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2_regresiones.R
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2_regresiones.R
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library(tidyverse)
install.packages("stargazer")
#Modelos trabajo ---------------------
t0_tr<-
summary(lm(
h40.huellaPreWrk ~
as.factor(P3_1) +
as.factor(`P5[{_1}].Rp`) +
as.factor(`P5[{_2}].Rp`) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_4}].Rp`) +
as.factor(`P5[{_5}].Rp`) +
as.factor(`P5[{_6}].Rp`) +
as.factor(`F0_Coronel (Bíobío)`)+
as.factor(`F0_Osorno (Los Lagos)`)+
as.factor(`F0_Temuco / Padre las Casas (La Araucanía)`)+
as.factor(`F0_Valdivia (Los Ríos)`)+
as.factor(GSE_C1)+
as.factor(GSE_C2)+
as.factor(GSE_C3)+
as.factor(GSE_D)+
as.factor(GSE_E)+
as.factor(P5_1) +
as.factor(P5_2), +
`Densidad del barrio (hab)` +
`Distancia al centro Network`,
data=bd1hogarDummy))
t1_tr<-summary(lm(
h40.huellaPstWrk ~
as.factor(P3_1) +
as.factor(`P5[{_1}].Rp`) +
as.factor(`P5[{_2}].Rp`) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_4}].Rp`) +
as.factor(`P5[{_5}].Rp`) +
as.factor(`P5[{_6}].Rp`)+
as.factor(P5_1) +
as.factor(P5_2)+
as.factor(F0)+
as.factor(GSE)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`, data=bd1hogarDummy))
t2_tr<-summary(lm(huellaDicWrk_v2~
as.factor(F0)+
as.factor(GSE)+
as.factor(P3_1)+
as.factor(`P5[{_1}].Rp`)+
as.factor(`P5[{_2}].Rp`)+
as.factor(`P5[{_3}].Rp`)+
as.factor(`P5[{_4}].Rp`)+
as.factor(`P5[{_5}].Rp`)+
as.factor(`P5[{_6}].Rp`)+
as.factor(`P5[{_7}].Rp`)+
as.factor(P5_1)+
as.factor(P5_2)+
as.factor(P5_3)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`, data=bd2hogarDummy))
#Modelos estudio ---------------------
t0_es<-summary(lm(
h40.huellaPreStu ~
as.factor(P3_1) +
as.factor(`P5[{_1}].Rp`) +
as.factor(`P5[{_2}].Rp`) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_4}].Rp`) +
as.factor(`P5[{_5}].Rp`) +
as.factor(`P5[{_6}].Rp`) +
as.factor(P5_1) +
as.factor(P5_2)+
as.factor(F0)+
as.factor(GSE)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`,
data=bd1hogarDummy))
t1_es<-summary(lm(
h40.huellaPstStu ~
as.factor(P3_1) +
as.factor(`P5[{_1}].Rp`) +
as.factor(`P5[{_2}].Rp`) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_4}].Rp`) +
as.factor(`P5[{_5}].Rp`) +
as.factor(`P5[{_6}].Rp`) +
as.factor(P5_1) +
as.factor(P5_2)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`+
as.factor(F0)+
as.factor(GSE)+,
data=bd1hogarDummy))
t2_es<-summary(lm(huellaDicStu_v2~
as.factor(F0)+
as.factor(GSE)+
as.factor(P3_1)+
as.factor(`P5[{_1}].Rp`)+
as.factor(`P5[{_2}].Rp`)+
as.factor(`P5[{_3}].Rp`)+
as.factor(`P5[{_4}].Rp`)+
as.factor(`P5[{_5}].Rp`)+
as.factor(`P5[{_6}].Rp`)+
as.factor(`P5[{_7}].Rp`)+
as.factor(P5_1)+
as.factor(P5_2)+
as.factor(P5_3)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`,
data=bd2hogarDummy))
# Desempeño de los modelos ------------------------------------------------
library(see)
library(performance)
performance::compare_performance(t0_es, t1_es,t2_es)
performance::compare_performance(t0_tr, t1_tr,t2_tr)
plot(performance::compare_performance(t0_es, t1_es,t2_es))
plot(performance::compare_performance(t0_tr, t1_tr,t2_tr))
# Modelos de trabajo más acotados -----------------------------------------
# Poner tipo de combustible
# Ver una reducción de variables
# Solo trabajo
# Auto Renta Densidad Distancia Compra presencial Cantidad de personas
# Contaminación Compra de alimentos
t0_tr<-lm(
