Aca cargos las 8 tablas de datos de spss a R Como no todas estan etiquetadas importo los spss sin las etiquetas
Hago una tabla maestra llamada Comunidades con 901 hogares A cada tabal le pego un vector que tiene el codigo de localidad, que para las 8 loclaidades estan en orden alfabetico
Lectura de datos y Verificacion
dir(pattern = "*.sav")
## [1] "Caracarai_senasa.sav" "Melgarejo_senasa.sav"
## [3] "Nupyahu_senasa.sav" "PasoYobai_senasa.sav"
## [5] "sanignacio_senansa.sav" "Sansalvador_senasa.sav"
## [7] "Signaciocaaz_senasa.sav" "Vizcaino_senasa.sav"
library(foreign)
Caracarai <- as.data.frame(read.spss("Caracarai_senasa.sav", use.value.labels = FALSE))
## Warning: Caracarai_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
Melgarejo <- as.data.frame(read.spss("Melgarejo_senasa.sav", use.value.labels = FALSE))
## Warning: Melgarejo_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
Nupyahu <- as.data.frame(read.spss("Nupyahu_senasa.sav", use.value.labels = FALSE))
## Warning: Nupyahu_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
PasoYobai <- as.data.frame(read.spss("PasoYobai_senasa.sav", use.value.labels = FALSE))
## Warning: PasoYobai_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
Sanignacio <- as.data.frame(read.spss("sanignacio_senansa.sav", use.value.labels = FALSE))
## Warning: sanignacio_senansa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
Sansalvador <- as.data.frame(read.spss("Sansalvador_senasa.sav", use.value.labels = FALSE))
## Warning: Sansalvador_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
Signaciocaaz <- as.data.frame(read.spss("Signaciocaaz_senasa.sav", use.value.labels = FALSE))
## Warning: Signaciocaaz_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
Vizcaino <- as.data.frame(read.spss("Vizcaino_senasa.sav", use.value.labels = FALSE))
## Warning: Vizcaino_senasa.sav: Unrecognized record type 7, subtype 18
## encountered in system file
## re-encoding from CP1252
save.image("Comunidades.RData")
Caracarai$comunidad <- 1
Melgarejo$comunidad <- 2
Nupyahu$comunidad <- 3
PasoYobai$comunidad <- 4
Sanignacio$comunidad <- 5
Sansalvador$comunidad <- 6
Signaciocaaz$comunidad <- 7
Vizcaino$comunidad <- 8
Crea archivo maestro
Comunidades <- rbind(Caracarai, Melgarejo, Nupyahu, PasoYobai, Sanignacio, Sansalvador,
Signaciocaaz, Vizcaino)
dim(Comunidades)
## [1] 901 244
names(Comunidades)
## [1] "CODIGO" "FECHA" "DEPTO" "ENCUEST1" "DISTRITO"
## [6] "ENCUESTA" "COMPANIA" "HORA" "LOCALIDA" "DISTANCI"
## [11] "DISTANC1" "NOMBRE" "ENTREVIS" "B1" "B2"
## [16] "MESES" "B3" "PARED" "TECHO" "PISO"
## [21] "ELECTRIC" "RED" "LETRINA" "TELEFONO" "C9"
## [26] "C10" "C11" "JEFE" "EDADJ" "SEXOJ"
## [31] "INSTJ" "LEEJ" "TRABAJAJ" "OCUPACIO" "PERS1"
## [36] "EDAD1" "SEXO1" "INST1" "LEE1" "TRABAJA1"
## [41] "OCUPACI1" "PERS2" "EDAD2" "SEXO2" "INST2"
## [46] "LEE2" "TRABAJA2" "OCUPACI2" "PERS3" "EDAD3"
## [51] "SEXO3" "INST3" "LEE3" "TRABAJA3" "OCUPACI3"
## [56] "PERS4" "EDAD4" "SEXO4" "INST4" "LEE4"
## [61] "TRABAJA4" "OCUPACI4" "PERS5" "EDAD5" "SEXO5"
## [66] "INST5" "LEE5" "TRABAJA5" "OCUPACI5" "C12"
## [71] "ABUESALA" "CUANTOME" "PADRESAL" "CUANTOM1" "MADRESAL"
## [76] "CUANTOM2" "HIJO1SAL" "CUANTOM3" "HIJO2SAL" "CUANTOM4"
## [81] "HIJO3SAL" "CUANTOM5" "OTROSALA" "CUANTOM6" "TOTALGUA"
## [86] "C14A1" "C14A2" "C14A3" "C14B1" "C14B2"
