dataokall
2024-06-19
DATA
library(readxl)
dataOK_all <- read_excel("dataOK_all.xlsx")## New names:
## • `` -> `...1`View(dataOK_all)PREGUNTA 1
Se quiere organizar un indicador complejo de probreza, asumiendo que las variables de calidad de techo, pared,piso y la disponibilidad de agua representa cada una un concepto o variable latente.
Ud hará ell con el archivo entregado (no considere las mediciones ‘otros’ en ningun caso, ni otras que su criterio de analista le dicte).
names(dataOK_all)## [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"dontselect=c("...1","key", "Código", "elec1_Sí", "elec2_No", "elec3_Total", "departamento", "provincia", "Castillo", "Keiko", "ganaCastillo", "countPositivos", "countFallecidos", "agua8_Otro", "pared9_Otro", "techo8_Otro", "piso7_Otro", "agua10_Total", "piso8_Total", "techo9_Total", "pared10_Total")
select=setdiff(names(dataOK_all),dontselect)
theData=dataOK_all[,select]
# usaremos:
library(magrittr)
head(theData,10)%>%
rmarkdown::paged_table()str(theData)## tibble [196 × 29] (S3: tbl_df/tbl/data.frame)
## $ pared1_Ladrillo : num [1:196] 4633 1602 3782 291 430 ...
## $ pared2_Piedra : num [1:196] 46 9 22 7 7 7 35 1 0 3 ...
## $ pared3_Adobe : num [1:196] 6639 2729 5881 672 5217 ...
## $ pared4_Tapia : num [1:196] 222 240 2476 8 6052 ...
## $ pared5_Quincha : num [1:196] 2518 157 309 386 346 ...
## $ pared6_Piedra : num [1:196] 127 36 168 7 54 28 518 65 7 6 ...
## $ pared7_Madera : num [1:196] 4484 2505 1270 8145 606 ...
## $ pared8_Triplay : num [1:196] 851 30 91 200 45 24 210 18 0 1 ...
## $ techo1_Concreto : num [1:196] 2187 692 2262 56 187 ...
## $ techo2_Madera : num [1:196] 294 75 160 188 43 48 340 57 12 8 ...
## $ techo3_Tejas : num [1:196] 179 382 3393 177 3071 ...
## $ techo4_Planchas : num [1:196] 13186 6084 8005 2036 9343 ...
## $ techo5_Caña : num [1:196] 160 38 50 15 26 15 196 10 8 5 ...
## $ techo6_Triplay : num [1:196] 106 5 14 10 12 5 62 17 4 3 ...
## $ techo7_Paja : num [1:196] 3408 32 115 7234 75 ...
## $ piso1_Parquet : num [1:196] 6 5 23 2 4 3 20 0 0 5 ...
## $ piso2_Láminas : num [1:196] 19 2 36 0 0 4 32 0 0 1 ...
## $ piso3_Losetas : num [1:196] 647 165 1077 20 46 ...
## $ piso4_Madera : num [1:196] 157 132 240 1523 295 ...
## $ piso5_Cemento : num [1:196] 7121 2917 6189 943 1911 ...
## $ piso6_Tierra : num [1:196] 11569 4087 6434 7228 10501 ...
## $ agua1_Red : num [1:196] 9429 4569 10647 1307 7172 ...
## $ agua2_Red_fueraVivienda: num [1:196] 4392 1497 1619 867 3097 ...
## $ agua3_Pilón : num [1:196] 793 215 184 1003 1112 ...
## $ agua4_Camión : num [1:196] 59 0 49 2 0 0 117 0 0 0 ...
## $ agua5_Pozo : num [1:196] 1792 474 876 2564 819 ...
## $ agua6_Manantial : num [1:196] 270 67 92 431 132 211 471 121 61 27 ...
## $ agua7_Río : num [1:196] 2648 388 488 3428 369 ...
## $ agua9_Vecino : num [1:196] 81 37 20 34 47 8 177 9 4 6 ...theData <- as.data.frame(lapply(theData, as.numeric))#install.packages("polycor")
library(polycor)## Warning: package 'polycor' was built under R version 4.3.3corMatrix=polycor::hetcor(theData)$correlations
