Prevalencia del VIH en mujeres a nivel mundial
Limpieza de datas
Data Prevalencia VIH (2008-2012)
link1="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ51CAVKCjF_48ylMXr4FJkuVOHpXlUhaGmIA44cdQeWt4cBNjfekSgrPMjZMXrZg/pub?gid=14954305&single=true&output=csv"
DataVIH=read.csv(link1, stringsAsFactors = F)
DataVIH1 = DataVIH[,c(1,53:57)]
names(DataVIH1) = c("Pais","2008","2009","2010","2011","2012")
DataVIH1$`2009` = gsub("\\,", ".", DataVIH1$`2009`)
DataVIH1$`2010` = gsub("\\,", ".", DataVIH1$`2010`)
DataVIH1$`2008` = gsub("\\,", ".", DataVIH1$`2008`)
DataVIH1$`2011` = gsub("\\,", ".", DataVIH1$`2011`)
DataVIH1$`2012` = gsub("\\,", ".", DataVIH1$`2012`)
DataVIH1[,c(2:6)]=lapply(DataVIH1[,c(2:6)],as.numeric) #volver numerico en grupo
DataVIH1 = DataVIH1[complete.cases(DataVIH1),]
row.names(DataVIH1) = NULL
DataVIH1$VIH = rowMeans(DataVIH1[,2:6])
DataVIH1 = DataVIH1[,c (1,7)]Data Población Activa Mujeres
link2="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ-T56gOlA6lVVHHnrqUR6bc_doAmUewuvSlG4CcNgYyeZVUbPiozpcciPpDS3SyQ/pub?gid=1993384276&single=true&output=csv"
DataAct=read.csv(link2,stringsAsFactors = F)
DataAct1 = DataAct[,c(1,48:52)]
names(DataAct1) = c("Pais","2003","2004","2005", "2006", "2007")
DataAct1$`2003` = gsub("\\,", ".", DataAct1$`2003`)
DataAct1$`2004` = gsub("\\,", ".", DataAct1$`2004`)
DataAct1$`2005` = gsub("\\,", ".", DataAct1$`2005`)
DataAct1$`2006` = gsub("\\,", ".", DataAct1$`2006`)
DataAct1$`2007` = gsub("\\,", ".", DataAct1$`2007`)
DataAct1[,c(2:6)]=lapply(DataAct1[,c(2:6)],as.numeric) #volver numerico en grupo
DataAct1 = DataAct1[complete.cases(DataAct1),]
row.names(DataAct1) = NULL
DataAct1$PoblacionActiva = rowMeans(DataAct1[,2:6])
DataAct1= DataAct1[,c (1,7)]Data Participación en la Fuerza Laboral Mujeres
link3="https://docs.google.com/spreadsheets/d/e/2PACX-1vTDcvi_z6RrnoATATdOGBLj2WlKRmVxqvx2hc4lUqkMwCcF3j9BLklmz0VjjIX4vA/pub?gid=1181245938&single=true&output=csv"
DataFLM= read.csv(link3, stringsAsFactors = F)
DataFLM1 = DataFLM[,c(1,48:52)]
names(DataFLM1) = c("Pais","2003","2004","2005", "2006", "2007")
DataFLM1$`2003` = gsub("\\,", ".", DataFLM1$`2003`)
DataFLM1$`2004` = gsub("\\,", ".", DataFLM1$`2004`)
DataFLM1$`2005` = gsub("\\,", ".", DataFLM1$`2005`)
DataFLM1$`2006` = gsub("\\,", ".", DataFLM1$`2006`)
DataFLM1$`2007` = gsub("\\,", ".", DataFLM1$`2007`)
DataFLM1[,c(2:6)]=lapply(DataFLM1[,c(2:6)],as.numeric)
DataFLM1$FLM = rowMeans(DataFLM1[,2:6],na.rm = TRUE)
DataFLM1= DataFLM1[,c (1,7)]
DataFLM1 = DataFLM1[complete.cases(DataFLM1),]
row.names(DataFLM1) = NULLData Prevalencia de Métodos anticoceptivos
link4="https://docs.google.com/spreadsheets/d/e/2PACX-1vRI-hkw-v7fdeFY_c1aS0c_DD86WJ-0k_G9Ti2lE_0_P3nGcPHagckLPhVM9SzD5g/pub?gid=932337199&single=true&output=csv"
DataMetodos= read.csv(link4, stringsAsFactors = F)
DataMetodos1 = DataMetodos[,c(1,48:52)]
names(DataMetodos1) = c("Pais","2003","2004","2005", "2006", "2007")
DataMetodos1$`2003` = gsub("\\,", ".", DataMetodos1$`2003`)
DataMetodos1$`2004` = gsub("\\,", ".", DataMetodos1$`2004`)
DataMetodos1$`2005` = gsub("\\,", ".", DataMetodos1$`2005`)
DataMetodos1$`2006` = gsub("\\,", ".", DataMetodos1$`2006`)
DataMetodos1$`2007` = gsub("\\,", ".", DataMetodos1$`2007`)
DataMetodos1[,c(2:6)]=lapply(DataMetodos1[,c(2:6)],as.numeric)
DataMetodos1$Metodos = rowMeans(DataMetodos1[,2:6],na.rm = TRUE)
DataMetodos1= DataMetodos1[,c (1,7)]
DataMetodos1 = DataMetodos1[complete.cases(DataMetodos1),]
row.names(DataMetodos1) = NULLData de población en barrios de tugurios
link5="https://docs.google.com/spreadsheets/d/e/2PACX-1vQnghOacrnZH200jpcJc-Vym7n8rrfuQlupJ470spsBTvZ0WUWvl63x2AYL1W92sw/pub?gid=1538383881&single=true&output=csv"
DataTugurios= read.csv(link5, stringsAsFactors = F)
DataTugurios1 = DataTugurios[,c(1,48:52)]
names(DataTugurios1) = c("Pais","2003","2004","2005", "2006", "2007")
DataTugurios1$`2003` = gsub("\\,", ".", DataTugurios1$`2003`)
DataTugurios1$`2004` = gsub("\\,", ".", DataTugurios1$`2004`)
DataTugurios1$`2005` = gsub("\\,", ".", DataTugurios1$`2005`)
DataTugurios1$`2006` = gsub("\\,", ".", DataTugurios1$`2006`)
DataTugurios1$`2007` = gsub("\\,", ".", DataTugurios1$`2007`)
DataTugurios1[,c(2:6)]=lapply(DataTugurios1[,c(2:6)],as.numeric)
DataTugurios1$BarriosTugurios = rowMeans(DataTugurios1[,2:6],na.rm = TRUE)
DataTugurios1= DataTugurios1[,c (1,7)]
DataTugurios1 = DataTugurios1[complete.cases(DataTugurios1),]
row.names(DataTugurios1) = NULLData de Gini
link6="https://docs.google.com/spreadsheets/d/e/2PACX-1vSOdz-vUkw9_yctGztLL_PS87cCS7GoU10PiLA3ywnO8-iNXG1OBi_8OBOpZ0r3AQ/pub?gid=826191890&single=true&output=csv"
DataGini= read.csv(link6, stringsAsFactors = F)
DataGini1 = DataGini[,c(1,48:52)]
names(DataGini1) = c("Pais","2003","2004","2005", "2006", "2007")
DataGini1$`2003` = gsub("\\,", ".", DataGini1$`2003`)
DataGini1$`2004` = gsub("\\,", ".", DataGini1$`2004`)
DataGini1$`2005` = gsub("\\,", ".", DataGini1$`2005`)
DataGini1$`2006` = gsub("\\,", ".", DataGini1$`2006`)
DataGini1$`2007` = gsub("\\,", ".", DataGini1$`2007`)
DataGini1[,c(2:6)]=lapply(DataGini1[,c(2:6)],as.numeric)
DataGini1$Gini = rowMeans(DataGini1[,2:6],na.rm = TRUE)
DataGini1= DataGini1[,c (1,7)]
DataGini1= DataGini1[complete.cases(DataGini1),]
row.names(DataGini1) = NULLDATA ALFABETIZACIÓN
link7="https://docs.google.com/spreadsheets/d/e/2PACX-1vTTojodRJwkAu-98fKnnuzUCJZE-Wj4tDFm7F2XQLDeT3CSifu-yWiHZuZv-uzL2Q/pub?gid=1142139153&single=true&output=csv"
EDU=read.csv(link7,stringsAsFactors = F)
EDU1=EDU[,c(1,48:52)]
names(EDU1)=c("Pais","2003","2004","2005","2006","2007")
EDU1$`2003`= gsub("\\,", ".",EDU1$`2003`)
EDU1$`2004`= gsub("\\,", ".",EDU1$`2004`)
EDU1$`2005`= gsub("\\,", ".",EDU1$`2005`)
EDU1$`2006`= gsub("\\,", ".",EDU1$`2006`)
EDU1$`2007`= gsub("\\,", ".",EDU1$`2007`)
EDU1[c(2:6)] = lapply(EDU1[c(2:6)], as.numeric)
EDU1$EDU = rowMeans(EDU1[,2:6],na.rm = TRUE)
EDU1= EDU1[,c (1,7)]
EDU1= EDU1[complete.cases(EDU1),]
row.names(EDU1) = NULLDATA DE ACCESO A LA ELECTRICIDAD
link8="https://docs.google.com/spreadsheets/d/e/2PACX-1vQrlQtEYBGaf0IhPz_H9oaM8uD1UaVoR1J_xP6bYe8ZqNAVSRJiLh4DXq52KomGsQ/pub?gid=2059415238&single=true&output=csv"
ENER=read.csv(link8,stringsAsFactors = F)
ENER1=ENER[,c(1,48:52)]
names(ENER1)=c("Pais","2003","2004","2005","2006","2007")
ENER1$`2003`= gsub("\\,", ".",ENER1$`2003`)
ENER1$`2004`= gsub("\\,", ".",ENER1$`2004`)
ENER1$`2005`= gsub("\\,", ".",ENER1$`2005`)
ENER1$`2006`= gsub("\\,", ".",ENER1$`2006`)
ENER1$`2007`= gsub("\\,", ".",ENER1$`2007`)
ENER1[c(2:6)] = lapply(ENER1[c(2:6)], as.numeric)
ENER1$ENER = rowMeans(ENER1[,2:6],na.rm = TRUE)
ENER1= ENER1[,c (1,7)]
ENER1= ENER1[complete.cases(ENER1),]
row.names(ENER1) = NULLDATA DE GASTO
link9="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ6hJgrir9ZBg6Qo5dfwQGluWp4oZLEfSVgz-sVzHEqav1pulMwix_2jNcjxcCkjw/pub?gid=1845220649&single=true&output=csv"
GAST=read.csv(link9,stringsAsFactors = F)
GAST1=GAST[,c(1,48:52)]
names(GAST1)=c("Pais","2003","2004","2005","2006","2007")
GAST1$`2003`= gsub("\\,", ".",GAST1$`2003`)
GAST1$`2004`= gsub("\\,", ".",GAST1$`2004`)
GAST1$`2005`= gsub("\\,", ".",GAST1$`2005`)
GAST1$`2006`= gsub("\\,", ".",GAST1$`2006`)
GAST1$`2007`= gsub("\\,", ".",GAST1$`2007`)
GAST1[c(2:6)] = lapply(GAST1[c(2:6)], as.numeric)
GAST1$GAST = rowMeans(GAST1[,2:6],na.rm = TRUE)
GAST1= GAST1[,c (1,7)]
GAST1= GAST1[complete.cases(GAST1),]
row.names(GAST1) = NULLBANDA ANCHA
link10="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJo2T8oxMssc3utiol3H4IZDOw4jq1sNu12vGh4LUz2aZU-BTtDvkhDaESiIwCJQ/pub?gid=508597872&single=true&output=csv"
ban=read.csv(link10,stringsAsFactors = F)
ban1=ban[,c(1,48:52)]
names(ban1)=c("Pais","2003","2004","2005","2006","2007")
ban1$`2003`= gsub("\\,", ".",ban1$`2003`)
ban1$`2004`= gsub("\\,", ".",ban1$`2004`)
ban1$`2005`= gsub("\\,", ".",ban1$`2005`)
ban1$`2006`= gsub("\\,", ".",ban1$`2006`)
ban1$`2007`= gsub("\\,", ".",ban1$`2007`)
ban1[c(2:6)] = lapply(ban1[c(2:6)], as.numeric)
ban1$ban = rowMeans(ban1[,2:6],na.rm = TRUE)
ban1= ban1[,c (1,7)]
ban1= ban1[complete.cases(ban1),]
row.names(ban1) = NULLCobertura de tratamiento antirretroviral
link11="https://docs.google.com/spreadsheets/d/e/2PACX-1vQ7mf0BRsmhcre-RKQseWRY_aGZrVve25Wmmm85m4OMF2Eb8_NyqGhDjrRmePcuWg/pub?gid=26033417&single=true&output=csv"
antiRetrov=read.csv(link11, stringsAsFactors = F)
antiRetrov1=antiRetrov[,c(1,48:52)]
names(antiRetrov1) = c("Pais","2003","2004","2005", "2006", "2007")
antiRetrov1[,c(2:6)]=lapply(antiRetrov1[,c(2:6)],as.numeric) #volver numerico en grupo## Warning in lapply(antiRetrov1[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov1[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov1[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov1[, c(2:6)], as.numeric): NAs introduced by
## coercion
## Warning in lapply(antiRetrov1[, c(2:6)], as.numeric): NAs introduced by
## coercionantiRetrov1$CobARet = rowMeans(antiRetrov1[,2:6], na.rm = TRUE)
antiRetrov1= antiRetrov1[,c (1,7)]
antiRetrov1 = antiRetrov1[complete.cases(antiRetrov1),]
row.names(antiRetrov1) = NULLEsperanza de vida en mujeres
link12="https://docs.google.com/spreadsheets/d/e/2PACX-1vQoWu6HPrX8qbiqsewwImM89BxWol-bI-b_ubT6v_hxbPG9JTxXFxaxX5nUJTm-bg/pub?gid=72965045&single=true&output=csv"
EspVida=read.csv(link12, stringsAsFactors = F)
EspVida1=EspVida[,c(1,48:52)]
names(EspVida1) = c("Pais","2003","2004","2005", "2006", "2007")
EspVida1$`2003` = gsub("\\,", ".", EspVida1$`2003`)
EspVida1$`2004` = gsub("\\,", ".", EspVida1$`2004`)
EspVida1$`2005` = gsub("\\,", ".", EspVida1$`2005`)
EspVida1$`2006` = gsub("\\,", ".", EspVida1$`2006`)
EspVida1$`2007` = gsub("\\,", ".", EspVida1$`2007`)
EspVida1[,c(2:6)]=lapply(EspVida1[,c(2:6)],as.numeric) #volver numerico en grupo
EspVida1$VidaM = rowMeans(EspVida1[,2:6], na.rm = TRUE)
EspVida1= EspVida1[,c (1,7)]
EspVida1 = EspVida1[complete.cases(EspVida1),]
row.names(EspVida1) = NULLMigracion neta
link13="https://docs.google.com/spreadsheets/d/e/2PACX-1vSri2T-73zRzhVczOzNqkAKmQ_qLcWrQHzuVl7QIFUE7fJRUbtKoDCeJ1zixpCjRA/pub?gid=1968467072&single=true&output=csv"
neta=read.csv(link13, stringsAsFactors = F)
migra1=neta[,c(1,52)]
names(migra1) = c("Pais","Migracion")
migra1 = migra1[complete.cases(migra1),]
row.names(migra1) = NULL
migra1[,c(2)]=as.numeric(migra1[,c(2)]) #volver numerico ODA
link14="https://docs.google.com/spreadsheets/d/e/2PACX-1vQMbPO51JV-DhQLOOX9GnTSN7Z4hlKuFjX6Ft9QRS07q1i28GeqAsAAhdfXkNq-uA/pub?gid=1457611805&single=true&output=csv"
ODA=read.csv(link14, stringsAsFactors = F)
ODA1=ODA[,c(1,48:52)]
names(ODA1) = c("Pais","2003","2004","2005", "2006", "2007")
ODA1$`2003` = gsub("\\,", ".", ODA1$`2003`)
ODA1$`2004` = gsub("\\,", ".", ODA1$`2004`)
ODA1$`2005` = gsub("\\,", ".", ODA1$`2005`)
ODA1$`2006` = gsub("\\,", ".", ODA1$`2006`)
ODA1$`2007` = gsub("\\,", ".", ODA1$`2007`)
ODA1[,c(2:6)]=lapply(ODA1[,c(2:6)],as.numeric) #volver numerico en grupo
ODA1$ODA = rowMeans(ODA1[,2:6], na.rm = TRUE)
ODA1= ODA1[,c (1,7)]
ODA1 = ODA1[complete.cases(ODA1),]
row.names(ODA1) = NULLPAIS AFRICANO
link15="https://docs.google.com/spreadsheets/d/e/2PACX-1vT-0H84cFoinLuU8HZaFS1Yln8HhKYcVtaKCbcKn3iShculk-vuU3QJ888lCbUd_Q/pub?gid=1165629081&single=true&output=csv"
AFRICA=read.csv(link15, stringsAsFactors = F)
names(AFRICA)=c("Pais", "Africa")Población total de mujeres
link16="https://docs.google.com/spreadsheets/d/e/2PACX-1vTnOQNQQsmZQ1Ru-bqF5mBAlnpbxQOOH_IUuMv7XylSyOIvQ_Ij1OB64-a-pXaSgg/pub?gid=871901844&single=true&output=csv"
women=read.csv(link16, stringsAsFactors = F)
women1=women[,c(1,48:52)]
names(women1) = c("Pais","2003","2004","2005", "2006", "2007")
women1$`2003` = gsub("\\,", ".", women1$`2003`)
women1$`2004` = gsub("\\,", ".", women1$`2004`)
women1$`2005` = gsub("\\,", ".", women1$`2005`)
women1$`2006` = gsub("\\,", ".", women1$`2006`)
women1$`2007` = gsub("\\,", ".", women1$`2007`)
women1[,c(2:6)]=lapply(women1[,c(2:6)],as.numeric) #volver numerico en grupo
women1$Women = rowMeans(women1[,2:6], na.rm = TRUE)
women1= women1[,c (1,7)]
women1 = women1[complete.cases(women1),]
row.names(women1) = NULLMerge
Act1FLM1=merge(DataAct1,DataFLM1,all.x=T,all.y=T)
desigualdad1=merge(DataTugurios1,DataGini1,all.x=T,all.y=T)
est1=merge(EDU1,ENER1,all.x=T,all.y=T)
estado1=merge(est1,GAST1,all.x=T,all.y=T)
densidad1=merge(estado1,ban1,all.x=T,all.y=T)
Calidad1=merge(desigualdad1,densidad1,all.x=T,all.y=T)
salud1=merge(EspVida1,antiRetrov1, all.x=T,all.y=T)Análisis Factorial
Análisis factorial empoderamiento
Act1FLM1_x=Act1FLM1
row.names(Act1FLM1) = Act1FLM1$Pais
Act1FLM1$Pais = NULL
head(Act1FLM1)## PoblacionActiva FLM
