rm(list = ls())
library(dplyr)
##
## 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
library(rio)
data= import("dataOK.xlsx")
## New names:
## • `` -> `...1`
library(BBmisc)
##
## Attaching package: 'BBmisc'
## The following objects are masked from 'package:dplyr':
##
## coalesce, collapse, symdiff
## The following object is masked from 'package:base':
##
## isFALSE
names(data)
## [1] "...1" "key"
## [3] "Código" "pared1_Ladrillo"
## [5] "pared2_Piedra" "pared3_Adobe"
## [7] "pared4_Tapia" "pared5_Quincha"
## [9] "pared6_Piedra" "pared7_Madera"
## [11] "pared8_Triplay" "pared9_Otro"
## [13] "pared10_Total" "techo1_Concreto"
## [15] "techo2_Madera" "techo3_Tejas"
## [17] "techo4_Planchas" "techo5_Caña"
## [19] "techo6_Triplay" "techo7_Paja"
## [21] "techo8_Otro" "techo9_Total"
## [23] "piso1_Parquet" "piso2_Láminas"
## [25] "piso3_Losetas" "piso4_Madera"
## [27] "piso5_Cemento" "piso6_Tierra"
## [29] "piso7_Otro" "piso8_Total"
## [31] "agua1_Red" "agua2_Red_fueraVivienda"
## [33] "agua3_Pilón" "agua4_Camión"
## [35] "agua5_Pozo" "agua6_Manantial"
## [37] "agua7_Río" "agua8_Otro"
## [39] "agua9_Vecino" "agua10_Total"
## [41] "elec1_Sí" "elec2_No"
## [43] "elec3_Total" "departamento"
## [45] "provincia" "Castillo"
## [47] "Keiko" "ganaCastillo"
## [49] "countPositivos" "countFallecidos"
str(data)
## 'data.frame': 196 obs. of 50 variables:
## $ ...1 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ key : chr "AMAZONAS+BAGUA" "AMAZONAS+BONGARA" "AMAZONAS+CHACHAPOYAS" "AMAZONAS+CONDORCANQUI" ...