## [91] "C14B3" "C14C1" "C14C2" "C14C3" "C14D1"
## [96] "C14D2" "C14D3" "C14E1" "C14E2" "C14E3"
## [101] "C14F1" "C14F2" "C14F3" "C14G1" "C14G2"
## [106] "C14G3" "C14H1" "C14H2" "C14H3" "C14I1"
## [111] "C14I2" "C14I3" "C14J1" "C14J2" "C14J3"
## [116] "TOTALMES" "TOTALANU" "D15" "E16" "E17"
## [121] "E18" "E19" "E20" "E21RECI1" "CAPARECI"
## [126] "FRERECI1" "CANTRECI" "PAGORECI" "E21RECI2" "CAPAREC1"
## [131] "FRERECI2" "CANTREC1" "PAGOREC1" "E21RECI3" "CAPAREC2"
## [136] "FRERECI3" "CANTREC2" "PAGOREC2" "CAPATOTA" "FRETOTAL"
## [141] "CANTTOTA" "PAGOTOTA" "E21B" "E22" "E23"
## [146] "E24" "E25" "E261" "E262" "E263"
## [151] "E264" "E265" "E266" "E267" "E268"
## [156] "E269" "E2610" "E26B" "E27" "E27B"
## [161] "E28" "F28B" "F29" "F30" "F31"
## [166] "F32" "F33" "F341" "F342" "F34B"
## [171] "F35" "G36" "G36B" "G37" "G37B"
## [176] "G38A" "G38B" "G38C" "G38D" "G39NIN"
## [181] "G39DIA" "G39DIAT" "G39EST" "G39ESTT" "G39INF"
## [186] "G39INFT" "G39TUB" "G39TUBT" "G39PAR" "G39PART"
## [191] "G39PIE" "G39PIET" "G39OJO" "G39OJOT" "G39OTR"
## [196] "G39OTRT" "G40" "G40A1" "G40A2" "G40B"
## [201] "G41" "G42" "G43RAD1" "G43RAD1H" "G43DIA1"
## [206] "G43DIA1F" "G43CAN1" "G43CAN1H" "G43RAD2" "G43RAD2H"
## [211] "G43DIA2" "G43DIA2F" "G43CAN2" "G43CAN2H" "G43RAD3"
## [216] "G43RAD3H" "G43DIA3" "G43DIA3F" "G43CAN3" "G43CAN3H"
## [221] "H44" "H45" "H46ORG1" "H46OR1AC" "LIDERORG"
## [226] "H46ORG2" "H46OR2AC" "LIDEROR1" "H46ORG3" "H46OR3AC"
## [231] "LIDEROR2" "H47ORG1" "H47OR1AC" "H47ORG2" "H47OR2AC"
## [236] "H47ORG3" "H47OR3AC" "H48" "I49" "I50"
## [241] "I51" "P52" "P53" "comunidad"
Crea identificador unico y selecciona variable de personas para acumular en los hogares
library(car)
## Loading required package: MASS Loading required package: nnet
ficha <- seq(1:901)
unique(ficha)
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## [18] 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## [35] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## [52] 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
## [69] 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
## [86] 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
## [103] 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
## [120] 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
## [137] 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153
## [154] 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170
## [171] 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187
## [188] 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204
## [205] 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221
## [222] 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238
## [239] 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255
## [256] 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## [273] 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289
## [290] 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
## [307] 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
## [324] 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340
## [341] 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357
## [358] 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374
## [375] 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391
## [392] 