round(corMatrix,2)## pared1_Ladrillo pared2_Piedra pared3_Adobe pared4_Tapia
## pared1_Ladrillo 1.00 0.65 0.35 -0.03
## pared2_Piedra 0.65 1.00 0.20 -0.05
## pared3_Adobe 0.35 0.20 1.00 0.00
## pared4_Tapia -0.03 -0.05 0.00 1.00
## pared5_Quincha 0.22 0.14 0.15 -0.11
## pared6_Piedra 0.07 0.10 0.18 0.06
## pared7_Madera 0.89 0.50 0.16 -0.09
## pared8_Triplay 0.85 0.50 0.27 -0.09
## techo1_Concreto 1.00 0.65 0.35 -0.02
## techo2_Madera 0.96 0.55 0.26 -0.04
## techo3_Tejas 0.13 0.04 0.45 0.41
## techo4_Planchas 0.92 0.62 0.46 -0.02
## techo5_Caña 0.44 0.24 0.38 -0.13
## techo6_Triplay 0.84 0.49 0.29 -0.10
## techo7_Paja 0.04 0.02 0.08 -0.12
## piso1_Parquet 0.99 0.59 0.31 -0.02
## piso2_Láminas 0.99 0.69 0.30 -0.03
## piso3_Losetas 1.00 0.62 0.36 -0.03
## piso4_Madera 0.57 0.32 0.12 -0.02
## piso5_Cemento 1.00 0.67 0.38 -0.03
## piso6_Tierra 0.69 0.45 0.72 0.14
## agua1_Red 1.00 0.64 0.39 -0.02
## agua2_Red_fueraVivienda 0.99 0.59 0.40 0.01
## agua3_Pilón 0.91 0.84 0.38 -0.02
## agua4_Camión 0.99 0.61 0.33 -0.06
## agua5_Pozo 0.37 0.25 0.45 0.02
## agua6_Manantial -0.05 -0.03 0.27 0.24
## agua7_Río 0.01 0.00 0.06 -0.06
## agua9_Vecino 0.94 0.55 0.41 -0.03
## pared5_Quincha pared6_Piedra pared7_Madera
## pared1_Ladrillo 0.22 0.07 0.89
## pared2_Piedra 0.14 0.10 0.50
## pared3_Adobe 0.15 0.18 0.16
## pared4_Tapia -0.11 0.06 -0.09
## pared5_Quincha 1.00 -0.03 0.17
## pared6_Piedra -0.03 1.00 0.04
## pared7_Madera 0.17 0.04 1.00
## pared8_Triplay 0.45 0.04 0.74
## techo1_Concreto 0.20 0.07 0.88
## techo2_Madera 0.18 0.05 0.94
## techo3_Tejas -0.05 0.00 0.03
## techo4_Planchas 0.43 0.12 0.87
## techo5_Caña 0.16 -0.05 0.31
## techo6_Triplay 0.25 0.03 0.73
## techo7_Paja -0.06 0.26 0.24
## piso1_Parquet 0.17 0.07 0.89
## piso2_Láminas 0.18 0.07 0.88
## piso3_Losetas 0.22 0.06 0.89
## piso4_Madera 0.04 0.02 0.76
## piso5_Cemento 0.24 0.07 0.89
## piso6_Tierra 0.50 0.23 0.58
## agua1_Red 0.24 0.07 0.89
## agua2_Red_fueraVivienda 0.21 0.10 0.90
## agua3_Pilón 0.28 0.11 0.79
## agua4_Camión 0.27 0.06 0.88
## agua5_Pozo 0.07 0.24 0.45
## agua6_Manantial -0.04 0.39 0.00
## agua7_Río 0.47 0.10 0.19
## agua9_Vecino 0.45 0.06 0.86
## pared8_Triplay techo1_Concreto techo2_Madera
## pared1_Ladrillo 0.85 1.00 0.96
## pared2_Piedra 0.50 0.65 0.55
## pared3_Adobe 0.27 0.35 0.26
## pared4_Tapia -0.09 -0.02 -0.04
## pared5_Quincha 0.45 0.20 0.18
## pared6_Piedra 0.04 0.07 0.05
## pared7_Madera 0.74 0.88 0.94
## pared8_Triplay 1.00 0.84 0.79
## techo1_Concreto 0.84 1.00 0.96
## techo2_Madera 0.79 0.96 1.00
## techo3_Tejas 0.04 0.13 0.10
## techo4_Planchas 0.85 0.91 0.88
## techo5_Caña 0.51 0.44 0.39
## techo6_Triplay 0.93 0.83 0.80
## techo7_Paja 0.01 0.04 0.08
## piso1_Parquet 0.81 0.99 0.96
## piso2_Láminas 0.81 0.99 0.96
## piso3_Losetas 0.85 1.00 0.97
## piso4_Madera 0.47 0.56 0.61
## piso5_Cemento 0.85 0.99 0.96
## piso6_Tierra 0.72 0.68 0.61
## agua1_Red 0.85 1.00 0.96
## agua2_Red_fueraVivienda 0.82 0.99 0.96
## agua3_Pilón 0.79 0.91 0.86
## agua4_Camión 0.89 0.99 0.95
## agua5_Pozo 0.29 0.37 0.36
## agua6_Manantial -0.08 -0.05 -0.05
## agua7_Río 0.22 -0.02 0.00
## agua9_Vecino 0.88 0.93 0.90
## techo3_Tejas techo4_Planchas techo5_Caña techo6_Triplay
## pared1_Ladrillo 0.13 0.92 0.44 0.84
## pared2_Piedra 0.04 0.62 0.24 0.49
## pared3_Adobe 0.45 0.46 0.38 0.29
## pared4_Tapia 0.41 -0.02 -0.13 -0.10
## pared5_Quincha -0.05 0.43 0.16 0.25
## pared6_Piedra 0.00 0.12 -0.05 0.03
## pared7_Madera 0.03 0.87 0.31 0.73
## pared8_Triplay 0.04 0.85 0.51 0.93
## techo1_Concreto 0.13 0.91 0.44 0.83
## techo2_Madera 0.10 0.88 0.39 0.80
## techo3_Tejas 1.00 0.09 -0.05 0.03
## techo4_Planchas 0.09 1.00 0.37 0.76
## techo5_Caña -0.05 0.37 1.00 0.66
## techo6_Triplay 0.03 0.76 0.66 1.00
## techo7_Paja -0.13 0.13 -0.11 -0.02
## piso1_Parquet 0.13 0.89 0.38 0.80
## piso2_Láminas 0.12 0.89 0.38 0.80
## piso3_Losetas 0.14 0.92 0.44 0.84
## piso4_Madera 0.16 0.63 0.14 0.43
## piso5_Cemento 0.13 0.93 0.47 0.85
## piso6_Tierra 0.32 0.84 0.36 0.62
## agua1_Red 0.16 0.93 0.46 0.84
## agua2_Red_fueraVivienda 0.19 0.92 0.40 0.81
## agua3_Pilón 0.11 0.90 0.38 0.76
## agua4_Camión 0.10 0.91 0.46 0.88
## agua5_Pozo 0.04 0.51 0.15 0.26
## agua6_Manantial 0.19 0.06 -0.13 -0.10
## agua7_Río -0.10 0.27 -0.03 0.04
## agua9_Vecino 0.13 0.96 0.45 0.81
## techo7_Paja piso1_Parquet piso2_Láminas piso3_Losetas
## pared1_Ladrillo 0.04 0.99 0.99 1.00
## pared2_Piedra 0.02 0.59 0.69 0.62
## pared3_Adobe 0.08 0.31 0.30 0.36
## pared4_Tapia -0.12 -0.02 -0.03 -0.03
## pared5_Quincha -0.06 0.17 0.18 0.22
## pared6_Piedra 0.26 0.07 0.07 0.06
## pared7_Madera 0.24 0.89 0.88 0.89
## pared8_Triplay 0.01 0.81 0.81 0.85
## techo1_Concreto 0.04 0.99 0.99 1.00
## techo2_Madera 0.08 0.96 0.96 0.97
## techo3_Tejas -0.13 0.13 0.12 0.14
## techo4_Planchas 0.13 0.89 0.89 0.92
## techo5_Caña -0.11 0.38 0.38 0.44
## techo6_Triplay -0.02 0.80 0.80 0.84
## techo7_Paja 1.00 0.05 0.05 0.04
## piso1_Parquet 0.05 1.00 0.99 0.99
## piso2_Láminas 0.05 0.99 1.00 0.99
## piso3_Losetas 0.04 0.99 0.99 1.00
