library(rio)
library(psych)

data = import("datainei.xlsx")

## New names:
## • `Total` -> `Total...5`
## • `Total` -> `Total...8`
## • `Total` -> `Total...11`
## • `Total` -> `Total...14`

colnames(data)[colnames(data) == "No usa electricidad"] <- "NO_ELECTRICIDAD"
colnames(data)[colnames(data) == "Sí usa electricidad"] <- "SI_ELECTRICIDAD"
colnames(data)[colnames(data) == "No usa gas (balón GLP)"] <- "NO_GAS"
colnames(data)[colnames(data) == "Sí usa gas (balón GLP)"] <- "SI_GAS"
colnames(data)[colnames(data) == "No usa carbón"] <- "NO_CARBON"
colnames(data)[colnames(data) == "Sí usa carbón"] <- "SI_CARBON"
colnames(data)[colnames(data) == "No usa leña"] <- "NO_LENA"
colnames(data)[colnames(data) == "Sí usa leña"] <- "SI_LENA"
colnames(data)[colnames(data) == "Total...5"] <- "TOTAL_ELECTRICIDAD"
colnames(data)[colnames(data) == "Total...8"] <- "TOTAL_GAS"
colnames(data)[colnames(data) == "Total...11"] <- "TOTAL_CARBON"
colnames(data)[colnames(data) == "Total...14"] <- "TOTAL_LENA"

data$PORCENTAJE_ELECTRICIDAD <- data$SI_ELECTRICIDAD / data$TOTAL_ELECTRICIDAD * 100
data$PORCENTAJE_GAS <- data$SI_GAS / data$TOTAL_GAS * 100
data$PORCENTAJE_CARBON <- data$SI_CARBON / data$TOTAL_CARBON * 100
data$PORCENTAJE_LENA <- data$SI_LENA / data$TOTAL_LENA * 100

variables <- data[, c("PORCENTAJE_ELECTRICIDAD", "PORCENTAJE_GAS", "PORCENTAJE_CARBON", "PORCENTAJE_LENA")]

kmo <- KMO(variables)
bartlett <- cortest.bartlett(variables)

## R was not square, finding R from data

kmo

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = variables)
## Overall MSA =  0.63
## MSA for each item = 
## PORCENTAJE_ELECTRICIDAD          PORCENTAJE_GAS       PORCENTAJE_CARBON 
##                    0.91                    0.59                    0.72 
##         PORCENTAJE_LENA 
##                    0.59

bartlett

## $chisq
## [1] 338.9104
## 
## $p.value
## [1] 3.704739e-70
## 
## $df
## [1] 6

#Varimax
efa_varimax <- fa(variables, nfactors = 1, rotate = "varimax")

#Oblimin
efa_oblimin <- fa(variables, nfactors = 1, rotate = "oblimin")

# Comparar cargas factoriales
efa_varimax$loadings

## 
## Loadings:
##                         MR1   
## PORCENTAJE_ELECTRICIDAD  0.491
## PORCENTAJE_GAS           0.912
## PORCENTAJE_CARBON        0.280
## PORCENTAJE_LENA         -0.956
## 
##                  MR1
## SS loadings    2.065
## Proportion Var 0.516

efa_oblimin$loadings

## 
## Loadings:
##                         MR1   
## PORCENTAJE_ELECTRICIDAD  0.491
## PORCENTAJE_GAS           0.912
## PORCENTAJE_CARBON        0.280
## PORCENTAJE_LENA         -0.956
## 
##                  MR1
## SS loadings    2.065
## Proportion Var 0.516

# Verificar varianza
efa_varimax$Vaccounted  # Varimax

##                      MR1
## SS loadings    2.0645612
## Proportion Var 0.5161403

efa_oblimin$Vaccounted  # Oblimin

##                      MR1
## SS loadings    2.0645612
## Proportion Var 0.5161403