## $ Código : num 102 103 101 104 105 106 107 202 203 204 ...
## $ pared1_Ladrillo : num 4633 1602 3782 291 430 ...
## $ pared2_Piedra : num 46 9 22 7 7 7 35 1 0 3 ...
## $ pared3_Adobe : num 6639 2729 5881 672 5217 ...
## $ pared4_Tapia : num 222 240 2476 8 6052 ...
## $ pared5_Quincha : num 2518 157 309 386 346 ...
## $ pared6_Piedra : num 127 36 168 7 54 28 518 65 7 6 ...
## $ pared7_Madera : num 4484 2505 1270 8145 606 ...
## $ pared8_Triplay : num 851 30 91 200 45 24 210 18 0 1 ...
## $ pared9_Otro : num 0 0 0 0 0 0 0 0 0 0 ...
## $ pared10_Total : num 19520 7308 13999 9716 12757 ...
## $ techo1_Concreto : num 2187 692 2262 56 187 ...
## $ techo2_Madera : num 294 75 160 188 43 48 340 57 12 8 ...
## $ techo3_Tejas : num 179 382 3393 177 3071 ...
## $ techo4_Planchas : num 13186 6084 8005 2036 9343 ...
## $ techo5_Caña : num 160 38 50 15 26 15 196 10 8 5 ...
## $ techo6_Triplay : num 106 5 14 10 12 5 62 17 4 3 ...
## $ techo7_Paja : num 3408 32 115 7234 75 ...
## $ techo8_Otro : num 0 0 0 0 0 0 0 0 0 0 ...
## $ techo9_Total : num 19520 7308 13999 9716 12757 ...
## $ piso1_Parquet : num 6 5 23 2 4 3 20 0 0 5 ...
## $ piso2_Láminas : num 19 2 36 0 0 4 32 0 0 1 ...
## $ piso3_Losetas : num 647 165 1077 20 46 ...
## $ piso4_Madera : num 157 132 240 1523 295 ...
## $ piso5_Cemento : num 7121 2917 6189 943 1911 ...
## $ piso6_Tierra : num 11569 4087 6434 7228 10501 ...
## $ piso7_Otro : num 1 0 0 0 0 0 0 0 0 0 ...
## $ piso8_Total : num 19520 7308 13999 9716 12757 ...
## $ agua1_Red : num 9429 4569 10647 1307 7172 ...
## $ agua2_Red_fueraVivienda: num 4392 1497 1619 867 3097 ...
## $ agua3_Pilón : num 793 215 184 1003 1112 ...
## $ agua4_Camión : num 59 0 49 2 0 0 117 0 0 0 ...
## $ agua5_Pozo : num 1792 474 876 2564 819 ...
## $ agua6_Manantial : num 270 67 92 431 132 211 471 121 61 27 ...
## $ agua7_Río : num 2648 388 488 3428 369 ...
## $ agua8_Otro : num 56 61 24 80 9 29 104 2 1 6 ...
## $ agua9_Vecino : num 81 37 20 34 47 8 177 9 4 6 ...
## $ agua10_Total : num 19520 7308 13999 9716 12757 ...
## $ elec1_Sí : num 13204 6025 12248 1792 10886 ...
## $ elec2_No : num 6316 1283 1751 7924 1871 ...
## $ elec3_Total : num 19520 7308 13999 9716 12757 ...
## $ departamento : chr "AMAZONAS" "AMAZONAS" "AMAZONAS" "AMAZONAS" ...
## $ provincia : chr "BAGUA" "BONGARA" "CHACHAPOYAS" "CONDORCANQUI" ...
## $ Castillo : num 25629 8374 15671 13154 12606 ...
## $ Keiko : num 10770 5209 10473 1446 7840 ...
## $ ganaCastillo : num 1 1 1 1 1 1 1 1 1 1 ...
## $ countPositivos : num 8126 389 2174 3481 456 ...
## $ countFallecidos : num 462 72 281 111 88 60 336 26 31 21 ...
cor(data[,c(4:11, 14:20, 23:28, 31:37,39 )])
## pared1_Ladrillo pared2_Piedra pared3_Adobe pared4_Tapia
## pared1_Ladrillo 1.00000000 0.6531304075 0.353719710 -0.028441269
## pared2_Piedra 0.65313041 1.0000000000 0.202919582 -0.049282201
## pared3_Adobe 0.35371971 0.2029195823 1.000000000 -0.004149731
## pared4_Tapia -0.02844127 -0.0492822015 -0.004149731 1.000000000
## pared5_Quincha 0.22318873 0.1380474858 0.148902281 -0.108766809
## pared6_Piedra 0.06663338 0.1032200591 0.178622319 0.055097671
## pared7_Madera 0.88895732 0.5023937898 0.157709831 -0.086020384
## pared8_Triplay 0.84967877 0.5041851637 0.274356092 -0.093543394
## techo1_Concreto 0.99895777 0.6475530197 0.354369780 -0.022979167
## techo2_Madera 0.96134482 0.5544758307 0.255641939 -0.042858436
## techo3_Tejas 0.13065272 0.0424043920 0.452572804 0.406675472
## techo4_Planchas 0.92241165 0.6181716798 0.457713911 -0.020523849
## techo5_Caña 0.43978726 0.2414297477 0.379420639 -0.130629381
## techo6_Triplay 0.83773155 0.4864905433 0.293214074 -0.095166861
## techo7_Paja 0.04330660 0.0223855427 0.083448461 -0.119761652
## piso1_Parquet 0.98932418 0.5942830300 0.306203092 -0.024715356
## piso2_Láminas 0.99183728 0.6943500824 0.298837801 -0.032107090
## piso3_Losetas 0.99778010 0.6175524308 0.361288369 -0.027440616
## piso4_Madera 0.57442769 0.3229830485 0.117851587 -0.022105601
## piso5_Cemento 0.99711647 0.6705103532 0.381281093 -0.029507485
## piso6_Tierra 0.69091898 0.4488614500 0.716499338 0.142515418
## agua1_Red 0.99829658 0.6423855521 0.392068843 -0.015368882
## agua2_Red_fueraVivienda 0.98520928 0.5877128787 0.396090900 0.014701847
## agua3_Pilón 0.91424116 0.8433274954 0.378993545 -0.022967354