## Afghanistan 43.55220 NA
## Albania 48.17600 29.15748
## Algeria 12.87380 NA
## Angola 75.08880 62.67960
## Arab World 19.62438 20.61435
## Argentina 49.57260 49.55246Act1FLM1[is.na(Act1FLM1$PoblacionActiva), "PoblacionActiva"]=mean(Act1FLM1$PoblacionActiva, na.rm=T)
Act1FLM1[is.na(Act1FLM1$FLM), "FLM"]=mean(Act1FLM1$FLM, na.rm=T)Act1FLM1=as.data.frame(scale(Act1FLM1[,c(1,2)]))
head(Act1FLM1)## PoblacionActiva FLM
## Afghanistan -0.43930039 0.00000000
## Albania -0.13370714 -1.37188100
## Algeria -2.46687804 0.00000000
## Angola 1.64499686 0.96623135
## Arab World -2.02072269 -1.96774987
## Argentina -0.04140391 0.05063512library(psych)pearson1 = cor(Act1FLM1) #sacar la correlación de los puntajes estandarizadas
pearson1## PoblacionActiva FLM
## PoblacionActiva 1.0000000 0.7868555
## FLM 0.7868555 1.0000000cor.plot(pearson1,
numbers=T,
upper=FALSE,
main = "Correlation",
show.legend = FALSE) #verlo en un gráfico
KMO(Act1FLM1) #nos indica que mientras más cercano a uno hay una división subyacente, a partir de 0.7. Que tan buena idea es juntarlos en un indice. ver el Overall MSA(más cercano a 1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = Act1FLM1)
## Overall MSA = 0.5
## MSA for each item =
## PoblacionActiva FLM
## 0.5 0.5#Prueba de esfericidad de bartlett
library(psych)
cortest.bartlett(Act1FLM1, n=nrow(Act1FLM1)) ## R was not square, finding R from data## $chisq
## [1] 231.196
##
## $p.value
## [1] 3.2698e-52
##
## $df
## [1] 1fa.parallel(pearson1, fm="pa", fa="fa", main = "Scree Plot",n.obs = nrow(Act1FLM1)) #cuantos indices deberia formar
## Parallel analysis suggests that the number of factors = 1 and the number of components = NAAct1FLM1 = fa(Act1FLM1,
nfactors=1,
rotate="varimax") #codigo para el analisis factorial solo cambiar la data y el numero de factoresAct1FLM1$loadings##
## Loadings:
## MR1
## PoblacionActiva 0.887
## FLM 0.887
##
## MR1
## SS loadings 1.574
## Proportion Var 0.787fa.diagram(Act1FLM1)
#Para ver el tipo de análisis factorial:
# mientras mas grande mejor (lo que aporta)
sort(Act1FLM1$communalities)## PoblacionActiva FLM
## 0.7868555 0.7868555# mientras mas grande peor (lo que mantiene)
sort(Act1FLM1$uniquenesses)## PoblacionActiva FLM
## 0.2131445 0.2131445sort(Act1FLM1$complexity)## PoblacionActiva FLM
## 1 1Act1FLM1$scores## MR1
## Afghanistan -0.218081911
## Albania -0.747419191
## Algeria -1.224632385
## Angola 1.296292143
## Arab World -1.979997621
## Argentina 0.004582650
## Armenia -0.310238745
## Aruba 0.367840847
## Australia 0.498514225
## Austria 0.105783795
## Azerbaijan 0.302854548
## Bahamas, The 0.623402726
## Bahrain -0.853641697
## Bangladesh -1.472732756
## Barbados 0.960750286
## Belarus 0.329622731
## Belgium -0.306988194
## Belize -0.867062954
## Benin 1.580277249
## Bhutan 0.567385718
## Bolivia 0.743099218
## Bosnia and Herzegovina -1.034565215
## Botswana 0.336834077
## Brazil 0.394759215
## Brunei Darussalam 0.195202744
## Bulgaria -0.243377536
## Burkina Faso 1.389544683
## Burundi 1.045368902
## Cabo Verde 0.136991681
## Cambodia 1.808782549
## Cameroon 1.760772259
## Canada 0.819332764
## Caribbean small states 0.072862091
## Cayman Islands 1.053765461
## Central African Republic 0.509861315
## Central Europe and the Baltics -0.125440772
## Chad 0.448841779
## Channel Islands 0.001251200
## Chile -0.593441335
## China 0.546450719
## Colombia 0.227261289
## Comoros -1.114769780
## Congo, Dem. Rep. 1.415986742
## Congo, Rep. 1.166573364
## Costa Rica -0.397352481
## Cote d'Ivoire -0.061868183
## Croatia -0.336865361
## Cuba -0.959734638
## Cyprus 0.310985908
## Czech Republic 0.069977696
## Denmark 0.739197542
## Djibouti -0.037241149
## Dominican Republic -0.655151160
## Early-demographic dividend -0.865927088
## East Asia & Pacific 0.386330446
## East Asia & Pacific (excluding high income) 0.442014163
## East Asia & Pacific (IDA & IBRD countries) 0.436908551
## Ecuador 0.176081095
## Egypt, Arab Rep. -2.005033677
## El Salvador -0.264923711
## Equatorial Guinea 0.060289066
## Eritrea 0.744175438
## Estonia 0.277800750
## Eswatini -0.498152426
## Ethiopia 1.679320454
## Euro area -0.106349196
## Europe & Central Asia -0.006807899
## Europe & Central Asia (excluding high income) -0.001337942
## Europe & Central Asia (IDA & IBRD countries) -0.019361503
## European Union -0.039837413
## Fiji -0.946866807
## Finland 0.515624322
## Fragile and conflict affected situations -0.034553476
## France 0.052207623
## French Polynesia -0.151777904
## Gabon -0.271022288
## Gambia, The -0.054741891
## Georgia 0.411265626
## Germany 0.092597943
## Ghana 1.324422734
## Greece -0.488210709
## Guam 0.340044186
## Guatemala -0.378624796
## Guinea 0.416189510
## Guinea-Bissau 0.491520630
## Guyana -0.513258095
## Haiti 0.195356957
## Heavily indebted poor countries (HIPC) 0.485561463
## High income 0.149685853
## Honduras -0.589130873
## Hong Kong SAR, China 0.171289897
## Hungary -0.456806438
## IBRD only -0.023631135
## Iceland 1.693784620
## IDA & IBRD total -0.018782865
## IDA blend -0.806941564
## IDA only 0.161754091
## IDA total 0.001778396
## India -1.185765411
## Indonesia -0.162841980
## Iran, Islamic Rep. -2.112360416
## Iraq -2.536029376
## Ireland 0.238920337
## Isle of Man 0.256001551
## Israel 0.377123027
## Italy -0.782635384
## Jamaica 0.421407379
## Japan -0.072704909
## Jordan -2.490151045
## Kazakhstan 1.027408228
## Kenya 0.735505219
## Kiribati -0.985310951
## Korea, Dem. People’s Rep. 0.830878402
## Korea, Rep. 0.034976626
## Kosovo -0.698268280
## Kuwait 0.046047582
## Kyrgyz Republic 0.300847727
## Lao PDR 1.999875482
## Late-demographic dividend 0.388485135
## Latin America & Caribbean 0.041142445
## Latin America & Caribbean (excluding high income) 0.075280389
## Latin America & the Caribbean (IDA & IBRD countries) 0.065569377
## Latvia 0.128623266
## Least developed countries: UN classification 0.216709160
## Lebanon -1.946961775
## Lesotho -0.137693307
## Liberia 0.765510910
## Libya -0.941884570
## Liechtenstein 0.141807689
## Lithuania 0.134496532
## Low & middle income -0.014293141
## Low income 0.508704767
## Lower middle income -0.818704121
## Luxembourg -0.227072385
## Macao SAR, China 0.539375369
## Madagascar 2.273801315
## Malawi 1.975846557
## Malaysia -0.280719425
## Maldives -0.319897128
## Mali 0.730988288
## Malta -1.298600132
## Mauritania -0.703685818
## Mauritius -0.575270084
## Mexico -0.573230839
## Middle East & North Africa -1.975443501
## Middle East & North Africa (excluding high income) -2.028850299
## Middle East & North Africa (IDA & IBRD countries) -2.025291777
## Middle income -0.060565269
## Moldova -0.238247760
## Mongolia 0.432687495
## Montenegro -0.414569055
## Morocco -1.568033163
## Mozambique 2.542142687
## Myanmar 0.199796113
## Namibia -0.249277877
## Nepal 2.538850436
## Netherlands 0.521080833
## New Caledonia 0.334361949
## New Zealand 0.725181542
## Nicaragua -0.361433382
## Niger 0.251317114
## Nigeria -0.091427247
## North America 0.648117672
## North Macedonia -0.468344263
## Northern Mariana Islands 1.125439692
## Norway 1.054652072
## OECD members 0.039050741
## Oman -1.671541746
## Other small states -0.024994858
## Pacific island small states -0.169651496
## Pakistan -2.086227681
## Palau 0.321096783
## Panama -0.182764593
## Papua New Guinea 0.305338522
## Paraguay 0.319355648
## Peru 0.900286201
## Philippines -0.147202953
## Poland -0.151082728
## Portugal 0.382453534
## Post-demographic dividend 0.202798162
## Pre-demographic dividend 0.260775226
## Puerto Rico -0.831745121
## Qatar -0.272388758
## Romania -0.120953027
## Russian Federation 0.597349047
## Rwanda 2.350623422
## Samoa -1.374880549
## San Marino 0.172208561
## Sao Tome and Principe -1.019860577
## Saudi Arabia -2.129877574
## Senegal -1.054152738
## Serbia -0.308830749
## Sierra Leone 1.023502680
## Singapore 0.186636237
## Slovak Republic 0.138050914
## Slovenia 0.203810232
## Small states -0.010767330
## Solomon Islands 0.444523960
## Somalia -1.074265326
## South Africa -0.416041508
## South Asia -1.199783982
## South Asia (IDA & IBRD) -1.199783982
## South Sudan 0.664178656
## Spain -0.221935582
## Sri Lanka -0.897814432
## St. Lucia 0.649728560
## St. Vincent and the Grenadines 0.131420593
## Sub-Saharan Africa 0.379954847
## Sub-Saharan Africa (excluding high income) 0.379954847
## Sub-Saharan Africa (IDA & IBRD countries) 0.379954847
## Sudan -0.843744066
## Suriname -0.436831030
## Sweden 0.641036800
## Switzerland 0.680393591
## Syrian Arab Republic -2.245588328
## Tajikistan -0.674952343
## Tanzania 2.512556173
## Thailand 1.073992684
## Timor-Leste -0.395653700
## Togo 2.104439749
## Tonga -0.658657852
## Trinidad and Tobago 0.152339251
## Tunisia -1.785198120
## Turkey -1.729348682
## Turkmenistan 0.063497873
## Uganda 0.677071249
## Ukraine 0.250958153
## United Arab Emirates -0.805208024
## United Kingdom 0.368314436
## United States 0.628777497
## Upper middle income 0.246886943
## Uruguay 0.147303509
## Uzbekistan -0.351101194
## Vanuatu 0.365019405
## Venezuela, RB -0.001698803
## Vietnam 1.399045475
## Virgin Islands (U.S.) 0.282411076
## West Bank and Gaza -2.388327075
## World -0.003717115
## Yemen, Rep. -2.505323817
## Zambia 0.760219351
## Zimbabwe 1.866854539scores1=as.data.frame(Act1FLM1$scores)
names(scores1) = c("Empoderamiento")
head(scores1)## Empoderamiento
## Afghanistan -0.21808191
## Albania -0.74741919
## Algeria -1.22463238
## Angola 1.29629214
## Arab World -1.97999762
## Argentina 0.00458265scores1$Pais=row.names(scores1)
row.names(scores1) = NULLAnálisis factorial calidad de vida
#Ponemos las variables en forma intuitiva restando con el mayor valor
Calidad1$BarriosTugurios= 100 - Calidad1$BarriosTugurios
Calidad1$Gini= 65 - Calidad1$GiniCalidad1_X=Calidad1
row.names(Calidad1) = Calidad1$Pais
Calidad1$Pais = NULL
head(Calidad1)## BarriosTugurios Gini EDU ENER GAST
## Afghanistan NA NA NA 22.79312 NA
## Albania NA 34.4 NA 100.00000 0.0873700
## Algeria NA NA 72.64868 98.80855 0.1419467
## American Samoa NA NA NA NA 0.3170075
## Andorra NA NA NA 100.00000 NA
## Angola 18.65 NA NA 29.52772 NA
## ban
## Afghanistan 0.00137906
## Albania 0.16976576
## Algeria 0.38217637
## American Samoa NA
## Andorra 13.34488004
## Angola 0.04626376Calidad1[is.na(Calidad1$BarriosTugurios), "BarriosTugurios"]=mean(Calidad1$BarriosTugurios, na.rm=T)
Calidad1[is.na(Calidad1$Gini), "Gini"]=mean(Calidad1$Gini, na.rm=T)
Calidad1[is.na(Calidad1$EDU), "EDU"]=mean(Calidad1$EDU, na.rm=T)
Calidad1[is.na(Calidad1$ENER), "ENER"]=mean(Calidad1$ENER, na.rm=T)
Calidad1[is.na(Calidad1$GAST), "GAST"]=mean(Calidad1$GAST, na.rm=T)
Calidad1[is.na(Calidad1$ban), "ban"]=mean(Calidad1$ban, na.rm=T)Calidad1=as.data.frame(scale(Calidad1[,c(1:6)]))
head(Calidad1)## BarriosTugurios Gini EDU ENER GAST
## Afghanistan 0.000000 0.000000 0.0000000 -1.7233986 0.0000000
## Albania 0.000000 1.394347 0.0000000 0.7551778 -1.1905817
## Algeria 0.000000 0.000000 -0.2212941 0.7169287 -1.1038070
## American Samoa 0.000000 0.000000 0.0000000 0.0000000 -0.8254673
## Andorra 0.000000 0.000000 0.0000000 0.7551778 0.0000000
## Angola -2.469433 0.000000 0.0000000 -1.5071972 0.0000000
## ban
## Afghanistan -0.6777603
## Albania -0.6512634
## Algeria -0.6178389
## American Samoa 0.0000000
## Andorra 1.4219446
## Angola -0.6706974library(psych)promedio1 = cor(Calidad1) #sacar la correlación de los puntajes estandarizadas
promedio1## BarriosTugurios Gini EDU ENER
## BarriosTugurios 1.000000000 -0.13270600 0.49417768 0.5779805
## Gini -0.132706004 1.00000000 -0.09252552 0.2145609
## EDU 0.494177679 -0.09252552 1.00000000 0.5721154
## ENER 0.577980511 0.21456087 0.57211541 1.0000000
## GAST -0.007642321 0.27747953 0.04709207 0.1967345
## ban 0.011836126 0.31819474 0.10412853 0.3545951
## GAST ban
## BarriosTugurios -0.007642321 0.01183613
## Gini 0.277479531 0.31819474
## EDU 0.047092075 0.10412853
## ENER 0.196734526 0.35459509
## GAST 1.000000000 0.62000846
## ban 0.620008458 1.00000000cor.plot(promedio1,
numbers=T,
upper=FALSE,
main = "Correlation",
show.legend = FALSE) #verlo en un gráfico
KMO(Calidad1) #nos indica que mientras más cercano a uno hay una división subyacente, a partir de 0.7. Que tan buena idea es juntarlos en un indice. ver el Overall MSA(más cercano a 1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = Calidad1)
## Overall MSA = 0.6
## MSA for each item =
## BarriosTugurios Gini EDU ENER
## 0.62 0.54 0.70 0.58
## GAST ban
## 0.59 0.58fa.parallel(promedio1, fm="pa", fa="fa", main = "Scree Plot",n.obs = nrow(Calidad1)) #cuantos indices deberia formar## 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 extraction method.
