 32 1
## 1950 2000 2050 2100 2200 2300
## 1 34 1 1 3 2
## 2350 2400 2500 2600 2700 2800
## 1 8 18 1 1 6
## 3000 3400 3500 or more <NA>
## 12 1 13 2311
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_hhpartner)[na.exclude(mydata$inc_hhpartner)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_hhpartner", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_hhpartner. Monthly Labor Income (PEN) - Partner
## 0 6 97 100 120 150 180 200 250 280 300 350 400 450
## 6 1 6 6 1 3 1 7 2 1 19 5 22 3
## 480 500 508 528 560 570 580 600 650 700 720 740 750 800
## 3 54 1 1 1 1 1 52 1 49 2 1 11 167
## 820 850 858 860 900 910 930 950 980 1000 1025 1030 1050 1070
## 1 230 2 3 77 1 1 2 1 241 1 1 3 1
## 1090 1100 1120 1200 1240 1250 1280 1300 1350 1400 1500 1508 1588 1600
## 1 16 1 196 1 1 1 34 2 30 159 1 1 29
## 1700 1800 1900 2000 2200 2400 2500 2580 2700 2800 2900 3000 3100 3200
## 10 36 2 80 8 3 12 1 1 6 1 16 1 2
## 3500 4000 4500 4900 12050 <NA>
## 3 4 1 1 1 2456
## [1] "Frequency table after encoding"
## inc_hhpartner. Monthly Labor Income (PEN) - Partner
## 0 6 97 100 120 150
## 6 1 6 6 1 3
## 180 200 250 280 300 350
## 1 7 2 1 19 5
## 400 450 480 500 508 528
## 22 3 3 54 1 1
## 560 570 580 600 650 700
## 1 1 1 52 1 49
## 720 740 750 800 820 850
## 2 1 11 167 1 230
## 858 860 900 910 930 950
## 2 3 77 1 1 2
## 980 1000 1025 1030 1050 1070
## 1 241 1 1 3 1
## 1090 1100 1120 1200 1240 1250
## 1 16 1 196 1 1
## 1280 1300 1350 1400 1500 1508
## 1 34 2 30 159 1
## 1588 1600 1700 1800 1900 2000
## 1 29 10 36 2 80
## 2200 2400 2500 2580 2700 2800
## 8 3 12 1 1 6
## 2900 3000 3100 3200 3500 or more <NA>
## 1 16 1 2 10 2456
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_b1)[na.exclude(mydata$inc_b1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_b1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_b1. Monthly Labor Income (PEN) - Grandparent 1
## 0 20 60 97 100 150 200 250 280 300 350 400 500 600 700 750 800
## 2 1 2 1 1 2 3 1 1 2 1 5 15 6 4 3 12
## 850 900 950 1000 1100 1200 1300 1500 1600 1800 2000 2500 3000 6300 <NA>
## 19 5 1 12 1 8 1 6 1 3 1 2 1 1 3987
## [1] "Frequency table after encoding"
## inc_b1. Monthly Labor Income (PEN) - Grandparent 1
## 0 20 60 97 100 150
## 2 1 2 1 1 2
## 200 250 280 300 350 400
## 3 1 1 2 1 5
## 500 600 700 750 800 850
## 15 6 4 3 12 19
## 900 950 1000 1100 1200 1300
## 5 1 12 1 8 1
## 1500 1600 1800 2000 2500 3000
## 6 1 3 1 2 1
## 4270 or more <NA>
## 1 3987
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_c1)[na.exclude(mydata$inc_c1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_c1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_c1. Monthly Labor Income (PEN) - Uncle 1
## 30 97 100 200 240 300 320 400 450 480 500 600 700 720 750 770 780
## 1 4 1 4 1 2 1 4 1 1 8 6 10 1 1 1 1
## 800 850 858 860 900 950 1000 1100 1200 1300 1400 1500 1600 1800 2000 2400 2500
## 24 59 1 3 14 1 19 2 26 4 3 24 1 6 1 1 2
## 2800 3000 <NA>
## 1 2 3869
## [1] "Frequency table after encoding"
## inc_c1. Monthly Labor Income (PEN) - Uncle 1
## 30 97 100 200 240 300
## 1 4 1 4 1 2
## 320 400 450 480 500 600
## 1 4 1 1 8 6
## 700 720 750 770 780 800
## 10 1 1 1 1 24
## 850 858 860 900 950 1000
## 59 1 3 14 1 19
## 1100 1200 1300 1400 1500 1600
## 2 26 4 3 24 1
## 1800 2000 2400 2500 2800 2958 or more
## 6 1 1 2 1 2
## <NA>
## 3869
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_c2)[na.exclude(mydata$inc_c2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_c2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_c2. Monthly Labor Income (PEN) - Uncle 2
## 0 60 97 400 500 550 600 700 750 800 850 858 860 900 950 1000 1100
## 5 1 4 1 5 1 2 5 3 11 27 1 2 5 2 8 2
## 1200 1400 1500 1600 1800 2000 2400 2500 <NA>
## 9 1 4 1 2 4 1 2 4002
## [1] "Frequency table after encoding"
## inc_c2. Monthly Labor Income (PEN) - Uncle 2
## 0 60 97 400 500 550
## 5 1 4 1 5 1
## 600 700 750 800 850 858
## 2 5 3 11 27 1
## 860 900 950 1000 1100 1200
## 2 5 2 8 2 9
## 1400 1500 1600 1800 2000 2400
## 1 4 1 2 4 1
## 2500 or more <NA>
## 2 4002
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_c3)[na.exclude(mydata$inc_c3)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_c3", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_c3. Monthly Labor Income (PEN) - Uncle 3
## 97 200 500 600 700 750 800 850 860 900 1000 1100 1200 1500 2200 <NA>
## 2 1 4 1 3 1 5 9 1 2 2 1 1 3 1 4074
## [1] "Frequency table after encoding"
## inc_c3. Monthly Labor Income (PEN) - Uncle 3
## 97 200 500 600 700 750
## 2 1 4 1 3 1
## 800 850 860 900 1000 1100
## 5 9 1 2 2 1
## 1200 1500 2074 or more <NA>
## 1 3 1 4074
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_c4)[na.exclude(mydata$inc_c4)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_c4", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_c4. Monthly Labor Income (PEN) - Uncle 4
## 97 200 500 850 860 900 1100 1200 <NA>
## 1 1 2 1 1 2 1 3 4099
## [1] "Frequency table after encoding"
## inc_c4. Monthly Labor Income (PEN) - Uncle 4
## 97 200 500 850 860 900
## 1 1 2 1 1 2
## 1100 1200 or more <NA>
## 1 3 4099
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_c5)[na.exclude(mydata$inc_c5)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_c5", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_c5. Monthly Labor Income (PEN) - Uncle 5
## 200 500 600 850 860 900 3200 <NA>
## 1 2 1 1 1 1 1 4103
## [1] "Frequency table after encoding"
## inc_c5. Monthly Labor Income (PEN) - Uncle 5
## 200 500 600 850 860 900
## 1 2 1 1 1 1
## 3119 or more <NA>
## 1 4103
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_c6)[na.exclude(mydata$inc_c6)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_c6", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_c6. Monthly Labor Income (PEN) - Uncle 6
## 750 860 1500 <NA>
## 1 1 1 4108
## [1] "Frequency table after encoding"
## inc_c6. Monthly Labor Income (PEN) - Uncle 6
## 750 860 1493 or more <NA>
## 1 1 1 4108
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_d1)[na.exclude(mydata$inc_d1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_d1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_d1. Monthly Labor Income (PEN) - Nephew 1
## 97 400 500 850 900 920 950 1000 1500 2000 <NA>
## 1 1 2 6 2 1 1 3 1 1 4092
## [1] "Frequency table after encoding"
## inc_d1. Monthly Labor Income (PEN) - Nephew 1
## 97 400 500 850 900 920
## 1 1 2 6 2 1
## 950 1000 1500 1955 or more <NA>
## 1 3 1 1 4092
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_d2)[na.exclude(mydata$inc_d2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_d2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_d2. Monthly Labor Income (PEN) - Nephew 2
## 500 850 1000 <NA>
## 1 1 1 4108
## [1] "Frequency table after encoding"
## inc_d2. Monthly Labor Income (PEN) - Nephew 2
## 500 850 998 or more <NA>
## 1 1 1 4108
percentile_99.5 <- floor(quantile(na.exclude(mydata$inc_total)[na.exclude(mydata$inc_total)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="inc_total", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## inc_total.
