rm(list = ls())
library(rio)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data1= import("reporte.xlsx")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
data1 = data1[,-c(1,2)]
filas_a_eliminar <- c(1, 2, 3, 4, 202, 203, 204)
data1 <- data1[-filas_a_eliminar, ]
str(data1)
## 'data.frame': 197 obs. of 13 variables:
## $ ...3 : chr "Provincia" "Amazonas, provincia: Chachapoyas" "Amazonas, provincia: Bagua" "Amazonas, provincia: Bongara" ...
## $ ...4 : chr "No usa electricidad" "14763" "20313" "7689" ...
## $ ...5 : chr "Sí usa electricidad" "574" "161" "124" ...
## $ ...6 : chr "Total" "15337" "20474" "7813" ...
## $ ...7 : chr "No usa gas (balón GLP)" "4696" "10557" "3154" ...
## $ ...8 : chr "Sí usa gas (balón GLP)" "10641" "9917" "4659" ...
## $ ...9 : chr "Total" "15337" "20474" "7813" ...
## $ ...10: chr "No usa carbón" "15161" "20185" "7755" ...
## $ ...11: chr "Sí usa carbón" "176" "289" "58" ...
## $ ...12: chr "Total" "15337" "20474" "7813" ...
## $ ...13: chr "No usa leña" "7236" "7357" "2345" ...
## $ ...14: chr "Sí usa leña" "8101" "13117" "5468" ...
## $ ...15: chr "Total" "15337" "20474" "7813" ...
data1 <- data1 %>%
rename (provincia = "...3",
no_electricidad = "...4",
si_electricidad = "...5",
total_electricidad = "...6",
no_gas = "...7",
si_gas = "...8",
total_gas = "...9",
no_carbon = "...10",
si_carbon = "...11",
total_carbon = "...12",
no_leña = "...13",
si_leña = "...14",
total_leña = "...15")
data1 <- data1[-1, ]
str(data1)
## 'data.frame': 196 obs. of 13 variables:
## $ provincia : chr "Amazonas, provincia: Chachapoyas" "Amazonas, provincia: Bagua" "Amazonas, provincia: Bongara" "Amazonas, provincia: Condorcanqui" ...
## $ no_electricidad : chr "14763" "20313" "7689" "9853" ...
## $ si_electricidad : chr "574" "161" "124" "14" ...
## $ total_electricidad: chr "15337" "20474" "7813" "9867" ...
## $ no_gas : chr "4696" "10557" "3154" "8331" ...
## $ si_gas : chr "10641" "9917" "4659" "1536" ...
## $ total_gas : chr "15337" "20474" "7813" "9867" ...
## $ no_carbon : chr "15161" "20185" "7755" "9841" ...
## $ si_carbon : chr "176" "289" "58" "26" ...
## $ total_carbon : chr "15337" "20474" "7813" "9867" ...
## $ no_leña : chr "7236" "7357" "2345" "1059" ...
## $ si_leña : chr "8101" "13117" "5468" "8808" ...
## $ total_leña : chr "15337" "20474" "7813" "9867" ...
data1[,-c(1)]=lapply(data1[,-c(1)], readr::parse_number)
str(data1)
## 'data.frame': 196 obs. of 13 variables:
## $ provincia : chr "Amazonas, provincia: Chachapoyas" "Amazonas, provincia: Bagua" "Amazonas, provincia: Bongara" "Amazonas, provincia: Condorcanqui" ...