## Parallel analysis suggests that the number of factors = 3 and the number of components = NACalidad1 = fa(Calidad1,
nfactors=3,
rotate="varimax") #codigo para el analisis factorial solo cambiar la data y el numero de factores## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefullyCalidad1$loadings##
## Loadings:
## MR1 MR2 MR3
## BarriosTugurios 0.717 -0.143
## Gini 0.241 0.551
## EDU 0.677
## ENER 0.888 0.157 0.432
## GAST 0.863 0.118
## ban 0.121 0.673 0.313
##
## MR1 MR2 MR3
## SS loadings 1.782 1.285 0.632
## Proportion Var 0.297 0.214 0.105
## Cumulative Var 0.297 0.511 0.616fa.diagram(Calidad1)
#Para ver el tipo de análisis factorial:
# mientras mas grande mejor (lo que aporta)
sort(Calidad1$communalities)## Gini EDU BarriosTugurios ban
## 0.3668824 0.4712142 0.5351012 0.5657713
## GAST ENER
## 0.7585527 0.9950000# mientras mas grande peor (lo que mantiene)
sort(Calidad1$uniquenesses)## ENER GAST ban BarriosTugurios
## -0.0006972736 0.2413269718 0.4340153963 0.4648857180
## EDU Gini
## 0.5288417029 0.6330720986sort(Calidad1$complexity)## GAST EDU BarriosTugurios Gini
## 1.038257 1.055293 1.080547 1.408544
## ban ENER
## 1.487721 1.520918Calidad1$scores## MR1
## Afghanistan -1.28872296
## Albania 0.47328987
## Algeria 0.63538286
## American Samoa 0.02928735
## Andorra 0.45131879
## Angola -1.54618516
## Antigua and Barbuda 0.87478567
## Arab World 0.37962060
## Argentina 1.26624404
## Armenia 0.52788649
## Aruba 0.31144886
## Australia 0.29572552
## Austria 0.10617704
## Azerbaijan 0.58259044
## Bahamas, The 0.52927301
## Bahrain 0.61196199
## Bangladesh -1.47593965
## Barbados 0.70408381
## Belarus 0.36794990
## Belgium 0.03415665
## Belize 0.69510370
## Benin -1.98448749
## Bermuda 0.12295387
## Bhutan -0.70295308
## Bolivia 0.58528978
## Bosnia and Herzegovina 0.53055154
## Botswana -0.84727758
## Brazil 1.35087042
## Brunei Darussalam 0.67504403
## Bulgaria 0.53877126
## Burkina Faso -2.10020389
## Burundi -2.17378997
## Cabo Verde 0.10482152
## Cambodia -1.66772678
## Cameroon -0.64171528
## Canada 0.12337850
## Caribbean small states 0.55891719
## Cayman Islands 0.83042617
## Central African Republic -2.18979013
## Central Europe and the Baltics 0.58641461
## Chad -2.81787378
## Channel Islands 0.59464125
## Chile 1.29221255
## China 0.73851168
## Colombia 1.47579858
## Comoros -0.38736668
## Congo, Dem. Rep. -2.03120474
## Congo, Rep. -0.99528190
## Costa Rica 1.30392674
## Cote d'Ivoire -0.63845748
## Croatia 0.60387032
## Cuba 0.64590704
## Curacao 0.59464125
## Cyprus 0.39557968
## Czech Republic 0.19795345
## Denmark -0.18758402
## Djibouti -0.47181901
## Dominica 0.26900652
## Dominican Republic 1.21052802
## Early-demographic dividend 0.07268648
## East Asia & Pacific 0.66249277
## East Asia & Pacific (excluding high income) 0.73458314
## East Asia & Pacific (IDA & IBRD countries) 0.75694924
## Ecuador 1.41726441
## Egypt, Arab Rep. 0.73174179
## El Salvador 0.88835512
## Equatorial Guinea -0.50421759
## Eritrea -1.06508883
## Estonia 0.30025367
## Eswatini -1.05000765
## Ethiopia -2.49421098
## Euro area 0.63086605
## Europe & Central Asia 0.70916626
## Europe & Central Asia (excluding high income) 0.84447754
## Europe & Central Asia (IDA & IBRD countries) 0.84506294
## European Union 0.65020917
## Faroe Islands 0.41899491
## Fiji 0.22077322
## Finland -0.11250373
## Fragile and conflict affected situations -1.23880669
## France 0.12113052
## French Polynesia 0.59118472
## Gabon 0.34357663
## Gambia, The -0.73692487
## Georgia 0.61040154
## Germany 0.13578600
## Ghana -0.35724373
## Gibraltar 0.59464125
## Greece 0.67609049
## Greenland 0.50864662
## Grenada 0.96658799
## Guam 0.66853342
## Guatemala 0.64850773
## Guinea -1.87316603
## Guinea-Bissau -2.14797483
## Guyana 0.19093545
## Haiti -1.60348315
## Heavily indebted poor countries (HIPC) -1.74380376
## High income 0.34907271
## Honduras 0.57011946
## Hong Kong SAR, China 0.29062705
## Hungary 0.29210942
## IBRD only 0.57996366
## Iceland -0.11265529
## IDA & IBRD total 0.21730463
## IDA blend -0.44649028
## IDA only -1.48465665
## IDA total -1.13635754
## India -0.30704108
## Indonesia 0.62143023
## Iran, Islamic Rep. 1.01081734
## Iraq 0.26255170
## Ireland 0.34338351
## Isle of Man 0.59464125
## Israel 0.27011902
## Italy 0.33678435
## Jamaica 0.33957689
## Japan 0.25298583
## Jordan 0.96984775
## Kazakhstan 0.51310632
## Kenya -1.25419578
## Kiribati -0.31494032
## Korea, Dem. People’s Rep. -1.42845827
## Korea, Rep. -0.05648612
## Kosovo 0.25791023
## Kuwait 0.86760922
## Kyrgyz Republic 0.53437903
## Lao PDR -0.98977441
## Late-demographic dividend 0.86466749
## Latin America & Caribbean 0.86535543
## Latin America & Caribbean (excluding high income) 0.84915828
## Latin America & the Caribbean (IDA & IBRD countries) 0.85893282
## Latvia 0.54913602
## Least developed countries: UN classification -1.69519780
## Lebanon 0.69299911
## Lesotho -1.52439532
## Liberia -2.31629524
## Libya 0.67416460
## Liechtenstein 0.30930217
## Lithuania 0.43919362
## Low & middle income 0.19720627
## Low income -1.75727091
## Lower middle income -0.11636657
## Luxembourg 0.15262039
## Macao SAR, China 0.66434442
## Madagascar -1.79495531
## Malawi -1.94670000
## Malaysia 0.79011432
## Maldives 0.65082253
## Mali -2.15849881
## Malta 0.37533013
## Marshall Islands -0.05030317
## Mauritania -1.50561264
## Mauritius 0.55225015
## Mexico 1.38852627
## Micronesia, Fed. Sts. -0.37486694
## Middle East & North Africa 0.69788251
## Middle East & North Africa (excluding high income) 0.65634623
## Middle East & North Africa (IDA & IBRD countries) 0.65288652
## Middle income 0.39082017
## Moldova 0.55469328
## Monaco 0.27095935
## Mongolia -0.09745198
## Montenegro 0.60195399
## Morocco 0.41115604
## Mozambique -2.24022606
## Myanmar -0.66759847
## Namibia -0.03481467
## Nauru 0.58503834
## Nepal -0.75003491
## Netherlands -0.03316193
## New Caledonia 0.59263892
## New Zealand 0.50010167
## Nicaragua 0.31084864
## Niger -2.48974118
## Nigeria -1.01291027
## North America 0.29694702
## North Macedonia 0.62233366
## Northern Mariana Islands 0.59464125
## Norway 0.01619126
## OECD members 0.36242030
## Oman 0.71428723
## Other small states -0.37371102
## Pacific island small states -0.32538152
## Pakistan -0.19247573
## Palau 0.61747321
## Panama 0.97063199
## Papua New Guinea -1.56504359
## Paraguay 1.45990858
## Peru 0.67546043
## Philippines 0.43908948
## Poland 0.47860696
## Portugal 0.47708332
## Post-demographic dividend 0.33697908
## Pre-demographic dividend -1.54547211
## Puerto Rico 0.61309036
## Qatar 0.76566564
## Romania 0.61917325
## Russian Federation 0.67829303
## Rwanda -1.58772460
## Samoa 0.47755835
## San Marino 0.59846417
## Sao Tome and Principe -0.45879920
## Saudi Arabia 1.07603800
## Senegal -1.02471482
## Serbia 0.46794911
## Seychelles 0.56230626
## Sierra Leone -2.58758124
## Singapore 0.35540711
## Sint Maarten (Dutch part) 0.59464125
## Slovak Republic 0.28001107
## Slovenia 0.32304036
## Small states -0.18116503
## Solomon Islands -1.28031868
## Somalia -1.94086688
## South Africa 1.22630549
## South Asia -0.29271171
## South Asia (IDA & IBRD) -0.29271171
## South Sudan -1.92960790
## Spain 0.52657555
## Sri Lanka 0.31471714
## St. Kitts and Nevis 0.34076772
## St. Lucia 0.77375456
## St. Martin (French part) -0.43812641
## St. Vincent and the Grenadines 0.26280349
## Sub-Saharan Africa -1.44087447
## Sub-Saharan Africa (excluding high income) -1.44110644
## Sub-Saharan Africa (IDA & IBRD countries) -1.44087447
## Sudan -0.99364835
## Suriname 1.10695892
## Sweden -0.18950717
## Switzerland 0.08076505
## Syrian Arab Republic 0.78990348
## Tajikistan 0.51670612
## Tanzania -1.72998746
## Thailand 0.97906030
## Timor-Leste -1.59996695
## Togo -1.50179459
## Tonga 0.62244336
## Trinidad and Tobago 0.80215679
## Tunisia 0.60999492
## Turkey 1.08646735
## Turkmenistan 0.58593378
## Turks and Caicos Islands 0.42401618
## Tuvalu 0.52636842
## Uganda -1.74553044
## Ukraine 0.34677614
## United Arab Emirates 0.75863772
## United Kingdom 0.26678538
## United States 0.35139421
## Upper middle income 0.90063013
## Uruguay 1.02914988
## Uzbekistan 0.58813642
## Vanuatu -1.10820402
## Venezuela, RB 1.27890147
## Vietnam 0.46795125
## Virgin Islands (U.S.) 0.65726233
## West Bank and Gaza 0.72894882
## World 0.23193789
## Yemen, Rep. -1.13072094
## Zambia -1.17865742
## Zimbabwe -0.68533868
## MR2
## Afghanistan 0.038673556
## Albania -1.141039814
## Algeria -1.066187218
## American Samoa -0.596071750
## Andorra 0.283324544
## Angola -0.057611355
## Antigua and Barbuda -0.036392766
## Arab World -0.183969970
## Argentina -0.457542618
## Armenia -0.943961150
## Aruba 0.177247485
## Australia 1.460774186
## Austria 1.968257989
## Azerbaijan -0.872896588
## Bahamas, The -0.078658359
## Bahrain -0.142086253
## Bangladesh -0.225756689
## Barbados 0.360420947
## Belarus -0.422674500
## Belgium 1.633200756
## Belize -0.119811492
## Benin -0.230122721
## Bermuda 0.501509377
## Bhutan -0.018350604
## Bolivia -0.088063431
## Bosnia and Herzegovina -1.184580709
## Botswana -0.340734870
## Brazil 0.130761512
## Brunei Darussalam -1.116576217
## Bulgaria -0.555813083
## Burkina Faso -0.892291173
## Burundi -0.479159014
## Cabo Verde -0.093544371
## Cambodia -0.041797330
## Cameroon -0.082428270
## Canada 1.891593845
## Caribbean small states -0.073788621
## Cayman Islands 0.003016415
## Central African Republic 0.191304163
## Central Europe and the Baltics -0.168878154
## Chad 0.005344313
## Channel Islands -0.096217949
## Chile -0.593597253
## China 0.385816828
## Colombia -0.846490324
## Comoros -0.101454119
## Congo, Dem. Rep. -0.648856511
## Congo, Rep. 0.179012241
## Costa Rica -0.665401900
## Cote d'Ivoire -0.110569877
## Croatia -0.083026606
## Cuba -0.631253489
## Curacao -0.096217949
## Cyprus -0.661495798
## Czech Republic 0.415452766
## Denmark 2.594244773
## Djibouti -0.091467854
## Dominica -0.005702867
## Dominican Republic -0.083529206
## Early-demographic dividend -0.371289965
## East Asia & Pacific 1.799223841
## East Asia & Pacific (excluding high income) 0.309168324
## East Asia & Pacific (IDA & IBRD countries) 0.305156655
## Ecuador -0.952885612
## Egypt, Arab Rep. -0.910446395
## El Salvador -0.962811287
## Equatorial Guinea -0.169953945
## Eritrea 0.172340320
## Estonia 0.412675050
## Eswatini 0.169900059
## Ethiopia -0.949868930
## Euro area 1.479329669
## Europe & Central Asia 1.126543872
## Europe & Central Asia (excluding high income) -0.143604277
## Europe & Central Asia (IDA & IBRD countries) -0.173638980
## European Union 1.402028670
## Faroe Islands 0.393879398
## Fiji -0.162821343
## Finland 3.445077161
## Fragile and conflict affected situations 0.083130493
## France 1.783262297
## French Polynesia -0.087064473
## Gabon -0.659268842
## Gambia, The 0.014934555
## Georgia -0.975507084
## Germany 2.141283612
## Ghana -0.775304309
## Gibraltar -0.096217949
## Greece -0.399083943
## Greenland -0.127756291
## Grenada 0.118049191
## Guam -0.867542228
## Guatemala -1.057025754
## Guinea 0.029361244
## Guinea-Bissau 0.201196511
## Guyana -0.147604999
## Haiti 0.032591706
## Heavily indebted poor countries (HIPC) -0.090908368
## High income 1.940143363
## Honduras -0.804265058
## Hong Kong SAR, China 0.617148333
## Hungary 0.148211597
## IBRD only -0.103249413
## Iceland 2.912597749
## IDA & IBRD total -0.113469380
## IDA blend -0.717257212
## IDA only -0.111048709
## IDA total -0.126674662
## India -0.223974750
## Indonesia -0.130333434
## Iran, Islamic Rep. -0.505986899
## Iraq -1.204075513
## Ireland 0.474287072
## Isle of Man -0.096217949
## Israel 4.178094497
## Italy 0.453768093
## Jamaica -0.178293277
## Japan 3.143684121
## Jordan -0.135870655
## Kazakhstan -0.942233950
## Kenya -0.529287367
## Kiribati -0.113211581
## Korea, Dem. People’s Rep. 0.231136548
## Korea, Rep. 2.920839185
## Kosovo -0.092597574
## Kuwait -0.991964148
## Kyrgyz Republic -0.991741225
## Lao PDR -0.179464201
## Late-demographic dividend 0.136836954
## Latin America & Caribbean -0.358214098
## Latin America & Caribbean (excluding high income) -0.337341831
## Latin America & the Caribbean (IDA & IBRD countries) -0.356369239
## Latvia -0.448307153
## Least developed countries: UN classification -0.112001232
## Lebanon -0.222165075
## Lesotho -0.801214149
## Liberia 0.166900102
## Libya -0.047277648
## Liechtenstein 0.659408947
## Lithuania -0.035043486
## Low & middle income -0.089096894
## Low income -0.091611216
## Lower middle income -0.407146159
## Luxembourg 1.233445607
## Macao SAR, China -0.490710996
## Madagascar -0.720709798
## Malawi 0.065261626
## Malaysia -0.439603013
## Maldives -0.089877854
## Mali -0.903897635
## Malta -0.118937774
## Marshall Islands 0.008139475
## Mauritania -0.095554341
## Mauritius -0.770523574
## Mexico -0.557484070
## Micronesia, Fed. Sts. -0.087187079
## Middle East & North Africa -0.189530020
## Middle East & North Africa (excluding high income) -0.219404712
## Middle East & North Africa (IDA & IBRD countries) -0.220215144
## Middle income -0.093949608
## Moldova -0.756074758
