library(rio)
data=import("dataOK_all.Xlsx")
## New names:
## • `` -> `...1`
head(data)
## ...1 key Código pared1_Ladrillo pared2_Piedra
## 1 1 AMAZONAS+BAGUA 102 4633 46
## 2 2 AMAZONAS+BONGARA 103 1602 9
## 3 3 AMAZONAS+CHACHAPOYAS 101 3782 22
## 4 4 AMAZONAS+CONDORCANQUI 104 291 7
## 5 5 AMAZONAS+LUYA 105 430 7
## 6 6 AMAZONAS+RODRIGUEZ DE MENDOZA 106 1546 7
## pared3_Adobe pared4_Tapia pared5_Quincha pared6_Piedra pared7_Madera
## 1 6639 222 2518 127 4484
## 2 2729 240 157 36 2505
## 3 5881 2476 309 168 1270
## 4 672 8 386 7 8145
## 5 5217 6052 346 54 606
## 6 2778 155 720 28 3646
## pared8_Triplay pared9_Otro pared10_Total techo1_Concreto techo2_Madera
## 1 851 0 19520 2187 294
## 2 30 0 7308 692 75
## 3 91 0 13999 2262 160
## 4 200 0 9716 56 188
## 5 45 0 12757 187 43
## 6 24 0 8904 480 48
## techo3_Tejas techo4_Planchas techo5_Caña techo6_Triplay techo7_Paja
## 1 179 13186 160 106 3408
## 2 382 6084 38 5 32
## 3 3393 8005 50 14 115
## 4 177 2036 15 10 7234
## 5 3071 9343 26 12 75
## 6 2810 5495 15 5 51
## techo8_Otro techo9_Total piso1_Parquet piso2_Láminas piso3_Losetas
## 1 0 19520 6 19 647
## 2 0 7308 5 2 165
## 3 0 13999 23 36 1077
## 4 0 9716 2 0 20
## 5 0 12757 4 0 46
## 6 0 8904 3 4 264
## piso4_Madera piso5_Cemento piso6_Tierra piso7_Otro piso8_Total agua1_Red
## 1 157 7121 11569 1 19520 9429
## 2 132 2917 4087 0 7308 4569
## 3 240 6189 6434 0 13999 10647
## 4 1523 943 7228 0 9716 1307
## 5 295 1911 10501 0 12757 7172
## 6 176 2974 5483 0 8904 5256
## agua2_Red_fueraVivienda agua3_Pilón agua4_Camión agua5_Pozo agua6_Manantial
## 1 4392 793 59 1792 270
## 2 1497 215 0 474 67
## 3 1619 184 49 876 92
## 4 867 1003 2 2564 431
## 5 3097 1112 0 819 132
## 6 1278 154 0 1020 211
## agua7_Río agua8_Otro agua9_Vecino agua10_Total elec1_Sí elec2_No elec3_Total
## 1 2648 56 81 19520 13204 6316 19520
## 2 388 61 37 7308 6025 1283 7308
## 3 488 24 20 13999 12248 1751 13999
## 4 3428 80 34 9716 1792 7924 9716
## 5 369 9 47 12757 10886 1871 12757
## 6 948 29 8 8904 6895 2009 8904
## departamento provincia Castillo Keiko ganaCastillo covidPositivos
## 1 AMAZONAS BAGUA 25629 10770 1 8126
## 2 AMAZONAS BONGARA 8374 5209 1 389
## 3 AMAZONAS CHACHAPOYAS 15671 10473 1 2174
## 4 AMAZONAS CONDORCANQUI 13154 1446 1 3481
## 5 AMAZONAS LUYA 12606 7840 1 456
## 6 AMAZONAS RODRÍGUEZ DE MENDOZA 7967 5491 1 110
## covidFallecidos
## 1 462
## 2 72
## 3 281
## 4 111
## 5 88
## 6 60
data <- na.omit(data)
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 ...