## piso4_Madera 0.34 0.57 0.56 0.57
## piso5_Cemento 0.04 0.98 0.98 0.99
## piso6_Tierra 0.20 0.63 0.64 0.68
## agua1_Red 0.04 0.98 0.99 1.00
## agua2_Red_fueraVivienda 0.06 0.99 0.98 0.99
## agua3_Pilón 0.08 0.87 0.92 0.90
## agua4_Camión 0.03 0.98 0.98 0.99
## agua5_Pozo 0.51 0.35 0.35 0.35
## agua6_Manantial 0.33 -0.04 -0.05 -0.06
## agua7_Río 0.43 -0.03 -0.03 0.01
## agua9_Vecino 0.06 0.91 0.91 0.94
## piso4_Madera piso5_Cemento piso6_Tierra agua1_Red
## pared1_Ladrillo 0.57 1.00 0.69 1.00
## pared2_Piedra 0.32 0.67 0.45 0.64
## pared3_Adobe 0.12 0.38 0.72 0.39
## pared4_Tapia -0.02 -0.03 0.14 -0.02
## pared5_Quincha 0.04 0.24 0.50 0.24
## pared6_Piedra 0.02 0.07 0.23 0.07
## pared7_Madera 0.76 0.89 0.58 0.89
## pared8_Triplay 0.47 0.85 0.72 0.85
## techo1_Concreto 0.56 0.99 0.68 1.00
## techo2_Madera 0.61 0.96 0.61 0.96
## techo3_Tejas 0.16 0.13 0.32 0.16
## techo4_Planchas 0.63 0.93 0.84 0.93
## techo5_Caña 0.14 0.47 0.36 0.46
## techo6_Triplay 0.43 0.85 0.62 0.84
## techo7_Paja 0.34 0.04 0.20 0.04
## piso1_Parquet 0.57 0.98 0.63 0.98
## piso2_Láminas 0.56 0.98 0.64 0.99
## piso3_Losetas 0.57 0.99 0.68 1.00
## piso4_Madera 1.00 0.58 0.38 0.58
## piso5_Cemento 0.58 1.00 0.71 1.00
## piso6_Tierra 0.38 0.71 1.00 0.72
## agua1_Red 0.58 1.00 0.72 1.00
## agua2_Red_fueraVivienda 0.59 0.98 0.72 0.99
## agua3_Pilón 0.51 0.92 0.73 0.91
## agua4_Camión 0.56 0.98 0.69 0.99
## agua5_Pozo 0.43 0.40 0.59 0.38
## agua6_Manantial -0.01 -0.05 0.30 -0.05
## agua7_Río 0.35 0.02 0.37 0.02
## agua9_Vecino 0.59 0.94 0.79 0.95
## agua2_Red_fueraVivienda agua3_Pilón agua4_Camión
## pared1_Ladrillo 0.99 0.91 0.99
## pared2_Piedra 0.59 0.84 0.61
## pared3_Adobe 0.40 0.38 0.33
## pared4_Tapia 0.01 -0.02 -0.06
## pared5_Quincha 0.21 0.28 0.27
## pared6_Piedra 0.10 0.11 0.06
## pared7_Madera 0.90 0.79 0.88
## pared8_Triplay 0.82 0.79 0.89
## techo1_Concreto 0.99 0.91 0.99
## techo2_Madera 0.96 0.86 0.95
## techo3_Tejas 0.19 0.11 0.10
## techo4_Planchas 0.92 0.90 0.91
## techo5_Caña 0.40 0.38 0.46
## techo6_Triplay 0.81 0.76 0.88
## techo7_Paja 0.06 0.08 0.03
## piso1_Parquet 0.99 0.87 0.98
## piso2_Láminas 0.98 0.92 0.98
## piso3_Losetas 0.99 0.90 0.99
## piso4_Madera 0.59 0.51 0.56
## piso5_Cemento 0.98 0.92 0.98
## piso6_Tierra 0.72 0.73 0.69
## agua1_Red 0.99 0.91 0.99
## agua2_Red_fueraVivienda 1.00 0.88 0.97
## agua3_Pilón 0.88 1.00 0.89
## agua4_Camión 0.97 0.89 1.00
## agua5_Pozo 0.39 0.40 0.34
## agua6_Manantial 0.03 0.01 -0.07
## agua7_Río 0.01 0.10 0.03
## agua9_Vecino 0.93 0.86 0.94
## agua5_Pozo agua6_Manantial agua7_Río agua9_Vecino
## pared1_Ladrillo 0.37 -0.05 0.01 0.94
## pared2_Piedra 0.25 -0.03 0.00 0.55
## pared3_Adobe 0.45 0.27 0.06 0.41
## pared4_Tapia 