## 6 40 60 80 97 100 120 150 160 180 194 200 240 250
## 1 1 1 1 8 1 3 4 1 2 1 9 4 2
## 280 291 297 300 320 329 350 360 380 400 425 440 450 458
## 1 1 1 18 1 1 6 3 1 33 2 1 2 1
## 460 480 500 520 540 550 590 600 620 630 650 700 720 728
## 2 5 59 1 1 1 1 68 1 1 4 47 3 1
## 740 750 760 770 780 794 800 820 850 858 860 880 900 920
## 1 18 1 1 1 1 170 1 195 2 1 3 78 1
## 950 960 996 1000 1015 1025 1040 1050 1070 1080 1090 1100 1120 1130
## 15 5 1 205 1 1 2 9 1 1 1 31 2 2
## 1140 1145 1150 1160 1200 1230 1240 1250 1270 1280 1290 1300 1332 1350
## 1 1 10 1 171 2 1 9 1 1 1 48 1 16
## 1360 1370 1378 1390 1400 1408 1420 1440 1450 1458 1480 1500 1550 1580
## 2 1 1 1 56 1 3 1 16 2 1 161 4 1
## 1588 1600 1650 1700 1708 1710 1720 1730 1731 1750 1780 1800 1820 1840
## 1 78 19 127 1 1 3 1 1 12 1 87 1 1
## 1850 1900 1920 1930 1944 1950 1960 1970 1990 2000 2008 2050 2060 2100
## 34 20 2 1 1 8 2 1 1 110 1 26 1 24
## 2108 2120 2130 2140 2150 2200 2210 2230 2240 2250 2280 2300 2308 2310
## 1 2 1 2 5 39 1 1 1 15 1 34 1 1
## 2320 2350 2360 2400 2450 2500 2520 2533 2550 2558 2580 2600 2650 2660
## 2 24 1 49 7 50 1 1 25 2 1 23 5 1
## 2680 2700 2708 2720 2750 2800 2810 2835 2850 2897 2900 2930 2950 2980
## 1 37 1 1 5 29 1 1 7 1 23 1 7 2
## 3000 3050 3100 3150 3200 3208 3250 3300 3350 3380 3400 3416 3450 3500
## 54 4 6 1 24 1 7 10 4 1 23 1 5 23
## 3550 3590 3600 3650 3700 3750 3760 3800 3850 3900 3950 4000 4100 4200
## 3 1 10 3 5 5 1 8 6 10 1 20 5 10
## 4250 4300 4340 4350 4400 4500 4537 4550 4600 4650 4700 4750 4800 4850
## 3 5 1 1 6 9 1 1 4 1 2 1 4 1
## 4900 5000 5030 5100 5200 5250 5350 5400 5450 5500 5550 5600 5700 5800
## 2 6 1 3 1 1 2 1 1 3 1 1 2 2
## 5900 5950 6000 6200 6320 6400 6500 6650 6700 6800 6950 7060 7200 7300
## 2 1 3 1 1 1 1 1 1 1 1 1 2 2
## 8000 8150 8400 8500 8600 8800 9200 10000 10600 10750 14250 16500 <NA>
## 1 1 1 1 1 1 1 1 1 1 1 1 1209
## [1] "Frequency table after encoding"
## inc_total. 7200
## 6 40 60 80 97 100
## 1 1 1 1 8 1
## 120 150 160 180 194 200
## 3 4 1 2 1 9
## 240 250 280 291 297 300
## 4 2 1 1 1 18
## 320 329 350 360 380 400
## 1 1 6 3 1 33
## 425 440 450 458 460 480
## 2 1 2 1 2 5
## 500 520 540 550 590 600
## 59 1 1 1 1 68
## 620 630 650 700 720 728
## 1 1 4 47 3 1
## 740 750 760 770 780 794
## 1 18 1 1 1 1
## 800 820 850 858 860 880
## 170 1 195 2 1 3
## 900 920 950 960 996 1000
## 78 1 15 5 1 205
## 1015 1025 1040 1050 1070 1080
## 1 1 2 9 1 1
## 1090 1100 1120 1130 1140 1145
## 1 31 2 2 1 1
## 1150 1160 1200 1230 1240 1250
## 10 1 171 2 1 9
## 1270 1280 1290 1300 1332 1350
## 1 1 1 48 1 16
## 1360 1370 1378 1390 1400 1408
## 2 1 1 1 56 1
## 1420 1440 1450 1458 1480 1500
## 3 1 16 2 1 161
## 1550 1580 1588 1600 1650 1700
## 4 1 1 78 19 127
## 1708 1710 1720 1730 1731 1750
## 1 1 3 1 1 12
## 1780 1800 1820 1840 1850 1900
## 1 87 1 1 34 20
## 1920 1930 1944 1950 1960 1970
## 2 1 1 8 2 1
## 1990 2000 2008 2050 2060 2100
## 1 110 1 26 1 24
## 2108 2120 2130 2140 2150 2200
## 1 2 1 2 5 39
## 2210 2230 2240 2250 2280 2300
## 1 1 1 15 1 34
## 2308 2310 2320 2350 2360 2400
## 1 1 2 24 1 49
## 2450 2500 2520 2533 2550 2558
## 7 50 1 1 25 2
## 2580 2600 2650 2660 2680 2700
## 1 23 5 1 1 37
## 2708 2720 2750 2800 2810 2835
## 1 1 5 29 1 1
## 2850 2897 2900 2930 2950 2980
## 7 1 23 1 7 2
## 3000 3050 3100 3150 3200 3208
## 54 4 6 1 24 1
## 3250 3300 3350 3380 3400 3416
## 7 10 4 1 23 1
## 3450 3500 3550 3590 3600 3650
## 5 23 3 1 10 3
## 3700 3750 3760 3800 3850 3900
## 5 5 1 8 6 10
## 3950 4000 4100 4200 4250 4300
## 1 20 5 10 3 5
## 4340 4350 4400 4500 4537 4550
## 1 1 6 9 1 1
## 4600 4650 4700 4750 4800 4850
## 4 1 2 1 4 1
## 4900 5000 5030 5100 5200 5250
## 2 6 1 3 1 1
## 5350 5400 5450 5500 5550 5600
## 2 1 1 3 1 1
## 5700 5800 5900 5950 6000 6200
## 2 2 2 1 3 1
## 6320 6400 6500 6650 6700 6800
## 1 1 1 1 1 1
## 6950 7060 7200 or more <NA>
## 1 1 16 1209
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_hh)[na.exclude(mydata$whour_hh)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_hh", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_hh. Avg. Hourly Wage (PEN) - Household Head
## 0 0.0765306130051613 0.20833332836628 0.238095238804817
## 4 1 1 1
## 0.297619044780731 0.336805552244186 0.357142865657806 0.404166668653488
## 2 1 1 1
## 0.416666656732559 0.428571432828903 0.476190477609634 0.5
## 1 1 1 1
## 0.505208313465118 0.595238089561462 0.669642865657806 0.714285731315613
## 1 2 1 3
## 0.74404764175415 0.75 0.765306115150452 0.78125
## 1 2 1 3
## 0.833333313465118 0.892857134342194 0.9375 1.02040815353394
## 2 2 1 3
## 1.04166662693024 1.06944441795349 1.07142853736877 1.11607146263123
## 4 1 2 3
## 1.13636362552643 1.19047617912292 1.25 1.27551019191742
## 1 8 7 2
## 1.30208337306976 1.33928573131561 1.38888883590698 1.42857146263123
## 1 3 2 5
## 1.4880952835083 1.5 1.5306122303009 1.5625
## 5 2 1 4
## 1.60714280605316 1.62037038803101 1.66666662693024 1.71428573131561
## 1 1 11 1
## 1.73611116409302 1.78571426868439 1.80952382087708 1.82291662693024
## 4 10 1 2
## 1.85185182094574 1.875 1.89732146263123 1.90476191043854
## 2 4 1 1
## 1.92307686805725 1.96759259700775 1.98412692546844 2
## 1 1 1 1
## 2.00892853736877 2.04081630706787 2.06349205970764 2.08333325386047
## 1 3 1 24
## 2.11111116409302 2.14285707473755 2.16836738586426 2.19298243522644
## 1 6 1 1
## 2.22222232818604 2.23214292526245 2.25694441795349 2.27272725105286
## 1 13 1 1
## 2.3148148059845 2.33333325386047 2.36111116409302 2.38095235824585
## 1 2 2 23
## 2.40000009536743 2.43055558204651 2.5 2.52976179122925
## 1 7 21 11
## 2.53333330154419 2.54166674613953 2.54629635810852 2.55102038383484
## 1 1 1 1
## 2.56410264968872 2.59740257263184 2.60416674613953 2.65151524543762
## 1 1 18 2
## 2.67857146263123 2.72435903549194 2.72727274894714 2.76666665077209
## 18 2 1 1
## 2.77777767181396 2.8125 2.85714292526245 2.875
## 26 1 8 1
## 2.90178561210632 2.91666674613953 2.95138883590698 2.96875
## 1 9 30 1
## 2.9761905670166 2.97916674613953 3 3.03030300140381
## 14 1 6 2
## 3.03571438789368 3.06122446060181 3.11813187599182 3.125
## 12 2 1 49
## 3.17460322380066 3.21428561210632 3.21969699859619 3.24074077606201
## 1 3 4 1
## 3.2738094329834 3.29670333862305 3.31632661819458 3.33333325386047
## 3 1 1 26
## 3.34821438789368 3.37301588058472 3.38541674613953 3.40909099578857
## 2 3 1 1
## 3.47222232818604 3.5 3.54166674613953 3.57142853736877
## 29 2 20 34
## 3.63636374473572 3.64583325386047 3.66666674613953 3.70370364189148
## 2 13 2 11
## 3.75 3.78787875175476 3.79464292526245 3.81944441795349
## 10 3 22 3
## 3.82653069496155 3.83333325386047 3.86904764175415 3.88888883590698
## 3 1 5 1
## 3.89610385894775 3.90625 3.9351851940155 3.95833325386047
## 2 9 13 4
## 3.96825385093689 4 4.01785707473755 4.0625
## 3 5 4 1
## 4.16666650772095 4.21875 4.24107122421265 4.25
## 97 1 1 6
## 4.2857141494751 4.33673477172852 4.375 4.39814805984497
## 9 2 4 1
## 4.42708349227905 4.44444465637207 4.4642858505249 4.47916650772095
## 121 1 20 2
## 4.5 4.51388883590698 4.54545450210571 4.58333349227905
## 2 3 5 3
## 4.59183692932129 4.61538457870483 4.62962961196899 4.64285707473755
## 1 3 5 1
## 4.6875 4.72222232818604 4.7619047164917 4.79166650772095
## 28 5 5 2
## 4.86111116409302 4.91666650772095 4.94791650772095 5
## 5 1 4 51
## 5.03999996185303 5.0595235824585 5.06944465637207 5.10204076766968
## 1 6 1 1
## 5.20833349227905 5.30303049087524 5.3125 5.35714292526245
## 67 1 20 15
## 5.375 5.41666650772095 5.45454549789429 5.51470565795898
## 3 5 1 1
## 5.55555534362793 5.57291650772095 5.625 5.7142858505249
## 15 2 7 3
## 5.72916650772095 5.80357122421265 5.83333349227905 5.84415578842163
## 7 1 8 1
## 5.859375 5.90277767181396 5.9375 5.9523811340332
## 1 4 1 12
## 5.96354150772095 6 6.07142877578735 6.11111116409302
## 1 8 1 1
## 6.12244892120361 6.25 6.3125 6.31666660308838
## 3 87 1 1
## 6.34920644760132 6.36363649368286 6.42857122421265 6.4732141494751
## 1 1 2 1
## 6.48148155212402 6.5 6.51041650772095 6.5625
## 2 1 1 1
## 6.640625 6.66666650772095 6.69642877578735 6.77083349227905
## 1 12 5 10
## 6.81818199157715 6.875 6.94444465637207 7
## 1 1 10 1
## 7.08333349227905 7.14285707473755 7.29166650772095 7.40740728378296
## 7 8 5 2
## 7.4404764175415 7.5 7.55208349227905 7.57575750350952
## 2 25 1 1
## 7.5892858505249 7.63888883590698 7.6530613899231 7.8125
## 1 1 2 22
## 7.87037038803101 7.93650770187378 8 8.0357141494751
## 2 1 4 3
## 8.125 8.22500038146973 8.33333301544189 8.5
## 5 1 48 6
## 8.5714282989502 8.68055534362793 8.75 8.85416698455811
## 1 2 5 1
## 8.88888931274414 8.9285717010498 9.02777767181396 9.25925922393799
## 1 5 1 1
## 9.375 9.46969699859619 9.49166679382324 9.72222232818604
## 21 1 1 2
## 10 10.4166669845581 10.625 10.7142858505249
## 13 18 3 3
## 10.8333330154419 11.1111106872559 11.25 11.4583330154419
## 1 1 6 2
## 11.5740737915039 11.6666669845581 11.71875 11.875
## 1 1 3 2
## 12 12.152777671814 12.5 12.8571424484253
## 1 1 31 1
## 13 13.0208330154419 13.0952377319336 13.28125
## 1 4 1 1
## 13.3333330154419 13.8888893127441 14 14.0625
## 4 7 1 1
## 14.1666669845581 14.2857141494751 14.3333330154419 14.375
## 3 2 1 2
## 14.5833330154419 14.6428575515747 15 15.1785717010498
## 1 1 4 1
## 15.625 16 16.2037029266357 16.6666660308838
## 5 1 1 4
## 16.875 17 17.5 17.8571434020996
## 1 1 2 1
## 18.75 19.0476188659668 