## agua4_Camión 0.98857983 0.6134251834 0.328883084 -0.055134724
## agua5_Pozo 0.37406346 0.2506213775 0.445056601 0.021149053
## agua6_Manantial -0.05230168 -0.0346340044 0.265363626 0.235734210
## agua7_Río 0.01052770 0.0001916141 0.059652469 -0.058424224
## agua9_Vecino 0.93746903 0.5515121854 0.411211941 -0.029156352
## pared5_Quincha pared6_Piedra pared7_Madera
## pared1_Ladrillo 0.22318873 0.066633377 0.888957322
## pared2_Piedra 0.13804749 0.103220059 0.502393790
## pared3_Adobe 0.14890228 0.178622319 0.157709831
## pared4_Tapia -0.10876681 0.055097671 -0.086020384
## pared5_Quincha 1.00000000 -0.026273029 0.170311934
## pared6_Piedra -0.02627303 1.000000000 0.035535282
## pared7_Madera 0.17031193 0.035535282 1.000000000
## pared8_Triplay 0.44570008 0.039816573 0.744713107
## techo1_Concreto 0.19527128 0.069792583 0.883161427
## techo2_Madera 0.17862751 0.053037313 0.941476827
## techo3_Tejas -0.05309366 -0.001301362 0.032824915
## techo4_Planchas 0.42894200 0.119895509 0.869075213
## techo5_Caña 0.15514103 -0.046225996 0.312733003
## techo6_Triplay 0.25087205 0.026429158 0.725503574
## techo7_Paja -0.06002296 0.261592675 0.242050977
## piso1_Parquet 0.16624455 0.070321007 0.891160659
## piso2_Láminas 0.17602057 0.071823074 0.882083317
## piso3_Losetas 0.21954880 0.062592052 0.891094201
## piso4_Madera 0.03746477 0.019869483 0.762214370
## piso5_Cemento 0.23548831 0.065790066 0.890530043
## piso6_Tierra 0.49536175 0.226419900 0.578371145
## agua1_Red 0.23668107 0.067800217 0.886993167
## agua2_Red_fueraVivienda 0.21298304 0.099070259 0.896724456
## agua3_Pilón 0.28442189 0.105897661 0.786723617
## agua4_Camión 0.26851383 0.063823367 0.877312286
## agua5_Pozo 0.06622468 0.235064440 0.450563996
## agua6_Manantial -0.03649775 0.386226692 0.002887509
## agua7_Río 0.47127241 0.097287175 0.192537686
## agua9_Vecino 0.45416242 0.061389980 0.859526544
## pared8_Triplay techo1_Concreto techo2_Madera
## pared1_Ladrillo 0.84967877 0.99895777 0.961344818
## pared2_Piedra 0.50418516 0.64755302 0.554475831
## pared3_Adobe 0.27435609 0.35436978 0.255641939
## pared4_Tapia -0.09354339 -0.02297917 -0.042858436
## pared5_Quincha 0.44570008 0.19527128 0.178627506
## pared6_Piedra 0.03981657 0.06979258 0.053037313
## pared7_Madera 0.74471311 0.88316143 0.941476827
## pared8_Triplay 1.00000000 0.83590363 0.794328071
## techo1_Concreto 0.83590363 1.00000000 0.961581686
## techo2_Madera 0.79432807 0.96158169 1.000000000
## techo3_Tejas 0.03780376 0.13279160 0.101237912
## techo4_Planchas 0.84848052 0.90871357 0.878105184
## techo5_Caña 0.50990128 0.43728397 0.390803808
## techo6_Triplay 0.93355385 0.83270784 0.802264531
## techo7_Paja 0.01251506 0.03913805 0.077059348
## piso1_Parquet 0.81322920 0.99207493 0.964158881
## piso2_Láminas 0.81358173 0.99296224 0.956534403
## piso3_Losetas 0.84624538 0.99775192 0.966516260
## piso4_Madera 0.47329269 0.55886513 0.613180887
## piso5_Cemento 0.85093335 0.99480170 0.958653705
## piso6_Tierra 0.72230336 0.67690594 0.607794833
## agua1_Red 0.85195472 0.99671838 0.959502210
## agua2_Red_fueraVivienda 0.82195202 0.98578532 0.962206573
## agua3_Pilón 0.79397034 0.90688087 0.858428968
## agua4_Camión 0.89011698 0.98648237 0.949513734
## agua5_Pozo 0.28971580 0.36629882 0.356536589
## agua6_Manantial -0.08325702 -0.05033923 -0.051148104
## agua7_Río 0.22006947 -0.01649647 -0.000496056
## agua9_Vecino 0.88257997 0.92777533 0.896083631
## techo3_Tejas techo4_Planchas techo5_Caña techo6_Triplay
## pared1_Ladrillo 0.130652715 0.92241165 0.43978726 0.83773155
## pared2_Piedra 0.042404392 0.61817168 0.24142975 0.48649054
## pared3_Adobe 0.452572804 0.45771391 0.37942064 0.29321407
## pared4_Tapia 0.406675472 -0.02052385 -0.13062938 -0.09516686
## pared5_Quincha -0.053093661 0.42894200 0.15514103 0.25087205
## pared6_Piedra -0.001301362 0.11989551 -0.04622600 0.02642916
## pared7_Madera 0.032824915 0.86907521 0.31273300 0.72550357
## pared8_Triplay 0.037803764 0.84848052 0.50990128 0.93355385
## techo1_Concreto 0.132791602 0.90871357 0.43728397 0.83270784
## techo2_Madera 0.101237912 0.87810518 0.39080381 0.80226453
## techo3_Tejas 1.000000000 0.09472707 -0.05434360 0.03229131
## techo4_Planchas 0.094727069 1.00000000 0.36667138 0.75919949
## techo5_Caña -0.054343597 0.36667138 1.00000000 0.65797536