0.02 0.24 -0.06 -0.03
## pared5_Quincha 0.07 -0.04 0.47 0.45
## pared6_Piedra 0.24 0.39 0.10 0.06
## pared7_Madera 0.45 0.00 0.19 0.86
## pared8_Triplay 0.29 -0.08 0.22 0.88
## techo1_Concreto 0.37 -0.05 -0.02 0.93
## techo2_Madera 0.36 -0.05 0.00 0.90
## techo3_Tejas 0.04 0.19 -0.10 0.13
## techo4_Planchas 0.51 0.06 0.27 0.96
## techo5_Caña 0.15 -0.13 -0.03 0.45
## techo6_Triplay 0.26 -0.10 0.04 0.81
## techo7_Paja 0.51 0.33 0.43 0.06
## piso1_Parquet 0.35 -0.04 -0.03 0.91
## piso2_Láminas 0.35 -0.05 -0.03 0.91
## piso3_Losetas 0.35 -0.06 0.01 0.94
## piso4_Madera 0.43 -0.01 0.35 0.59
## piso5_Cemento 0.40 -0.05 0.02 0.94
## piso6_Tierra 0.59 0.30 0.37 0.79
## agua1_Red 0.38 -0.05 0.02 0.95
## agua2_Red_fueraVivienda 0.39 0.03 0.01 0.93
## agua3_Pilón 0.40 0.01 0.10 0.86
## agua4_Camión 0.34 -0.07 0.03 0.94
## agua5_Pozo 1.00 0.29 0.27 0.42
## agua6_Manantial 0.29 1.00 0.31 -0.06
## agua7_Río 0.27 0.31 1.00 0.16
## agua9_Vecino 0.42 -0.06 0.16 1.00#install.packages("ggcorrplot")
library(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.3.3## Loading required package: ggplot2ggcorrplot(corMatrix)
#install.packages("psych")
library(psych)## Warning: package 'psych' was built under R version 4.3.3##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:polycor':
##
## polyserialpsych::KMO(corMatrix) ## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.43
## MSA for each item =
## pared1_Ladrillo pared2_Piedra pared3_Adobe
## 0.49 0.29 0.16
## pared4_Tapia pared5_Quincha pared6_Piedra
## 0.02 0.10 0.03
## pared7_Madera pared8_Triplay techo1_Concreto
## 0.44 0.43 0.49
## techo2_Madera techo3_Tejas techo4_Planchas
## 0.47 0.05 0.47
## techo5_Caña techo6_Triplay techo7_Paja
## 0.18 0.42 0.06
## piso1_Parquet piso2_Láminas piso3_Losetas
## 0.74 0.74 0.75
## piso4_Madera piso5_Cemento piso6_Tierra
## 0.54 0.75 0.65
## agua1_Red agua2_Red_fueraVivienda agua3_Pilón
## 0.59 0.59 0.54
## agua4_Camión agua5_Pozo agua6_Manantial
## 0.59 0.25 0.07
## agua7_Río agua9_Vecino
## 0.10 0.60cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05## [1] FALSE#install.packages("matrixcalc")
library(matrixcalc)
is.singular.matrix(corMatrix)## [1] TRUEfa.parallel(theData, fa = 'fa',correct = T,plot = F)## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Parallel analysis suggests that the number of factors = 4 and the number of components = NA#install.packages("GPArotation")
library(GPArotation)## Warning: package 'GPArotation' was built under R version 4.3.3##
## Attaching package: 'GPArotation'## The following objects are masked from 'package:psych':
##
## equamax, variminresfa <- fa(theData,
nfactors = 4,
cor = 'mixed',
rotate = "varimax", #oblimin?