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408
## [409] 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425
## [426] 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442
## [443] 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459
## [460] 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476
## [477] 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493
## [494] 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510
## [511] 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527
## [528] 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## [545] 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561
## [562] 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578
## [579] 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595
## [596] 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612
## [613] 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629
## [630] 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646
## [647] 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663
## [664] 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680
## [681] 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697
## [698] 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714
## [715] 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731
## [732] 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748
## [749] 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765
## [766] 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782
## [783] 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799
## [800] 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## [817] 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833
## [834] 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850
## [851] 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867
## [868] 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884
## [885] 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901
ficha <- Comunidades$comunidad * 10000 + ficha
Comunidades <- cbind(ficha, Comunidades)
Comunidades1 <- Comunidades[, c(1, 29:70, 245)]
names(Comunidades1)
## [1] "ficha" "JEFE" "EDADJ" "SEXOJ" "INSTJ"
## [6] "LEEJ" "TRABAJAJ" "OCUPACIO" "PERS1" "EDAD1"
## [11] "SEXO1" "INST1" "LEE1" "TRABAJA1" "OCUPACI1"
## [16] "PERS2" "EDAD2" "SEXO2" "INST2" "LEE2"
## [21] "TRABAJA2" "OCUPACI2" "PERS3" "EDAD3" "SEXO3"
## [26] "INST3" "LEE3" "TRABAJA3" "OCUPACI3" "PERS4"
## [31] "EDAD4" "SEXO4" "INST4" "LEE4" "TRABAJA4"
## [36] "OCUPACI4" "PERS5" "EDAD5" "SEXO5" "INST5"
## [41] "LEE5" "TRABAJA5" "OCUPACI5" "comunidad"
personas <- reshape(Comunidades1, idvar = "ficha", varying = list(c(2, 9, 16,
23, 30, 37), c(3, 10, 17, 24, 31, 38), c(4, 11, 18, 25, 32, 39), c(5, 12,
19, 26, 33, 40), c(6, 13, 20, 27, 34, 41), c(7, 14, 21, 28, 35, 42), c(8,
15, 22, 29, 36, 43)), v.names = c("PERS", "EDAD", "SEXO", "INST", "LEE",
"TRABAJA", "OCUPA"), direction = "long")
head(personas)
## ficha comunidad time PERS EDAD SEXO INST LEE TRABAJA OCUPA
## 10001.1 10001 1 1 0 31 2 5 1 2 NA
## 10002.1 10002 1 1 0 45 1 8 1 1 2
## 10003.1 10003 1 1 0 38 1 12 1 1 1
## 10004.1 10004 1 1 0 58 1 12 1 1 1
## 10005.1 10005 1 1 0 60 1 2 1 1 2
## 10006.1 10006 1 1 0 40 2 4 1 2 NA
names(personas)
## [1] "ficha" "comunidad" "time" "PERS" "EDAD"
## [6] "SEXO" "INST" "LEE" "TRABAJA" "OCUPA"
Tablas de control en los 7 atributos evaluados en los 6 integrantes por hogar