19.1666660308838 20
## 4 1 1 1
## 20.8333339691162 21.25 22.2222213745117 23.5
## 3 1 1 1
## 23.8095245361328 25 27.7777786254883 35
## 1 1 1 1
## 41.6666679382324 44.6428565979004 93.75 <NA>
## 2 1 1 2320
## [1] "Frequency table after encoding"
## whour_hh. Avg. Hourly Wage (PEN) - Household Head
## 0 0.0765306130051613 0.20833332836628 0.238095238804817
## 4 1 1 1
## 0.297619044780731 0.336805552244186 0.357142865657806 0.404166668653488
## 2 1 1 1
## 0.416666656732559 0.428571432828903 0.476190477609634 0.5
## 1 1 1 1
## 0.505208313465118 0.595238089561462 0.669642865657806 0.714285731315613
## 1 2 1 3
## 0.74404764175415 0.75 0.765306115150452 0.78125
## 1 2 1 3
## 0.833333313465118 0.892857134342194 0.9375 1.02040815353394
## 2 2 1 3
## 1.04166662693024 1.06944441795349 1.07142853736877 1.11607146263123
## 4 1 2 3
## 1.13636362552643 1.19047617912292 1.25 1.27551019191742
## 1 8 7 2
## 1.30208337306976 1.33928573131561 1.38888883590698 1.42857146263123
## 1 3 2 5
## 1.4880952835083 1.5 1.5306122303009 1.5625
## 5 2 1 4
## 1.60714280605316 1.62037038803101 1.66666662693024 1.71428573131561
## 1 1 11 1
## 1.73611116409302 1.78571426868439 1.80952382087708 1.82291662693024
## 4 10 1 2
## 1.85185182094574 1.875 1.89732146263123 1.90476191043854
## 2 4 1 1
## 1.92307686805725 1.96759259700775 1.98412692546844 2
## 1 1 1 1
## 2.00892853736877 2.04081630706787 2.06349205970764 2.08333325386047
## 1 3 1 24
## 2.11111116409302 2.14285707473755 2.16836738586426 2.19298243522644
## 1 6 1 1
## 2.22222232818604 2.23214292526245 2.25694441795349 2.27272725105286
## 1 13 1 1
## 2.3148148059845 2.33333325386047 2.36111116409302 2.38095235824585
## 1 2 2 23
## 2.40000009536743 2.43055558204651 2.5 2.52976179122925
## 1 7 21 11
## 2.53333330154419 2.54166674613953 2.54629635810852 2.55102038383484
## 1 1 1 1
## 2.56410264968872 2.59740257263184 2.60416674613953 2.65151524543762
## 1 1 18 2
## 2.67857146263123 2.72435903549194 2.72727274894714 2.76666665077209
## 18 2 1 1
## 2.77777767181396 2.8125 2.85714292526245 2.875
## 26 1 8 1
## 2.90178561210632 2.91666674613953 2.95138883590698 2.96875
## 1 9 30 1
## 2.9761905670166 2.97916674613953 3 3.03030300140381
## 14 1 6 2
## 3.03571438789368 3.06122446060181 3.11813187599182 3.125
## 12 2 1 49
## 3.17460322380066 3.21428561210632 3.21969699859619 3.24074077606201
## 1 3 4 1
## 3.2738094329834 3.29670333862305 3.31632661819458 3.33333325386047
## 3 1 1 26
## 3.34821438789368 3.37301588058472 3.38541674613953 3.40909099578857
## 2 3 1 1
## 3.47222232818604 3.5 3.54166674613953 3.57142853736877
## 29 2 20 34
## 3.63636374473572 3.64583325386047 3.66666674613953 3.70370364189148
## 2 13 2 11
## 3.75 3.78787875175476 3.79464292526245 3.81944441795349
## 10 3 22 3
## 3.82653069496155 3.83333325386047 3.86904764175415 3.88888883590698
## 3 1 5 1
## 3.89610385894775 3.90625 3.9351851940155 3.95833325386047
## 2 9 13 4
## 3.96825385093689 4 4.01785707473755 4.0625
## 3 5 4 1
## 4.16666650772095 4.21875 4.24107122421265 4.25
## 97 1 1 6
## 4.2857141494751 4.33673477172852 4.375 4.39814805984497
## 9 2 4 1
## 4.42708349227905 4.44444465637207 4.4642858505249 4.47916650772095
## 121 1 20 2
## 4.5 4.51388883590698 4.54545450210571 4.58333349227905
## 2 3 5 3
## 4.59183692932129 4.61538457870483 4.62962961196899 4.64285707473755
## 1 3 5 1
## 4.6875 4.72222232818604 4.7619047164917 4.79166650772095
## 28 5 5 2
## 4.86111116409302 4.91666650772095 4.94791650772095 5
## 5 1 4 51
## 5.03999996185303 5.0595235824585 5.06944465637207 5.10204076766968
## 1 6 1 1
## 5.20833349227905 5.30303049087524 5.3125 5.35714292526245
## 67 1 20 15
## 5.375 5.41666650772095 5.45454549789429 5.51470565795898
## 3 5 1 1
## 5.55555534362793 5.57291650772095 5.625 5.7142858505249
## 15 2 7 3
## 5.72916650772095 5.80357122421265 5.83333349227905 5.84415578842163
## 7 1 8 1
## 5.859375 5.90277767181396 5.9375 5.9523811340332
## 1 4 1 12
## 5.96354150772095 6 6.07142877578735 6.11111116409302
## 1 8 1 1
## 6.12244892120361 6.25 6.3125 6.31666660308838
## 3 87 1 1
## 6.34920644760132 6.36363649368286 6.42857122421265 6.4732141494751
## 1 1 2 1
## 6.48148155212402 6.5 6.51041650772095 6.5625
## 2 1 1 1
## 6.640625 6.66666650772095 6.69642877578735 6.77083349227905
## 1 12 5 10
## 6.81818199157715 6.875 6.94444465637207 7
## 1 1 10 1
## 7.08333349227905 7.14285707473755 7.29166650772095 7.40740728378296
## 7 8 5 2
## 7.4404764175415 7.5 7.55208349227905 7.57575750350952
## 2 25 1 1
## 7.5892858505249 7.63888883590698 7.6530613899231 7.8125
## 1 1 2 22
## 7.87037038803101 7.93650770187378 8 8.0357141494751
## 2 1 4 3
## 8.125 8.22500038146973 8.33333301544189 8.5
## 5 1 48 6
## 8.5714282989502 8.68055534362793 8.75 8.85416698455811
## 1 2 5 1
## 8.88888931274414 8.9285717010498 9.02777767181396 9.25925922393799
## 1 5 1 1
## 9.375 9.46969699859619 9.49166679382324 9.72222232818604
## 21 1 1 2
## 10 10.4166669845581 10.625 10.7142858505249
## 13 18 3 3
## 10.8333330154419 11.1111106872559 11.25 11.4583330154419
## 1 1 6 2
## 11.5740737915039 11.6666669845581 11.71875 11.875
## 1 1 3 2
## 12 12.152777671814 12.5 12.8571424484253
## 1 1 31 1
## 13 13.0208330154419 13.0952377319336 13.28125
## 1 4 1 1
## 13.3333330154419 13.8888893127441 14 14.0625
## 4 7 1 1
## 14.1666669845581 14.2857141494751 14.3333330154419 14.375
## 3 2 1 2
## 14.5833330154419 14.6428575515747 15 15.1785717010498
## 1 1 4 1
## 15.625 16 16.2037029266357 16.6666660308838
## 5 1 1 4
## 16.875 17 17.5 17.8571434020996
## 1 1 2 1
## 18.75 19.0476188659668 19.1666660308838 20
## 4 1 1 1
## 20.8333339691162 21.25 22 or more <NA>
## 3 1 10 2320
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_hhpartner)[na.exclude(mydata$whour_hhpartner)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_hhpartner", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_hhpartner. Avg. Hourly Wage (PEN) - Partner
## 0 0.025000000372529 0.288690477609634 0.314935058355331
## 3 1 1 1
## 0.336805552244186 0.347222208976746 0.462962955236435 0.505208313465118
## 1 1 1 2
## 0.595238089561462 0.606249988079071 0.625 0.714285731315613
## 1 1 1 2
## 0.765306115150452 0.833333313465118 0.892857134342194 1.02040815353394
## 1 1 2 1
## 1.04166662693024 1.07142853736877 1.19047617912292 1.21527779102325
## 6 2 3 2
## 1.25 1.30208337306976 1.33928573131561 1.38888883590698
## 2 1 1 2
## 1.42857146263123 1.4880952835083 1.5 1.5306122303009
## 3 1 1 1
## 1.5625 1.60256409645081 1.60714280605316 1.66666662693024
## 7 1 1 5
## 1.73611116409302 1.76388883590698 1.78571426868439 1.82291662693024
## 6 1 9 1
## 1.83333337306976 1.875 1.89732146263123 1.90476191043854
## 1 3 2 2
## 1.91326534748077 1.92307686805725 1.96759259700775 1.97916662693024
## 1 1 1 1
## 2 2.0238094329834 2.04081630706787 2.08333325386047
## 2 1 2 16
## 2.1008403301239 2.14285707473755 2.22222232818604 2.23214292526245
## 1 1 2 12
## 2.3148148059845 2.33333325386047 2.33516478538513 2.38095235824585
## 1 1 1 11
## 2.40000009536743 2.41666674613953 2.43055558204651 2.45535707473755
## 1 1 9 1
## 2.5 2.52976179122925 2.55102038383484 2.56410264968872
## 14 16 1 1
## 2.60416674613953 2.64285707473755 2.65151524543762 2.66666674613953
## 11 1 1 1
## 2.67857146263123 2.77777767181396 2.84722232818604 2.85714292526245
## 13 25 1 3
## 2.91666674613953 2.92207789421082 2.95138883590698 2.9761905670166
## 9 2 32 20
## 3 3.03030300140381 3.03571438789368 3.06122446060181
## 4 2 4 3
## 3.125 3.17460322380066 3.18181824684143 3.20512819290161
## 26 2 1 1
## 3.21428561210632 3.21969699859619 3.2738094329834 3.29670333862305
## 3 1 2 1
## 3.29861116409302 3.33333325386047 3.37301588058472 3.40909099578857
## 1 32 2 1
## 3.47222232818604 3.5 3.54166674613953 3.57142853736877
## 43 1 29 34
## 3.58333325386047 3.64583325386047 3.66666674613953 3.69047617912292
## 1 12 1 1
## 3.70370364189148 3.75 3.78787875175476 3.79464292526245
## 4 11 4 9
## 3.81944441795349 3.82653069496155 3.8461537361145 3.86363625526428
## 2 2 1 1
## 3.86904764175415 3.88888883590698 3.90625 3.9351851940155
## 2 1 2 6
## 3.95833325386047 3.96825385093689 4 4.01785707473755
## 1 1 4 3
## 4.08163261413574 4.09090900421143 4.16666650772095 4.25
## 1 1 138 7
## 4.27083349227905 4.2857141494751 4.375 4.42708349227905
## 1 13 1 94
## 4.44444465637207 4.4642858505249 4.46875 4.47916650772095
## 1 34 2 2
## 4.5 4.51388883590698 4.54545450210571 4.58333349227905
## 1 6 4 3
## 4.62962961196899 4.64285707473755 4.6875 4.73958349227905
## 9 2 20 1
## 4.7619047164917 4.807692527771 4.84375 4.86111116409302
## 4 1 1 7
## 4.87013006210327 5 5.0595235824585 5.10204076766968
## 4 43 3 4
## 5.20833349227905 5.3125 5.33333349227905 5.35714292526245
## 92 12 1 20
## 5.36458349227905 5.41666650772095 5.46875 5.5
## 1 1 1 1
## 5.55555534362793 5.57291650772095 5.625 5.68181800842285
## 21 1 7 5
## 5.7142858505249 5.72916650772095 5.80357122421265 5.83333349227905
## 2 4 2 5
## 5.90277767181396 5.90909099578857 5.9523811340332 6
## 4 1 7 3
## 6.01851844787598 6.04395627975464 6.06060600280762 6.25
## 2 1 1 111
## 6.42857122421265 6.43939399719238 6.48148155212402 6.5
## 3 1 1 1
## 6.61666679382324 6.66666650772095 6.69642877578735 6.77083349227905
## 1 16 2 13
## 6.8125 6.875 6.94444465637207 7
## 1 1 22 1
## 7.03125 7.08333349227905 7.14285707473755 7.29166650772095
## 2 3 5 8
## 7.40740728378296 