## Monaco -0.036862855
## Mongolia -0.900740075
## Montenegro 0.109209165
## Morocco -0.461744104
## Mozambique -0.537220431
## Myanmar -0.065475439
## Namibia 0.022964864
## Nauru -0.094664119
## Nepal -0.078425541
## Netherlands 1.806020389
## New Caledonia -0.090915436
## New Zealand 0.459543824
## Nicaragua -0.143254254
## Niger -0.176345947
## Nigeria -0.902908831
## North America 2.350717020
## North Macedonia -0.891880407
## Northern Mariana Islands -0.096217949
## Norway 1.382424532
## OECD members 1.878486670
## Oman -0.235193576
## Other small states 0.032677782
## Pacific island small states -0.095241171
## Pakistan -0.670270791
## Palau -0.251454186
## Panama -0.763408637
## Papua New Guinea 0.253237200
## Paraguay -0.976977489
## Peru -0.856131578
## Philippines -0.925876097
## Poland -0.408627324
## Portugal 0.173693825
## Post-demographic dividend 2.031560590
## Pre-demographic dividend -0.104061880
## Puerto Rico -0.145074361
## Qatar -0.070090611
## Romania -0.600563764
## Russian Federation 0.134042632
## Rwanda 0.066749205
## Samoa -0.245410754
## San Marino -0.106341704
## Sao Tome and Principe -0.090399048
## Saudi Arabia -1.064260348
## Senegal -0.187050785
## Serbia -0.618589436
## Seychelles -0.730837807
## Sierra Leone -0.015009675
## Singapore 1.851834413
## Sint Maarten (Dutch part) -0.096217949
## Slovak Republic -0.507357440
## Slovenia 0.866347934
## Small states 0.015180312
## Solomon Islands 0.083274903
## Somalia 0.203971234
## South Africa -0.046605946
## South Asia -0.254725198
## South Asia (IDA & IBRD) -0.254725198
## South Sudan 0.312226770
## Spain 0.605472539
## Sri Lanka -0.867624443
## St. Kitts and Nevis 0.279233760
## St. Lucia -0.025436339
## St. Martin (French part) 0.070892534
## St. Vincent and the Grenadines -0.069702830
## Sub-Saharan Africa -0.476288869
## Sub-Saharan Africa (excluding high income) -0.476296136
## Sub-Saharan Africa (IDA & IBRD countries) -0.476288869
## Sudan -0.605447725
## Suriname -0.098510133
## Sweden 3.637355430
## Switzerland 2.739352668
## Syrian Arab Republic -0.177805992
## Tajikistan -1.147706733
## Tanzania -0.527397330
## Thailand -0.797765656
## Timor-Leste -0.123815067
## Togo -0.085046474
## Tonga -0.113593468
## Trinidad and Tobago -1.024744966
## Tunisia -0.432702257
## Turkey -0.351652098
## Turkmenistan -0.094809008
## Turks and Caicos Islands -0.068609380
## Tuvalu -0.185046003
## Uganda -0.596488142
## Ukraine -0.051874817
## United Arab Emirates -0.082306122
## United Kingdom 1.236198046
## United States 2.390895483
## Upper middle income -0.017229040
## Uruguay -0.563391821
## Uzbekistan -0.960016648
## Vanuatu 0.026823609
## Venezuela, RB -0.773894179
## Vietnam -0.223627404
## Virgin Islands (U.S.) -1.065345509
## West Bank and Gaza -0.900469413
## World 1.321749952
## Yemen, Rep. -0.242455987
## Zambia -0.952795992
## Zimbabwe 0.039555393
## MR3
## Afghanistan -1.383960928
## Albania 1.178861819
## Algeria 0.754605654
## American Samoa 0.128088771
## Andorra 0.683137249
## Angola -0.236433583
## Antigua and Barbuda -0.683118279
## Arab World -0.179963663
## Argentina -0.942786035
## Armenia 0.963556281
## Aruba 0.459208943
## Australia 0.651910080
## Austria 0.831483487
## Azerbaijan 0.786529608
## Bahamas, The 0.526419633
## Bahrain 0.574474833
## Bangladesh 0.764994519
## Barbados 0.128232469
## Belarus 1.160071049
## Belgium 1.038849324
## Belize -0.370858809
## Benin 0.522825990
## Bermuda 1.091048380
## Bhutan 0.015232477
## Bolivia -1.234556271
## Bosnia and Herzegovina 1.024539946
## Botswana -1.069705024
## Brazil -1.221833260
## Brunei Darussalam 0.762116022
## Bulgaria 0.841523706
## Burkina Faso -0.149873263
## Burundi -0.819435531
## Cabo Verde -0.845431725
## Cambodia -0.382786898
## Cameroon -0.801709174
## Canada 0.769278501
## Caribbean small states -0.105548614
## Cayman Islands 0.054513173
## Central African Republic -0.632472148
## Central Europe and the Baltics 0.618695037
## Chad 0.470415508
## Channel Islands 0.586190945
## Chile -0.852608812
## China -0.040508722
## Colombia -1.328451123
## Comoros -0.943916083
## Congo, Dem. Rep. -0.581961726
## Congo, Rep. -1.354452257
## Costa Rica -0.786354981
## Cote d'Ivoire -0.292241319
## Croatia 0.569214302
## Cuba 0.583579714
## Curacao 0.586190945
## Cyprus 1.152618643
## Czech Republic 1.184434735
## Denmark 1.132539236
## Djibouti -0.577301661
## Dominica 0.290470676
## Dominican Republic -1.345349390
## Early-demographic dividend -0.316100657
## East Asia & Pacific -0.716923160
## East Asia & Pacific (excluding high income) -0.437616863
## East Asia & Pacific (IDA & IBRD countries) -0.412530805
## Ecuador -1.143672951
## Egypt, Arab Rep. 0.431403759
## El Salvador -0.603729738
## Equatorial Guinea 0.217032222
## Eritrea -1.049953113
## Estonia 0.936862620
## Eswatini -1.035086251
## Ethiopia 1.004805727
## Euro area -0.078829412
## Europe & Central Asia -0.105810970
## Europe & Central Asia (excluding high income) 0.046998852
## Europe & Central Asia (IDA & IBRD countries) 0.057283047
## European Union -0.086086701
## Faroe Islands 0.697883506
## Fiji 0.132666700
## Finland 0.748508138
## Fragile and conflict affected situations -0.355859383
## France 0.846353003
## French Polynesia 0.588529012
## Gabon -0.179273317
## Gambia, The -1.687206678
## Georgia 0.770867295
## Germany 0.718331186
## Ghana -0.679193777
## Gibraltar 0.586190945
## Greece 0.500045908
## Greenland 0.718312028
## Grenada -1.276780628
## Guam 0.700404055
## Guatemala -0.689192163
## Guinea -0.326705660
## Guinea-Bissau -0.650666462
## Guyana -0.335769989
## Haiti 0.113529884
## Heavily indebted poor countries (HIPC) -0.362488848
## High income 0.345650041
## Honduras -1.444686610
## Hong Kong SAR, China 0.817993167
## Hungary 1.076314940
## IBRD only -0.349395494
## Iceland 0.889037429
## IDA & IBRD total -0.364471552
## IDA blend 0.138062611
## IDA only -0.237938503
## IDA total -0.185638204
## India -0.033948424
## Indonesia -0.323572689
## Iran, Islamic Rep. -0.252916997
## Iraq 1.449952559
## Ireland 0.869974599
## Isle of Man 0.586190945
## Israel -0.202936334
## Italy 0.871391219
## Jamaica 0.342901540
## Japan 0.151225406
## Jordan -0.298676127
## Kazakhstan 0.988832083
## Kenya -1.364526917
## Kiribati -0.253662086
## Korea, Dem. People’s Rep. -1.408158796
## Korea, Rep. 0.762875295
## Kosovo 0.967715353
## Kuwait 0.324087068
## Kyrgyz Republic 0.977190058
## Lao PDR 0.619132617
## Late-demographic dividend -0.345568118
## Latin America & Caribbean -0.392300789
## Latin America & Caribbean (excluding high income) -0.396025578
## Latin America & the Caribbean (IDA & IBRD countries) -0.391042338
## Latvia 0.778301578
## Least developed countries: UN classification -0.151443646
## Lebanon 0.441057518
## Lesotho -1.649729968
## Liberia -1.006682271
## Libya 0.320822834
## Liechtenstein 0.779200255
## Lithuania 0.843147136
## Low & middle income -0.375756194
## Low income -0.356444171
## Lower middle income -0.198881175
## Luxembourg 0.960119455
## Macao SAR, China 0.471478055
## Madagascar -0.517044049
## Malawi -1.253257793
## Malaysia 0.183166587
## Maldives -0.186738700
## Mali 0.358788767
## Malta 0.937184926
## Marshall Islands -0.049588321
## Mauritania -0.704343048
## Mauritius 0.816614067
## Mexico -1.045446428
## Micronesia, Fed. Sts. -0.760889089
## Middle East & North Africa -0.033870126
## Middle East & North Africa (excluding high income) 0.020981082
## Middle East & North Africa (IDA & IBRD countries) 0.023876188
## Middle income -0.326434824
## Moldova 0.831938072
## Monaco 1.032701150
## Mongolia 0.671252783
## Montenegro 0.454084268
## Morocco -0.217708272
## Mozambique -0.004036439
## Myanmar -0.732649978
## Namibia -2.562420851
## Nauru 0.576724508
## Nepal -0.656085832
## Netherlands 1.077840606
## New Caledonia 0.587545363
## New Zealand 0.563026743
## Nicaragua -0.641379681
## Niger 0.250479955
## Nigeria 0.378940499
## North America 0.314466352
## North Macedonia 0.683715113
## Northern Mariana Islands 0.586190945
## Norway 1.141598448
## OECD members 0.336713186
## Oman 0.413776918
## Other small states -0.560287944
## Pacific island small states -0.419627022
## Pakistan 1.178012067
## Palau 0.507388095
## Panama -1.064040118
## Papua New Guinea -1.542803135
## Paraguay -1.308855259
## Peru -0.980928543
## Philippines -0.417693447
## Poland 0.907377573
## Portugal 0.688788610
## Post-demographic dividend 0.346218254
## Pre-demographic dividend -0.260756521
## Puerto Rico 0.573711582
## Qatar 0.224126264
## Romania 0.700843001
## Russian Federation 0.371514089
## Rwanda -1.770416315
## Samoa 0.358366615
## San Marino 0.583605041
## Sao Tome and Principe -0.562343942
## Saudi Arabia -0.129714659
## Senegal -0.117831889
## Serbia 0.988274980
## Seychelles 0.586335510
## Sierra Leone 0.688862756
## Singapore 0.373064303
## Sint Maarten (Dutch part) 0.586190945
## Slovak Republic 1.330889297
## Slovenia 0.737181709
## Small states -0.444235755
## Solomon Islands -1.952007208
## Somalia -0.810146772
## South Africa -2.135850646
## South Asia -0.129643613
## South Asia (IDA & IBRD) -0.129643613
## South Sudan -1.902186720
## Spain 0.412542452
## Sri Lanka -0.281662162
## St. Kitts and Nevis 0.559463640
## St. Lucia -0.497928704
## St. Martin (French part) -0.431900302
## St. Vincent and the Grenadines 0.240898853
## Sub-Saharan Africa -0.326364007
## Sub-Saharan Africa (excluding high income) -0.326425128
## Sub-Saharan Africa (IDA & IBRD countries) -0.326364007
## Sudan -0.991866448
## Suriname -1.021303372
## Sweden 0.826477655
## Switzerland 0.583417148
## Syrian Arab Republic -0.317970857
## Tajikistan 1.037788850
## Tanzania -1.028535221
## Thailand -0.489601496
## Timor-Leste 0.039750570
## Togo -0.571379380
## Tonga -0.256525312
## Trinidad and Tobago 0.090949028
## Tunisia 0.623837993
## Turkey -0.607938503
## Turkmenistan 0.577607216
## Turks and Caicos Islands 0.417990591
## Tuvalu 0.452118712
## Uganda -1.127900527
## Ukraine 1.057440069
## United Arab Emirates 0.244735236
## United Kingdom 0.730646427
## United States 0.198425817
## Upper middle income -0.324783747
## Uruguay -0.259468252
## Uzbekistan 0.877257285
## Vanuatu -1.295507926
## Venezuela, RB -0.704418912
## Vietnam 0.394349770
## Virgin Islands (U.S.) 0.772962562
## West Bank and Gaza 0.519147512
## World -0.611312688
## Yemen, Rep. 0.871877662
## Zambia -1.424985708
## Zimbabwe -1.744006562promedio1=as.data.frame(Calidad1$scores)
names(promedio1) = c("Densidad","Informacion", "Desigualdad")
head(promedio1)## Densidad Informacion Desigualdad
## Afghanistan -1.28872296 0.03867356 -1.3839609
## Albania 0.47328987 -1.14103981 1.1788618
## Algeria 0.63538286 -1.06618722 0.7546057
## American Samoa 0.02928735 -0.59607175 0.1280888
## Andorra 0.45131879 0.28332454 0.6831372
## Angola -1.54618516 -0.05761135 -0.2364336promedio1$Pais=row.names(promedio1)
row.names(promedio1) = NULLAnálisis factorial de salud
salud1_X=salud1
row.names(salud1) = salud1$Pais
salud1$Pais = NULL
head(salud1)## VidaM CobARet
## Afghanistan 59.71540 NA
## Albania 78.16240 6.2
## Algeria 74.10120 14.6
## Angola 55.38620 1.4
## Antigua and Barbuda 76.90380 NA
## Arab World 70.78677 NAsalud1[is.na(salud1$VidaM), "VidaM"]=mean(salud1$VidaM, na.rm=T)
salud1[is.na(salud1$CobARet), "CobARet"]=mean(salud1$CobARet, na.rm=T)salud1=as.data.frame(scale(salud1[,c(1,2)]))
head(salud1)## VidaM CobARet
## Afghanistan -1.133702486 0.00000000
## Albania 0.739051823 -0.64026360
## Algeria 0.326755526 -0.05502504
## Angola -1.573206359 -0.97468563
## Antigua and Barbuda 0.611277737 0.00000000
## Arab World -0.009727659 0.00000000library(psych)puntaje1 = cor(salud1) #sacar la correlación de los puntajes estandarizadas
puntaje1## VidaM CobARet
## VidaM 1.0000000 0.4826546
## CobARet 0.4826546 1.0000000cor.plot(puntaje1,
numbers=T,
upper=FALSE,
main = "Correlation",
show.legend = FALSE) #verlo en un gráfico
KMO(salud1) #nos indica que mientras más cercano a uno hay una división subyacente, a partir de 0.7. Que tan buena idea es juntarlos en un indice. ver el Overall MSA(más cercano a 1)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = salud1)
## Overall MSA = 0.5
## MSA for each item =
## VidaM CobARet
## 0.5 0.5fa.parallel(puntaje1, fm="pa", fa="fa", main = "Scree Plot",n.obs = nrow(salud1)) #cuantos indices deberia formar
## Parallel analysis suggests that the number of factors = 1 and the number of components = NAsalud1 = fa(salud1,
nfactors=1,
rotate="varimax") #codigo para el analisis factorial solo cambiar la data y el numero de factoressalud1$loadings##
## Loadings:
## MR1
## VidaM 0.695
## CobARet 0.695
##
## MR1
## SS loadings 0.965
## Proportion Var 0.483fa.diagram(salud1)
#Para ver el tipo de análisis factorial:
# mientras mas grande mejor (lo que aporta)
sort(salud1$communalities)## VidaM CobARet
## 0.4826546 0.4826546# mientras mas grande peor (lo que mantiene)
sort(salud1$uniquenesses)## VidaM CobARet