addmargins(table(personas$PERS, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 0 120 249 26 210 26 174 36 60 0 901
## 1 90 169 20 128 20 97 26 40 0 590
## 2 9 15 0 20 0 19 7 5 0 75
## 3 82 148 30 120 14 95 23 28 0 540
## 4 104 213 23 181 26 119 28 60 0 754
## 5 2 5 0 10 1 8 0 4 0 30
## 6 0 1 0 0 0 3 0 0 0 4
## 7 13 38 5 13 1 36 8 12 0 126
## 8 4 5 0 3 0 4 0 1 0 17
## 9 3 1 2 5 0 3 1 5 0 20
## <NA> 293 650 50 570 68 486 87 145 0 2349
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(table(personas$EDAD, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 0 0 8 0 11 0 0 0 0 0 19
## 1 1 9 3 10 3 4 3 4 0 37
## 2 2 17 1 6 0 7 3 3 0 39
## 3 4 20 2 12 1 4 2 5 0 50
## 4 7 13 1 10 6 6 3 2 0 48
## 5 7 12 1 7 0 3 2 3 0 35
## 6 2 10 1 14 0 8 2 2 0 39
## 7 8 5 5 18 1 10 1 4 0 52
## 8 10 14 3 13 1 4 0 6 0 51
## 9 7 14 4 19 1 4 2 4 0 55
## 10 6 11 6 15 4 8 3 3 0 56
## 11 8 13 0 15 1 5 2 5 0 49
## 12 2 11 1 14 0 8 1 4 0 41
## 13 14 13 1 11 1 13 2 5 0 60
## 14 6 8 1 8 0 8 4 5 0 40
## 15 11 12 2 18 2 10 2 5 0 62
## 16 15 23 2 9 2 17 3 5 0 76
## 17 12 20 0 19 5 11 1 3 0 71
## 18 8 19 3 19 4 11 5 11 0 80
## 19 13 17 2 13 3 12 3 9 0 72
## 20 9 13 4 8 3 15 4 3 0 59
## 21 5 11 1 5 2 6 1 2 0 33
## 22 10 20 2 11 2 8 4 3 0 60
## 23 7 12 2 8 1 7 1 4 0 42
## 24 7 10 1 10 1 7 1 2 0 39
## 25 4 21 6 12 3 10 1 4 0 61
## 26 3 12 4 10 1 5 3 3 0 41
## 27 3 18 1 19 1 9 0 5 0 56
## 28 3 14 1 12 2 5 1 5 0 43
## 29 11 10 2 12 0 2 2 1 0 40
## 30 12 28 1 25 2 8 2 4 0 82
## 31 7 8 0 4 1 3 2 1 0 26
## 32 7 19 1 12 1 7 2 1 0 50
## 33 1 10 5 15 3 9 1 2 0 46
## 34 3 9 1 10 0 7 1 2 0 33
## 35 8 31 3 25 0 8 1 6 0 82
## 36 4 16 0 7 0 4 2 3 0 36
## 37 5 6 1 6 3 4 0 1 0 26
## 38 9 12 3 19 1 6 2 2 0 54
## 39 6 7 1 10 1 3 1 4 0 33
## 40 12 21 1 14 1 16 2 4 0 71
## 41 1 2 0 7 0 4 0 2 0 16
## 42 3 17 1 14 0 4 2 2 0 43
## 43 8 5 2 11 2 5 4 2 0 39
## 44 4 9 1 5 0 5 2 2 0 28
## 45 12 16 0 20 2 10 2 1 0 63
## 46 3 6 1 4 0 3 1 2 0 20
## 47 6 9 1 3 0 11 2 2 0 34
## 48 7 12 0 6 2 10 3 4 0 44
## 49 7 9 0 3 1 4 0 2 0 26
## 50 10 18 0 16 1 17 3 3 0 68
## 51 0 5 1 5 2 4 0 3 0 20
## 52 2 7 1 10 1 16 0 1 0 38
## 53 6 5 1 5 1 4 1 3 0 26
## 54 4 5 2 3 0 6 4 0 0 24
## 55 8 11 1 4 1 4 4 2 0 35
## 56 6 5 0 5 3 4 1 0 0 24
## 57 4 7 1 3 1 6 3 2 0 27
## 58 4 17 0 1 0 9 1 1 0 33
## 59 4 3 0 4 0 3 1 1 0 16
## 60 6 9 2 5 1 11 0 2 0 36
## 61 2 8 0 3 0 4 0 1 0 18
## 62 2 8 1 4 0 10 2 1 0 28
## 63 3 11 2 1 0 9 3 0 0 29
## 64 1 6 0 2 0 7 0 0 0 16
## 65 4 9 1 5 0 6 1 6 0 32
## 66 1 3 0 3 0 5 1 0 0 13
## 67 0 2 3 1 0 6 1 1 0 14
## 68 3 7 0 3 1 6 0 2 0 22
## 69 1 5 0 1 0 6 0 0 0 13
## 70 4 10 2 4 0 4 2 1 0 27
## 71 0 1 0 0 0 2 0 0 0 3
## 72 1 4 0 2 0 5 1 1 0 14
## 73 4 0 0 6 0 5 2 2 0 19
## 74 1 2 1 2 2 5 0 1 0 14
## 75 1 3 0 2 0 2 0 2 0 10
## 76 4 1 0 3 1 2 0 1 0 12
## 77 1 1 0 0 1 3 1 2 0 9
## 78 1 0 0 1 0 4 0 1 0 7
## 79 1 2 0 2 0 2 0 1 0 8
## 80 2 4 0 3 0 3 0 0 0 12
## 81 0 0 1 0 0 2 1 0 0 4
## 82 0 0 0 0 0 3 1 1 0 5
## 83 0 1 0 0 1 0 0 1 0 3
## 84 3 1 0 0 0 1 0 0 0 5
## 85 0 1 0 1 0 2 0 2 0 6
## 86 1 0 0 0 0 1 0 0 0 2
## 87 1 1 0 0 0 3 0 0 0 5
## 88 0 1 0 0 0 1 0 0 0 2
## 89 0 2 0 0 0 1 0 1 0 4
## 90 0 1 0 0 0 0 0 0 0 1
## 91 0 0 0 0 0 0 1 0 0 1
## 92 0 1 0 0 0 1 0 0 0 2
## 94 0 0 0 0 0 1 0 0 0 1
## 96 0 0 0 0 0 1 0 0 0 1
## 99 0 0 0 0 0 2 0 0 0 2
## <NA> 294 644 50 562 68 487 87 145 0 2337
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(table(personas$SEXO, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 1 228 453 48 376 49 279 69 121 0 1623