7.4404764175415 7.5 7.63888883590698
## 1 1 17 1
## 7.6530613899231 7.8125 7.85416650772095 7.87037038803101
## 1 30 1 1
## 8 8.0357141494751 8.125 8.33333301544189
## 1 2 2 30
## 8.5 8.51648330688477 8.5714282989502 8.68055534362793
## 2 1 5 1
## 8.75 8.85416698455811 8.9285717010498 9
## 3 3 8 1
## 9.16666698455811 9.25925922393799 9.375 9.44444465637207
## 1 7 27 1
## 9.64285755157471 9.80000019073486 9.89583301544189 10
## 1 1 1 9
## 10.1851854324341 10.2040815353394 10.4166669845581 10.625
## 1 1 33 2
## 10.7142858505249 10.8333330154419 10.9375 11
## 1 1 1 1
## 11.1607141494751 11.25 11.4583330154419 11.5740737915039
## 1 1 2 1
## 11.71875 11.875 12 12.0833330154419
## 1 1 2 1
## 12.5 13.0208330154419 13.0952377319336 13.3333330154419
## 21 2 1 2
## 13.5416669845581 13.8888893127441 14 14.5833330154419
## 1 2 1 4
## 15.5555553436279 15.625 16.6666660308838 16.875
## 1 4 4 1
## 17.5 17.9166660308838 18.518518447876 18.75
## 1 1 3 4
## 20 20.8333339691162 21.25 21.4285717010498
## 1 1 2 1
## 21.875 22 23.4375 25
## 1 1 2 5
## 31.25 41.8402786254883 <NA>
## 1 1 2471
## [1] "Frequency table after encoding"
## whour_hhpartner. Avg. Hourly Wage (PEN) - Partner
## 0 0.025000000372529 0.288690477609634 0.314935058355331
## 3 1 1 1
## 0.336805552244186 0.347222208976746 0.462962955236435 0.505208313465118
## 1 1 1 2
## 0.595238089561462 0.606249988079071 0.625 0.714285731315613
## 1 1 1 2
## 0.765306115150452 0.833333313465118 0.892857134342194 1.02040815353394
## 1 1 2 1
## 1.04166662693024 1.07142853736877 1.19047617912292 1.21527779102325
## 6 2 3 2
## 1.25 1.30208337306976 1.33928573131561 1.38888883590698
## 2 1 1 2
## 1.42857146263123 1.4880952835083 1.5 1.5306122303009
## 3 1 1 1
## 1.5625 1.60256409645081 1.60714280605316 1.66666662693024
## 7 1 1 5
## 1.73611116409302 1.76388883590698 1.78571426868439 1.82291662693024
## 6 1 9 1
## 1.83333337306976 1.875 1.89732146263123 1.90476191043854
## 1 3 2 2
## 1.91326534748077 1.92307686805725 1.96759259700775 1.97916662693024
## 1 1 1 1
## 2 2.0238094329834 2.04081630706787 2.08333325386047
## 2 1 2 16
## 2.1008403301239 2.14285707473755 2.22222232818604 2.23214292526245
## 1 1 2 12
## 2.3148148059845 2.33333325386047 2.33516478538513 2.38095235824585
## 1 1 1 11
## 2.40000009536743 2.41666674613953 2.43055558204651 2.45535707473755
## 1 1 9 1
## 2.5 2.52976179122925 2.55102038383484 2.56410264968872
## 14 16 1 1
## 2.60416674613953 2.64285707473755 2.65151524543762 2.66666674613953
## 11 1 1 1
## 2.67857146263123 2.77777767181396 2.84722232818604 2.85714292526245
## 13 25 1 3
## 2.91666674613953 2.92207789421082 2.95138883590698 2.9761905670166
## 9 2 32 20
## 3 3.03030300140381 3.03571438789368 3.06122446060181
## 4 2 4 3
## 3.125 3.17460322380066 3.18181824684143 3.20512819290161
## 26 2 1 1
## 3.21428561210632 3.21969699859619 3.2738094329834 3.29670333862305
## 3 1 2 1
## 3.29861116409302 3.33333325386047 3.37301588058472 3.40909099578857
## 1 32 2 1
## 3.47222232818604 3.5 3.54166674613953 3.57142853736877
## 43 1 29 34
## 3.58333325386047 3.64583325386047 3.66666674613953 3.69047617912292
## 1 12 1 1
## 3.70370364189148 3.75 3.78787875175476 3.79464292526245
## 4 11 4 9
## 3.81944441795349 3.82653069496155 3.8461537361145 3.86363625526428
## 2 2 1 1
## 3.86904764175415 3.88888883590698 3.90625 3.9351851940155
## 2 1 2 6
## 3.95833325386047 3.96825385093689 4 4.01785707473755
## 1 1 4 3
## 4.08163261413574 4.09090900421143 4.16666650772095 4.25
## 1 1 138 7
## 4.27083349227905 4.2857141494751 4.375 4.42708349227905
## 1 13 1 94
## 4.44444465637207 4.4642858505249 4.46875 4.47916650772095
## 1 34 2 2
## 4.5 4.51388883590698 4.54545450210571 4.58333349227905
## 1 6 4 3
## 4.62962961196899 4.64285707473755 4.6875 4.73958349227905
## 9 2 20 1
## 4.7619047164917 4.807692527771 4.84375 4.86111116409302
## 4 1 1 7
## 4.87013006210327 5 5.0595235824585 5.10204076766968
## 4 43 3 4
## 5.20833349227905 5.3125 5.33333349227905 5.35714292526245
## 92 12 1 20
## 5.36458349227905 5.41666650772095 5.46875 5.5
## 1 1 1 1
## 5.55555534362793 5.57291650772095 5.625 5.68181800842285
## 21 1 7 5
## 5.7142858505249 5.72916650772095 5.80357122421265 5.83333349227905
## 2 4 2 5
## 5.90277767181396 5.90909099578857 5.9523811340332 6
## 4 1 7 3
## 6.01851844787598 6.04395627975464 6.06060600280762 6.25
## 2 1 1 111
## 6.42857122421265 6.43939399719238 6.48148155212402 6.5
## 3 1 1 1
## 6.61666679382324 6.66666650772095 6.69642877578735 6.77083349227905
## 1 16 2 13
## 6.8125 6.875 6.94444465637207 7
## 1 1 22 1
## 7.03125 7.08333349227905 7.14285707473755 7.29166650772095
## 2 3 5 8
## 7.40740728378296 7.4404764175415 7.5 7.63888883590698
## 1 1 17 1
## 7.6530613899231 7.8125 7.85416650772095 7.87037038803101
## 1 30 1 1
## 8 8.0357141494751 8.125 8.33333301544189
## 1 2 2 30
## 8.5 8.51648330688477 8.5714282989502 8.68055534362793
## 2 1 5 1
## 8.75 8.85416698455811 8.9285717010498 9
## 3 3 8 1
## 9.16666698455811 9.25925922393799 9.375 9.44444465637207
## 1 7 27 1
## 9.64285755157471 9.80000019073486 9.89583301544189 10
## 1 1 1 9
## 10.1851854324341 10.2040815353394 10.4166669845581 10.625
## 1 1 33 2
## 10.7142858505249 10.8333330154419 10.9375 11
## 1 1 1 1
## 11.1607141494751 11.25 11.4583330154419 11.5740737915039
## 1 1 2 1
## 11.71875 11.875 12 12.0833330154419
## 1 1 2 1
## 12.5 13.0208330154419 13.0952377319336 13.3333330154419
## 21 2 1 2
## 13.5416669845581 13.8888893127441 14 14.5833330154419
## 1 2 1 4
## 15.5555553436279 15.625 16.6666660308838 16.875
## 1 4 4 1
## 17.5 17.9166660308838 18.518518447876 18.75
## 1 1 3 4
## 20 20.8333339691162 21.25 21.4285717010498
## 1 1 2 1
## 21.875 22 23 or more <NA>
## 1 1 9 2471
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_b1)[na.exclude(mydata$whour_b1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_b1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_b1. Avg. Hourly Wage (PEN) - Grandparent 1
## 0 0.102040819823742 0.306122452020645 0.333333343267441
## 1 1 1 1
## 0.505208313465118 0.74404764175415 0.892857134342194 1
## 1 1 1 1
## 1.04166662693024 1.19047617912292 1.38888883590698 1.58730161190033
## 2 1 1 1
## 1.73611116409302 1.78571426868439 1.85185182094574 1.90476191043854
## 1 1 1 1
## 2 2.08333325386047 2.23214292526245 2.27272725105286
## 1 1 2 1
## 2.34375 2.38095235824585 2.52976179122925 2.60416674613953
## 1 1 2 2
## 2.67857146263123 2.77777767181396 2.95138883590698 2.9761905670166
## 1 2 1 2
## 3 3.125 3.33333325386047 3.38541674613953
## 1 3 1 1
## 3.47222232818604 3.54166674613953 3.57142853736877 3.64583325386047
## 3 2 2 3
## 3.75 3.79464292526245 3.90625 4
## 2 5 3 1
## 4.08163261413574 4.16666650772095 4.24107122421265 4.25
## 1 7 1 1
## 4.375 4.42708349227905 4.4642858505249 4.6875
## 1 5 1 4
## 5 5.20833349227905 5.3125 5.35714292526245
## 4 4 2 3
## 5.625 5.72916650772095 6.25 6.42857122421265
## 1 1 2 1
## 6.66666650772095 6.69642877578735 7.03125 7.5
## 1 1 1 3
## 7.8125 8.33333301544189 8.5714282989502 8.88888931274414
## 3 2 1 1
## 10 12.5 13.0208330154419 13.8888893127441
## 1 2 2 1
## 15.625 39.375 53.125 <NA>
## 1 1 1 3989
## [1] "Frequency table after encoding"
## whour_b1. Avg. Hourly Wage (PEN) - Grandparent 1
## 0 0.102040819823742 0.306122452020645 0.333333343267441
## 1 1 1 1
## 0.505208313465118 0.74404764175415 0.892857134342194 1
## 1 1 1 1
## 1.04166662693024 1.19047617912292 1.38888883590698 1.58730161190033
## 2 1 1 1
## 1.73611116409302 1.78571426868439 1.85185182094574 1.90476191043854
## 1 1 1 1
## 2 2.08333325386047 2.23214292526245 2.27272725105286
## 1 1 2 1
## 2.34375 2.38095235824585 2.52976179122925 2.60416674613953
## 1 1 2 2
## 2.67857146263123 2.77777767181396 2.95138883590698 2.9761905670166
## 1 2 1 2
## 3 3.125 3.33333325386047 3.38541674613953
## 1 3 1 1
## 3.47222232818604 3.54166674613953 3.57142853736877 3.64583325386047
## 3 2 2 3
## 3.75 3.79464292526245 3.90625 4
## 2 5 3 1
## 4.08163261413574 4.16666650772095 4.24107122421265 4.25
## 1 7 1 1
## 4.375 4.42708349227905 4.4642858505249 4.6875
## 1 5 1 4
## 5 5.20833349227905 5.3125 5.35714292526245
## 4 4 2 3
## 5.625 5.72916650772095 6.25 6.42857122421265
## 1 1 2 1
## 6.66666650772095 6.69642877578735 7.03125 7.5
## 1 1 1 3
## 7.8125 8.33333301544189 8.5714282989502 8.88888931274414
## 3 2 1 1
## 10 12.5 13.0208330154419 13.8888893127441
## 1 2 2 1
## 15.625 39.375 44 or more <NA>
## 1 1 1 3989
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_b2)[na.exclude(mydata$whour_b2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_b2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_b2. Avg. Hourly Wage (PEN) - Grandparent 2