## $ covidPositivos : num 8126 389 2174 3481 456 ...
## $ covidFallecidos : num 462 72 281 111 88 60 336 26 31 21 ...
data['% viviendas_con_agua_redpublica'] = (data['agua1_Red'] / data['agua10_Total']) * 100
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
data <- data %>%
mutate(RazonKEIKO_CASTILLO = Keiko / Castillo)
library(dplyr)
data <- data %>%
mutate(Tasa = (covidFallecidos / covidPositivos) * 1000)
library(dplyr)
datos_sin_lima <- data %>%
filter(provincia != "LIMA")
boxplot(datos_sin_lima[,c(51:53)], method='standardize',horizontal = F,las=2,cex.axis = 0.5)
cor(datos_sin_lima[,c(51:53)])
## % viviendas_con_agua_redpublica
## % viviendas_con_agua_redpublica 1.0000000
## RazonKEIKO_CASTILLO 0.1195803
## Tasa 0.1035734
## RazonKEIKO_CASTILLO Tasa
## % viviendas_con_agua_redpublica 0.11958032 0.10357342
## RazonKEIKO_CASTILLO 1.00000000 -0.09694139
## Tasa -0.09694139 1.00000000
library(dplyr)
dataClus=datos_sin_lima[,c(51:53)]
row.names(dataClus)=datos_sin_lima$provincia
library(cluster)
g.dist = daisy(dataClus, metric="gower")
library(factoextra)
## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(ggplot2)
fviz_nbclust(dataClus, pam,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F)
library(kableExtra)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
set.seed(123)
res.pam=pam(g.dist,1,cluster.only = F)
#nueva columna
dataClus$pam=res.pam$cluster
# ver
head(dataClus,15)%>%kbl()%>%kable_styling()
|
|
% viviendas_con_agua_redpublica
|
RazonKEIKO_CASTILLO
|
Tasa
|
pam
|
|
BAGUA
|
48.30430
|
0.4202271
|
56.85454
|
1
|
|
BONGARA
|
62.52053
|
0.6220444
|
185.08997
|
1
|
|
CHACHAPOYAS
|
76.05543
|
0.6683045
|
129.25483
|
1
|
|
CONDORCANQUI
|
13.45204
|
0.1099285
|
31.88739
|
1
|
|
LUYA
|
56.22011
|
0.6219261
|
192.98246
|
1
|
|
RODRÍGUEZ DE MENDOZA
|
59.02965
|
0.6892180
|
545.45455
|
1
|
|
UTCUBAMBA
|
48.71039
|
0.5260536
|
89.62390
|
1
|
|
AIJA
|
74.75528
|
0.6077419
|
329.11392
|
1
|
|
ANTONIO RAYMONDI
|
85.28790
|
0.1558544
|
574.07407
|
1
|
|
ASUNCIÓN
|
71.32928
|
0.2891608
|
355.93220
|
1
|
|
BOLOGNESI
|
72.30859
|
0.5193758
|
396.69421
|
1
|
|
CARHUAZ
|
73.07544
|
0.4573771
|
295.28986
|
1
|
|
CARLOS FERMÍN FITZCARRALD
|
66.24904
|
0.2626122
|
607.14286
|
1
|
|
CASMA
|
60.43541
|
1.3723517
|
375.90862
|
1
|
|
CORONGO
|