h40.huellaPreWrk ~ as.factor(P3_1) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_6}].Rp`)+
as.factor(GSE)+
as.factor(P5_1)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`+
`P4[{_1}].Rp`, data=bd1hogarDummy[bd1hogarDummy$`P4[{_1}].Rp`!=99,]) %>% summary()
t1_tr<-lm(
h40.huellaPstWrk ~ as.factor(P3_1) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_6}].Rp`)+
as.factor(GSE)+
as.factor(P5_1)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`+
`P4[{_2}].Rp`, data=bd1hogarDummy[bd1hogarDummy$`P4[{_2}].Rp`!=99,]) %>% summary()
t2_tr<-lm(huellaDicWrk_v2~
as.factor(GSE)+
as.factor(P3_1)+
as.factor(`P5[{_3}].Rp`)+
as.factor(`P5[{_6}].Rp`)+
as.factor(P5_1)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`+
`P4[{_1}].Rp`, data=bd2hogarDummy[bd2hogarDummy$`P4[{_1}].Rp`!=99,])%>% summary()
# Gráficos ----------------------------------------------------------------
library(tidyverse)
# GSE
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=GSE))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=GSE))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=GSE))+
geom_point()
# Auto
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=P3_1))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=P3_1))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=P3_1))+
geom_point()
# + Bicicleta
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=`P5[{_3}].Rp`))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=`P5[{_3}].Rp`))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=`P5[{_3}].Rp`))+
geom_point()
# Consciencia
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=`P5[{_6}].Rp`))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=`P5[{_6}].Rp`))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=`P5[{_6}].Rp`))+
geom_point()
# Ha bajado el ingreso
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=P5_1))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=P5_1))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=P5_1))+
geom_point()
#Densidad
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=`Densidad del barrio (hab)`))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=`Densidad del barrio (hab)`))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=`Densidad del barrio (hab)`))+
geom_point()
#Distancia
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPreWrk,x=`Distancia al centro Network`))+
geom_point()
ggplot(data=bd1hogarDummy,aes(y=h40.huellaPstWrk,x=`Distancia al centro Network`))+
geom_point()
ggplot(data=bd2hogarDummy, aes(y=huellaDicWrk_v2,x=`Distancia al centro Network`))+
geom_point()
# Sintetizando bases de datos ---------------------------------------------
t0<-select(bd1hogarDummy,
c("h40.huellaPreWrk",
"F0",
"GSE",
"P3_1",
"P5[{_3}].Rp",
"P5[{_6}].Rp",
"P5_1",
"Densidad del barrio (hab)",
"Distancia al centro Network",
"P4[{_1}].Rp"
)) %>% mutate(., data="T0")
t1<-select(bd1hogarDummy,
c("h40.huellaPstWrk",
"F0",
"GSE",
"P3_1",
"P5[{_3}].Rp",
"P5[{_6}].Rp",
"P5_1",
"Densidad del barrio (hab)",
"Distancia al centro Network",
"P4[{_2}].Rp"))%>% mutate(., data="T1")
t2<-select(bd2hogarDummy,
c("huellaDicWrk_v2",
"F0",
"GSE",
"P3_1",
"P5[{_3}].Rp",
"P5[{_6}].Rp",
"P5_1",
"Densidad del barrio (hab)",
"Distancia al centro Network",
"P4[{_1}].Rp"))%>% mutate(., data="T2")