## 0.267857134342194 0.625 0.74404764175415 1.04166662693024
## 1 1 1 1
## 1.33928573131561 1.4880952835083 1.71428573131561 1.85185182094574
## 1 1 1 1
## 2.23214292526245 2.67857146263123 2.95138883590698 3.125
## 2 1 1 2
## 3.33333325386047 3.54166674613953 3.57142853736877 3.64583325386047
## 1 1 2 2
## 3.75 3.79464292526245 4 4.16666650772095
## 1 3 2 2
## 4.24107122421265 4.42708349227905 4.4642858505249 5.20833349227905
## 1 1 2 2
## 5.55555534362793 5.625 5.9523811340332 6.25
## 1 1 1 3
## 7.08333349227905 7.5 11.6666669845581 12.5
## 1 1 1 1
## 13.8888893127441 166.66667175293 <NA>
## 1 1 4065
## [1] "Frequency table after encoding"
## whour_b2. Avg. Hourly Wage (PEN) - Grandparent 2
## 0.267857134342194 0.625 0.74404764175415 1.04166662693024
## 1 1 1 1
## 1.33928573131561 1.4880952835083 1.71428573131561 1.85185182094574
## 1 1 1 1
## 2.23214292526245 2.67857146263123 2.95138883590698 3.125
## 2 1 1 2
## 3.33333325386047 3.54166674613953 3.57142853736877 3.64583325386047
## 1 1 2 2
## 3.75 3.79464292526245 4 4.16666650772095
## 1 3 2 2
## 4.24107122421265 4.42708349227905 4.4642858505249 5.20833349227905
## 1 1 2 2
## 5.55555534362793 5.625 5.9523811340332 6.25
## 1 1 1 3
## 7.08333349227905 7.5 11.6666669845581 12.5
## 1 1 1 1
## 13.8888893127441 132 or more <NA>
## 1 1 4065
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_c1)[na.exclude(mydata$whour_c1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_c1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_c1. Avg. Hourly Wage (PEN) - Uncle 1
## 0.133928567171097 0.336805552244186 0.505208313465118 0.694444417953491
## 1 1 1 1
## 0.808333337306976 1.19047617912292 1.25 1.33928573131561
## 1 1 1 1
## 1.38888883590698 1.73611116409302 1.875 1.89393937587738
## 1 1 1 1
## 2.08333325386047 2.23214292526245 2.38095235824585 2.5
## 3 1 1 3
## 2.52976179122925 2.60416674613953 2.67857146263123 2.74725270271301
## 3 5 2 1
## 2.75974035263062 2.77777767181396 2.85714292526245 2.95138883590698
## 1 6 1 10
## 2.98611116409302 3.125 3.20000004768372 3.21969699859619
## 1 7 1 1
## 3.24074077606201 3.33333325386047 3.47222232818604 3.54166674613953
## 1 6 2 6
## 3.57142853736877 3.64583325386047 3.75 3.79464292526245
## 3 4 2 2
## 3.8461537361145 4.01785707473755 4.0625 4.16666650772095
## 1 1 1 15
## 4.42708349227905 4.4642858505249 4.46875 4.47916650772095
## 29 4 1 1
## 4.51388883590698 4.59183692932129 4.6875 4.72222232818604
## 1 1 7 1
## 4.86111116409302 4.94791650772095 5 5.20833349227905
## 1 1 8 12
## 5.3125 5.35714292526245 5.375 5.46875
## 3 3 1 1
## 5.55555534362793 5.625 5.72916650772095 6
## 2 1 2 1
## 6.25 6.77083349227905 7.14285707473755 7.29166650772095
## 15 2 1 2
## 7.5 7.69999980926514 7.8125 8.33333301544189
## 2 1 11 1
## 8.5714282989502 8.75 8.9285717010498 9.2857141494751
## 1 1 1 1
## 9.375 10 10.4166669845581 10.625
## 4 1 1 1
## 11.1111106872559 11.25 12.5 13.0208330154419
## 1 1 2 2
## 14 14.1666669845581 15 17.7083339691162
## 1 1 1 1
## 20 <NA>
## 1 3870
## [1] "Frequency table after encoding"
## whour_c1. Avg. Hourly Wage (PEN) - Uncle 1
## 0.133928567171097 0.336805552244186 0.505208313465118 0.694444417953491
## 1 1 1 1
## 0.808333337306976 1.19047617912292 1.25 1.33928573131561
## 1 1 1 1
## 1.38888883590698 1.73611116409302 1.875 1.89393937587738
## 1 1 1 1
## 2.08333325386047 2.23214292526245 2.38095235824585 2.5
## 3 1 1 3
## 2.52976179122925 2.60416674613953 2.67857146263123 2.74725270271301
## 3 5 2 1
## 2.75974035263062 2.77777767181396 2.85714292526245 2.95138883590698
## 1 6 1 10
## 2.98611116409302 3.125 3.20000004768372 3.21969699859619
## 1 7 1 1
## 3.24074077606201 3.33333325386047 3.47222232818604 3.54166674613953
## 1 6 2 6
## 3.57142853736877 3.64583325386047 3.75 3.79464292526245
## 3 4 2 2
## 3.8461537361145 4.01785707473755 4.0625 4.16666650772095
## 1 1 1 15
## 4.42708349227905 4.4642858505249 4.46875 4.47916650772095
## 29 4 1 1
## 4.51388883590698 4.59183692932129 4.6875 4.72222232818604
## 1 1 7 1
## 4.86111116409302 4.94791650772095 5 5.20833349227905
## 1 1 8 12
## 5.3125 5.35714292526245 5.375 5.46875
## 3 3 1 1
## 5.55555534362793 5.625 5.72916650772095 6
## 2 1 2 1
## 6.25 6.77083349227905 7.14285707473755 7.29166650772095
## 15 2 1 2
## 7.5 7.69999980926514 7.8125 8.33333301544189
## 2 1 11 1
## 8.5714282989502 8.75 8.9285717010498 9.2857141494751
## 1 1 1 1
## 9.375 10 10.4166669845581 10.625
## 4 1 1 1
## 11.1111106872559 11.25 12.5 13.0208330154419
## 1 1 2 2
## 14 14.1666669845581 15 17 or more
## 1 1 1 2
## <NA>
## 3870
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_c2)[na.exclude(mydata$whour_c2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_c2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_c2. Avg. Hourly Wage (PEN) - Uncle 2
## 0.404166668653488 0.606249988079071 0.673611104488373 2.0238094329834
## 1 1 1 1
## 2.23214292526245 2.60416674613953 2.67857146263123 2.77777767181396
## 1 3 1 3
## 2.86458325386047 2.91666674613953 2.95138883590698 2.9761905670166
## 1 1 3 1
## 3 3.125 3.29861116409302 3.33333325386047
## 1 3 1 1
## 3.47222232818604 3.54166674613953 3.57142853736877 3.64583325386047
## 1 2 1 3
## 3.79464292526245 3.90625 3.9351851940155 4.16666650772095
## 2 1 1 8
## 4.2857141494751 4.42708349227905 4.46875 4.47916650772095
## 1 14 1 2
## 4.5 4.6875 4.72222232818604 4.7619047164917
## 1 2 1 1
## 4.94791650772095 5 5.20833349227905 5.3125
## 1 1 3 2
## 5.72916650772095 6.25 6.875 7.14285707473755
## 1 6 1 1
## 7.8125 8.0357141494751 8.33333301544189 8.75
## 2 1 5 1
## 8.9285717010498 9.375 10.4166669845581 10.625
## 1 2 1 1
## 12.5 13.0208330154419 15.625 16.6666660308838
## 2 1 2 1
## <NA>
## 4009
## [1] "Frequency table after encoding"
## whour_c2. Avg. Hourly Wage (PEN) - Uncle 2
## 0.404166668653488 0.606249988079071 0.673611104488373 2.0238094329834
## 1 1 1 1
## 2.23214292526245 2.60416674613953 2.67857146263123 2.77777767181396
## 1 3 1 3
## 2.86458325386047 2.91666674613953 2.95138883590698 2.9761905670166
## 1 1 3 1
## 3 3.125 3.29861116409302 3.33333325386047
## 1 3 1 1
## 3.47222232818604 3.54166674613953 3.57142853736877 3.64583325386047
## 1 2 1 3
## 3.79464292526245 3.90625 3.9351851940155 4.16666650772095
## 2 1 1 8
## 4.2857141494751 4.42708349227905 4.46875 4.47916650772095
## 1 14 1 2
## 4.5 4.6875 4.72222232818604 4.7619047164917
## 1 2 1 1
## 4.94791650772095 5 5.20833349227905 5.3125
## 1 1 3 2
## 5.72916650772095 6.25 6.875 7.14285707473755
## 1 6 1 1
## 7.8125 8.0357141494751 8.33333301544189 8.75
## 2 1 5 1
## 8.9285717010498 9.375 10.4166669845581 10.625
## 1 2 1 1
## 12.5 13.0208330154419 15.625 16 or more
## 2 1 2 1
## <NA>
## 4009
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_c3)[na.exclude(mydata$whour_c3)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_c3", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_c3. Avg. Hourly Wage (PEN) - Uncle 3
## 0.404166668653488 0.714285731315613 2.08333325386047 2.60416674613953
## 1 1 1 4
## 2.77777767181396 3.125 3.54166674613953 3.64583325386047
## 1 1 1 2
## 3.79464292526245 3.90625 4.16666650772095 4.42708349227905
## 1 1 4 7
## 4.47916650772095 4.6875 5 5.20833349227905
## 1 1 1 1
## 5.72916650772095 6.25 7.29166650772095 7.8125
## 1 1 1 2
## 11.4583330154419 12.5 <NA>
## 1 1 4075
## [1] "Frequency table after encoding"
## whour_c3. Avg. Hourly Wage (PEN) - Uncle 3
## 0.404166668653488 0.714285731315613 2.08333325386047 2.60416674613953
## 1 1 1 4
## 2.77777767181396 3.125 3.54166674613953 3.64583325386047
## 1 1 1 2
## 3.79464292526245 3.90625 4.16666650772095 4.42708349227905
## 1 1 4 7
## 4.47916650772095 4.6875 5 5.20833349227905
## 1 1 1 1
## 5.72916650772095 6.25 7.29166650772095 7.8125
## 1 1 1 2
## 11.4583330154419 12 or more <NA>
## 1 1 4075
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_c4)[na.exclude(mydata$whour_c4)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_c4", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_c4. Avg. Hourly Wage (PEN) - Uncle 4
## 0.404166668653488 0.595238089561462 2.60416674613953 3.125
## 1 1 2 1
## 3.81944441795349 4.16666650772095 4.42708349227905 4.47916650772095
## 1 1 1 1
## 4.6875 5 6.25 <NA>
## 1 1 1 4099
## [1] "Frequency table after encoding"
## whour_c4. Avg. Hourly Wage (PEN) - Uncle 4
## 0.404166668653488 0.595238089561462 2.60416674613953 3.125
## 1 1 2 1
## 3.81944441795349 4.16666650772095 4.42708349227905 4.47916650772095
## 1 1 1 1
## 4.6875 5 6 or more <NA>
## 1 1 1 4099
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_c5)[na.exclude(mydata$whour_c5)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_c5", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_c5. Avg. Hourly Wage (PEN) - Uncle 5
## 0.714285731315613 2.60416674613953 3.125 3.79464292526245
## 1 2 1 1
## 4.47916650772095 8.33333301544189 13.3333330154419 <NA>
## 1 1 1 4103
## [1] "Frequency table after encoding"
## whour_c5. Avg. Hourly Wage (PEN) - Uncle 5
## 0.714285731315613 2.60416674613953 3.125 3.79464292526245
## 1 2 1 1
## 4.47916650772095 8.33333301544189 13 or more <NA>
## 1 1 1 4103
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_c6)[na.exclude(mydata$whour_c6)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_c6", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_c6. Avg. Hourly Wage (PEN) - Uncle 6
## 3.34821438789368 4.47916650772095 9.375 <NA>
## 1 1 1 4108
## [1] "Frequency table after encoding"
## whour_c6. Avg. Hourly Wage (PEN) - Uncle 6
## 3.34821438789368 4.47916650772095 9 or more <NA>
## 1 1 1 4108
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_d1)[na.exclude(mydata$whour_d1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_d1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_d1. Avg. Hourly Wage (PEN) - Nephew 1