## 2 198 396 58 327 39 278 59 94 0 1449
## <NA> 294 645 50 557 68 487 88 145 0 2334
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(table(personas$INST, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 0 17 94 9 88 1 24 1 11 0 245
## 1 11 19 4 20 0 14 7 3 0 78
## 2 24 29 14 29 1 22 6 15 0 140
## 3 40 55 15 48 5 47 12 16 0 238
## 4 38 39 13 48 8 20 13 12 0 191
## 5 32 31 8 38 11 32 11 13 0 176
## 6 112 159 28 151 19 125 29 67 0 690
## 7 12 21 1 19 4 12 4 7 0 80
## 8 10 26 2 28 5 19 2 7 0 99
## 9 19 68 1 38 3 32 6 7 0 174
## 10 12 22 0 15 1 25 5 9 0 89
## 11 17 26 1 15 4 18 0 4 0 85
## 12 46 133 2 107 9 59 14 25 0 395
## 13 23 120 3 53 6 89 6 6 0 306
## 15 0 0 0 0 0 0 1 0 0 1
## 31 0 0 0 0 0 1 0 0 0 1
## <NA> 307 652 55 563 79 505 99 158 0 2418
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(table(personas$LEE, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 1 381 735 85 604 75 504 111 187 0 2682
## 2 15 103 8 95 2 19 2 7 0 251
## <NA> 324 656 63 561 79 521 103 166 0 2473
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(table(personas$TRABAJA, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 1 162 394 39 302 29 223 45 77 0 1271
## 2 232 441 49 392 46 286 68 117 0 1631
## <NA> 326 659 68 566 81 535 103 166 0 2504
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(table(personas$OCUPA, personas$comunidad, useNA = "always"))
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 1 13 53 2 27 1 26 5 5 0 132
## 2 80 46 36 42 18 36 18 45 0 321
## 3 0 4 0 2 0 0 0 0 0 6
## 4 1 1 1 1 0 5 0 0 0 9
## 6 0 0 0 0 0 1 0 0 0 1
## 7 18 13 0 21 2 9 5 7 0 75
## 8 0 1 0 1 0 5 0 0 0 7
## 9 2 4 0 10 0 3 1 0 0 20
## 10 15 50 0 56 3 27 4 2 0 157
## 11 0 2 0 0 0 0 0 0 0 2
## 12 2 42 0 15 0 8 1 1 0 69
## 14 2 0 0 1 3 0 1 0 0 7
## 15 4 5 0 5 0 2 1 3 0 20
## 16 0 0 0 0 1 3 0 0 0 4
## 17 4 21 0 16 0 12 0 3 0 56
## 19 0 1 0 0 0 1 0 0 0 2
## 21 0 1 0 1 0 2 0 1 0 5
## 22 12 33 0 31 1 24 7 6 0 114
## 23 0 1 0 0 0 0 0 0 0 1
## 24 0 1 0 0 0 0 0 0 0 1
## 25 3 65 0 41 0 19 2 2 0 132
## 26 0 14 0 7 0 12 0 0 0 33
## 27 3 22 0 10 0 34 0 0 0 69
## 28 0 0 0 0 0 4 1 0 0 5
## 29 0 9 0 8 0 0 0 0 0 17
## 30 0 0 0 0 0 1 0 0 0 1
## 31 0 0 0 3 0 0 0 0 0 3
## 32 0 0 0 0 0 1 0 0 0 1
## 33 2 0 0 0 0 0 0 0 0 2
## 35 0 3 0 3 0 0 0 0 0 6
## <NA> 559 1102 117 959 127 809 170 285 0 4128
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
Crea variable de edad binaria
personas$EDAD.rec <- recode(personas$EDAD, "lo:17=0;18:hi=1")
personas$EDAD.rec <- as.factor(personas$EDAD.rec)
levels(personas$EDAD.rec) <- c("0-Menor a 18", "1- 18 años o mas")
addmargins(table(personas$EDAD, personas$EDAD.rec, useNA = "always"))
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 0 19 0 0 19