75.16049
|
0.6715731
|
513.51351
|
1
|
silPAM=data.frame(res.pam$silinfo$widths)
silPAM$country_name=row.names(silPAM)
poorPAM=silPAM[silPAM$sil_width<0,'provincia']%>%sort()
poorPAM
## NULL
#fviz_silhouette(res.pam,print.summary = F)
fviz_nbclust(dataClus, hcut,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F,hc_func = "agnes")
set.seed(123)
library(factoextra)
res.agnes<- hcut(g.dist, k = 1,hc_func='agnes',hc_method = "ward.D")
dataClus$agnes=res.agnes$cluster
# ver
head(dataClus,15)%>%kbl()%>%kable_styling()
|
|
% viviendas_con_agua_redpublica
|
RazonKEIKO_CASTILLO
|
Tasa
|
pam
|
agnes
|
|
BAGUA
|
48.30430
|
0.4202271
|
56.85454
|
1
|
1
|
|
BONGARA
|
62.52053
|
0.6220444
|
185.08997
|
1
|
1
|
|
CHACHAPOYAS
|
76.05543
|
0.6683045
|
129.25483
|
1
|
1
|
|
CONDORCANQUI
|
13.45204
|
0.1099285
|
31.88739
|
1
|
1
|
|
LUYA
|
56.22011
|
0.6219261
|
192.98246
|
1
|
1
|
|
RODRÍGUEZ DE MENDOZA
|
59.02965
|
0.6892180
|
545.45455
|
1
|
1
|
|
UTCUBAMBA
|
48.71039
|
0.5260536
|
89.62390
|
1
|
1
|
|
AIJA
|
74.75528
|
0.6077419
|
329.11392
|
1
|
1
|
|
ANTONIO RAYMONDI
|
85.28790
|
0.1558544
|
574.07407
|
1
|
1
|
|
ASUNCIÓN
|
71.32928
|
0.2891608
|
355.93220
|
1
|
1
|
|
BOLOGNESI
|
72.30859
|
0.5193758
|
396.69421
|
1
|
1
|
|
CARHUAZ
|
73.07544
|
0.4573771
|
295.28986
|
1
|
1
|
|
CARLOS FERMÍN FITZCARRALD
|
66.24904
|
0.2626122
|
607.14286
|
1
|
1
|
|
CASMA
|
60.43541
|
1.3723517
|
375.90862
|
1
|
1
|
|
CORONGO
|
75.16049
|
0.6715731
|
513.51351
|
1
|
1
|
silAGNES=data.frame(res.agnes$silinfo$widths)
silAGNES$country=row.names(silAGNES)
poorAGNES=silAGNES[silAGNES$sil_width<0,'provincia']%>%sort()
poorAGNES
## NULL
#fviz_silhouette(res.agnes,print.summary = F)
fviz_nbclust(dataClus, hcut,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F,hc_func = "diana")
set.seed(123)
res.diana <- hcut(g.dist, k = 1,hc_func='diana')
dataClus$diana=res.diana$cluster
# veamos
head(dataClus,150)%>%kbl%>%kable_styling()
|
|
% viviendas_con_agua_redpublica
|
RazonKEIKO_CASTILLO
|
Tasa
|
pam
|
agnes
|
diana
|
|
BAGUA
|
48.304303
|
0.4202271
|
56.85454
|
1
|
1
|
1
|
|
BONGARA
|
62.520525
|
0.6220444
|
185.08997
|
1
|
1
|
1
|
|
CHACHAPOYAS
|
76.055432
|
0.6683045
|
129.25483
|
1
|
1
|
1
|
|
CONDORCANQUI
|
13.452038
|
0.1099285
|
31.88739
|
1
|
1
|
1
|
|
LUYA
|
56.220114
|
0.6219261