## 1.01041662693024 1.66666662693024 2.08333325386047 2.83333325386047 3.47222232818604
## 1 2 1 1 2
## 3.54166674613953 4.10714292526245 4.16666650772095 4.42708349227905 4.6875
## 1 1 1 3 1
## 5.20833349227905 5.3125 6.25 6.59722232818604 <NA>
## 1 1 2 1 4092
## [1] "Frequency table after encoding"
## whour_d1. Avg. Hourly Wage (PEN) - Nephew 1
## 1.01041662693024 1.66666662693024 2.08333325386047 2.83333325386047 3.47222232818604
## 1 2 1 1 2
## 3.54166674613953 4.10714292526245 4.16666650772095 4.42708349227905 4.6875
## 1 1 1 3 1
## 5.20833349227905 5.3125 6 or more <NA>
## 1 1 3 4092
percentile_99.5 <- floor(quantile(na.exclude(mydata$whour_d2)[na.exclude(mydata$whour_d2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="whour_d2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## whour_d2. Avg. Hourly Wage (PEN) - Nephew 2
## 2.08333325386047 5.3125 5.55555534362793 <NA>
## 1 1 1 4108
## [1] "Frequency table after encoding"
## whour_d2. Avg. Hourly Wage (PEN) - Nephew 2
## 2.08333325386047 5 or more <NA>
## 1 2 4108
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_hh)[na.exclude(mydata$wsL_hh)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_hh", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_hh. Perc. Shadow Price of Help (Monthly, PEN) - Household Head
## 0 1 10 15 20 25 30 40 50 80 90 100 108 120 150 158 200
## 188 2 7 1 1 1 4 1 13 4 1 27 1 2 10 1 55
## 208 240 250 280 300 308 350 400 408 450 480 500 600 650 660 700 750
## 5 1 17 1 73 2 7 57 1 3 1 110 62 1 1 29 17
## 800 850 900 950 1000 1100 1200 1300 1400 1500 1600 1800 1900 2000 2500 3000 3500
## 93 115 31 2 83 2 34 1 3 28 2 2 1 9 1 2 1
## <NA>
## 2994
## [1] "Frequency table after encoding"
## wsL_hh. Perc. Shadow Price of Help (Monthly, PEN) - Household Head
## 0 1 10 15 20 25
## 188 2 7 1 1 1
## 30 40 50 80 90 100
## 4 1 13 4 1 27
## 108 120 150 158 200 208
## 1 2 10 1 55 5
## 240 250 280 300 308 350
## 1 17 1 73 2 7
## 400 408 450 480 500 600
## 57 1 3 1 110 62
## 650 660 700 750 800 850
## 1 1 29 17 93 115
## 900 950 1000 1100 1200 1300
## 31 2 83 2 34 1
## 1400 1500 1600 1800 1900 2000 or more
## 3 28 2 2 1 13
## <NA>
## 2994
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_hhpartner)[na.exclude(mydata$wsL_hhpartner)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_hhpartner", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_hhpartner. Perc. Shadow Price of Help (Monthly, PEN) - Partner
## 0 50 60 70 100 200 240 250 300 350 400 450 500 600 700 750 800
## 30 2 1 1 6 18 1 1 18 1 13 1 19 14 4 4 25
## 850 900 1000 1200 1400 1500 1800 2000 3000 <NA>
## 26 7 20 8 2 6 1 3 1 3878
## [1] "Frequency table after encoding"
## wsL_hhpartner. Perc. Shadow Price of Help (Monthly, PEN) - Partner
## 0 50 60 70 100 200
## 30 2 1 1 6 18
## 240 250 300 350 400 450
## 1 1 18 1 13 1
## 500 600 700 750 800 850
## 19 14 4 4 25 26
## 900 1000 1200 1400 1500 1800
## 7 20 8 2 6 1
## 2000 or more <NA>
## 4 3878
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_b1)[na.exclude(mydata$wsL_b1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_b1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_b1. Perc. Shadow Price of Help (Monthly, PEN) - Grandparent 1
## 0 10 20 30 50 80 100 120 150 200 250 280 300 350 400 500 600
## 21 2 3 1 2 1 5 1 3 20 1 2 16 2 13 14 6
## 700 750 800 850 900 1000 1200 1500 1800 <NA>
## 2 2 3 18 2 6 3 5 1 3956
## [1] "Frequency table after encoding"
## wsL_b1. Perc. Shadow Price of Help (Monthly, PEN) - Grandparent 1
## 0 10 20 30 50 80
## 21 2 3 1 2 1
## 100 120 150 200 250 280
## 5 1 3 20 1 2
## 300 350 400 500 600 700
## 16 2 13 14 6 2
## 750 800 850 900 1000 1200
## 2 3 18 2 6 3
## 1500 1568 or more <NA>
## 5 1 3956
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_b2)[na.exclude(mydata$wsL_b2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_b2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_b2. Perc. Shadow Price of Help (Monthly, PEN) - Grandparent 2
## 0 10 20 50 80 100 150 200 208 250 300 400 500 600 700 750 800
## 8 2 1 1 1 1 1 1 1 1 8 3 9 3 1 2 3
## 850 900 1000 1500 <NA>
## 9 1 5 4 4045
## [1] "Frequency table after encoding"
## wsL_b2. Perc. Shadow Price of Help (Monthly, PEN) - Grandparent 2
## 0 10 20 50 80 100
## 8 2 1 1 1 1
## 150 200 208 250 300 400
## 1 1 1 1 8 3
## 500 600 700 750 800 850
## 9 3 1 2 3 9
## 900 1000 1500 or more <NA>
## 1 5 4 4045
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_b3)[na.exclude(mydata$wsL_b3)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_b3", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_b3. Perc. Shadow Price of Help (Monthly, PEN) - Grandparent 3
## 0 300 <NA>
## 2 1 4108
## [1] "Frequency table after encoding"
## wsL_b3. Perc. Shadow Price of Help (Monthly, PEN) - Grandparent 3
## 0 297 or more <NA>
## 2 1 4108
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_c1)[na.exclude(mydata$wsL_c1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_c1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_c1. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 1
## 0 10 20 50 100 150 200 220 250 300 350 400
## 16 2 2 2 2 3 3 1 1 6 1 2
## 450 500 600 700 800 850 1000 400400 <NA>
## 2 3 3 1 3 5 2 1 4050
## [1] "Frequency table after encoding"
## wsL_c1. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 1
## 0 10 20 50 100
## 16 2 2 2 2
## 150 200 220 250 300
## 3 3 1 1 6
## 350 400 450 500 600
## 1 2 2 3 3
## 700 800 850 1000 280580 or more
## 1 3 5 2 1
## <NA>
## 4050
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_c2)[na.exclude(mydata$wsL_c2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_c2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_c2. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 2
## 0 50 200 220 250 300 400 500 588 600 800 850 1000 1200 1500 2000 <NA>
## 6 3 2 1 1 2 5 3 1 2 1 4 2 1 3 1 4073
## [1] "Frequency table after encoding"
## wsL_c2. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 2
## 0 50 200 220 250 300
## 6 3 2 1 1 2
## 400 500 588 600 800 850
## 5 3 1 2 1 4
## 1000 1200 1500 1907 or more <NA>
## 2 1 3 1 4073
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_c3)[na.exclude(mydata$wsL_c3)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_c3", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_c3. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 3
## 200 400 500 600 750 850 900 1000 <NA>
## 1 2 1 1 1 2 1 1 4101
## [1] "Frequency table after encoding"
## wsL_c3. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 3
## 200 400 500 600 750 850 900
## 1 2 1 1 1 2 1
## 995 or more <NA>
## 1 4101
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_c4)[na.exclude(mydata$wsL_c4)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_c4", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_c4. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 4
## 300 400 500 600 900 <NA>
## 1 1 1 1 1 4106
## [1] "Frequency table after encoding"
## wsL_c4. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 4
## 300 400 500 600 894 or more <NA>
## 1 1 1 1 1 4106
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_c5)[na.exclude(mydata$wsL_c5)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_c5", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_c5. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 5
## 40 <NA>
## 1 4110
## [1] "Frequency table after encoding"
## wsL_c5. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 5
## 40 or more <NA>
## 1 4110
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_c6)[na.exclude(mydata$wsL_c6)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_c6", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_c6. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 6
## 400 <NA>
## 1 4110
## [1] "Frequency table after encoding"
## wsL_c6. Perc. Shadow Price of Help (Monthly, PEN) - Uncle 6
## 400 or more <NA>
## 1 4110
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_d1)[na.exclude(mydata$wsL_d1)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_d1", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_d1. Perc. Shadow Price of Help (Monthly, PEN) - Nephew 1
## 0 10 20 30 40 50 100 200 250 360 400 500 800 850 <NA>
## 15 2 1 2 2 3 2 3 1 1 3 3 2 2 4069
## [1] "Frequency table after encoding"
## wsL_d1. Perc. Shadow Price of Help (Monthly, PEN) - Nephew 1
## 0 10 20 30 40 50 100
## 15 2 1 2 2 3 2
## 200 250 360 400 500 800 850 or more
## 3 1 1 3 3 2 2
## <NA>
## 4069
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_d2)[na.exclude(mydata$wsL_d2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_d2", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_d2. Perc. Shadow Price of Help (Monthly, PEN) - Nephew 2
## 0 10 20 40 200 300 <NA>
## 8 1 1 1 1 1 4098
## [1] "Frequency table after encoding"
## wsL_d2. Perc. Shadow Price of Help (Monthly, PEN) - Nephew 2
## 0 10 20 40 200 293 or more <NA>
## 8 1 1 1 1 1 4098
percentile_99.5 <- floor(quantile(na.exclude(mydata$wsL_d3)[na.exclude(mydata$wsL_d3)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="wsL_d3", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## wsL_d3. Perc. Shadow Price of Help (Monthly, PEN) - Nephew 3
## 0 10 200 800 <NA>
## 4 1 1 1 4104
## [1] "Frequency table after encoding"
## wsL_d3. Perc. Shadow Price of Help (Monthly, PEN) - Nephew 3
## 0 10 200 781 or more <NA>
## 4 1 1 1 4104
# Top code high education outlay to the 99.5 percentile
percentile_99.5 <- floor(quantile(na.exclude(mydata$p49)[na.exclude(mydata$p9)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="p49", break_point=percentile_99.5, missing=999999)