## $ no_electricidad : num 14763 20313 7689 9853 13112 ...
## $ si_electricidad : num 574 161 124 14 90 65 255 921 16 33 ...
## $ total_electricidad: num 15337 20474 7813 9867 13202 ...
## $ no_gas : num 4696 10557 3154 8331 6863 ...
## $ si_gas : num 10641 9917 4659 1536 6339 ...
## $ total_gas : num 15337 20474 7813 9867 13202 ...
## $ no_carbon : num 15161 20185 7755 9841 13169 ...
## $ si_carbon : num 176 289 58 26 33 26 335 218 4 4 ...
## $ total_carbon : num 15337 20474 7813 9867 13202 ...
## $ no_leña : num 7236 7357 2345 1059 1833 ...
## $ si_leña : num 8101 13117 5468 8808 11369 ...
## $ total_leña : num 15337 20474 7813 9867 13202 ...
data1$por_electricidad <- (data1$si_electricidad / data1$total_electricidad) * 100
data1$por_gas <- (data1$si_gas / data1$total_gas) * 100
data1$por_carbon <- (data1$si_carbon / data1$total_carbon) * 100
data1$por_leña <- (data1$si_leña / data1$total_leña) * 100
dontselect=c("provincia","no_electricidad","si_electricidad","total_electricidad","no_gas","si_gas", "total_gas", "si_carbon", "no_carbon","total_carbon","si_leña","total_leña", "no_leña")
select=setdiff(names(data1),dontselect)
theData= data1[,select]
corMatrix=polycor::hetcor(theData)$correlations
round(corMatrix,2)
## por_electricidad por_gas por_carbon por_leña
## por_electricidad 1.00 0.47 0.10 -0.46
## por_gas 0.47 1.00 0.23 -0.87
## por_carbon 0.10 0.23 1.00 -0.31
## por_leña -0.46 -0.87 -0.31 1.00
library(psych)
psych::KMO(corMatrix)
## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.63
## MSA for each item =
## por_electricidad por_gas por_carbon por_leña
## 0.91 0.59 0.72 0.59
cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05
## [1] FALSE
library(matrixcalc)
is.singular.matrix(corMatrix)
## [1] FALSE
fa.parallel(theData, fa = 'fa',correct = T,plot = F)
## Parallel analysis suggests that the number of factors = 1 and the number of components = NA
library(GPArotation)
##
## Attaching package: 'GPArotation'
## The following objects are masked from 'package:psych':
##
## equamax, varimin
resfa <- fa(theData,
nfactors = 1,
cor = 'mixed',
rotate = "varimax",
fm="minres")
print(resfa$loadings)
##
## Loadings:
## MR1
## por_electricidad 0.491
## por_gas 0.912
## por_carbon 0.280
## por_leña -0.956
##
## MR1
## SS loadings 2.065
## Proportion Var 0.516
print(resfa$loadings,cutoff = 0.5)
##
## Loadings:
## MR1
## por_electricidad
## por_gas 0.912
## por_carbon
## por_leña -0.956
##
## MR1
## SS loadings 2.065
## Proportion Var 0.516
library(GPArotation)
resfa2<- fa(theData,
nfactors = 1,
cor = 'mixed',
rotate = "oblimin",
fm="minres")
print(resfa2$loadings)
##
## Loadings:
## MR1
## por_electricidad 0.491
## por_gas 0.912
## por_carbon 0.280
## por_leña -0.956
##
## MR1
## SS loadings 2.065
## Proportion Var 0.516
print(resfa2$loadings,cutoff = 0.5)
##
## Loadings:
## MR1
## por_electricidad
## por_gas 0.912
## por_carbon
## por_leña -0.956
##
## MR1
## SS loadings 2.065
## Proportion Var 0.516
fa.diagram(resfa,main = "Resultados del EFA")
as.data.frame(resfa$scores)%>%head()
## MR1
## 6 0.4475047
## 7 -0.1693039
## 8 -0.1326194
## 9 -1.2306794
## 10 -0.6894937
## 11 -0.6444460
data1$resfa=resfa$scores[,1]
resfa$TLI
## [1] 0.9607796
resfa$rms
## [1] 0.03000416
resfa$RMSEA
## RMSEA lower upper confidence
## 0.10505967 0.01732496 0.20271043 0.90000000
resfa$BIC
## [1] -4.21926