## techo6_Triplay 0.032291306 0.75919949 0.65797536 1.00000000
## techo7_Paja -0.127532085 0.13498599 -0.10843201 -0.01979360
## piso1_Parquet 0.133642693 0.88536545 0.37954398 0.80425936
## piso2_Láminas 0.121516097 0.89437518 0.37680835 0.80040365
## piso3_Losetas 0.137798305 0.91661265 0.44402234 0.83725324
## piso4_Madera 0.160424162 0.63394934 0.14239733 0.43011029
## piso5_Cemento 0.134501379 0.93238504 0.47116404 0.84592008
## piso6_Tierra 0.316997243 0.84457447 0.35974747 0.62026735
## agua1_Red 0.158712998 0.93123070 0.45513900 0.84040829
## agua2_Red_fueraVivienda 0.194065573 0.91918607 0.40465632 0.80897283
## agua3_Pilón 0.110096827 0.89903722 0.38290612 0.75742867
## agua4_Camión 0.097045593 0.91376978 0.45951274 0.87754627
## agua5_Pozo 0.043586108 0.51371228 0.14862510 0.26004518
## agua6_Manantial 0.187194399 0.05604603 -0.12964685 -0.09880946
## agua7_Río -0.095874511 0.27005171 -0.03088737 0.03804366
## agua9_Vecino 0.126065743 0.95684216 0.44988953 0.80608463
## techo7_Paja piso1_Parquet piso2_Láminas piso3_Losetas
## pared1_Ladrillo 0.04330660 0.98932418 0.99183728 0.99778010
## pared2_Piedra 0.02238554 0.59428303 0.69435008 0.61755243
## pared3_Adobe 0.08344846 0.30620309 0.29883780 0.36128837
## pared4_Tapia -0.11976165 -0.02471536 -0.03210709 -0.02744062
## pared5_Quincha -0.06002296 0.16624455 0.17602057 0.21954880
## pared6_Piedra 0.26159268 0.07032101 0.07182307 0.06259205
## pared7_Madera 0.24205098 0.89116066 0.88208332 0.89109420
## pared8_Triplay 0.01251506 0.81322920 0.81358173 0.84624538
## techo1_Concreto 0.03913805 0.99207493 0.99296224 0.99775192
## techo2_Madera 0.07705935 0.96415888 0.95653440 0.96651626
## techo3_Tejas -0.12753208 0.13364269 0.12151610 0.13779831
## techo4_Planchas 0.13498599 0.88536545 0.89437518 0.91661265
## techo5_Caña -0.10843201 0.37954398 0.37680835 0.44402234
## techo6_Triplay -0.01979360 0.80425936 0.80040365 0.83725324
## techo7_Paja 1.00000000 0.05309059 0.04633085 0.03941692
## piso1_Parquet 0.05309059 1.00000000 0.99065357 0.99103260
## piso2_Láminas 0.04633085 0.99065357 1.00000000 0.98752333
## piso3_Losetas 0.03941692 0.99103260 0.98752333 1.00000000
## piso4_Madera 0.34381863 0.56951134 0.56203796 0.57292657
## piso5_Cemento 0.04326973 0.97772920 0.98449487 0.99354882
## piso6_Tierra 0.19756408 0.63421141 0.63900132 0.68434083
## agua1_Red 0.04067676 0.98411589 0.98526683 0.99757039
## agua2_Red_fueraVivienda 0.06474771 0.98589969 0.97699716 0.98734773
## agua3_Pilón 0.07705249 0.86828274 0.91865407 0.89712584
## agua4_Camión 0.03345529 0.98116063 0.97711545 0.98828751
## agua5_Pozo 0.50703976 0.34557827 0.34685544 0.35335673
## agua6_Manantial 0.32756695 -0.04463570 -0.05062350 -0.05659592
## agua7_Río 0.43462456 -0.03279737 -0.02608500 0.00614929
## agua9_Vecino 0.06495528 0.91217924 0.90579537 0.93886366
## piso4_Madera piso5_Cemento piso6_Tierra agua1_Red
## pared1_Ladrillo 0.57442769 0.99711647 0.6909190 0.99829658
## pared2_Piedra 0.32298305 0.67051035 0.4488614 0.64238555
## pared3_Adobe 0.11785159 0.38128109 0.7164993 0.39206884
## pared4_Tapia -0.02210560 -0.02950749 0.1425154 -0.01536888
## pared5_Quincha 0.03746477 0.23548831 0.4953617 0.23668107
## pared6_Piedra 0.01986948 0.06579007 0.2264199 0.06780022
## pared7_Madera 0.76221437 0.89053004 0.5783711 0.88699317
## pared8_Triplay 0.47329269 0.85093335 0.7223034 0.85195472
## techo1_Concreto 0.55886513 0.99480170 0.6769059 0.99671838
## techo2_Madera 0.61318089 0.95865370 0.6077948 0.95950221
## techo3_Tejas 0.16042416 0.13450138 0.3169972 0.15871300
## techo4_Planchas 0.63394934 0.93238504 0.8445745 0.93123070
## techo5_Caña 0.14239733 0.47116404 0.3597475 0.45513900
## techo6_Triplay 0.43011029 0.84592008 0.6202674 0.84040829
## techo7_Paja 0.34381863 0.04326973 0.1975641 0.04067676
## piso1_Parquet 0.56951134 0.97772920 0.6342114 0.98411589
## piso2_Láminas 0.56203796 0.98449487 0.6390013 0.98526683
## piso3_Losetas 0.57292657 0.99354882 0.6843408 0.99757039
## piso4_Madera 1.00000000 0.58193718 0.3777415 0.57916009
## piso5_Cemento 0.58193718 1.00000000 0.7088272 0.99789026
## piso6_Tierra 0.37774147 0.70882717 1.0000000 0.71563959
## agua1_Red 0.57916009 0.99789026 0.7156396 1.00000000
## agua2_Red_fueraVivienda 0.58582633 0.98027897 0.7198307 0.98638141
## agua3_Pilón 0.51010998 