## [1] "Frequency table before encoding"
## p49. ¿Cuánto gasta cada mes en total en la educación de todos sus hijos que viven en
## 0 1 8 10 12 15 18 20 22 25 30 33 34 40 45 50 55
## 72 1 2 6 1 2 1 25 1 4 45 1 1 24 4 199 1
## 58 60 70 72 75 80 84 90 95 100 108 110 120 130 140 150 160
## 3 51 25 1 2 73 2 7 1 361 2 2 31 6 2 202 5
## 170 180 190 200 208 210 215 220 230 240 250 260 270 280 282 300 305
## 2 15 2 448 3 3 1 4 3 6 110 3 2 10 1 423 1
## 308 310 320 330 350 360 372 375 380 385 390 400 408 410 440 450 460
## 2 1 5 3 65 2 1 1 2 1 2 247 3 1 1 30 1
## 473 480 500 508 515 520 530 540 550 560 580 600 608 650 700 730 750
## 1 2 320 2 1 1 1 1 6 1 1 154 3 5 69 1 8
## 757 770 800 840 850 870 884 900 960 1000 1008 1050 1100 1200 1230 1300 1400
## 1 1 104 1 5 1 1 25 1 142 1 1 5 39 1 5 3
## 1470 1500 1508 1550 1600 1700 1800 2000 2100 2200 2300 2400 2500 2800 3000 3400 3500
## 1 78 1 1 3 1 7 45 2 1 1 1 12 1 17 1 1
## 4000 5000 6000 7000 <NA>
## 4 1 1 1 445
## [1] "Frequency table after encoding"
## p49. ¿Cuánto gasta cada mes en total en la educación de todos sus hijos que viven en
## 0 1 8 10 12 15
## 72 1 2 6 1 2
## 18 20 22 25 30 33
## 1 25 1 4 45 1
## 34 40 45 50 55 58
## 1 24 4 199 1 3
## 60 70 72 75 80 84
## 51 25 1 2 73 2
## 90 95 100 108 110 120
## 7 1 361 2 2 31
## 130 140 150 160 170 180
## 6 2 202 5 2 15
## 190 200 208 210 215 220
## 2 448 3 3 1 4
## 230 240 250 260 270 280
## 3 6 110 3 2 10
## 282 300 305 308 310 320
## 1 423 1 2 1 5
## 330 350 360 372 375 380
## 3 65 2 1 1 2
## 385 390 400 408 410 440
## 1 2 247 3 1 1
## 450 460 473 480 500 508
## 30 1 1 2 320 2
## 515 520 530 540 550 560
## 1 1 1 1 6 1
## 580 600 608 650 700 730
## 1 154 3 5 69 1
## 750 757 770 800 840 850
## 8 1 1 104 1 5
## 870 884 900 960 1000 1008
## 1 1 25 1 142 1
## 1050 1100 1200 1230 1300 1400
## 1 5 39 1 5 3
## 1470 1500 1508 1550 1600 1700
## 1 78 1 1 3 1
## 1800 2000 2100 2200 2300 2400
## 7 45 2 1 1 1
## 2500 2800 3000 or more <NA>
## 12 1 26 445
# !!!Include relevant variables in list below (Indirect PII - Categorical, and Ordinal if not processed yet)
indirect_PII <- c("p4c1",
"p4_2",
"p4b1",
"p4c2",
"p4_1",
"p4c3",
"p4c4",
"p8",
"p4b3",
"p4c5",
"p4c6",
"idioma1",
"idioma2")
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="p4c1", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4c1. TÃo / TÃa 1
## Estudia
## 25
## Estudia y tiene un trabajo remunerado
## 13
## Trabajo remunerado
## 299
## Quehaceres del hogar o trabajo no remunerado
## 56
## No hace nada
## 27
## <NA>
## 3691
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 25 0 0 0 0
## 2 0 13 0 0 0
## 3 0 0 299 0 0
## 4 0 0 0 56 0
## 5 0 0 0 0 27
## [1] "Frequency table after encoding"
## p4c1. TÃo / TÃa 1
## Otros
## 65
## Trabajo remunerado
## 299
## Quehaceres del hogar o trabajo no remunerado
## 56
## <NA>
## 3691
## [1] "Inspect value labels and relabel as necessary"
## Otros
## 1
## Otros
## 2
## Trabajo remunerado
## 3
## Quehaceres del hogar o trabajo no remunerado
## 4
## Otros
## 5
break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Estudia y tiene un trabajo remunerado"=2,
"Trabajo remunerado"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"No hace nada"=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
## 9
## Estudia y tiene un trabajo remunerado
## 36
## Trabajo remunerado
## 1761
## Quehaceres del hogar o trabajo no remunerado
## 1468
## No hace nada
## 61
## <NA>
## 776
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 9 0 0 0 0
## 2 0 36 0 0 0
## 3 0 0 1761 0 0
## 4 0 0 0 1468 0
## 5 0 0 0 0 61
## [1] "Frequency table after encoding"
## p4_2. Madre
## Otros
## 9
## Estudia y tiene un trabajo remunerado
## 36
## Trabajo remunerado
## 1761
## Quehaceres del hogar o trabajo no remunerado
## 1468
## No hace nada
## 61
## <NA>
## 776
## [1] "Inspect value labels and relabel as necessary"
## Otros
## 1
## Estudia y tiene un trabajo remunerado
## 2
## Trabajo remunerado
## 3
## Quehaceres del hogar o trabajo no remunerado
## 4
## No hace nada
## 5
break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Estudia y tiene un trabajo remunerado"=2,
"Trabajo remunerado"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"No hace nada"=5)
mydata <- ordinal_recode (variable="p4b1", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4b1. Abuelo / Abuela 1
## Estudia
## 5
## Trabajo remunerado
## 153
## Quehaceres del hogar o trabajo no remunerado
## 188
## No hace nada
## 206
## <NA>
## 3559
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 5 0 0 0 0
## 3 0 0 153 0 0
## 4 0 0 0 188 0
## 5 0 0 0 0 206
## [1] "Frequency table after encoding"
## p4b1. Abuelo / Abuela 1
## Otros
## 5
## Trabajo remunerado
## 153
## Quehaceres del hogar o trabajo no remunerado
## 188
## No hace nada
## 206
## <NA>
## 3559
## [1] "Inspect value labels and relabel as necessary"
## Otros
## 1
## Estudia y tiene un trabajo remunerado
## 2
## Trabajo remunerado
## 3
## Quehaceres del hogar o trabajo no remunerado
## 4
## No hace nada
## 5
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="p4c2", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4c2. TÃo / TÃa 2
## Estudia
## 10
## Estudia y tiene un trabajo remunerado
## 3
## Trabajo remunerado
## 140
## Quehaceres del hogar o trabajo no remunerado
## 35
## No hace nada
## 8
## <NA>
## 3915
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 10 0 0 0 0
## 2 0 3 0 0 0
## 3 0 0 140 0 0
## 4 0 0 0 35 0
## 5 0 0 0 0 8
## [1] "Frequency table after encoding"
## p4c2. TÃo / TÃa 2
## Otros
## 21
## Trabajo remunerado
## 140
## Quehaceres del hogar o trabajo no remunerado
## 35
## <NA>
## 3915
## [1] "Inspect value labels and relabel as necessary"
## Otros
## 1
## Otros
## 2
## Trabajo remunerado
## 3
## Quehaceres del hogar o trabajo no remunerado
## 4
## Otros
## 5
break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Estudia y tiene un trabajo remunerado"=2,
"Trabajo remunerado"=3,
"Quehaceres del hogar o trabajo no remunerado"=4,
"No hace nada"=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
## 2
## Estudia y tiene un trabajo remunerado
## 40
## Trabajo remunerado
## 2264
## Quehaceres del hogar o trabajo no remunerado
## 48
## No hace nada
## 43
## <NA>
## 1714
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 2 0 0 0 0
## 2 0 40 0 0 0
## 3 0 0 2264 0 0
## 4 0 0 0 48 0
## 5 0 0 0 0 43
## [1] "Frequency table after encoding"
## p4_1. Padre
## Otros
## 2
## Estudia y tiene un trabajo remunerado
## 40
## Trabajo remunerado
## 2264
## Quehaceres del hogar o trabajo no remunerado
## 48
## No hace nada
## 43
## <NA>
## 1714
## [1] "Inspect value labels and relabel as necessary"
## Otros
## 1
## Estudia y tiene un trabajo remunerado
## 2
## Trabajo remunerado
## 3
## Quehaceres del hogar o trabajo no remunerado
## 4
## No hace nada
## 5
break_activity <- c(1,2,3,4,5)
labels_activity <- c("Otros"=1,
"Otros"=2,
"Trabajo remunerado"=3,
"Otros"=4,
"Otros"=5)
mydata <- ordinal_recode (variable="p4c3", break_points=break_activity, missing=999999, value_labels=labels_activity)
## [1] "Frequency table before encoding"
## p4c3. TÃo / TÃa 3
## Estudia
## 5
## Estudia y tiene un trabajo remunerado
## 2
## Trabajo remunerado
## 50
## Quehaceres del hogar o trabajo no remunerado
## 9
## No hace nada
## 2
## <NA>
## 4043
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,1e+06)
## 1 5 0 0 0 0
## 2 0 2 0 0 0
## 3 0 0 50 0 0
## 4 0 0 0 9 0
## 5 0 0 0 0 2
## [1] "Frequency table after encoding"
## p4c3. TÃo / TÃa 3
## Otros Trabajo remunerado <NA>
## 18 50 4043
## [1] "Inspect value labels and relabel as necessary"
## Otros Otros Trabajo remunerado Otros
## 1 2 3 4
## Otros
## 5
break_material <- c(1,2,3,4,5,6,7,8,99)
labels_material <- c("Cemento"=1,
"Tejas"=2,
"Calamina de metal o metal"=3,
"Calamina de plastico o plastico"=4,
"Madera"=5,
"Otro"=6,
"Adobe"=7,
"Otro"=8,
"Otro"=9)
mydata <- ordinal_recode (variable="p8", break_points=break_material, missing=999999, value_labels=labels_material)
## [1] "Frequency table before encoding"
## p8. Material principal de construcción del techo del hogar
## Concreto, ladrillos o cemento Tejas
## 2315 121
## Calamina de metal o metal Calamina de plástico o plástico
## 844 296
## Madera Cartón
## 368 12
## Adobe Paja
## 96 2
## Otro <NA>
## 43 14
## recoded
## [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,99) [99,1e+06)
## 1 2315 0 0 0 0 0 0 0 0
## 2 0 121 0 0 0 0 0 0 0
## 3 0 0 844 0 0 0 0 0 0
## 4 0 0 0 296 0 0 0 0 0
## 5 0 0 0 0 368 0 0 0 0
## 6 0 0 0 0 0 12 0 0 0
## 7 0 0 0 0 0 0 96 0 0
## 8 0 0 0 0 0 0 0 2 0
## 99 0 0 0 0 0 0 0 0 43
## [1] "Frequency table after encoding"
## p8. Material principal de construcción del techo del hogar
## Cemento Tejas
## 2315 121
## Calamina de metal o metal Calamina de plastico o plastico
## 844 296
## Madera Otro
## 368 57
## Adobe <NA>
## 96 14
## [1] "Inspect value labels and relabel as necessary"
## Cemento Tejas
## 1 2
## Calamina de metal o metal Calamina de plastico o plastico
## 3 4
## Madera Otro
## 5 6
## Adobe Otro
## 7 8
## Otro
## 9
break_language <- c(-11,-10,-9,-8,-7,1,2)
labels_language <- c("Missing - Zenekon"=1,
"Missing - IPA"=2,
"No indica"=3,
"No se puede leer"=4,
"Error"=5,
"Castellano - Espanol"=6,
"Otro"=7)
mydata <- ordinal_recode (variable="idioma1", break_points=break_language, missing=999999, value_labels=labels_language)