## 1 37 0 0 37
## 2 39 0 0 39
## 3 50 0 0 50
## 4 48 0 0 48
## 5 35 0 0 35
## 6 39 0 0 39
## 7 52 0 0 52
## 8 51 0 0 51
## 9 55 0 0 55
## 10 56 0 0 56
## 11 49 0 0 49
## 12 41 0 0 41
## 13 60 0 0 60
## 14 40 0 0 40
## 15 62 0 0 62
## 16 76 0 0 76
## 17 71 0 0 71
## 18 0 80 0 80
## 19 0 72 0 72
## 20 0 59 0 59
## 21 0 33 0 33
## 22 0 60 0 60
## 23 0 42 0 42
## 24 0 39 0 39
## 25 0 61 0 61
## 26 0 41 0 41
## 27 0 56 0 56
## 28 0 43 0 43
## 29 0 40 0 40
## 30 0 82 0 82
## 31 0 26 0 26
## 32 0 50 0 50
## 33 0 46 0 46
## 34 0 33 0 33
## 35 0 82 0 82
## 36 0 36 0 36
## 37 0 26 0 26
## 38 0 54 0 54
## 39 0 33 0 33
## 40 0 71 0 71
## 41 0 16 0 16
## 42 0 43 0 43
## 43 0 39 0 39
## 44 0 28 0 28
## 45 0 63 0 63
## 46 0 20 0 20
## 47 0 34 0 34
## 48 0 44 0 44
## 49 0 26 0 26
## 50 0 68 0 68
## 51 0 20 0 20
## 52 0 38 0 38
## 53 0 26 0 26
## 54 0 24 0 24
## 55 0 35 0 35
## 56 0 24 0 24
## 57 0 27 0 27
## 58 0 33 0 33
## 59 0 16 0 16
## 60 0 36 0 36
## 61 0 18 0 18
## 62 0 28 0 28
## 63 0 29 0 29
## 64 0 16 0 16
## 65 0 32 0 32
## 66 0 13 0 13
## 67 0 14 0 14
## 68 0 22 0 22
## 69 0 13 0 13
## 70 0 27 0 27
## 71 0 3 0 3
## 72 0 14 0 14
## 73 0 19 0 19
## 74 0 14 0 14
## 75 0 10 0 10
## 76 0 12 0 12
## 77 0 9 0 9
## 78 0 7 0 7
## 79 0 8 0 8
## 80 0 12 0 12
## 81 0 4 0 4
## 82 0 5 0 5
## 83 0 3 0 3
## 84 0 5 0 5
## 85 0 6 0 6
## 86 0 2 0 2
## 87 0 5 0 5
## 88 0 2 0 2
## 89 0 4 0 4
## 90 0 1 0 1
## 91 0 1 0 1
## 92 0 2 0 2
## 94 0 1 0 1
## 96 0 1 0 1
## 99 0 2 0 2
## <NA> 0 0 2337 2337
## Sum 880 2189 2337 5406
personas$comunidad.rec <- personas$comunidad
personas$comunidad.rec <- as.factor(personas$comunidad.rec)
levels(personas$comunidad.rec) <- c("1-Caracarai", "2-Melgarejo", "3-Nupyahu",
"4-PasoYobai", "5-Sanignacio", "6-Sansalvador$comunidad", "7-Signaciocaaz",
"8-Vizcaino")
Cuadros para el informe para las personas
tablaperson.1 <- table(personas$SEXO, personas$EDAD.rec, personas$comunidad.rec)
tablaperson.2 <- table(personas$INST, personas$comunidad, useNA = "always")
tablaperson.3 <- table(personas$LEE, personas$comunidad, useNA = "always")
tablaperson.4 <- table(personas$TRABAJA, personas$comunidad, useNA = "always")
tablaperson.5 <- table(personas$TRABAJA, personas$EDAD.rec, personas$comunidad,
useNA = "always")
tablaperson.6 <- table(personas$OCUPA, personas$TRABAJA, personas$comunidad,
useNA = "always")
addmargins(tablaperson.1)
## , , = 1-Caracarai
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 58 169 227
## 2 63 135 198
## Sum 121 304 425
##
## , , = 2-Melgarejo
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 131 322 453
## 2 101 295 396
## Sum 232 617 849
##
## , , = 3-Nupyahu
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 12 36 48
## 2 22 36 58
## Sum 34 72 106
##
## , , = 4-PasoYobai
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 129 245 374
## 2 100 224 324
## Sum 229 469 698
##
## , , = 5-Sanignacio
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 17 32 49
## 2 11 28 39
## Sum 28 60 88
##
## , , = 6-Sansalvador$comunidad
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 68 210 278
## 2 62 216 278
## Sum 130 426 556
##
## , , = 7-Signaciocaaz
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 18 51 69
## 2 18 41 59
## Sum 36 92 128
##
## , , = 8-Vizcaino
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 42 79 121
## 2 26 68 94
## Sum 68 147 215
##
## , , = Sum
##
##
## 0-Menor a 18 1- 18 años o mas Sum
## 1 475 1144 1619
## 2 403 1043 1446
## Sum 878 2187 3065
addmargins(tablaperson.2)
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 0 17 94 9 88 1 24 1 11 0 245