colnames(t0)[1]<-"Huella"
colnames(t1)[1]<-"Huella"
colnames(t2)[1]<-"Huella"
colnames(t0)[10]<-"tripsFood"
colnames(t1)[10]<-"tripsFood"
colnames(t2)[10]<-"tripsFood"
transporte<-rbind(t0,t1,t2)
# Gráficos comparados -----------------------------------------------------
# GSE
g1<-ggplot(data=transporte,aes(y=Huella,x=GSE, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de Carbono & GSE")+
xlab("GSE")
g2<-ggplot(data=transporte,aes(y=Huella,x=P3_1, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & Tenencia de auto")+
xlab("Tiene auto")
g3<-ggplot(data=transporte,aes(y=Huella,x=F0, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+ theme(axis.text.x = element_text(angle=90, hjust = 1))+
labs(title = "Huella de carbono & Ciudad")+
xlab("Ciudad")
g4<-ggplot(data=transporte,aes(y=Huella,x=`P5[{_3}].Rp`, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & Camina y anda más en bicicleta")+
xlab("Camina y anda más en bicicleta")
g5<-ggplot(data=transporte,aes(y=Huella,x=`P5[{_6}].Rp`, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & Conoce la calidad del aire de su ciudad")+
xlab("Conoce la calidad del aire de su ciudad")
g6<-ggplot(data=transporte,aes(y=Huella,x=P5_1, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & ha bajado el ingreso promedio del hogar")+
xlab("Ha bajado el ingreso promedio del hogar")
g7<-ggplot(data=transporte,aes(y=Huella,x=`Densidad del barrio (hab)`, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & Densidad del barrio")+
xlab("Densidad del barrio")
g8<-ggplot(data=transporte,aes(y=Huella,x=`Distancia al centro Network`, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & Distancia (network) al centro")+
xlab("Distancia (network) al centro")
g9<-ggplot(data=transporte[transporte$tripsFood!=99,],aes(y=Huella,x=tripsFood, col=factor(data),group=factor(data)))+
geom_point(position = position_dodge(width = .5))+
scale_color_viridis_d(name="Tiempo")+
labs(title = "Huella de carbono & Viajes a comprar alimentos")+
xlab("Viajes a comprar alimentos")
ggplot(data=transporte, aes(x=Huella, col=factor(data),group=factor(data)))+
geom_histogram(position = "dodge")
# Nuevo desempeño ---------------------------------------------------------
p1<-performance::compare_performance(t0_tr, t1_tr,t2_tr)
p2<-plot(performance::compare_performance(t0_tr, t1_tr,t2_tr))
# Divide and coquer (pendiente) -------------------------------------------------------
pivot_wider(transporte, id_cols = colnames(transporte)[1])
# Reducción de variables --------------------------------------------------
p1
# Guardando figuras y datos -----------------------------------------------
save(g1,g2,g3,g4,g5,g6,g7,g8,g9,t0_tr,t1_tr,t2_tr,p1,p2, st1, st1b, tr_spl, p1_spl, p2_spl,file="output/graphic.RData")
# Divide y vencerás -------------------------------------------------------
tr_spl<-split.data.frame(transporte,list(transporte$F0,transporte$data)) %>%
lapply(.,function(x){
x<-x
lm(Huella~
as.factor(GSE)+
as.factor(P3_1)+
as.factor(`P5[{_3}].Rp`)+
as.factor(`P5[{_6}].Rp`)+
as.factor(P5_1)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`+
tripsFood, data=x) %>% summary()})
lapply(tr_spl,performance::compare_performance)
p1_spl<-plot(performance::compare_performance(tr_spl[[1]],
tr_spl[[2]],
tr_spl[[3]],
tr_spl[[4]],
tr_spl[[5]],
tr_spl[[6]],
tr_spl[[7]],
tr_spl[[8]],
tr_spl[[9]],