## [1] "Frequency table before encoding"
## idioma1. P2. Cual fue el idioma con el que aprendiste a hablar? 1
## Missing-MINEDU Castellano - Español Quechua Aymara
## 1557 2124 23 2
## Japones Ingles Portugues Machiguenga
## 1 5 1 1
## Aleman <NA>
## 2 395
## recoded
## [-11,-10) [-10,-9) [-9,-8) [-8,-7) [-7,1) [1,2) [2,1e+06)
## -99999 0 0 0 0 0 0 0
## 1 0 0 0 0 0 2124 0
## 2 0 0 0 0 0 0 23
## 3 0 0 0 0 0 0 2
## 4 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 5
## 6 0 0 0 0 0 0 1
## 11 0 0 0 0 0 0 1
## 12 0 0 0 0 0 0 2
## [1] "Frequency table after encoding"
## idioma1. P2. Cual fue el idioma con el que aprendiste a hablar? 1
## Castellano - Espanol Otro <NA>
## 2124 35 1952
## [1] "Inspect value labels and relabel as necessary"
## Missing - Zenekon Missing - IPA No indica No se puede leer
## 1 2 3 4
## Error Castellano - Espanol Otro
## 5 6 7
break_language <- c(-11,-10,-9,-8,-7,1)
labels_language <- c("Missing - Zenekon"=1,
"Missing - IPA"=2,
"No indica"=3,
"No se puede leer"=4,
"Error"=5,
"Otros"=6)
mydata <- ordinal_recode (variable="idioma2", break_points=break_language, missing=999999, value_labels=labels_language)
## [1] "Frequency table before encoding"
## idioma2. P2. Cual fue el idioma con el que aprendiste a hablar? 2
## Missing-MINEDU Quechua Aymara Ingles Chino
## 1557 7 1 4 1
## <NA>
## 2541
## recoded
## [-11,-10) [-10,-9) [-9,-8) [-8,-7) [-7,1) [1,1e+06)
## -99999 0 0 0 0 0 0
## 2 0 0 0 0 0 7
## 3 0 0 0 0 0 1
## 5 0 0 0 0 0 4
## 16 0 0 0 0 0 1
## [1] "Frequency table after encoding"
## idioma2. P2. Cual fue el idioma con el que aprendiste a hablar? 2
## Otros <NA>
## 13 4098
## [1] "Inspect value labels and relabel as necessary"
## Missing - Zenekon Missing - IPA No indica No se puede leer
## 1 2 3 4
## Error Otros
## 5 6
# selected categorical key variables: gender, occupation/education and age
selectedKeyVars= c('hh_ageinyears', 'd_mujer','grado2016_admin') ##!!! Replace with candidate categorical demo vars
# creating the sdcMicro object with the assigned variables
sdcInitial <- createSdcObj(dat = mydata, keyVars = selectedKeyVars)
sdcInitial
## The input dataset consists of 4111 rows and 2020 variables.
## --> Categorical key variables: hh_ageinyears, d_mujer, grado2016_admin
## ----------------------------------------------------------------------
## 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
## hh_ageinyears 43 (43) 87.286 (87.286)
## d_mujer 2 (2) 2055.500 (2055.500)
## grado2016_admin 7 (7) 587.286 (587.286)
## Size of smallest (>0)
## 6 (6)
## 2029 (2029)
## 13 (13)
## ----------------------------------------------------------------------
## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 8 (0.195%)
## - 3-anonymity: 12 (0.292%)
## - 5-anonymity: 12 (0.292%)
##
## ----------------------------------------------------------------------
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]
## Registered S3 method overwritten by 'cli':
## method from
## print.boxx spatstat.geom
## # A tibble: 8 x 3
## hh_ageinyears d_mujer grado2016_admin
## <dbl+lbl> <dbl> <dbl>
## 1 41 0 5
## 2 32 0 5
## 3 56 0 5
## 4 30 0 5
## 5 42 0 5
## 6 49 0 5
## 7 43 0 5
## 8 31 0 5
sdcFinal <- localSuppression(sdcInitial)
extractManipData(sdcFinal)[notAnon,selectedKeyVars] # manipulated variables HH
## Warning in if (cc != class(v_p)) {: the condition has length > 1 and only the first
## element will be used
## hh_ageinyears d_mujer grado2016_admin
## 1380 NA 0 5
## 1484 NA 0 5
## 2283 NA 0 5
## 2366 NA 0 5
## 2431 NA 0 5
## 2926 NA 0 5
## 3115 NA 0 5
## 4076 31 0 NA
mydata [notAnon,"d_mujer"] <- NA
createSdcObj(dat = mydata, keyVars = selectedKeyVars)
## The input dataset consists of 4111 rows and 2020 variables.
## --> Categorical key variables: hh_ageinyears, d_mujer, grado2016_admin
## ----------------------------------------------------------------------
## 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
## hh_ageinyears 43 (43) 87.286 (87.286)
## d_mujer 3 (3) 2051.500 (2051.500)
## grado2016_admin 7 (7) 587.286 (587.286)
## Size of smallest (>0)
## 6 (6)
## 2029 (2029)
## 13 (13)
## ----------------------------------------------------------------------
## Infos on 2/3-Anonymity:
##
## Number of observations violating
## - 2-anonymity: 0 (0.000%)
## - 3-anonymity: 8 (0.195%)
## - 5-anonymity: 12 (0.292%)
##
## ----------------------------------------------------------------------
# !!! Identify open-end variables here:
open_ends <- c("hh_parentesco_other",
"p_ave_finance_2a",
"p_sc_info_2",
"p_sc_info_312",
"p_sc_info_322",
"p_sc_info_332",
"p_sc_info_342",
"p_sc_info_352",
"p_sc_info_362",
"p_sc_info_372",
"p_sc_info_382",
"p_sc_info_392",
"p_sc_info_5_1st",
"p_sc_info_5_2nd",
"p_sc_info_5_3rd",
"p_sc_info_8",
"p_sc_info_74",
"p_sc_info_310",
"p8a",
"pref65f",
"pref66f",
"p44c",
"i19a1",
"p_ave_plans_45",
"p_ave_returns_10_3",
"name_schship",
"p4b",
"p13c1",
"s_ave_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",
"pref19a",
"pref15f",
"pref16f",
"p35a1",
"hs_gps_whereother",
"s_ave_returns_11_3",
"ss_gps_whereother")
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% "hh_parentesco_other"]
mydata <- mydata[!names(mydata) %in% "p_ave_finance_2a"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_2"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_312"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_322"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_332"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_342"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_352"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_362"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_372"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_382"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_392"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_5_1st"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_5_2nd"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_5_3rd"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_8"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_74"]
mydata <- mydata[!names(mydata) %in% "p_sc_info_310"]
mydata <- mydata[!names(mydata) %in% "p8a"]
mydata <- mydata[!names(mydata) %in% "pref65f"]
mydata <- mydata[!names(mydata) %in% "pref66f"]
mydata <- mydata[!names(mydata) %in% "p44c"]
mydata <- mydata[!names(mydata) %in% "i19a1"]
mydata <- mydata[!names(mydata) %in% "p_ave_plans_45"]
mydata <- mydata[!names(mydata) %in% "p_ave_returns_10_3"]
mydata <- mydata[!names(mydata) %in% "p13c1"]
mydata <- mydata[!names(mydata) %in% "s_ave_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% "pref19a"]
mydata <- mydata[!names(mydata) %in% "pref15f"]
mydata <- mydata[!names(mydata) %in% "pref16f"]
mydata <- mydata[!names(mydata) %in% "hs_gps_whereother"]
mydata <- mydata[!names(mydata) %in% "s_ave_returns_11_3"]
mydata <- mydata[!names(mydata) %in% "ss_gps_whereother"]
mydata <- mydata[!names(mydata) %in% "p4b"]
mydata <- mydata[!names(mydata) %in% "p35a1"]
mydata <- mydata[!names(mydata) %in% "name_schship"]
# Setup map
countrymap <- map_data("world") %>% filter(region=="Peru") #!!! Select correct country
admin <- raster::getData("GADM", country="PE", level=0) #!!! Select correct country map using standard 2-letter country codes: https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2
# Displace all pairs of GPS variables (Longitude, Latitude). Check summary statistics and maps before and after displacement.
gps.vars <- c("i19longitude", "i19latitude") # !!!Include relevant variables, always longitude first, latitude second.
mydata <- displace(gps.vars, admin=admin, samp_num=1, other_num=100000) # May take a few minutes to process.
## Warning: Removed 1139 rows containing missing values (geom_point).
## [1] "Summary Long/Lat statistics before displacement"
## i19longitude i19latitude
## Min. :-77.17 Min. :-13.60
## 1st Qu.:-77.07 1st Qu.:-12.12
## Median :-77.01 Median :-12.03
## Mean :-76.75 Mean :-12.10
## 3rd Qu.:-76.95 3rd Qu.:-11.96
## Max. :-71.89 Max. :-11.73
## NA's :1139 NA's :1139
## Warning: Removed 1139 rows containing missing values (geom_point).
## Warning: Removed 1139 rows containing missing values (geom_point).
## Warning: Removed 1139 rows containing missing values (geom_point).
## Warning: Removed 1139 rows containing missing values (geom_point).
## [1] "Summary Long/Lat statistics after displacement"
## i19longitude i19latitude
## Min. :-77.18 Min. :-13.58
## 1st Qu.:-77.07 1st Qu.:-12.11
## Median :-77.01 Median :-12.03
## Mean :-76.75 Mean :-12.10
## 3rd Qu.:-76.95 3rd Qu.:-11.96
## Max. :-71.86 Max. :-11.71
## NA's :1139 NA's :1139
## [1] "Processing time = 4.81036257889536"
mydata <- mydata[!names(mydata) %in% "i19altitude"]
mydata <- mydata[!names(mydata) %in% "gpsaltitude_hh"]
mydata <- mydata[!names(mydata) %in% "gpsaltitude"]
haven::write_dta(mydata, paste0(filename, "_PU.dta"))
colnames(mydata) <- gsub('^_', '', colnames(mydata))
names(mydata)[names(mydata) == "ANEXO_2016"] <- "ANEXO_2016_1"
names(mydata)[names(mydata) == "COD_MOD_2015"] <- "COD_MOD_2015_1"
names(mydata)[names(mydata) == "COD_MOD_2016"] <- "COD_MOD_2016_1"
mydata[is.na(mydata)] <- NA
haven::write_sav(mydata, paste0(filename, "_PU.sav"))
# Add report title dynamically
title_var <- paste0("DOL-ILAB SDC - ", filename)