0.92487525 0.7279764 0.91246278
## agua4_Camión 0.55739897 0.98364180 0.6854607 0.98626095
## agua5_Pozo 0.42802544 0.39573418 0.5945324 0.37823473
## agua6_Manantial -0.01461525 -0.04956959 0.3041149 -0.04576082
## agua7_Río 0.35384303 0.02218880 0.3719876 0.02110242
## agua9_Vecino 0.59348853 0.94155307 0.7883890 0.94569160
## agua2_Red_fueraVivienda agua3_Pilón agua4_Camión
## pared1_Ladrillo 0.98520928 0.914241155 0.98857983
## pared2_Piedra 0.58771288 0.843327495 0.61342518
## pared3_Adobe 0.39609090 0.378993545 0.32888308
## pared4_Tapia 0.01470185 -0.022967354 -0.05513472
## pared5_Quincha 0.21298304 0.284421889 0.26851383
## pared6_Piedra 0.09907026 0.105897661 0.06382337
## pared7_Madera 0.89672446 0.786723617 0.87731229
## pared8_Triplay 0.82195202 0.793970340 0.89011698
## techo1_Concreto 0.98578532 0.906880868 0.98648237
## techo2_Madera 0.96220657 0.858428968 0.94951373
## techo3_Tejas 0.19406557 0.110096827 0.09704559
## techo4_Planchas 0.91918607 0.899037215 0.91376978
## techo5_Caña 0.40465632 0.382906118 0.45951274
## techo6_Triplay 0.80897283 0.757428670 0.87754627
## techo7_Paja 0.06474771 0.077052486 0.03345529
## piso1_Parquet 0.98589969 0.868282741 0.98116063
## piso2_Láminas 0.97699716 0.918654066 0.97711545
## piso3_Losetas 0.98734773 0.897125837 0.98828751
## piso4_Madera 0.58582633 0.510109980 0.55739897
## piso5_Cemento 0.98027897 0.924875253 0.98364180
## piso6_Tierra 0.71983065 0.727976372 0.68546068
## agua1_Red 0.98638141 0.912462784 0.98626095
## agua2_Red_fueraVivienda 1.00000000 0.879761248 0.97365926
## agua3_Pilón 0.87976125 1.000000000 0.88899419
## agua4_Camión 0.97365926 0.888994189 1.00000000
## agua5_Pozo 0.39026333 0.401347128 0.33670399
## agua6_Manantial 0.02845610 0.009365479 -0.07159749
## agua7_Río 0.01369634 0.096817588 0.02735650
## agua9_Vecino 0.93305331 0.855812361 0.94449827
## agua5_Pozo agua6_Manantial agua7_Río agua9_Vecino
## pared1_Ladrillo 0.37406346 -0.052301676 0.0105277014 0.93746903
## pared2_Piedra 0.25062138 -0.034634004 0.0001916141 0.55151219
## pared3_Adobe 0.44505660 0.265363626 0.0596524695 0.41121194
## pared4_Tapia 0.02114905 0.235734210 -0.0584242237 -0.02915635
## pared5_Quincha 0.06622468 -0.036497754 0.4712724110 0.45416242
## pared6_Piedra 0.23506444 0.386226692 0.0972871753 0.06138998
## pared7_Madera 0.45056400 0.002887509 0.1925376861 0.85952654
## pared8_Triplay 0.28971580 -0.083257024 0.2200694706 0.88257997
## techo1_Concreto 0.36629882 -0.050339225 -0.0164964678 0.92777533
## techo2_Madera 0.35653659 -0.051148104 -0.0004960560 0.89608363
## techo3_Tejas 0.04358611 0.187194399 -0.0958745114 0.12606574
## techo4_Planchas 0.51371228 0.056046030 0.2700517058 0.95684216
## techo5_Caña 0.14862510 -0.129646855 -0.0308873665 0.44988953
## techo6_Triplay 0.26004518 -0.098809458 0.0380436569 0.80608463
## techo7_Paja 0.50703976 0.327566948 0.4346245642 0.06495528
## piso1_Parquet 0.34557827 -0.044635695 -0.0327973692 0.91217924
## piso2_Láminas 0.34685544 -0.050623497 -0.0260850003 0.90579537
## piso3_Losetas 0.35335673 -0.056595918 0.0061492902 0.93886366
## piso4_Madera 0.42802544 -0.014615250 0.3538430310 0.59348853
## piso5_Cemento 0.39573418 -0.049569592 0.0221887989 0.94155307
## piso6_Tierra 0.59453238 0.304114863 0.3719876192 0.78838904
## agua1_Red 0.37823473 -0.045760824 0.0211024200 0.94569160
## agua2_Red_fueraVivienda 0.39026333 0.028456097 0.0136963424 0.93305331
## agua3_Pilón 0.40134713 0.009365479 0.0968175876 0.85581236
## agua4_Camión 0.33670399 -0.071597493 0.0273564998 0.94449827
## agua5_Pozo 1.00000000 0.291063373 0.2658177285 0.41928935
## agua6_Manantial 0.29106337 1.000000000 0.3114887990 -0.06376963
## agua7_Río 0.26581773 0.311488799 1.0000000000 0.16068237
## agua9_Vecino 0.41928935 -0.063769632 0.1606823680 1.00000000
dat <- data.frame(
x1 = c(2, 4, 3, 1),
x2 = c(4, 5, 3, 2),
x3 = c(3, 2, 4, 4),
y = c(7, 9, 8, 5)
)
# Calcular las correlaciones entre cada columna y la variable y
correlations <- sapply(dat[, -ncol(dat)], function(col) cor(col, dat$y))
# Identificar las columnas con correlación negativa
neg_cor_cols <- names(correlations[correlations < 0])
# Invertir el rango de las columnas con correlación negativa
for (col in neg_cor_cols) {
max_value <- max(dat[[col]])
min_value <- min(dat[[col]])