|
192.98246
|
1
|
1
|
1
|
|
RODRÍGUEZ DE MENDOZA
|
59.029650
|
0.6892180
|
545.45455
|
1
|
1
|
1
|
|
UTCUBAMBA
|
48.710393
|
0.5260536
|
89.62390
|
1
|
1
|
1
|
|
AIJA
|
74.755281
|
0.6077419
|
329.11392
|
1
|
1
|
1
|
|
ANTONIO RAYMONDI
|
85.287903
|
0.1558544
|
574.07407
|
1
|
1
|
1
|
|
ASUNCIÓN
|
71.329279
|
0.2891608
|
355.93220
|
1
|
1
|
1
|
|
BOLOGNESI
|
72.308595
|
0.5193758
|
396.69421
|
1
|
1
|
1
|
|
CARHUAZ
|
73.075444
|
0.4573771
|
295.28986
|
1
|
1
|
1
|
|
CARLOS FERMÍN FITZCARRALD
|
66.249036
|
0.2626122
|
607.14286
|
1
|
1
|
1
|
|
CASMA
|
60.435410
|
1.3723517
|
375.90862
|
1
|
1
|
1
|
|
CORONGO
|
75.160494
|
0.6715731
|
513.51351
|
1
|
1
|
1
|
|
HUARAZ
|
81.149992
|
0.5661629
|
167.18588
|
1
|
1
|
1
|
|
HUARI
|
80.850787
|
0.2461527
|
560.00000
|
1
|
1
|
1
|
|
HUARMEY
|
64.756230
|
1.2108647
|
151.68897
|
1
|
1
|
1
|
|
HUAYLAS
|
66.396818
|
1.0606972
|
202.05479
|
1
|
1
|
1
|
|
MARISCAL LUZURIAGA
|
37.689040
|
0.1942026
|
279.06977
|
1
|
1
|
1
|
|
OCROS
|
57.826599
|
1.0529842
|
301.20482
|
1
|
1
|
1
|
|
PALLASCA
|
73.458236
|
0.3696148
|
975.90361
|
1
|
1
|
1
|
|
POMABAMBA
|
61.376728
|
0.2844439
|
265.36313
|
1
|
1
|
1
|
|
RECUAY
|
66.463415
|
0.3644416
|
233.12883
|
1
|
1
|
1
|
|
SANTA
|
72.928390
|
1.0465242
|
252.26473
|
1
|
1
|
1
|
|
SIHUAS
|
62.757175
|
0.3581546
|
276.00000
|
1
|
1
|
1
|
|
YUNGAY
|
65.807536
|
0.4717077
|
326.38889
|
1
|
1
|
1
|
|
ABANCAY
|
68.205772
|
0.3471464
|
151.01850
|
1
|
1
|
1
|
|
ANDAHUAYLAS
|
59.121772
|
0.2073769
|
290.02193
|
1
|
1
|
1
|
|
ANTABAMBA
|
28.909595
|
0.1897019
|
464.28571
|
1
|
1
|
1
|
|
AYMARAES
|
53.799604
|
0.2510340
|
380.00000
|
1
|
1
|
1
|
|
CHINCHEROS
|
65.432007
|
0.1989793
|
467.04871
|
1
|
1
|
1
|
|
COTABAMBAS
|
46.206326
|
0.0978689
|
204.58265
|
1
|
1
|
1
|
|
GRAU
|
7.133685
|
0.1520148
|
574.80315
|
1
|
1
|
1
|
|
AREQUIPA
|
74.813441
|
0.5886095
|
209.12356
|
1
|
1
|
1
|
|
CAMANÁ
|
68.610312
|
0.6884061
|
319.16427
|
1
|
1
|
1
|
|
CARAVELÍ
|
56.758130
|
1.1201615
|
301.69051
|
1
|
1
|
1
|
|
CASTILLA
|
77.025956
|
0.3192157
|
310.16043
|
1
|
1
|
1
|
|
CAYLLOMA
|
57.597393
|
0.1612449
|
283.32404
|
1
|
1
|
1
|
|
CONDESUYOS
|
45.183203
|
0.2194998
|
278.38828
|
1
|
1
|
1
|
|
ISLAY
|
79.099345
|
0.4034536
|
271.04377
|
1
|
1
|
1
|
|
LA UNIÓN
|
65.040075
|
0.1635849
|