## 1 11 19 4 20 0 14 7 3 0 78
## 2 24 29 14 29 1 22 6 15 0 140
## 3 40 55 15 48 5 47 12 16 0 238
## 4 38 39 13 48 8 20 13 12 0 191
## 5 32 31 8 38 11 32 11 13 0 176
## 6 112 159 28 151 19 125 29 67 0 690
## 7 12 21 1 19 4 12 4 7 0 80
## 8 10 26 2 28 5 19 2 7 0 99
## 9 19 68 1 38 3 32 6 7 0 174
## 10 12 22 0 15 1 25 5 9 0 89
## 11 17 26 1 15 4 18 0 4 0 85
## 12 46 133 2 107 9 59 14 25 0 395
## 13 23 120 3 53 6 89 6 6 0 306
## 15 0 0 0 0 0 0 1 0 0 1
## 31 0 0 0 0 0 1 0 0 0 1
## <NA> 307 652 55 563 79 505 99 158 0 2418
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(tablaperson.3)
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 1 381 735 85 604 75 504 111 187 0 2682
## 2 15 103 8 95 2 19 2 7 0 251
## <NA> 324 656 63 561 79 521 103 166 0 2473
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(tablaperson.4)
##
## 1 2 3 4 5 6 7 8 <NA> Sum
## 1 162 394 39 302 29 223 45 77 0 1271
## 2 232 441 49 392 46 286 68 117 0 1631
## <NA> 326 659 68 566 81 535 103 166 0 2504
## Sum 720 1494 156 1260 156 1044 216 360 0 5406
addmargins(tablaperson.5)
## , , = 1
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 0 162 0 162
## 2 93 139 0 232
## <NA> 29 3 294 326
## Sum 122 304 294 720
##
## , , = 2
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 10 384 0 394
## 2 208 233 0 441
## <NA> 15 0 644 659
## Sum 233 617 644 1494
##
## , , = 3
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 1 38 0 39
## 2 22 27 0 49
## <NA> 11 7 50 68
## Sum 34 72 50 156
##
## , , = 4
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 9 293 0 302
## 2 211 176 5 392
## <NA> 9 0 557 566
## Sum 229 469 562 1260
##
## , , = 5
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 1 28 0 29
## 2 14 32 0 46
## <NA> 13 0 68 81
## Sum 28 60 68 156
##
## , , = 6
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 2 221 0 223
## 2 94 192 0 286
## <NA> 34 14 487 535
## Sum 130 427 487 1044
##
## , , = 7
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 0 45 0 45
## 2 23 45 0 68
## <NA> 13 3 87 103
## Sum 36 93 87 216
##
## , , = 8
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 1 76 0 77
## 2 47 70 0 117
## <NA> 20 1 145 166
## Sum 68 147 145 360
##
## , , = NA
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 0 0 0 0
## 2 0 0 0 0
## <NA> 0 0 0 0
## Sum 0 0 0 0
##
## , , = Sum
##
##
## 0-Menor a 18 1- 18 años o mas <NA> Sum
## 1 24 1247 0 1271
## 2 712 914 5 1631
## <NA> 144 28 2332 2504
## Sum 880 2189 2337 5406
addmargins(tablaperson.6)
## , , = 1
##
##
## 1 2 <NA> Sum
## 1 13 0 0 13
## 2 80 0 0 80
## 3 0 0 0 0
## 4 1 0 0 1
## 6 0 0 0 0
## 7 18 0 0 18
## 8 0 0 0 0
## 9 2 0 0 2
## 10 15 0 0 15
## 11 0 0 0 0
## 12 2 0 0 2
## 14 2 0 0 2
## 15 4 0 0 4
## 16 0 0 0 0
## 17 4 0 0 4
## 19 0 0 0 0
## 21 0 0 0 0
## 22 12 0 0 12
## 23 0 0 0 0
## 24 0 0 0 0
## 25 3 0 0 3
## 26 0 0 0 0
## 27 3 0 0 3
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 2 0 0 2
## 35 0 0 0 0
## <NA> 1 232 326 559
## Sum 162 232 326 720
##
## , , = 2
##
##
## 1 2 <NA> Sum
## 1 53 0 0 53
## 2 46 0 0 46
## 3 4 0 0 4
## 4 1 0 0 1
## 6 0 0 0 0
## 7 13 0 0 13
## 8 1 0 0 1
## 9 4 0 0 4
## 10 50 0 0 50
## 11 2 0 0 2
## 12 41 1 0 42
## 14 0 0 0 0
## 15 5 0 0 5
## 16 0 0 0 0
## 17 21 0 0 21
## 19 1 0 0 1
## 21 1 0 0 1
## 22 33 0 0 33
## 23 1 0 0 1
## 24 1 0 0 1
## 25 65 0 0 65
## 26 14 0 0 14
## 27 22 0 0 22
## 28 0 0 0 0
## 29 7 2 0 9
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 35 3 0 0 3