dat[[col]] <- (max_value + min_value) - dat[[col]]
}
# Mostrar el dataframe con las columnas transformadas
print(dat)
## x1 x2 x3 y
## 1 2 4 3 7
## 2 4 5 4 9
## 3 3 3 2 8
## 4 1 2 2 5
dataClus=data[,c(4:11, 14:20, 23:28, 31:37,39)]
row.names(dataClus)=data$key
library(cluster)
g.dist = daisy(dataClus, metric="gower")
library(factoextra)
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
fviz_nbclust(dataClus, pam,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F)
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
set.seed(123)
res.pam=pam(g.dist,3,cluster.only = F)
dataClus$pam=res.pam$cluster
head(dataClus,15)%>%kbl()%>%kable_styling()
|
|
pared1_Ladrillo
|
pared2_Piedra
|
pared3_Adobe
|
pared4_Tapia
|
pared5_Quincha
|
pared6_Piedra
|
pared7_Madera
|
pared8_Triplay
|
techo1_Concreto
|
techo2_Madera
|
techo3_Tejas
|
techo4_Planchas
|
techo5_Caña
|
techo6_Triplay
|
techo7_Paja
|
piso1_Parquet
|
piso2_Láminas
|
piso3_Losetas
|
piso4_Madera
|
piso5_Cemento
|
piso6_Tierra
|
agua1_Red
|
agua2_Red_fueraVivienda
|
agua3_Pilón
|
agua4_Camión
|
agua5_Pozo
|
agua6_Manantial
|
agua7_Río
|
agua9_Vecino
|
pam
|
|
AMAZONAS+BAGUA
|
4633
|
46
|
6639
|
222
|
2518
|
127
|
4484
|
851
|
2187
|
294
|
179
|
13186
|
160
|
106
|
3408
|
6
|
19
|
647
|
157
|
7121
|
11569
|
9429
|
4392
|
793
|
59
|
1792
|
270
|
2648
|
81
|
1
|
|
AMAZONAS+BONGARA
|
1602
|
9
|
2729
|
240
|
157
|
36
|
2505
|
30
|
692
|
75
|
382
|
6084
|
38
|
5
|
32
|
5
|
2
|
165
|
132
|
2917
|
4087
|
4569
|
1497
|
215
|
0
|
474
|
67
|
388
|
37
|
1
|
|
AMAZONAS+CHACHAPOYAS
|
3782
|
22
|
5881
|
2476
|
309
|
168
|
1270
|
91
|
2262
|
160
|
3393
|
8005
|
50
|
14
|
115
|
23
|
36
|
1077
|
240
|
6189
|
6434
|
10647
|
1619
|
184
|
49
|
876
|
92
|
488
|
20
|
1
|
|
AMAZONAS+CONDORCANQUI
|
291
|
7
|
672
|
8
|
386
|
7
|
8145
|
200
|
56
|
188
|
177
|
2036
|
15
|
10
|
7234
|
2
|
0
|
20
|
1523
|
943
|
7228
|
1307
|
867
|
1003
|
2
|
2564
|
431
|
3428
|
34
|
1
|
|
AMAZONAS+LUYA
|
430
|
7
|
5217
|
6052
|
346
|
54
|
606
|
45
|
187
|
43
|
3071
|
9343
|
26
|
12
|
75
|
4
|
0
|
46
|
295
|
1911
|
10501
|
7172
|
3097
|
1112
|
0
|
819
|
132
|
369
|
47
|
1
|
|
AMAZONAS+RODRIGUEZ DE MENDOZA
|
1546
|
7
|
2778
|
155
|
720
|
28
|
3646
|
24
|
480
|
48
|
2810
|
5495
|
15
|
5
|
51
|
3
|
4
|
264
|
176
|
2974
|
5483
|
5256
|
1278
|
154
|
0
|
1020
|
211
|
948
|
8
|
1
|
|
AMAZONAS+UTCUBAMBA
|
4727
|
35
|
17199
|
2964
|
1836
|
518
|
2714
|
210
|
2595
|
340
|
308
|
26620
|
196
|
62
|
82
|
20
|
32
|
940
|
328
|
10631
|
18252
|
14712
|
8760
|
1308
|
117
|
2502
|
471
|
2052
|
177
|
2
|
|
ANCASH+AIJA
|
15
|
1
|
1763
|
70
|
7
|
65
|
2
|
18
|
9
|
57
|
403
|
1297
|
10
|
17
|
148
|
0
|
0
|
4
|
24
|
195
|
1718
|
1451
|
42
|
10
|
0
|
230
|
121
|
76
|
9
|
1
|
|
ANCASH+ANTONIO RAIMONDI
|
97
|
0
|
658
|
3014
|
7
|
7
|
3
|
0
|
29
|
12
|
1146
|
2341
|
8
|
4
|
246
|
0
|
0
|
16
|
17
|
314
|
3439
|
3229
|
222
|
40
|
0
|
190
|
61
|
39
|
4
|
1
|
|
ANCASH+ASUNCION
|
215
|
3
|
368
|
1701
|
4
|
6
|
4
|
1
|
76
|
8
|
1893
|
314
|
5
|
3
|
3
|
5
|
1
|
41
|
12
|
409
|
1834
|
1642
|
444
|
4
|
0
|
124
|
27
|
49
|
6
|
1
|
|
ANCASH+BOLOGNESI
|
506
|
12
|
3703
|
2332
|
27
|
183
|
14
|
41
|
261
|
46
|
1365
|
4554
|
40
|
35
|
517
|
6
|
8
|
91
|
86
|
1854
|
4773
|
4930
|
479
|
113
|
0
|
579
|
242
|
456
|
8
|
1
|
|
ANCASH+CARHUAZ
|
1913
|
3
|
10846
|
140
|
9
|
15
|
48
|
29
|
1543
|
118
|
5794
|
5376
|
68
|
60
|
44
|
14
|
8
|
394
|
20
|
3212
|
9355
|
9502
|
1735
|
190
|
4
|
865
|
296
|
283
|
102
|
1
|
|
ANCASH+CARLOS FERMIN FITZCARRALD
|
107
|
0
|
1326
|
3728
|
0
|
20
|
4
|
3
|
62
|
14
|
3559
|
1254
|
6
|
14
|
279
|
1
|
1
|
15
|
11
|
609
|
4551
|
3437
|
1014
|
85
|
0
|
274
|
76
|
216
|
58
|
1
|
|
ANCASH+CASMA
|
5614
|
29
|
4418
|
11
|
1097
|
15
|
312
|
3065
|
2884
|
78
|
153
|
3549
|
4806
|
3059
|
32
|
49
|
19
|
998
|
19
|
6173
|
7303
|
8800
|
639
|
1608
|
1538
|
1421
|
150
|
275
|
101
|
1
|
|
ANCASH+CORONGO
|
43
|
1
|
1963
|
11
|
0
|
5
|
1
|
1
|
19
|
4
|
610
|
1353
|
16
|
1
|
22
|
0
|
0
|
4
|
4
|
417
|
1600
|
1522
|
90
|
10
|
0
|
194
|
100
|
100
|
6
|
1
|
fviz_silhouette(res.pam,print.summary = F)
silPAM=data.frame(res.pam$silinfo$widths)
silPAM$country=row.names(silPAM)
poorPAM=silPAM[silPAM$sil_width<0,'country']%>%sort()
poorPAM