779.66102
|
1
|
1
|
1
|
|
CANGALLO
|
60.932179
|
0.1224750
|
342.10526
|
1
|
1
|
1
|
|
HUAMANGA
|
75.055539
|
0.2153937
|
130.42613
|
1
|
1
|
1
|
|
HUANCA SANCOS
|
70.488981
|
0.1327329
|
272.72727
|
1
|
1
|
1
|
|
HUANTA
|
62.340212
|
0.2046569
|
158.05627
|
1
|
1
|
1
|
|
LA MAR
|
55.598286
|
0.1736800
|
185.30351
|
1
|
1
|
1
|
|
LUCANAS
|
62.349206
|
0.3739643
|
263.29442
|
1
|
1
|
1
|
|
PARINACOCHAS
|
70.882656
|
0.2217028
|
175.62254
|
1
|
1
|
1
|
|
PÁUCAR DEL SARA SARA
|
86.100386
|
0.3700426
|
282.72251
|
1
|
1
|
1
|
|
SUCRE
|
38.548820
|
0.3039416
|
1055.55556
|
1
|
1
|
1
|
|
VÍCTOR FAJARDO
|
64.642263
|
0.1108062
|
309.64467
|
1
|
1
|
1
|
|
VILCAS HUAMÁN
|
57.529776
|
0.1740456
|
532.60870
|
1
|
1
|
1
|
|
CAJABAMBA
|
49.781594
|
0.7951866
|
357.14286
|
1
|
1
|
1
|
|
CAJAMARCA
|
71.117636
|
0.6957371
|
180.94622
|
1
|
1
|
1
|
|
CELENDÍN
|
50.995878
|
0.2391852
|
511.73709
|
1
|
1
|
1
|
|
CHOTA
|
40.106251
|
0.1676515
|
299.80080
|
1
|
1
|
1
|
|
CONTUMAZÁ
|
59.835221
|
0.6437295
|
466.36771
|
1
|
1
|
1
|
|
CUTERVO
|
42.253994
|
0.3680159
|
260.57143
|
1
|
1
|
1
|
|
HUALGAYOC
|
34.909991
|
0.1070236
|
156.87919
|
1
|
1
|
1
|
|
JAÉN
|
60.014835
|
0.4289170
|
122.87614
|
1
|
1
|
1
|
|
SAN IGNACIO
|
36.382069
|
0.2684679
|
122.97872
|
1
|
1
|
1
|
|
SAN MARCOS
|
57.000823
|
0.3584337
|
460.37736
|
1
|
1
|
1
|
|
SAN MIGUEL
|
48.238877
|
0.4068323
|
372.43402
|
1
|
1
|
1
|
|
SAN PABLO
|
70.255930
|
0.2651058
|
329.26829
|
1
|
1
|
1
|
|
SANTA CRUZ
|
49.640479
|
0.3927163
|
276.16279
|
1
|
1
|
1
|
|
CALLAO
|
78.614492
|
2.0697924
|
264.59022
|
1
|
1
|
1
|
|
ACOMAYO
|
69.630702
|
0.0752867
|
397.16312
|
1
|
1
|
1
|
|
ANTA
|
50.421846
|
0.1335659
|
421.68675
|
1
|
1
|
1
|
|
CALCA
|
49.551533
|
0.1417012
|
423.78049
|
1
|
1
|
1
|
|
CANAS
|
39.224261
|
0.0460008
|
720.58824
|
1
|
1
|
1
|
|
CANCHIS
|
75.596944
|
0.0903048
|
381.42748
|
1
|
1
|
1
|
|
CHUMBIVILCAS
|
40.392252
|
0.0379983
|
533.33333
|
1
|
1
|
1
|
|
CUSCO
|
80.128668
|
0.3979290
|
158.57509
|
1
|
1
|
1
|
|
ESPINAR
|
43.961137
|
0.0844538
|
158.38150
|
1
|
1
|
1
|
|
LA CONVENCIÓN
|
41.494362
|
0.1825468
|
171.05653
|
1
|
1
|
1
|
|
PARURO
|
49.619843
|
0.0866401
|
694.44444
|
1
|
1
|
1
|
|
PAUCARTAMBO
|
23.273979
|
0.0812639
|
514.28571
|
1
|
1
|
1
|
|
QUISPICANCHI
|
46.889059
|
0.1147212
|
515.94203