## 0.5173454 0.5173454sort(salud1$complexity)## VidaM CobARet
## 1 1salud1$scores## MR1
## Afghanistan -0.531223568
## Albania 0.046289599
## Algeria 0.127325856
## Angola -1.193876075
## Antigua and Barbuda 0.286428886
## Arab World -0.004558128
## Argentina 1.136605835
## Armenia -0.250722761
## Aruba 0.277495237
## Australia 1.816622019
## Austria 1.746740690
## Azerbaijan -0.446383479
## Bahamas, The 0.435434166
## Bahrain 0.263627063
## Bangladesh -0.594973180
## Barbados 0.976052458
## Belarus -0.037640984
## Belgium 0.526952398
## Belize -0.084709744
## Benin -1.077452549
## Bermuda 0.499361789
## Bhutan -0.279283252
## Bolivia -0.678960846
## Bosnia and Herzegovina 0.329260928
## Botswana -0.695994685
## Brazil 0.825928745
## Brunei Darussalam 0.337909157
## Bulgaria 0.492009979
## Burkina Faso -1.065722512
## Burundi -1.079721209
## Cabo Verde -0.195891973
## Cambodia -0.248401824
## Cameroon -1.165292105
## Canada 0.554543007
## Caribbean small states 0.153951841
## Central African Republic -1.648226807
## Central Europe and the Baltics 0.336295082
## Chad -1.441149955
## Channel Islands 0.487478799
## Chile 2.008619424
## China 0.221505329
## Colombia -0.048372110
## Comoros -0.868593956
## Congo, Dem. Rep. -1.188587936
## Congo, Rep. -1.110690571
## Costa Rica 0.745081791
## Cote d'Ivoire -1.347554179
## Croatia 0.383481231
## Cuba 0.679105473
## Curacao 0.374252648
## Cyprus 1.123172427
## Czech Republic 1.442572154
## Denmark 1.516128510
## Djibouti -0.950432434
## Dominican Republic -0.219300325
## Early-demographic dividend -0.150445711
## East Asia & Pacific 0.213396793
## East Asia & Pacific (excluding high income) 0.160686354
## East Asia & Pacific (IDA & IBRD countries) 0.162369841
## Ecuador -0.045767225
## Egypt, Arab Rep. -0.405086485
## El Salvador 0.176765861
## Equatorial Guinea -1.152752955
## Eritrea -0.832730127
## Estonia 0.093521685
## Eswatini -1.289354093
## Ethiopia -0.991857074
## Euro area 0.557465684
## Europe & Central Asia 0.348775182
## Europe & Central Asia (excluding high income) 0.136991308
## Europe & Central Asia (IDA & IBRD countries) 0.162594996
## European Union 0.506593020
## Faroe Islands 0.526000998
## Fiji 0.021397302
## Finland 0.548834605
## Fragile and conflict affected situations -0.522476279
## France 2.233242721
## French Polynesia 0.281196184
## Gabon -0.621277868
## Gambia, The -1.015193583
## Georgia -0.086135140
## Germany 2.014868172
## Ghana -0.991006365
## Greece 1.522321820
## Greenland 0.043545901
## Grenada 0.139171146
## Guam 0.406923735
## Guatemala 0.058762705
## Guinea -1.228136671
## Guinea-Bissau -1.264182912
## Guyana 0.030613334
## Haiti -0.788298757
## Heavily indebted poor countries (HIPC) -0.671600820
## High income 0.509147448
## Honduras 0.049510519
## Hong Kong SAR, China 0.668711044
## Hungary 0.944261599
## IBRD only 0.053640720
## Iceland 0.581182216
## IDA & IBRD total -0.078103905
## IDA blend -0.607383275
## IDA only -0.489339559
## IDA total -0.527963387
## India -0.701492628
## Indonesia -0.562927395
## Iran, Islamic Rep. -0.358385842
## Iraq 0.008334576
## Ireland 1.857774963
## Israel 0.541223403
## Italy 1.484023077
## Jamaica -0.118597895
## Japan 1.871170737
## Jordan 0.157533172
## Kazakhstan -0.388250968
## Kenya -0.951841847
## Kiribati -0.144555454
## Korea, Dem. People’s Rep. 0.034288776
## Korea, Rep. 0.511729993
## Kosovo 0.003682228
## Kuwait 2.062804848
## Kyrgyz Republic -0.423014465
## Lao PDR -0.707901162
## Late-demographic dividend 0.223937384
## Latin America & Caribbean 0.256480255
## Latin America & Caribbean (excluding high income) 0.237142353
## Latin America & the Caribbean (IDA & IBRD countries) 0.250725234
## Latvia 0.286248120
## Least developed countries: UN classification -0.519093785
## Lebanon 0.429039473
## Lesotho -1.408489722
## Liberia -1.177488693
## Libya 0.130580001
## Liechtenstein 0.600210222
## Lithuania -0.012289095
## Low & middle income -0.086007665
## Low income -0.605906529
## Lower middle income -0.236671017
## Luxembourg 1.805112027
## Macao SAR, China 0.636116069
## Madagascar -0.887243354
## Malawi -1.266635266
## Malaysia 0.011518846
## Maldives 0.224378558
## Mali -1.194080502
## Malta 0.506021591
## Mauritania -0.830770549
## Mauritius 0.237155864
## Mexico 0.641142284
## Micronesia, Fed. Sts. -0.106309162
## Middle East & North Africa 0.099066974
## Middle East & North Africa (excluding high income) 0.078120053
## Middle East & North Africa (IDA & IBRD countries) 0.077658702
## Middle income -0.027404434
## Moldova -0.357086387
## Mongolia -0.505037002
## Montenegro 0.593335394
## Morocco -0.157634214
## Mozambique -1.278797091
## Myanmar -0.694994564
## Namibia -0.848907283
## Nepal -0.653252364
## Netherlands 1.679844135
## New Caledonia 0.387096553
## New Zealand 1.470218565
## Nicaragua -0.233348239
## Niger -1.237056537
## Nigeria -1.436488093
## North America 0.452428819
## North Macedonia 0.013030596
## Norway 2.579758332
## OECD members 0.491026088
## Oman 0.271177939
## Other small states -0.364012688
## Pacific island small states -0.017075781
## Pakistan -0.707142970
## Palau 0.057912046
## Panama 0.430264157
## Papua New Guinea -0.642356878
## Paraguay -0.263644117
## Peru -0.109136401
## Philippines -0.419045173
## Poland 0.403270358
## Portugal 1.121744350
## Post-demographic dividend 0.507833903
## Pre-demographic dividend -0.760460376
## Puerto Rico 0.549405445
## Qatar 1.609931278
## Romania 2.189087140
## Russian Federation -0.299733353
## Rwanda -0.845681366
## Samoa 0.170472216
## Sao Tome and Principe -0.209773945
## Saudi Arabia 0.183972587
## Senegal -0.792198828
## Serbia 1.342320787
## Seychelles 0.271025715
## Sierra Leone -1.716780444
## Singapore 0.178774408
## Sint Maarten (Dutch part) 0.286248120
## Slovak Republic 1.040259842
## Slovenia 0.920420235
## Small states -0.232259117
## Solomon Islands -0.162213444
## Somalia -1.293614968
## South Africa -1.110207003
## South Asia -0.241991799
## South Asia (IDA & IBRD) -0.241991799
## South Sudan -1.387028696
## Spain 1.694730622
## Sri Lanka -0.146130080
## St. Lucia 0.210706935
## St. Martin (French part) 0.490799186
## St. Vincent and the Grenadines 0.129229013
## Sub-Saharan Africa -0.777661864
## Sub-Saharan Africa (excluding high income) -0.777775639
## Sub-Saharan Africa (IDA & IBRD countries) -0.777661864
## Sudan -0.912118204
## Suriname -0.104952250
## Sweden 0.565008410
## Switzerland 0.614481226
## Syrian Arab Republic 0.276610435
## Tajikistan -0.501243844
## Tanzania -1.014317989
## Thailand 0.242204151
## Timor-Leste -0.256877774
## Togo -1.120817739
## Tonga 0.154545775
## Trinidad and Tobago 0.107926134
## Tunisia 0.356074937
## Turkey 0.241798697
## Turkmenistan -0.094644994
## Uganda -1.040429046
## Ukraine -0.304992281
## United Arab Emirates 0.282804051
## United Kingdom 0.489847786
## United States 0.441326370
## Upper middle income 0.190969536
## Uruguay 0.572336401
## Uzbekistan -0.448759358
## Vanuatu 0.016278768
## Venezuela, RB 0.314019495
## Vietnam -0.021868355
## Virgin Islands (U.S.) 0.479382383
## West Bank and Gaza 0.120485644
## World -0.239476561
## Yemen, Rep. -0.362502237
## Zambia -1.163770229
## Zimbabwe -1.545252176puntaje1=as.data.frame(salud1$scores)
names(puntaje1) = c("Salud")
head(puntaje1)## Salud
## Afghanistan -0.531223568
## Albania 0.046289599
## Algeria 0.127325856
## Angola -1.193876075
## Antigua and Barbuda 0.286428886
## Arab World -0.004558128puntaje1$Pais=row.names(puntaje1)
row.names(puntaje1) = NULLMerge final e imputación a las variables faltantes
responsabilidad1 = merge(scores1, DataMetodos1, by= "Pais")
dendes1 = merge(responsabilidad1, promedio1, by= "Pais")
sal1 = merge(dendes1, puntaje1, by= "Pais")
movilidad1 = merge(sal1, migra1, by= "Pais")
movi1 = merge(movilidad1, ODA1, by= "Pais")
control1 = merge(movi1, women1, by= "Pais")
final1 = merge(control1, AFRICA, by= "Pais")
VIH1 = merge(final1, DataVIH1,by= "Pais")VIH1[is.na(VIH1$Metodos), "Metodos"]=mean(VIH1$Metodos, na.rm=T)
VIH1[is.na(VIH1$Migracion), "Migracion"]=mean(VIH1$Migracion, na.rm=T)
VIH1[is.na(VIH1$ODA), "ODA"]=mean(VIH1$ODA, na.rm=T)
VIH1[is.na(VIH1$Women), "Women"]=mean(VIH1$Women, na.rm=T)
VIH1[is.na(VIH1$Africa), "Africa"]=mean(VIH1$Africa, na.rm=T)## Warning in mean.default(VIH1$Africa, na.rm = T): argument is not numeric or
## logical: returning NAVIH1$Metodos=scale(VIH1$Metodos)
VIH1$Migracion=scale(VIH1$Migracion)
VIH1$ODA=scale(VIH1$ODA)
VIH1$Women=scale(VIH1$Women)VIH1=VIH1[-grep("San|high|Pacific|French|Caribbean|Early|Late|Island|Small|West|Sint|Other|OECD|North|World|Euro|Latin|Upper|High|Heavily|IBR|IDA|Least|Low|Middle|East|Central|Fragile|Post|Pre",VIH1$Pais),] #Eliminamos los casos que no son paÃses
row.names(VIH1)=NULLRegresión
VIH1$VIH=VIH1$VIH/100summary(VIH1)## Pais Empoderamiento Metodos.V1
## Length:90 Min. :-2.1124 Min. :-1.7936258
## Class :character 1st Qu.:-0.4316 1st Qu.:-0.7444031
## Mode :character Median : 0.1976 Median : 0.1163042
## Mean : 0.2201 Mean : 0.0269795
## 3rd Qu.: 0.8666 3rd Qu.: 0.8617479
## Max. : 2.5421 Max. : 2.2222902
## Densidad Informacion Desigualdad Salud
## Min. :-2.8179 Min. :-1.1477 Min. :-2.5624 Min. :-1.71678
## 1st Qu.:-1.5047 1st Qu.:-0.8537 1st Qu.:-1.0176 1st Qu.:-1.05940
## Median : 0.1479 Median :-0.4596 Median :-0.3533 Median :-0.53398
## Mean :-0.3188 Mean :-0.4443 Mean :-0.3037 Mean :-0.50162
## 3rd Qu.: 0.6187 3rd Qu.:-0.0858 3rd Qu.: 0.4663 3rd Qu.:-0.08507
## Max. : 1.4758 Max. : 0.2532 Max. : 1.1789 Max. : 1.34232
## Migracion.V1 ODA.V1 Women.V1
## Min. :-5.823900 Min. :-0.885939 Min. :-2.383372
## 1st Qu.:-0.021024 1st Qu.:-0.778577 1st Qu.:-0.595795
## Median : 0.265644 Median :-0.341706 Median :-0.116951
## Mean :-0.006229 Mean : 0.001995 Mean : 0.002270
## 3rd Qu.: 0.381999 3rd Qu.: 0.466889 3rd Qu.: 0.392172
## Max. : 1.635120 Max. : 4.205506 Max. : 3.287269
## Africa VIH
## Length:90 Min. :0.00100
## Class :character 1st Qu.:0.00100
## Mode :character Median :0.00200
## Mean :0.01488
## 3rd Qu.:0.01100
## Max. :0.21720table(VIH1$Africa)##
## NO SI
## 52 38library(betareg)
modelo1=betareg(VIH ~ Empoderamiento + Metodos + Densidad + Desigualdad + Salud + Informacion + Migracion + ODA + Women + Africa,data=VIH1)
summary(modelo1)##
## Call:
## betareg(formula = VIH ~ Empoderamiento + Metodos + Densidad + Desigualdad +
## Salud + Informacion + Migracion + ODA + Women + Africa, data = VIH1)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -2.4120 -0.6356 -0.0050 0.6321 3.9196
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.94355 0.20460 -29.050 < 2e-16 ***
## Empoderamiento -0.16459 0.08228 -2.001 0.045443 *
## Metodos 0.48632 0.15781 3.082 0.002059 **
## Densidad -0.46753 0.13852 -3.375 0.000738 ***
## Desigualdad -0.53010 0.09153 -5.791 6.98e-09 ***
## Salud -1.45091 0.19841 -7.313 2.62e-13 ***
## Informacion -0.05573 0.17983 -0.310 0.756655
## Migracion 0.38492 0.10307 3.734 0.000188 ***
## ODA -0.36620 0.10634 -3.444 0.000574 ***
## Women 0.29142 0.08753 3.329 0.000871 ***
## AfricaSI 0.07138 0.18483 0.386 0.699346
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 126.92 22.15 5.73 1e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 352.4 on 12 Df
## Pseudo R-squared: 0.6222
## Number of iterations: 53 (BFGS) + 8 (Fisher scoring)library(margins)
(modelo1M = margins(modelo1))## Average marginal effects## betareg(formula = VIH ~ Empoderamiento + Metodos + Densidad + Desigualdad + Salud + Informacion + Migracion + ODA + Women + Africa, data = VIH1)## Empoderamiento Metodos Densidad Desigualdad Salud Informacion
## -0.002342 0.00692 -0.006652 -0.007542 -0.02064 -0.0007929
## Migracion ODA Women AfricaSI
## 0.005477 -0.00521 0.004146 0.001007resultado = summary(modelo1M)#salen los limites de su error
bet=summary(modelo1M)
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphaggplot(bet,aes(x=factor, y=AME)) + geom_point() + geom_errorbar(aes(ymin=lower, ymax=upper))
Mapa de Similitudes
VIH1_x=VIH1
row.names(VIH1_x) = VIH1_x$Pais
VIH1_x$Pais=NULL
head(VIH1_x) #resultado final## Empoderamiento Metodos Densidad Informacion Desigualdad
## Albania -0.74741919 0.6753870 0.4732899 -1.1410398 1.1788618
## Algeria -1.22463238 0.7314029 0.6353829 -1.0661872 0.7546057
## Argentina 0.00458265 1.4854644 1.2662440 -0.4575426 -0.9427860
## Armenia -0.31023875 0.3737624 0.5278865 -0.9439612 0.9635563
## Azerbaijan 0.30285455 0.2875839 0.5825904 -0.8728966 0.7865296
## Bangladesh -1.47273276 0.5482737 -1.4759396 -0.2257567 0.7649945
## Salud Migracion ODA Women Africa VIH
## Albania 0.0462896 0.01754680 -0.3312780 -0.6752982 NO 0.001
## Algeria 0.1273259 -0.22628024 -0.8194162 -1.0730844 SI 0.001
## Argentina 1.1366058 0.24809750 -0.8542239 0.6493450 NO 0.001
## Armenia -0.2507228 0.07676433 -0.1883990 2.6460604 NO 0.001
## Azerbaijan -0.4463835 0.48947898 -0.5907136 0.2936687 NO 0.001
## Bangladesh -0.5949732 -5.82389983 -0.6128485 -1.4247675 NO 0.001VIH1_x =dist(VIH1_x[-c(10)])VIH1_map = cmdscale(VIH1_x,eig=TRUE, k=2) # k sugiere dimensiones
VIH1_map$GOF # mientras mas cerca a 1 mejor.## [1] 0.6340295 0.6340295titulo="Mapa de Similitudes entre paises"
x = VIH1_map$points[,1]
y = VIH1_map$points[,2]
plot(x, y, main=titulo)
plot(x, y, xlab="Dimensión 1", ylab="Dimensión 2", main=titulo,
type="n") # 'n' evita que se pongan los puntos.