fm="minres")## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefullyprint(resfa$loadings)##
## Loadings:
## MR1 MR2 MR4 MR3
## pared1_Ladrillo 0.991
## pared2_Piedra 0.631
## pared3_Adobe 0.274 0.151 0.299 0.639
## pared4_Tapia -0.116 0.379
## pared5_Quincha 0.165 0.735
## pared6_Piedra 0.321 0.209
## pared7_Madera 0.905 0.317 -0.162
## pared8_Triplay 0.827 0.448
## techo1_Concreto 0.991
## techo2_Madera 0.970
## techo3_Tejas 0.591
## techo4_Planchas 0.882 0.256 0.335
## techo5_Caña 0.429 -0.179 0.280
## techo6_Triplay 0.833 0.286
## techo7_Paja 0.767 -0.115
## piso1_Parquet 0.985
## piso2_Láminas 0.990
## piso3_Losetas 0.989
## piso4_Madera 0.584 0.426 -0.125
## piso5_Cemento 0.987 0.112
## piso6_Tierra 0.592 0.346 0.560 0.502
## agua1_Red 0.985 0.110 0.119
## agua2_Red_fueraVivienda 0.970 0.165
## agua3_Pilón 0.891 0.107 0.176 0.119
## agua4_Camión 0.980 0.149
## agua5_Pozo 0.331 0.588 0.207
## agua6_Manantial -0.115 0.499 0.395
## agua7_Río 0.608 0.515 -0.196
## agua9_Vecino 0.908 0.339
##
## MR1 MR2 MR4 MR3
## SS loadings 15.787 2.237 1.929 1.621
## Proportion Var 0.544 0.077 0.067 0.056
## Cumulative Var 0.544 0.621 0.688 0.744PREGUNTA 2
Utilizando el porcentaje de viviendas que tiene electricidad, la razón de votacion de castillo entre keiko, y la tasa fallecidos por cada 1000 contagiados, Ud se propone agrupar a las provincias del Peru (sin la provincia de Lima) siguiendo una estrategia aglomerativa
colnames(dataOK_all)## [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"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# Usando el mismo df_original
data2 <- dataOK_all %>%
select("elec1_Sí", "elec2_No", "elec3_Total", "Castillo", "Keiko", "countPositivos", "countFallecidos", "provincia")electricidad
data2$porcentaje_electricidad <- (data2$elec1_Sí / data2$elec3_Total) * 100la razón de votacion de castillo entre keiko
data2$razonCASKEI <- (data2$Castillo / data2$Keiko)Tasa por 1000
data2$tasafallecidos1000 <- (data2$countFallecidos / data2$countPositivos) * 1000data2 <- data2 %>%
filter(provincia != "LIMA")Clusterizacion
nuevo_data <- data2 %>%
select("provincia", "porcentaje_electricidad", "razonCASKEI", "tasafallecidos1000")dataClus=nuevo_data[(2:4)]
row.names(dataClus)=nuevo_data$provincia## Warning: Setting row names on a tibble is deprecated.library(cluster)
g.dist = daisy(dataClus, metric="gower")PAM
## para PAM
#install.packages("factoextra")
library(factoextra)## Warning: package 'factoextra' was built under R version 4.3.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_nbclust(dataClus, pam,diss=g.dist,method = "gap_stat",k.max = 10,verbose = F)