|
1
|
1
|
1
|
|
URUBAMBA
|
72.571687
|
0.1562634
|
399.63834
|
1
|
1
|
1
|
|
ACOBAMBA
|
60.735923
|
0.1477964
|
216.66667
|
1
|
1
|
1
|
|
ANGARAES
|
48.758320
|
0.1275789
|
189.37644
|
1
|
1
|
1
|
|
CASTROVIRREYNA
|
43.481686
|
0.4782047
|
234.69388
|
1
|
1
|
1
|
|
CHURCAMPA
|
40.646627
|
0.1875499
|
201.68067
|
1
|
1
|
1
|
|
HUANCAVELICA
|
61.752606
|
0.1363864
|
134.49074
|
1
|
1
|
1
|
|
HUAYTARÁ
|
33.188383
|
0.4825835
|
269.23077
|
1
|
1
|
1
|
|
TAYACAJA
|
57.255274
|
0.1873777
|
192.20999
|
1
|
1
|
1
|
|
AMBO
|
44.498641
|
0.4777200
|
283.89155
|
1
|
1
|
1
|
|
DOS DE MAYO
|
44.260057
|
0.2389210
|
300.54645
|
1
|
1
|
1
|
|
HUACAYBAMBA
|
56.198547
|
0.2019061
|
187.50000
|
1
|
1
|
1
|
|
HUAMALÍES
|
51.428378
|
0.2100158
|
204.22535
|
1
|
1
|
1
|
|
HUÁNUCO
|
56.213964
|
0.5645689
|
145.80981
|
1
|
1
|
1
|
|
LAURICOCHA
|
13.791842
|
0.1675688
|
866.66667
|
1
|
1
|
1
|
|
LEONCIO PRADO
|
46.431935
|
0.8396717
|
357.83133
|
1
|
1
|
1
|
|
MARAÑÓN
|
36.468763
|
0.4439911
|
131.94444
|
1
|
1
|
1
|
|
PACHITEA
|
27.781123
|
0.2230469
|
174.02597
|
1
|
1
|
1
|
|
PUERTO INCA
|
16.641851
|
0.6230355
|
46.68305
|
1
|
1
|
1
|
|
YAROWILCA
|
27.659575
|
0.1031227
|
318.18182
|
1
|
1
|
1
|
|
CHINCHA
|
74.724332
|
1.1374797
|
474.33930
|
1
|
1
|
1
|
|
ICA
|
75.377321
|
1.1269777
|
258.90222
|
1
|
1
|
1
|
|
NAZCA
|
56.146743
|
1.0376981
|
288.25911
|
1
|
1
|
1
|
|
PALPA
|
69.777024
|
0.9940707
|
162.30366
|
1
|
1
|
1
|
|
PISCO
|
77.275893
|
1.0422555
|
258.49411
|
1
|
1
|
1
|
|
CHANCHAMAYO
|
56.555276
|
0.8721607
|
249.10851
|
1
|
1
|
1
|
|
CHUPACA
|
74.053561
|
0.4682792
|
198.26590
|
1
|
1
|
1
|
|
CONCEPCIÓN
|
72.551454
|
0.7603576
|
343.24324
|
1
|
1
|
1
|
|
HUANCAYO
|
83.596437
|
0.6609567
|
283.78576
|
1
|
1
|
1
|
|
JAUJA
|
78.413781
|
0.9410858
|
540.24052
|
1
|
1
|
1
|
|
JUNÍN
|
70.100162
|
0.4679828
|
261.81818
|
1
|
1
|
1
|
|
SATIPO
|
34.209847
|
0.7983922
|
116.63849
|
1
|
1
|
1
|
|
TARMA
|
70.400320
|
0.9432162
|
501.20482
|
1
|
1
|
1
|
|
YAULI
|
72.849883
|
0.4825304
|
296.10829
|
1
|
1
|
1
|
|
ASCOPE
|
81.023151
|
1.5004035
|
421.12421
|
1
|
1
|
1
|
|
BOLÍVAR
|
56.293797
|
0.6294719
|
1222.22222
|
1
|
1
|
1
|
|
CHEPÉN
|
75.522693
|
1.2978227
|
317.68650
|
1
|
1
|
1
|
|
GRAN CHIMÚ
|
35.566414
|
2.1461447
|
149.90138
|
1
|
1
|
1
|
|
JULCÁN
|
28.690557