## <NA> 5 438 659 1102
## Sum 394 441 659 1494
##
## , , = 3
##
##
## 1 2 <NA> Sum
## 1 2 0 0 2
## 2 36 0 0 36
## 3 0 0 0 0
## 4 1 0 0 1
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 19 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 35 0 0 0 0
## <NA> 0 49 68 117
## Sum 39 49 68 156
##
## , , = 4
##
##
## 1 2 <NA> Sum
## 1 27 0 0 27
## 2 42 0 0 42
## 3 2 0 0 2
## 4 1 0 0 1
## 6 0 0 0 0
## 7 21 0 0 21
## 8 1 0 0 1
## 9 10 0 0 10
## 10 56 0 0 56
## 11 0 0 0 0
## 12 15 0 0 15
## 14 1 0 0 1
## 15 5 0 0 5
## 16 0 0 0 0
## 17 16 0 0 16
## 19 0 0 0 0
## 21 1 0 0 1
## 22 31 0 0 31
## 23 0 0 0 0
## 24 0 0 0 0
## 25 41 0 0 41
## 26 7 0 0 7
## 27 10 0 0 10
## 28 0 0 0 0
## 29 8 0 0 8
## 30 0 0 0 0
## 31 3 0 0 3
## 32 0 0 0 0
## 33 0 0 0 0
## 35 3 0 0 3
## <NA> 1 392 566 959
## Sum 302 392 566 1260
##
## , , = 5
##
##
## 1 2 <NA> Sum
## 1 1 0 0 1
## 2 18 0 0 18
## 3 0 0 0 0
## 4 0 0 0 0
## 6 0 0 0 0
## 7 2 0 0 2
## 8 0 0 0 0
## 9 0 0 0 0
## 10 3 0 0 3
## 11 0 0 0 0
## 12 0 0 0 0
## 14 3 0 0 3
## 15 0 0 0 0
## 16 1 0 0 1
## 17 0 0 0 0
## 19 0 0 0 0
## 21 0 0 0 0
## 22 1 0 0 1
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 35 0 0 0 0
## <NA> 0 46 81 127
## Sum 29 46 81 156
##
## , , = 6
##
##
## 1 2 <NA> Sum
## 1 25 1 0 26
## 2 36 0 0 36
## 3 0 0 0 0
## 4 5 0 0 5
## 6 1 0 0 1
## 7 9 0 0 9
## 8 5 0 0 5
## 9 3 0 0 3
## 10 27 0 0 27
## 11 0 0 0 0
## 12 8 0 0 8
## 14 0 0 0 0
## 15 2 0 0 2
## 16 3 0 0 3
## 17 11 1 0 12
## 19 1 0 0 1
## 21 2 0 0 2
## 22 24 0 0 24
## 23 0 0 0 0
## 24 0 0 0 0
## 25 19 0 0 19
## 26 12 0 0 12
## 27 20 14 0 34
## 28 4 0 0 4
## 29 0 0 0 0
## 30 1 0 0 1
## 31 0 0 0 0
## 32 1 0 0 1
## 33 0 0 0 0
## 35 0 0 0 0
## <NA> 4 270 535 809
## Sum 223 286 535 1044
##
## , , = 7
##
##
## 1 2 <NA> Sum
## 1 5 0 0 5
## 2 17 1 0 18
## 3 0 0 0 0
## 4 0 0 0 0
## 6 0 0 0 0
## 7 5 0 0 5
## 8 0 0 0 0
## 9 1 0 0 1
## 10 4 0 0 4
## 11 0 0 0 0
## 12 1 0 0 1
## 14 1 0 0 1
## 15 1 0 0 1
## 16 0 0 0 0
## 17 0 0 0 0
## 19 0 0 0 0
## 21 0 0 0 0
## 22 7 0 0 7
## 23 0 0 0 0
## 24 0 0 0 0
## 25 2 0 0 2
## 26 0 0 0 0
## 27 0 0 0 0
## 28 1 0 0 1
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 35 0 0 0 0
## <NA> 0 67 103 170
## Sum 45 68 103 216
##
## , , = 8
##
##
## 1 2 <NA> Sum
## 1 5 0 0 5
## 2 45 0 0 45
## 3 0 0 0 0
## 4 0 0 0 0
## 6 0 0 0 0
## 7 7 0 0 7
## 8 0 0 0 0
## 9 0 0 0 0
## 10 2 0 0 2
## 11 0 0 0 0
## 12 1 0 0 1
## 14 0 0 0 0
## 15 3 0 0 3
## 16 0 0 0 0
## 17 3 0 0 3
## 19 0 0 0 0
## 21 1 0 0 1
## 22 6 0 0 6
## 23 0 0 0 0
## 24 0 0 0 0
## 25 2 0 0 2
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 35 0 0 0 0
## <NA> 2 117 166 285
## Sum 77 117 166 360
##
## , , = NA
##
##
## 1 2 <NA> Sum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 19 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 35 0 0 0 0
## <NA> 0 0 0 0
## Sum 0 0 0 0
##
## , , = Sum
##
##
## 1 2 <NA> Sum
## 1 131 1 0 132
## 2 320 1 0 321
## 3 6 0 0 6
## 4 9 0 0 9
## 6 1 0 0 1
## 7 75 0 0 75
## 8 7 0 0 7
## 9 20 0 0 20
## 10 157 0 0 157
## 11 2 0 0 2
## 12 68 1 0 69
## 14 7 0 0 7
## 15 20 0 0 20
## 16 4 0 0 4
## 17 55 1 0 56
## 19 2 0 0 2
## 21 5 0 0 5
## 22 114 0 0 114
## 23 1 0 0 1
## 24 1 0 0 1
## 25 132 0 0 132
## 26 33 0 0 33
## 27 55 14 0 69
## 28 5 0 0 5
## 29 15 2 0 17
## 30 1 0 0 1
## 31 3 0 0 3
## 32 1 0 0 1
## 33 2 0 0 2
## 35 6 0 0 6
## <NA> 13 1611 2504 4128
## Sum 1271 1631 2504 5406