# etiquetas y colores de los puntos
text(x, y,labels = rownames(VIH1_map$points),cex=1) 
VIH1_map_DF=as.data.frame(VIH1_map$points)install.packages("ggrepel")## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/3.6'
## (as 'lib' is unspecified)library(ggrepel)
base=ggplot(VIH1_map_DF,aes(x=V1,y=V2))
base+geom_point() + geom_text_repel(aes(label=row.names(VIH1_map_DF)),size=2.6)
Análisis de Conglomerados
Mapa de todas las variables (incluyendo las de control y la dependiente)
VIH1_x=VIH1
row.names(VIH1_x) = VIH1_x$Pais
VIH1_x$Pais=NULL
head(VIH1_x) #resultado final## Empoderamiento Metodos Densidad Informacion Desigualdad
## Albania -0.74741919 0.6753870 0.4732899 -1.1410398 1.1788618
## Algeria -1.22463238 0.7314029 0.6353829 -1.0661872 0.7546057
## Argentina 0.00458265 1.4854644 1.2662440 -0.4575426 -0.9427860
## Armenia -0.31023875 0.3737624 0.5278865 -0.9439612 0.9635563
## Azerbaijan 0.30285455 0.2875839 0.5825904 -0.8728966 0.7865296
## Bangladesh -1.47273276 0.5482737 -1.4759396 -0.2257567 0.7649945
## Salud Migracion ODA Women Africa VIH
## Albania 0.0462896 0.01754680 -0.3312780 -0.6752982 NO 0.001
## Algeria 0.1273259 -0.22628024 -0.8194162 -1.0730844 SI 0.001
## Argentina 1.1366058 0.24809750 -0.8542239 0.6493450 NO 0.001
## Armenia -0.2507228 0.07676433 -0.1883990 2.6460604 NO 0.001
## Azerbaijan -0.4463835 0.48947898 -0.5907136 0.2936687 NO 0.001
## Bangladesh -0.5949732 -5.82389983 -0.6128485 -1.4247675 NO 0.001VIH1_d = as.data.frame(scale(VIH1_x[,-c(10)])) #Estandarizamos y eliminamos la variable categóricaset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventadoPedimos las distancias como un criterio de agrupamiento
#Para pedir la distancia
VIH1_d=dist(VIH1_d[c(1:10)]) #Pedimos el numero de grupos:
VIH1_clus=kmeans(VIH1_d,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH1_clus$cluster## Albania Algeria Argentina
## 5 5 1
## Armenia Azerbaijan Bangladesh
## 5 5 3
## Belarus Belize Benin
## 5 1 2
## Bolivia Botswana Brazil
## 1 4 1
## Burkina Faso Burundi Cabo Verde
## 2 4 1
## Cambodia Cameroon Chad
## 2 2 2
## Colombia Congo, Dem. Rep. Congo, Rep.
## 1 2 2
## Costa Rica Cote d'Ivoire Cuba
## 1 2 1
## Dominican Republic Ecuador Egypt, Arab Rep.
## 1 1 1
## El Salvador Eswatini Ethiopia
## 5 3 2
## Georgia Ghana Guinea
## 5 2 2
## Guinea-Bissau Guyana Haiti
## 2 1 2
## Honduras India Indonesia
## 1 3 1
## Iran, Islamic Rep. Kazakhstan Kenya
## 1 5 2
## Kyrgyz Republic Lao PDR Lebanon
## 5 2 1
## Lesotho Liberia Madagascar
## 4 4 2
## Malawi Malaysia Mali
## 4 1 2
## Mauritania Mexico Moldova
## 2 1 5
## Mongolia Montenegro Morocco
## 5 1 1
## Mozambique Myanmar Namibia
## 4 2 4
## Nepal Nicaragua Niger
## 2 1 2
## Nigeria Pakistan Papua New Guinea
## 2 5 2
## Paraguay Peru Philippines
## 1 1 1
## Rwanda Senegal Serbia
## 4 2 5
## Sierra Leone South Africa Sri Lanka
## 4 4 1
## Sudan Suriname Tajikistan
## 2 1 5
## Tanzania Thailand Togo
## 2 1 2
## Trinidad and Tobago Tunisia Uganda
## 1 1 2
## Ukraine Uruguay Uzbekistan
## 5 1 5
## Vietnam Zambia Zimbabwe
## 1 2 4#Para ver la cantidad de paises en cada grupo:
table(VIH1_clus$cluster)##
## 1 2 3 4 5
## 31 29 3 11 16#Graficamos el mapa:
library(rgdal)## Loading required package: sp## rgdal: version: 1.4-4, (SVN revision 833)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.1.3, released 2017/20/01
## Path to GDAL shared files: /usr/share/gdal/2.1
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.2, 08 September 2015, [PJ_VERSION: 492]
## Path to PROJ.4 shared files: (autodetected)
## Linking to sp version: 1.3-1folderMap='MapaMundo' #Nombre del archivo que contiene al mapa
fileName='world_map.shp'
fileToRead=file.path(folderMap,fileName)
mapamundo = readOGR(fileToRead,stringsAsFactors=FALSE)## OGR data source with driver: ESRI Shapefile
## Source: "/cloud/project/MapaMundo/world_map.shp", layer: "world_map"
## with 246 features
## It has 11 fields
## Integer64 fields read as strings: POP2005plot(mapamundo, border='grey')
#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos1=as.data.frame(VIH1_clus$cluster)
grupos1## VIH1_clus$cluster
## Albania 5
## Algeria 5
## Argentina 1
## Armenia 5
## Azerbaijan 5
## Bangladesh 3
## Belarus 5
## Belize 1
## Benin 2
## Bolivia 1
## Botswana 4
## Brazil 1
## Burkina Faso 2
## Burundi 4
## Cabo Verde 1
## Cambodia 2
## Cameroon 2
## Chad 2
## Colombia 1
## Congo, Dem. Rep. 2
## Congo, Rep. 2
## Costa Rica 1
## Cote d'Ivoire 2
## Cuba 1
## Dominican Republic 1
## Ecuador 1
## Egypt, Arab Rep. 1
## El Salvador 5
## Eswatini 3
## Ethiopia 2
## Georgia 5
## Ghana 2
## Guinea 2
## Guinea-Bissau 2
## Guyana 1
## Haiti 2
## Honduras 1
## India 3
## Indonesia 1
## Iran, Islamic Rep. 1
## Kazakhstan 5
## Kenya 2
## Kyrgyz Republic 5
## Lao PDR 2
## Lebanon 1
## Lesotho 4
## Liberia 4
## Madagascar 2
## Malawi 4
## Malaysia 1
## Mali 2
## Mauritania 2
## Mexico 1
## Moldova 5
## Mongolia 5
## Montenegro 1
## Morocco 1
## Mozambique 4
## Myanmar 2
## Namibia 4
## Nepal 2
## Nicaragua 1
## Niger 2
## Nigeria 2
## Pakistan 5
## Papua New Guinea 2
## Paraguay 1
## Peru 1
## Philippines 1
## Rwanda 4
## Senegal 2
## Serbia 5
## Sierra Leone 4
## South Africa 4
## Sri Lanka 1
## Sudan 2
## Suriname 1
## Tajikistan 5
## Tanzania 2
## Thailand 1
## Togo 2
## Trinidad and Tobago 1
## Tunisia 1
## Uganda 2
## Ukraine 5
## Uruguay 1
## Uzbekistan 5
## Vietnam 1
## Zambia 2
## Zimbabwe 4names(grupos1)='cluster'
grupos1$NAME=row.names(grupos1)
head(grupos1)## cluster NAME
## Albania 5 Albania
## Algeria 5 Algeria
## Argentina 1 Argentina
## Armenia 5 Armenia
## Azerbaijan 5 Azerbaijan
## Bangladesh 3 Bangladesh#Creamos el objeto final:
mapamundo_VIH1=merge(mapamundo,grupos1)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "Set2")[mapamundo_VIH1$cluster],
border='gray',add=T)
#Para tener un mapa interactivo asignando colores a cada grupo:
library(leaflet)
#newMaps!
c1=mapamundo_VIH1[!is.na(mapamundo_VIH1$cluster) & mapamundo_VIH1$cluster==1,]
c2=mapamundo_VIH1[!is.na(mapamundo_VIH1$cluster) & mapamundo_VIH1$cluster==2,]
c3=mapamundo_VIH1[!is.na(mapamundo_VIH1$cluster) & mapamundo_VIH1$cluster==3,]
c4=mapamundo_VIH1[!is.na(mapamundo_VIH1$cluster) & mapamundo_VIH1$cluster==4,]
c5=mapamundo_VIH1[!is.na(mapamundo_VIH1$cluster) & mapamundo_VIH1$cluster==5,]
title="Clusters"
# base Layer
base= leaflet() %>% addProviderTiles("CartoDB.Positron")
layer1= base %>%
addPolygons(data=c1,color="firebrick",fillOpacity = 1,stroke = F,
group = "1")
layer_12= layer1%>%addPolygons(data=c2,color="darkred",fillOpacity = 1,stroke = F,
group = "2")
layer_123= layer_12%>%addPolygons(data=c3,color="indianred",fillOpacity = 1,stroke = F,
group = "3")
layer_1234= layer_123%>%addPolygons(data=c4,color="red",fillOpacity = 1,stroke = F,
group = "4")
layer_12345= layer_1234%>%addPolygons(data=c5,color="tomato",fillOpacity = 1,stroke = F,
group = "5")
layer_12345layer_12345%>% addLayersControl(
overlayGroups = c("1", "2","3","4","5"),
options = layersControlOptions(collapsed = FALSE)) VIH1_x=merge(VIH1_x, grupos1[-2], by=0)
row.names(VIH1_x) = VIH1_x$Row.names
VIH1_x=VIH1_x[-1]
head(VIH1_x)## Empoderamiento Metodos Densidad Informacion Desigualdad
## Albania -0.74741919 0.6753870 0.4732899 -1.1410398 1.1788618
## Algeria -1.22463238 0.7314029 0.6353829 -1.0661872 0.7546057
## Argentina 0.00458265 1.4854644 1.2662440 -0.4575426 -0.9427860
## Armenia -0.31023875 0.3737624 0.5278865 -0.9439612 0.9635563
## Azerbaijan 0.30285455 0.2875839 0.5825904 -0.8728966 0.7865296
## Bangladesh -1.47273276 0.5482737 -1.4759396 -0.2257567 0.7649945
## Salud Migracion ODA Women Africa VIH
## Albania 0.0462896 0.01754680 -0.3312780 -0.6752982 NO 0.001
## Algeria 0.1273259 -0.22628024 -0.8194162 -1.0730844 SI 0.001
## Argentina 1.1366058 0.24809750 -0.8542239 0.6493450 NO 0.001
## Armenia -0.2507228 0.07676433 -0.1883990 2.6460604 NO 0.001
## Azerbaijan -0.4463835 0.48947898 -0.5907136 0.2936687 NO 0.001
## Bangladesh -0.5949732 -5.82389983 -0.6128485 -1.4247675 NO 0.001
## cluster
## Albania 5
## Algeria 5
## Argentina 1
## Armenia 5
## Azerbaijan 5
## Bangladesh 3agg1=aggregate(cbind(Empoderamiento,Metodos,Densidad,Informacion,Desigualdad,Salud,Migracion,ODA,Women,VIH) ~ cluster, data=VIH1_x,FUN=mean)
names(agg1)=c("cluster", "Empoderamiento","Metodos","Densidad","DesarrolloTecno","Desigualdad","Salud","Migracion", "ODA", "Women","VIH")
agg1## cluster Empoderamiento Metodos Densidad DesarrolloTecno Desigualdad
## 1 1 -0.3548007 0.8507184 0.8148660 -0.47705777 -0.4850922
## 2 2 0.8969817 -0.9414249 -1.5074389 -0.33230315 -0.4234698
## 3 3 -1.0522169 0.4114654 -0.9443295 -0.09327713 -0.1013467
## 4 4 1.0094246 -0.4619728 -1.3379853 -0.16895572 -1.2115162
## 5 5 -0.1969331 0.4502816 0.4573547 -0.83902038 0.8511718
## Salud Migracion ODA Women VIH
## 1 0.07629879 0.06216519 -0.5236006 -0.3123196 0.002167742
## 2 -1.00648177 0.03956145 0.4115030 -0.1910575 0.013303448
## 3 -0.86193997 -3.37249651 -0.7179959 -0.8676613 0.073066667
## 4 -1.17945045 0.46550216 1.1246357 0.5304265 0.058727273
## 5 -0.17273172 0.08512182 -0.3587131 0.7621978 0.001337500Mapa de las variables significativas
Hacemos una data aparte donde solo se encuentren las variables significativas.