|
0.7859714
|
333.33333
|
1
|
1
|
1
|
|
OTUZCO
|
51.398222
|
0.9036741
|
227.37819
|
1
|
1
|
1
|
|
PACASMAYO
|
80.497294
|
1.3401658
|
481.50470
|
1
|
1
|
1
|
|
PATAZ
|
56.550355
|
0.7262353
|
145.09804
|
1
|
1
|
1
|
|
SÁNCHEZ CARRIÓN
|
49.197229
|
0.9640443
|
126.22549
|
1
|
1
|
1
|
|
SANTIAGO DE CHUCO
|
51.228096
|
0.7775074
|
235.95506
|
1
|
1
|
1
|
|
TRUJILLO
|
80.792443
|
1.9738840
|
292.82248
|
1
|
1
|
1
|
|
VIRÚ
|
74.647952
|
0.7782213
|
310.90652
|
1
|
1
|
1
|
|
CHICLAYO
|
83.054120
|
1.4803134
|
307.95371
|
1
|
1
|
1
|
|
FERREÑAFE
|
62.463488
|
0.9478664
|
202.71467
|
1
|
1
|
1
|
|
LAMBAYEQUE
|
59.261009
|
1.3191430
|
246.00264
|
1
|
1
|
1
|
|
BARRANCA
|
83.029953
|
1.2662657
|
259.48187
|
1
|
1
|
1
|
|
CAJATAMBO
|
65.493284
|
1.2171190
|
1742.85714
|
1
|
1
|
1
|
|
CAÑETE
|
68.828033
|
1.2069838
|
296.64812
|
1
|
1
|
1
|
|
CANTA
|
60.234987
|
0.9217826
|
228.11671
|
1
|
1
|
1
|
|
HUARAL
|
70.963774
|
1.2992243
|
224.55001
|
1
|
1
|
1
|
|
HUAROCHIRÍ
|
56.616303
|
0.8827611
|
374.67866
|
1
|
1
|
1
|
|
HUAURA
|
71.023371
|
1.1551929
|
274.28800
|
1
|
1
|
1
|
|
OYÓN
|
57.703993
|
0.8062910
|
853.21101
|
1
|
1
|
1
|
|
YAUYOS
|
63.963696
|
0.6868542
|
411.01695
|
1
|
1
|
1
|
|
ALTO AMAZONAS
|
46.898130
|
0.4496530
|
176.61488
|
1
|
1
|
1
|
|
DATEM DEL MARAÑÓN
|
4.925032
|
0.4871108
|
49.30468
|
1
|
1
|
1
|
|
LORETO
|
10.854486
|
1.2223729
|
47.92043
|
1
|
1
|
1
|
|
MARISCAL RAMÓN CASTILLA
|
15.522734
|
2.4747246
|
110.86798
|
1
|
1
|
1
|
|
MAYNAS
|
63.633149
|
1.2412949
|
244.18431
|
1
|
1
|
1
|
|
PUTUMAYO
|
4.593640
|
1.9345455
|
19.08714
|
1
|
1
|
1
|
|
REQUENA
|
19.858039
|
1.8000000
|
128.65497
|
1
|
1
|
1
|
|
UCAYALI
|
29.382737
|
0.7369900
|
197.40260
|
1
|
1
|
1
|
|
MANU
|
28.077203
|
0.2643328
|
79.15994
|
1
|
1
|
1
|
|
TAHUAMANU
|
45.533915
|
0.5501803
|
52.07329
|
1
|
1
|
1
|
|
TAMBOPATA
|
65.607713
|
0.4149462
|
99.60913
|
1
|
1
|
1
|
|
GENERAL SÁNCHEZ CERRO
|
33.676423
|
0.1816794
|
129.67033
|
1
|
1
|
1
|
|
ILO
|
81.072306
|
0.5475932
|
178.31638
|
1
|
1
|
1
|
|
MARISCAL NIETO
|
74.004445
|
0.2738522
|
78.14680
|
1
|
1
|
1
|
#fviz_silhouette(res.diana,print.summary = F)
silDIANA=data.frame(res.diana$silinfo$widths)
silDIANA$country=row.names(silDIANA)
poorDIANA=silDIANA[silDIANA$sil_width<0,'provincia']%>%sort()
poorDIANA
## NULL