tr_spl[[10]],
tr_spl[[11]],
tr_spl[[12]]))
p2_spl<-plot(performance::compare_performance(tr_spl[[1]],
tr_spl[[2]],
tr_spl[[3]],
tr_spl[[4]],
tr_spl[[5]],
tr_spl[[6]],
tr_spl[[7]],
tr_spl[[8]],
tr_spl[[9]],
tr_spl[[10]],
tr_spl[[11]],
tr_spl[[12]]))
# p2_spl<-plot(performance::compare_performance(tr_spl[[1]],
# tr_spl[[5]],
# tr_spl[[9]],
# tr_spl[[2]],
# tr_spl[[6]],
# tr_spl[[10]],
# tr_spl[[3]],
# tr_spl[[7]],
# tr_spl[[11]],
# tr_spl[[4]],
# tr_spl[[8]],
# tr_spl[[12]])) %>%
# legen d (1,95,legend=c("Coronel T0", "Coronel T1","Coronel T2",
# "Osorno T0", "Osorno T1","Osorno T2",
# "Temuco T0", "Temuco T1","Temuco T2",
# "Valdivia T0", "Valdivia T1","Valdivia T2"), lty=1:2, cex=0.8)
names(tr_spl)
# Performance -------------------------------------------------------------
p1_spl<-performance::compare_performance(tr_spl[[1]],tr_spl[[2]],tr_spl[[3]],tr_spl[[4]],tr_spl[[5]],tr_spl[[6]],
tr_spl[[7]],tr_spl[[8]],tr_spl[[9]],tr_spl[[10]],tr_spl[[11]],tr_spl[[12]])
p2_spl<-plot(performance::compare_performance(tr_spl[[1]],tr_spl[[2]],tr_spl[[3]],tr_spl[[4]],tr_spl[[5]],tr_spl[[6]],
tr_spl[[7]],tr_spl[[8]],tr_spl[[9]],tr_spl[[10]],tr_spl[[11]],tr_spl[[12]]))
# Appendix ----------------------------------------------------------------
summary(lm(
h40.huellaPreStu ~ as.factor(P3_1) + as.factor(`P5[{_1}].Rp`) + as.factor(`P5[{_2}].Rp`) + as.factor(`P5[{_3}].Rp`) + as.factor(F0)+ as.factor(GSE)+
as.factor(`P5[{_4}].Rp`) + as.factor(`P5[{_5}].Rp`) + as.factor(`P5[{_6}].Rp`) + as.factor(P5_1) + as.factor(P5_2)+`Densidad del barrio (hab)` + `Distancia al centro Network`+ as.factor(P1_3), data=bd1hogarDummy))
summary(lm(
h40.huellaPreStu ~ as.factor(P3_1) + as.factor(`P5[{_1}].Rp`) + as.factor(`P5[{_2}].Rp`) + as.factor(`P5[{_3}].Rp`) + as.factor(F0)+ as.factor(GSE)+
as.factor(`P5[{_4}].Rp`) + as.factor(`P5[{_5}].Rp`) + as.factor(`P5[{_6}].Rp`) + as.factor(P5_1) + as.factor(P5_2)+`Densidad del barrio (hab)` + `Distancia al centro Network`, data=bd1hogarDummy))
test1<-lm(
h40.huellaPreStu ~ as.factor(P3_1) + as.factor(`P5[{_1}].Rp`) + as.factor(`P5[{_2}].Rp`) + as.factor(`P5[{_3}].Rp`) + as.factor(F0)+ as.factor(GSE)+
as.factor(`P5[{_4}].Rp`) + as.factor(`P5[{_5}].Rp`) + as.factor(`P5[{_6}].Rp`) + as.factor(P5_1) + as.factor(P5_2)+`Densidad del barrio (hab)` + `Distancia al centro Network`, data=bd1hogarDummy)
test2<-lm(
h40.huellaPreStu ~ as.factor(P3_1) + as.factor(`P5[{_1}].Rp`) + as.factor(`P5[{_2}].Rp`) + as.factor(GSE)+
as.factor(`P5[{_4}].Rp`) + as.factor(`P5[{_5}].Rp`) + as.factor(`P5[{_6}].Rp`) + as.factor(P5_1) + as.factor(P5_2)+`Densidad del barrio (hab)` + `Distancia al centro Network`, data=bd1hogarDummy)
#Modelos - creando una fórmula
as.formula(paste("h40.huellaPreStu~ ",paste(ifelse(grepl(" ",colnames(bd1hogarDummy)[22:97])|grepl("\\{",colnames(bd1hogarDummy)[22:97]),paste0("`",colnames(bd1hogarDummy)[22:97],"`"),colnames(bd1hogarDummy)[22:97]),collapse = "+")))
st1<-stargazer::stargazer(t0_tr,t1_tr, t2_tr, type="text")
st1b<-stargazer::stargazer(t0_tr,t1_tr, t2_tr, type="text")
car::vif()
car::vif(t0_tr)
car::vif(t1_tr)
car::vif(t2_tr)
summary(lm(
h40.huellaPreWrk ~ as.factor(P3_1) +
as.factor(`P5[{_3}].Rp`) +
as.factor(`P5[{_6}].Rp`)+
as.factor(GSE)+
as.factor(P5_1)+
`Densidad del barrio (hab)` +
`Distancia al centro Network`
, data=bd1hogarDummy[bd1hogarDummy$`P4[{_1}].Rp`!=99,]))