VIH_Sig=VIH1_x
VIH_Sig=VIH_Sig[-c(4,10)]VIH1_ds = as.data.frame(scale(VIH_Sig)) #Estandarizamos set.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventadoPedimos las distancias como un criterio de agrupamiento
#Para pedir la distancia
VIH1_ds=dist(VIH1_ds[c(1:9)]) #Pedimos el numero de grupos:
VIH1s_clus=kmeans(VIH1_ds,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH1s_clus$cluster## Albania Algeria Argentina
## 1 1 1
## Armenia Azerbaijan Bangladesh
## 5 1 3
## Belarus Belize Benin
## 5 1 2
## Bolivia Botswana Brazil
## 1 4 1
## Burkina Faso Burundi Cabo Verde
## 2 4 1
## Cambodia Cameroon Chad
## 2 2 2
## Colombia Congo, Dem. Rep. Congo, Rep.
## 1 2 2
## Costa Rica Cote d'Ivoire Cuba
## 1 2 1
## Dominican Republic Ecuador Egypt, Arab Rep.
## 1 1 1
## El Salvador Eswatini Ethiopia
## 1 3 2
## Georgia Ghana Guinea
## 5 2 2
## Guinea-Bissau Guyana Haiti
## 2 1 2
## Honduras India Indonesia
## 1 3 1
## Iran, Islamic Rep. Kazakhstan Kenya
## 1 5 2
## Kyrgyz Republic Lao PDR Lebanon
## 1 2 1
## Lesotho Liberia Madagascar
## 4 4 2
## Malawi Malaysia Mali
## 4 1 2
## Mauritania Mexico Moldova
## 2 1 5
## Mongolia Montenegro Morocco
## 1 1 1
## Mozambique Myanmar Namibia
## 4 2 4
## Nepal Nicaragua Niger
## 2 1 2
## Nigeria Pakistan Papua New Guinea
## 2 5 2
## Paraguay Peru Philippines
## 1 1 1
## Rwanda Senegal Serbia
## 4 2 5
## Sierra Leone South Africa Sri Lanka
## 4 4 1
## Sudan Suriname Tajikistan
## 2 1 1
## Tanzania Thailand Togo
## 2 1 2
## Trinidad and Tobago Tunisia Uganda
## 1 1 2
## Ukraine Uruguay Uzbekistan
## 5 1 1
## Vietnam Zambia Zimbabwe
## 1 2 4#Para ver la cantidad de paises en cada grupo:
table(VIH1s_clus$cluster)##
## 1 2 3 4 5
## 39 29 3 11 8#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos1s=as.data.frame(VIH1s_clus$cluster)
grupos1s## VIH1s_clus$cluster
## Albania 1
## Algeria 1
## Argentina 1
## Armenia 5
## Azerbaijan 1
## Bangladesh 3
## Belarus 5
## Belize 1
## Benin 2
## Bolivia 1
## Botswana 4
## Brazil 1
## Burkina Faso 2
## Burundi 4
## Cabo Verde 1
## Cambodia 2
## Cameroon 2
## Chad 2
## Colombia 1
## Congo, Dem. Rep. 2
## Congo, Rep. 2
## Costa Rica 1
## Cote d'Ivoire 2
## Cuba 1
## Dominican Republic 1
## Ecuador 1
## Egypt, Arab Rep. 1
## El Salvador 1
## Eswatini 3
## Ethiopia 2
## Georgia 5
## Ghana 2
## Guinea 2
## Guinea-Bissau 2
## Guyana 1
## Haiti 2
## Honduras 1
## India 3
## Indonesia 1
## Iran, Islamic Rep. 1
## Kazakhstan 5
## Kenya 2
## Kyrgyz Republic 1
## Lao PDR 2
## Lebanon 1
## Lesotho 4
## Liberia 4
## Madagascar 2
## Malawi 4
## Malaysia 1
## Mali 2
## Mauritania 2
## Mexico 1
## Moldova 5
## Mongolia 1
## Montenegro 1
## Morocco 1
## Mozambique 4
## Myanmar 2
## Namibia 4
## Nepal 2
## Nicaragua 1
## Niger 2
## Nigeria 2
## Pakistan 5
## Papua New Guinea 2
## Paraguay 1
## Peru 1
## Philippines 1
## Rwanda 4
## Senegal 2
## Serbia 5
## Sierra Leone 4
## South Africa 4
## Sri Lanka 1
## Sudan 2
## Suriname 1
## Tajikistan 1
## Tanzania 2
## Thailand 1
## Togo 2
## Trinidad and Tobago 1
## Tunisia 1
## Uganda 2
## Ukraine 5
## Uruguay 1
## Uzbekistan 1
## Vietnam 1
## Zambia 2
## Zimbabwe 4names(grupos1s)='cluster'
grupos1s$NAME=row.names(grupos1s)
head(grupos1s)## cluster NAME
## Albania 1 Albania
## Algeria 1 Algeria
## Argentina 1 Argentina
## Armenia 5 Armenia
## Azerbaijan 1 Azerbaijan
## Bangladesh 3 Bangladesh#Creamos el objeto final:
mapamundo_VIH1s=merge(mapamundo,grupos1s)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "Accent")[mapamundo_VIH1s$cluster],
border='gray',add=T)
#Para tener un mapa interactivo asignando colores a cada grupo:
library(leaflet)
#newMaps!
c1=mapamundo_VIH1s[!is.na(mapamundo_VIH1s$cluster) & mapamundo_VIH1s$cluster==1,]
c2=mapamundo_VIH1s[!is.na(mapamundo_VIH1s$cluster) & mapamundo_VIH1s$cluster==2,]
c3=mapamundo_VIH1s[!is.na(mapamundo_VIH1s$cluster) & mapamundo_VIH1s$cluster==3,]
c4=mapamundo_VIH1s[!is.na(mapamundo_VIH1s$cluster) & mapamundo_VIH1s$cluster==4,]
c5=mapamundo_VIH1s[!is.na(mapamundo_VIH1s$cluster) & mapamundo_VIH1s$cluster==5,]
title="Clusters"
# base Layer
base= leaflet() %>% addProviderTiles("CartoDB.Positron")
layer1= base %>%
addPolygons(data=c1,color="lightseagreen",fillOpacity = 1,stroke = F,
group = "1")
layer_12= layer1%>%addPolygons(data=c2,color="slateblue",fillOpacity = 1,stroke = F,
group = "2")
layer_123= layer_12%>%addPolygons(data=c3,color="navy",fillOpacity = 1,stroke = F,
group = "3")
layer_1234= layer_123%>%addPolygons(data=c4,color="royalblue",fillOpacity = 1,stroke = F,
group = "4")
layer_12345= layer_1234%>%addPolygons(data=c5,color="turquoise",fillOpacity = 1,stroke = F,
group = "5")
layer_12345layer_12345%>% addLayersControl(
overlayGroups = c("1", "2","3","4","5"),
options = layersControlOptions(collapsed = FALSE)) agg12=aggregate(cbind(Empoderamiento,Metodos,Densidad,Desigualdad,Salud,Migracion,VIH) ~ cluster, data=VIH1_x,FUN=mean)
names(agg12)=c("cluster","Empoderamiento","Metodos","Densidad","Desigualdad","Salud","Migracion","VIH")
agg12## cluster Empoderamiento Metodos Densidad Desigualdad Salud
## 1 1 -0.3548007 0.8507184 0.8148660 -0.4850922 0.07629879
## 2 2 0.8969817 -0.9414249 -1.5074389 -0.4234698 -1.00648177
## 3 3 -1.0522169 0.4114654 -0.9443295 -0.1013467 -0.86193997
## 4 4 1.0094246 -0.4619728 -1.3379853 -1.2115162 -1.17945045
## 5 5 -0.1969331 0.4502816 0.4573547 0.8511718 -0.17273172
## Migracion VIH
## 1 0.06216519 0.002167742
## 2 0.03956145 0.013303448
## 3 -3.37249651 0.073066667
## 4 0.46550216 0.058727273
## 5 0.08512182 0.001337500Mapa de las hipotesis
Hacemos una data aparte donde solo se encuentre la relación entre las hipotesis
Reforzamiento de las capacidades femeninas
En esta hipotesis encontraremos las varibales de Empoderamiento y Uso de métodos anticonceptivos.
VIH_H1=VIH1_x[c(1,2,11)]
VIH1_dH1 = as.data.frame(scale(VIH_H1)) #Estandarizamos y nos quedamos con las variables de la hipótesis y la dependienteset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventado
#Para pedir la distancia
VIH1_dH1=dist(VIH1_dH1[c(1:3)])
#Pedimos las distancias como un criterio de agrupamiento#Pedimos el numero de grupos:
VIH1H_clus=kmeans(VIH1_dH1,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH1H_clus$cluster## Albania Algeria Argentina
## 1 3 1
## Armenia Azerbaijan Bangladesh
## 5 5 3
## Belarus Belize Benin
## 1 5 2
## Bolivia Botswana Brazil
## 5 4 1
## Burkina Faso Burundi Cabo Verde
## 2 2 1
## Cambodia Cameroon Chad
## 2 2 2
## Colombia Congo, Dem. Rep. Congo, Rep.
## 1 2 5
## Costa Rica Cote d'Ivoire Cuba
## 3 2 3
## Dominican Republic Ecuador Egypt, Arab Rep.
## 1 1 3
## El Salvador Eswatini Ethiopia
## 1 4 2
## Georgia Ghana Guinea
## 5 2 2
## Guinea-Bissau Guyana Haiti
## 2 5 5
## Honduras India Indonesia
## 1 3 1
## Iran, Islamic Rep. Kazakhstan Kenya
## 3 5 5
## Kyrgyz Republic Lao PDR Lebanon
## 5 2 3
## Lesotho Liberia Madagascar
## 4 2 2
## Malawi Malaysia Mali
## 2 5 2
## Mauritania Mexico Moldova
## 2 1 1
## Mongolia Montenegro Morocco
## 1 5 3
## Mozambique Myanmar Namibia
## 2 5 5
## Nepal Nicaragua Niger
## 2 1 2
## Nigeria Pakistan Papua New Guinea
## 2 3 5
## Paraguay Peru Philippines
## 1 1 5
## Rwanda Senegal Serbia
## 2 2 5
## Sierra Leone South Africa Sri Lanka
## 2 4 1
## Sudan Suriname Tajikistan
## 2 5 5
## Tanzania Thailand Togo
## 2 1 2
## Trinidad and Tobago Tunisia Uganda
## 5 3 2
## Ukraine Uruguay Uzbekistan
## 1 1 1
## Vietnam Zambia Zimbabwe
## 1 2 2#Para ver la cantidad de paises en cada grupo:
table(VIH1H_clus$cluster)##
## 1 2 3 4 5
## 23 31 11 4 21#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos1H=as.data.frame(VIH1H_clus$cluster)
grupos1H## VIH1H_clus$cluster
## Albania 1
## Algeria 3
## Argentina 1
## Armenia 5
## Azerbaijan 5
## Bangladesh 3
## Belarus 1
## Belize 5
## Benin 2
## Bolivia 5
## Botswana 4
## Brazil 1
## Burkina Faso 2
## Burundi 2
## Cabo Verde 1
## Cambodia 2
## Cameroon 2
## Chad 2
## Colombia 1
## Congo, Dem. Rep. 2
## Congo, Rep. 5
## Costa Rica 3
## Cote d'Ivoire 2
## Cuba 3
## Dominican Republic 1
## Ecuador 1
## Egypt, Arab Rep. 3
## El Salvador 1
## Eswatini 4
## Ethiopia 2
## Georgia 5
## Ghana 2
## Guinea 2
## Guinea-Bissau 2
## Guyana 5
## Haiti 5
## Honduras 1
## India 3
## Indonesia 1
## Iran, Islamic Rep. 3
## Kazakhstan 5
## Kenya 5
## Kyrgyz Republic 5
## Lao PDR 2
## Lebanon 3
## Lesotho 4
## Liberia 2
## Madagascar 2
## Malawi 2
## Malaysia 5
## Mali 2
## Mauritania 2
## Mexico 1
## Moldova 1
## Mongolia 1
## Montenegro 5
## Morocco 3
## Mozambique 2
## Myanmar 5
## Namibia 5
## Nepal 2
## Nicaragua 1
## Niger 2
## Nigeria 2
## Pakistan 3
## Papua New Guinea 5
## Paraguay 1
## Peru 1
## Philippines 5
## Rwanda 2
## Senegal 2
## Serbia 5
## Sierra Leone 2
## South Africa 4
## Sri Lanka 1
## Sudan 2
## Suriname 5
## Tajikistan 5
## Tanzania 2
## Thailand 1
## Togo 2
## Trinidad and Tobago 5
## Tunisia 3
## Uganda 2
## Ukraine 1
## Uruguay 1
## Uzbekistan 1
## Vietnam 1
## Zambia 2
## Zimbabwe 2names(grupos1H)='cluster'
grupos1H$NAME=row.names(grupos1H)
head(grupos1H)## cluster NAME
## Albania 1 Albania
## Algeria 3 Algeria
## Argentina 1 Argentina
## Armenia 5 Armenia
## Azerbaijan 5 Azerbaijan
## Bangladesh 3 Bangladesh#Creamos el objeto final:
mapamundo_VIH1H=merge(mapamundo,grupos1H)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "PiYG")[mapamundo_VIH1H$cluster],
border='gray',add=T)
#Para tener un mapa interactivo asignando colores a cada grupo:
library(leaflet)
#newMaps!
c1=mapamundo_VIH1H[!is.na(mapamundo_VIH1H$cluster) & mapamundo_VIH1H$cluster==1,]
c2=mapamundo_VIH1H[!is.na(mapamundo_VIH1H$cluster) & mapamundo_VIH1H$cluster==2,]
c3=mapamundo_VIH1H[!is.na(mapamundo_VIH1H$cluster) & mapamundo_VIH1H$cluster==3,]
c4=mapamundo_VIH1H[!is.na(mapamundo_VIH1H$cluster) & mapamundo_VIH1H$cluster==4,]
c5=mapamundo_VIH1H[!is.na(mapamundo_VIH1H$cluster) & mapamundo_VIH1H$cluster==5,]
title="Clusters"
# base Layer
base= leaflet() %>% addProviderTiles("CartoDB.Positron")
layer1= base %>%
addPolygons(data=c1,color="darkmagenta",fillOpacity = 1,stroke = F,
group = "1")
layer_12= layer1%>%addPolygons(data=c2,color="darkorchid",fillOpacity = 1,stroke = F,
group = "2")
layer_123= layer_12%>%addPolygons(data=c3,color="deeppink",fillOpacity = 1,stroke = F,
group = "3")
layer_1234= layer_123%>%addPolygons(data=c4,color="hotpink",fillOpacity = 1,stroke = F,
group = "4")
layer_12345= layer_1234%>%addPolygons(data=c5,color="mediumvioletred",fillOpacity = 1,stroke = F,
group = "5")
layer_12345layer_12345%>% addLayersControl(
overlayGroups = c("1", "2","3","4","5"),
options = layersControlOptions(collapsed = FALSE)) agg1H=aggregate(cbind(Empoderamiento,Metodos,VIH) ~ cluster, data=VIH1_x,FUN=mean)
names(agg1H)=c("cluster","Empoderamiento","Metodos","VIH")
agg1H## cluster Empoderamiento Metodos VIH
## 1 1 -0.3548007 0.8507184 0.002167742
## 2 2 0.8969817 -0.9414249 0.013303448
## 3 3 -1.0522169 0.4114654 0.073066667
## 4 4 1.0094246 -0.4619728 0.058727273
## 5 5 -0.1969331 0.4502816 0.001337500Calidad de vida
En esta hipotesis encontraremos las varibales de Densidad Estatal, Salud y Desigualdad.