## [1] "AMAZONAS+UTCUBAMBA" "APURIMAC+ANDAHUAYLAS"
## [3] "AREQUIPA+AREQUIPA" "AYACUCHO+HUAMANGA"
## [5] "CAJAMARCA+CUTERVO" "CAJAMARCA+JAEN"
## [7] "CAJAMARCA+SAN IGNACIO" "CUSCO+CHUMBIVILCAS"
## [9] "CUSCO+LA CONVENCION" "HUANUCO+LEONCIO PRADO"
## [11] "JUNIN+CHANCHAMAYO" "JUNIN+HUANCAYO"
## [13] "LA LIBERTAD+OTUZCO" "LA LIBERTAD+SANCHEZ CARRION"
## [15] "LA LIBERTAD+TRUJILLO" "LAMBAYEQUE+CHICLAYO"
## [17] "LAMBAYEQUE+LAMBAYEQUE" "PIURA+HUANCABAMBA"
## [19] "PIURA+MORROPON" "PIURA+SULLANA"
## [21] "PUNO+CARABAYA" "PUNO+MELGAR"
## [23] "PUNO+SANDIA"
aggregate(.~ pam, data=dataClus,mean)
## pam pared1_Ladrillo pared2_Piedra pared3_Adobe pared4_Tapia pared5_Quincha
## 1 1 8382.847 60.6319 5932.583 1669.681 514.2883
## 2 2 33794.875 699.4375 24159.625 2623.281 2300.6250
## 3 3 1850434.000 10905.0000 51710.000 562.000 7089.0000
## pared6_Piedra pared7_Madera pared8_Triplay techo1_Concreto techo2_Madera
## 1 241.2454 2642.534 829.9877 5320.736 433.1718
## 2 1157.0625 3105.781 1504.0625 25444.125 413.8750
## 3 1244.0000 197660.000 55594.0000 1616788.000 70951.0000
## techo3_Tejas techo4_Planchas techo5_Caña techo6_Triplay techo7_Paja
## 1 2484.080 9376.215 1178.387 562.2025 918.9939
## 2 5720.156 33452.969 1477.031 564.7500 2271.8438
## 3 12324.000 417514.000 21627.000 33153.0000 2841.0000
## piso1_Parquet piso2_Láminas piso3_Losetas piso4_Madera piso5_Cemento
## 1 285.0429 150.6135 2049.442 1081.9387 8245.405
## 2 1461.9375 878.8125 9149.000 861.3438 27643.344
## 3 298751.0000 91740.0000 609326.000 26720.0000 1017917.000
## piso6_Tierra agua1_Red agua2_Red_fueraVivienda agua3_Pilón agua4_Camión
## 1 8460.184 12726.58 2357.466 978.2147 707.1534
## 2 29348.406 43677.25 7827.812 4155.5312 1979.4688
## 3 130607.000 1690717.00 232583.000 69695.0000 146223.0000
## agua5_Pozo agua6_Manantial agua7_Río agua9_Vecino
## 1 2012.000 306.865 965.5767 130.7485
## 2 6603.219 1486.688 2865.1562 454.2188
## 3 23016.000 119.000 497.0000 10325.0000
sum(data$elec1_Sí+data$elec2_No)
## [1] 7698900
data$porcentaje_electrico <- (data$elec1_Sí / data$elec3_Total) * 100
data$porcentaje_electrico
## [1] 67.64344 82.44390 87.49196 18.44380 85.33354 77.43711 80.77012 78.72231
## [9] 81.59007 88.27107 78.83544 79.58164 65.49730 79.91896 89.67901 89.66251
## [17] 80.77924 83.94366 83.96259 69.49873 68.73284 81.37393 67.26671 79.66168
## [25] 90.85280 77.81418 79.90615 90.65506 79.09005 68.89952 82.35773 74.78474
## [33] 68.98904 79.15537 92.14882 89.42797 87.44072 87.78482 75.37346 68.46439
## [41] 89.11167 78.47713 69.70906 86.75668 72.89945 82.48046 70.30474 75.87302
## [49] 85.12609 86.71171 80.20305 78.36938 76.34198 75.20600 86.41630 73.92059
## [57] 87.76943 70.84335 73.89553 88.56824 80.13472 74.45562 79.01125 76.35064
## [65] 72.00375 79.82246 98.28078 77.68284 81.55471 77.21895 62.30630 82.81170
## [73] 61.46247 96.82175 61.32671 78.05704 72.86551 69.49437 78.82183 85.44950
## [81] 79.90021 68.19581 64.00158 76.51069 83.86897 57.29150 81.65788 72.21796
## [89] 64.66758 76.61017 63.93431 83.39664 49.09457 76.46809 54.97109 57.55683
## [97] 44.92040 53.83292 91.77197 91.04013 85.98952 83.76151 91.11282 83.62439
## [105] 83.34268 83.69814 93.11447 88.91416 81.60531 62.64424 91.01681 89.38624
## [113] 93.30889 68.77758 92.40647 84.45416 70.61077 84.66941 92.50522 70.76312
## [121] 71.80679 74.96151 94.79664 85.99579 95.65150 78.66870 85.32023 94.21655
## [129] 86.28995 83.81993 85.87467 91.88937 81.44267 89.85333 96.01232 93.40278
## [137] 85.64202 71.58794 42.18052 64.79486 60.72980 84.97648 43.87515 70.34500
## [145] 57.69488 67.11580 82.53862 86.95933 57.33355 91.25132 87.84567 68.34371
## [153] 67.96468 86.60623 69.76561 72.96044 85.39516 88.44072 87.65905 83.22978
## [161] 90.29782 91.19633 72.55965 54.03354 64.40475 71.58178 77.70734 60.35847
## [169] 69.32592 77.64073 77.38100 69.98782 90.66874 52.33462 81.38544 78.07711
## [177] 77.85411 72.60504 83.06654 86.29106 88.35394 84.54968 85.93354 94.46922
## [185] 83.29834 76.00497 86.52529 87.38263 70.82240 86.85755 91.21925 87.19316
## [193] 39.95383 82.73194 75.82967 41.58126
data$razon <- data$Castillo / data$Keiko
data$razon