VIH_H2=VIH1_x[c(3,5,6,11)]
VIH1_dH2 = as.data.frame(scale(VIH_H2)) #Estandarizamos y eliminamos la variable no significativa y la categóricaset.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventado
#Para pedir la distancia
VIH1_dH2=dist(VIH1_dH2[c(1:4)])
#Pedimos las distancias como un criterio de agrupamiento#Pedimos el numero de grupos:
VIH1H2_clus=kmeans(VIH1_dH2,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH1H2_clus$cluster## Albania Algeria Argentina
## 5 5 1
## Armenia Azerbaijan Bangladesh
## 5 5 2
## Belarus Belize Benin
## 5 3 2
## Bolivia Botswana Brazil
## 3 4 1
## Burkina Faso Burundi Cabo Verde
## 2 2 3
## Cambodia Cameroon Chad
## 3 2 2
## Colombia Congo, Dem. Rep. Congo, Rep.
## 1 2 2
## Costa Rica Cote d'Ivoire Cuba
## 1 2 5
## Dominican Republic Ecuador Egypt, Arab Rep.
## 1 1 5
## El Salvador Eswatini Ethiopia
## 1 4 2
## Georgia Ghana Guinea
## 5 3 2
## Guinea-Bissau Guyana Haiti
## 2 3 2
## Honduras India Indonesia
## 1 3 3
## Iran, Islamic Rep. Kazakhstan Kenya
## 3 5 2
## Kyrgyz Republic Lao PDR Lebanon
## 5 3 5
## Lesotho Liberia Madagascar
## 4 2 2
## Malawi Malaysia Mali
## 2 5 2
## Mauritania Mexico Moldova
## 2 1 5
## Mongolia Montenegro Morocco
## 5 5 3
## Mozambique Myanmar Namibia
## 2 3 4
## Nepal Nicaragua Niger
## 3 3 2
## Nigeria Pakistan Papua New Guinea
## 2 5 2
## Paraguay Peru Philippines
## 1 3 3
## Rwanda Senegal Serbia
## 2 3 1
## Sierra Leone South Africa Sri Lanka
## 2 4 3
## Sudan Suriname Tajikistan
## 2 1 5
## Tanzania Thailand Togo
## 2 1 2
## Trinidad and Tobago Tunisia Uganda
## 5 5 2
## Ukraine Uruguay Uzbekistan
## 5 1 5
## Vietnam Zambia Zimbabwe
## 5 2 4#Para ver la cantidad de paises en cada grupo:
table(VIH1H2_clus$cluster)##
## 1 2 3 4 5
## 14 30 18 6 22#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos1H2=as.data.frame(VIH1H2_clus$cluster)
grupos1H2## VIH1H2_clus$cluster
## Albania 5
## Algeria 5
## Argentina 1
## Armenia 5
## Azerbaijan 5
## Bangladesh 2
## Belarus 5
## Belize 3
## Benin 2
## Bolivia 3
## Botswana 4
## Brazil 1
## Burkina Faso 2
## Burundi 2
## Cabo Verde 3
## Cambodia 3
## Cameroon 2
## Chad 2
## Colombia 1
## Congo, Dem. Rep. 2
## Congo, Rep. 2
## Costa Rica 1
## Cote d'Ivoire 2
## Cuba 5
## Dominican Republic 1
## Ecuador 1
## Egypt, Arab Rep. 5
## El Salvador 1
## Eswatini 4
## Ethiopia 2
## Georgia 5
## Ghana 3
## Guinea 2
## Guinea-Bissau 2
## Guyana 3
## Haiti 2
## Honduras 1
## India 3
## Indonesia 3
## Iran, Islamic Rep. 3
## Kazakhstan 5
## Kenya 2
## Kyrgyz Republic 5
## Lao PDR 3
## Lebanon 5
## Lesotho 4
## Liberia 2
## Madagascar 2
## Malawi 2
## Malaysia 5
## Mali 2
## Mauritania 2
## Mexico 1
## Moldova 5
## Mongolia 5
## Montenegro 5
## Morocco 3
## Mozambique 2
## Myanmar 3
## Namibia 4
## Nepal 3
## Nicaragua 3
## Niger 2
## Nigeria 2
## Pakistan 5
## Papua New Guinea 2
## Paraguay 1
## Peru 3
## Philippines 3
## Rwanda 2
## Senegal 3
## Serbia 1
## Sierra Leone 2
## South Africa 4
## Sri Lanka 3
## Sudan 2
## Suriname 1
## Tajikistan 5
## Tanzania 2
## Thailand 1
## Togo 2
## Trinidad and Tobago 5
## Tunisia 5
## Uganda 2
## Ukraine 5
## Uruguay 1
## Uzbekistan 5
## Vietnam 5
## Zambia 2
## Zimbabwe 4names(grupos1H2)='cluster'
grupos1H2$NAME=row.names(grupos1H2)
head(grupos1H2)## cluster NAME
## Albania 5 Albania
## Algeria 5 Algeria
## Argentina 1 Argentina
## Armenia 5 Armenia
## Azerbaijan 5 Azerbaijan
## Bangladesh 2 Bangladesh#Creamos el objeto final:
mapamundo_VIH1H2=merge(mapamundo,grupos1H2)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "BrBG")[mapamundo_VIH1H2$cluster],
border='gray',add=T)
#Para tener un mapa interactivo asignando colores a cada grupo:
library(leaflet)
#newMaps!
c1=mapamundo_VIH1H2[!is.na(mapamundo_VIH1H2$cluster) & mapamundo_VIH1H2$cluster==1,]
c2=mapamundo_VIH1H2[!is.na(mapamundo_VIH1H2$cluster) & mapamundo_VIH1H2$cluster==2,]
c3=mapamundo_VIH1H2[!is.na(mapamundo_VIH1H2$cluster) & mapamundo_VIH1H2$cluster==3,]
c4=mapamundo_VIH1H2[!is.na(mapamundo_VIH1H2$cluster) & mapamundo_VIH1H2$cluster==4,]
c5=mapamundo_VIH1H2[!is.na(mapamundo_VIH1H2$cluster) & mapamundo_VIH1H2$cluster==5,]
title="Clusters"
# base Layer
base= leaflet() %>% addProviderTiles("CartoDB.Positron")
layer1= base %>%
addPolygons(data=c1,color="chocolate",fillOpacity = 1,stroke = F,
group = "1")
layer_12= layer1%>%addPolygons(data=c2,color="gold",fillOpacity = 1,stroke = F,
group = "2")
layer_123= layer_12%>%addPolygons(data=c3,color="sienna",fillOpacity = 1,stroke = F,
group = "3")
layer_1234= layer_123%>%addPolygons(data=c4,color="peru",fillOpacity = 1,stroke = F,
group = "4")
layer_12345= layer_1234%>%addPolygons(data=c5,color="orange",fillOpacity = 1,stroke = F,
group = "5")
layer_12345layer_12345%>% addLayersControl(
overlayGroups = c("1", "2","3","4","5"),
options = layersControlOptions(collapsed = FALSE)) agg1H2=aggregate(cbind(Densidad,Desigualdad,Salud,VIH) ~ cluster, data=VIH1_x,FUN=mean)
names(agg1H2)=c("cluster","Densidad","Desigualdad","Salud","VIH")
agg1H2## cluster Densidad Desigualdad Salud VIH
## 1 1 0.8148660 -0.4850922 0.07629879 0.002167742
## 2 2 -1.5074389 -0.4234698 -1.00648177 0.013303448
## 3 3 -0.9443295 -0.1013467 -0.86193997 0.073066667
## 4 4 -1.3379853 -1.2115162 -1.17945045 0.058727273
## 5 5 0.4573547 0.8511718 -0.17273172 0.001337500Movilidazación para el desarrollo en la población
En este analisis de conglomerados pondremos los componentes la hipoteisis tres que son el indice de Desarrollo tecnológico y migración
VIH_H3=VIH1_x[c(4,7,11)]
VIH1_dH3 = as.data.frame(scale(VIH_H3)) #Estandarizamos set.seed(15) #Para que todos los grupos tengan el mismo punto de inicio, el 15 es inventado
#Para pedir la distancia
VIH1_dH3=dist(VIH1_dH3[c(1:3)])
#Pedimos las distancias como un criterio de agrupamiento#Pedimos el numero de grupos:
VIH1H3_clus=kmeans(VIH1_dH3,centers = 5) #El 5 hace referencia al numero de grupos que se piden
VIH1H3_clus$cluster## Albania Algeria Argentina
## 5 5 1
## Armenia Azerbaijan Bangladesh
## 5 5 3
## Belarus Belize Benin
## 1 2 2
## Bolivia Botswana Brazil
## 2 4 2
## Burkina Faso Burundi Cabo Verde
## 5 1 2
## Cambodia Cameroon Chad
## 2 2 2
## Colombia Congo, Dem. Rep. Congo, Rep.
## 5 1 2
## Costa Rica Cote d'Ivoire Cuba
## 1 2 1
## Dominican Republic Ecuador Egypt, Arab Rep.
## 2 5 5
## El Salvador Eswatini Ethiopia
## 5 3 5
## Georgia Ghana Guinea
## 5 5 2
## Guinea-Bissau Guyana Haiti
## 2 2 2
## Honduras India Indonesia
## 5 3 2
## Iran, Islamic Rep. Kazakhstan Kenya
## 1 5 1
## Kyrgyz Republic Lao PDR Lebanon
## 5 2 2
## Lesotho Liberia Madagascar
## 4 2 1
## Malawi Malaysia Mali
## 2 1 5
## Mauritania Mexico Moldova
## 2 1 5
## Mongolia Montenegro Morocco
## 5 2 1
## Mozambique Myanmar Namibia
## 4 4 2
## Nepal Nicaragua Niger
## 2 2 2
## Nigeria Pakistan Papua New Guinea
## 5 4 2
## Paraguay Peru Philippines
## 5 5 4
## Rwanda Senegal Serbia
## 2 2 1
## Sierra Leone South Africa Sri Lanka
## 2 4 5
## Sudan Suriname Tajikistan
## 1 2 5
## Tanzania Thailand Togo
## 1 5 2
## Trinidad and Tobago Tunisia Uganda
## 5 1 1
## Ukraine Uruguay Uzbekistan
## 2 1 5
## Vietnam Zambia Zimbabwe
## 2 4 4#Para ver la cantidad de paises en cada grupo:
table(VIH1H3_clus$cluster)##
## 1 2 3 4 5
## 18 34 3 9 26#Creamos un objeto (cluster) que mezcla la informacion de los grupos con el mapa:
grupos1H3=as.data.frame(VIH1H3_clus$cluster)
grupos1H3## VIH1H3_clus$cluster
## Albania 5
## Algeria 5
## Argentina 1
## Armenia 5
## Azerbaijan 5
## Bangladesh 3
## Belarus 1
## Belize 2
## Benin 2
## Bolivia 2
## Botswana 4
## Brazil 2
## Burkina Faso 5
## Burundi 1
## Cabo Verde 2
## Cambodia 2
## Cameroon 2
## Chad 2
## Colombia 5
## Congo, Dem. Rep. 1
## Congo, Rep. 2
## Costa Rica 1
## Cote d'Ivoire 2
## Cuba 1
## Dominican Republic 2
## Ecuador 5
## Egypt, Arab Rep. 5
## El Salvador 5
## Eswatini 3
## Ethiopia 5
## Georgia 5
## Ghana 5
## Guinea 2
## Guinea-Bissau 2
## Guyana 2
## Haiti 2
## Honduras 5
## India 3
## Indonesia 2
## Iran, Islamic Rep. 1
## Kazakhstan 5
## Kenya 1
## Kyrgyz Republic 5
## Lao PDR 2
## Lebanon 2
## Lesotho 4
## Liberia 2
## Madagascar 1
## Malawi 2
## Malaysia 1
## Mali 5
## Mauritania 2
## Mexico 1
## Moldova 5
## Mongolia 5
## Montenegro 2
## Morocco 1
## Mozambique 4
## Myanmar 4
## Namibia 2
## Nepal 2
## Nicaragua 2
## Niger 2
## Nigeria 5
## Pakistan 4
## Papua New Guinea 2
## Paraguay 5
## Peru 5
## Philippines 4
## Rwanda 2
## Senegal 2
## Serbia 1
## Sierra Leone 2
## South Africa 4
## Sri Lanka 5
## Sudan 1
## Suriname 2
## Tajikistan 5
## Tanzania 1
## Thailand 5
## Togo 2
## Trinidad and Tobago 5
## Tunisia 1
## Uganda 1
## Ukraine 2
## Uruguay 1
## Uzbekistan 5
## Vietnam 2
## Zambia 4
## Zimbabwe 4names(grupos1H3)='cluster'
grupos1H3$NAME=row.names(grupos1H3)
head(grupos1H3)## cluster NAME
## Albania 5 Albania
## Algeria 5 Algeria
## Argentina 1 Argentina
## Armenia 5 Armenia
## Azerbaijan 5 Azerbaijan
## Bangladesh 3 Bangladesh#Creamos el objeto final:
mapamundo_VIH1H3=merge(mapamundo,grupos1H3)library("RColorBrewer")
plot(mapamundo,col='gray',border=NA)
plot(mapamundo,
col=brewer.pal(n = 5, name = "Greens")[mapamundo_VIH1H3$cluster],
border='gray',add=T)
#Para tener un mapa interactivo asignando colores a cada grupo:
library(leaflet)
#newMaps!
c1=mapamundo_VIH1H3[!is.na(mapamundo_VIH1H3$cluster) & mapamundo_VIH1H3$cluster==1,]
c2=mapamundo_VIH1H3[!is.na(mapamundo_VIH1H3$cluster) & mapamundo_VIH1H3$cluster==2,]
c3=mapamundo_VIH1H3[!is.na(mapamundo_VIH1H3$cluster) & mapamundo_VIH1H3$cluster==3,]
c4=mapamundo_VIH1H3[!is.na(mapamundo_VIH1H3$cluster) & mapamundo_VIH1H3$cluster==4,]
c5=mapamundo_VIH1H3[!is.na(mapamundo_VIH1H3$cluster) & mapamundo_VIH1H3$cluster==5,]
title="Clusters"
# base Layer
base= leaflet() %>% addProviderTiles("CartoDB.Positron")
layer1= base %>%
addPolygons(data=c1,color="darkgreen",fillOpacity = 1,stroke = F,
group = "1")
layer_12= layer1%>%addPolygons(data=c2,color="yellowgreen",fillOpacity = 1,stroke = F,
group = "2")
layer_123= layer_12%>%addPolygons(data=c3,color="forestgreen",fillOpacity = 1,stroke = F,
group = "3")
layer_1234= layer_123%>%addPolygons(data=c4,color="limegreen",fillOpacity = 1,stroke = F,
group = "4")
layer_12345= layer_1234%>%addPolygons(data=c5,color="lightgreen",fillOpacity = 1,stroke = F,
group = "5")
layer_12345layer_12345%>% addLayersControl(
overlayGroups = c("1", "2","3","4","5"),
options = layersControlOptions(collapsed = FALSE)) agg1H3=aggregate(cbind(Informacion,Migracion,VIH) ~ cluster, data=VIH1_x,FUN=mean)
names(agg1H3)=c("cluster","DesarrolloTecno","Migracion","VIH")
agg1H3## cluster DesarrolloTecno Migracion VIH
## 1 1 -0.47705777 0.06216519 0.002167742
## 2 2 -0.33230315 0.03956145 0.013303448
## 3 3 -0.09327713 -3.37249651 0.073066667
## 4 4 -0.16895572 0.46550216 0.058727273
## 5 5 -0.83902038 0.08512182 0.001337500