## [1] 2.3796657 1.6076022 1.4963239 9.0968188 1.6079082 1.4509197
## [7] 1.9009468 1.6454352 6.4162437 3.4582830 1.9253881 2.1863795
## [13] 3.8078963 0.7286762 1.4890411 1.7662760 4.0625186 0.8258561
## [19] 0.9427761 5.1492629 0.9496819 2.7055192 3.5156312 2.7439239
## [25] 0.9555440 2.7920907 2.1199570 2.8806288 4.8221388 5.2714286
## [31] 3.9835244 5.0256475 10.2177460 6.5783051 1.6989192 1.4526308
## [37] 0.8927284 3.1326781 6.2017463 4.5558122 2.4786000 6.1130334
## [43] 8.1649315 4.6426609 7.5339286 4.8862275 5.7577158 2.6740517
## [49] 4.5105422 2.7023915 3.2901057 9.0247678 5.7456202 1.2575664
## [55] 1.4373245 4.1808609 5.9647541 1.5534476 2.7172741 9.3437360
## [61] 2.3314535 3.7248398 2.7899160 2.4580153 3.7720787 2.5463673
## [67] 0.4831402 13.2825630 7.4869399 7.0571013 21.7387670 11.0736035
## [73] 26.3169839 2.5130113 11.8407979 5.4780481 11.5419968 12.3055848
## [79] 8.7167819 6.3994521 6.7660632 7.8382873 2.0911546 5.3319149
## [85] 7.3321089 2.0721805 5.3368150 2.0932762 4.1854839 4.9527972
## [91] 4.7615455 1.7712630 5.9676985 1.1909417 2.2522972 4.4833625
## [97] 1.6050449 9.6971831 0.8791366 0.8873290 0.9636714 1.0059647
## [103] 0.9594576 1.1465777 2.1354781 1.3151706 1.5129584 1.0626024
## [109] 2.1368307 1.2525173 1.0602023 2.0724083 0.6664874 1.5886333
## [115] 0.7705212 0.4659518 1.2723109 1.1065936 0.7461763 1.3769642
## [121] 1.0372968 1.2861614 0.5066154 1.2849815 0.6755326 1.0550010
## [127] 0.7580679 0.7897237 0.8216123 0.8285115 1.0848545 0.7696900
## [133] 1.1328093 0.8656563 0.5213297 1.2402470 1.4559132 2.2239371
## [139] 2.0529210 0.8180809 0.4040854 0.8056103 0.5169173 0.5555556
## [145] 1.3568704 3.7831094 1.8175860 2.4099509 5.5042017 1.8261733
## [151] 3.6516045 6.0134508 0.7435116 3.0984233 2.7399383 2.2203525
## [157] 0.7397454 0.7186292 0.5566541 0.6475959 0.3951030 0.7765315
## [163] 20.4238740 8.6549725 14.6956371 17.7529844 18.2880583 10.6007223
## [169] 14.0121689 17.8378033 5.7364652 13.8129496 5.0808733 8.4935858
## [175] 11.1696643 1.7610218 1.5915532 1.4060783 1.2011323 1.2609247
## [181] 1.3418081 1.1752912 1.5601314 0.8744723 1.9852570 9.3125000
## [187] 4.7396504 2.5177131 5.9096296 0.3755796 0.5881089 0.3764420
## [193] 0.8618825 0.8180703 2.2380675 0.3500949
tasa <- data$countFallecidos / data$countPositivos
tasa_mil <- tasa * 1000
tasa_mil
## [1] 56.85454 185.08997 129.25483 31.88739 192.98246 545.45455
## [7] 89.62390 329.11392 574.07407 355.93220 396.69421 295.28986
## [13] 607.14286 375.90862 513.51351 167.18588 560.00000 151.68897
## [19] 202.05479 279.06977 301.20482 975.90361 265.36313 233.12883
## [25] 252.26473 276.00000 326.38889 151.01850 290.02193 464.28571
## [31] 380.00000 467.04871 204.58265 574.80315 209.12356 319.16427
## [37] 301.69051 310.16043 283.32404 278.38828 271.04377 779.66102
## [43] 342.10526 130.42613 272.72727 158.05627 185.30351 263.29442
## [49] 175.62254 282.72251 1055.55556 309.64467 532.60870 357.14286
## [55] 180.94622 511.73709 299.80080 466.36771 260.57143 156.87919
## [61] 122.87614 122.97872 460.37736 372.43402 329.26829 276.16279
## [67] 264.59022 397.16312 421.68675 423.78049 720.58824 381.42748
## [73] 533.33333 158.57509 158.38150 171.05653 694.44444 514.28571
## [79] 515.94203 399.63834 216.66667 189.37644 234.69388 201.68067
## [85] 134.49074 269.23077 192.20999 283.89155 300.54645 187.50000
## [91] 204.22535 145.80981 866.66667 357.83133 131.94444 174.02597
## [97] 46.68305 318.18182 474.33930 258.90222 288.25911 162.30366
## [103] 258.49411 249.10851 198.26590 343.24324 283.78576 540.24052
## [109] 261.81818 116.63849 501.20482 296.10829 421.12421 1222.22222
## [115] 317.68650 149.90138 333.33333 227.37819 481.50470 145.09804
## [121] 126.22549 235.95506 292.82248 310.90652 307.95371 202.71467
## [127] 246.00264 259.48187 1742.85714 296.64812 228.11671 224.55001
## [133] 374.67866 274.28800 230.31283 853.21101 411.01695 176.61488
## [139] 49.30468 47.92043 110.86798 244.18431 19.08714 128.65497
## [145] 197.40260 79.15994 52.07329 99.60913 129.67033 178.31638
## [151] 78.14680 197.76119 230.90909 156.30975 300.95238 457.48988
## [157] 736.54709 240.96386 291.71745 426.70401 267.77977 371.02754
## [163] 313.24004 263.55140 316.86047 422.38267 308.90052 263.15789
## [169] 324.86631 359.37500 246.61556 222.22222 210.91881 297.22222
## [175] 604.57516 164.37247 45.19774 91.85606 117.50366 160.82359
## [181] 132.60160 189.18919 114.58333 140.73352 104.23305 135.33835
## [187] 105.91133 159.73742 242.85714 331.27572 179.40423 176.36023
## [193] 229.81366 180.20777 115.75179 17.09402