library(tidyverse)
library(haven) # importa, entre otros software, de Stata
library(sjlabelled) # manejar labels de las variables
library(openxlsx) # leer archivos de excel
library(DT) # visualizar a tablas
library(data.table) # convertir a tablas
library(highcharter) # gráficas interactivas
library(foreign)
library(stargazer)
library(ggplot2)
library(survival)
library(broom)
library(gridExtra)
Funciones de carga
cargar_esru<- function(){
return(read_stata(
path = "Datos/ESRU-EMOVI-2017/ESRU-EMOVI-2017-Entrevistado.dta",
atomic.to.fac = TRUE,
enc = NULL,
))
}
cargar_enigh<- function(docu){
return(read_stata(
path = paste("Datos/ENIGH-2016/",docu,".dta",sep=""),
atomic.to.fac = TRUE,
enc = NULL,
))
}
cargar_enigh_sav<- function(docu){
return(read_spss(
path = paste("Datos/ENIGH-2016/",docu,".sav",sep=""),
atomic.to.fac = TRUE,
enc = NULL,
))
}
cargar_enigh_18<- function(docu){
return(read_stata(
path = paste("Datos/ENIGH-2018/",docu,".dta",sep=""),
atomic.to.fac = TRUE,
enc = NULL,
))
}
cargar_enigh_sav_18<- function(docu){
return(read_spss(
path = paste("Datos/ENIGH-2018/",docu,".sav",sep=""),
atomic.to.fac = TRUE,
enc = NULL,
))
}
Filtrar hijos ESRU
Creamos per movilidad
data_emovi2017<-cargar_esru()
data_emovi2017<-data_emovi2017%>%
mutate(mov = ifelse(p147>p148,"mejor",
ifelse(p147<p148,"peor","igual")),
id_ho = as.integer(row.names(data_emovi2017)))
data_emovi2017$mov<-set_labels(
data_emovi2017$mov,
labels = c("mejor" = "mejor",
"peor" = "peor",
"igual" = "igual"))
seleccion de hijos
Criterios de filtrado - pregunta p2, comparten el mimso gasto para comer: 1 ) Si 2 ) No - pregunta 5 Edades entre 25 y 45 años - pregunta p08, es el jefe del hogar: 1 ) jefe 2) cónyuge NO IMPLEMENTADO - pregunta p12, actualmente estudia, 1)Si 2)No - p26 Había un jefe de hogar en la infancia (1 padre 2 madre) - p43 y p43m Reporta educación del padre o madre priciapl - p68, Personas que trabajan, 1. Si, 2. No - p69 si trabaja pero la semana pasada no - p132 numero de personas que aportan ingreso - p133 cohort de todas las personas y todos los ingresos (1 ingreso)
Variables de selección para estimar ingreso
Estado
pregunta 5, edad
pregunta 6, sexo
Pregunta 13, nivel de escuela
p76 ocupación –SINCO3 codificación ocupación entrevistado
p133 cohort de todas las personas y todos los ingresos
# pensamos que los datos se leeen como factores cando corresponda, si se leen como enteros no debe hacerse el casting
#data_emovi2017<-datos_e
hijos <- data_emovi2017 %>%
filter(p02 == 1) %>% #compartir gasot para comer
filter(between(p05,25,50)) %>% # edad, criterio 1 de 25 a 40,criterio2 aumentamos los ingreos, criterio 3 de 25 a 50
#filter(p08 == 1) %>%
filter(p12 == 2) %>% # ya no estudia
filter(p26 == 1 | p26==2 ) %>% # sosten principal papa, mama,
filter((!is.na(p43) & p43!=98 & p26 == 1 & cmo1_2!="." & !is.na(p38_11)) |
(!is.na(p43m) & p43m!=98 & p26 == 2 & cmo2_2!="." & !is.na(p38m_11) )) %>% # reporta educación de pp
filter(!is.na(p13))%>% # reporta educación
filter(SINCO3 !="") %>%# reporta oficio actual
filter(p68 == 1 | p69 == 1) %>% # si trabaja
filter(p132 %in% c(1)) %>%# persona que aportan ingreso
filter(!p133 %in% c(8,9,NA) ) # reporta ingresos
# no estan los ingresos hogar aislados.
#edad_promedio_padres <- mean(hijos$p38_11, na.rm = TRUE)
#edad_promedio_padres-21
unique(hijos$region)
unique(hijos$p43)
nrow(hijos)
hijos seleccionados
Hacemos un cohort del 1 y el 2
var<-c("Estado","p05","p06","p13","SINCO3","p133","mov","id_ho","region")
hijos_para_sal<-hijos%>%
select(var)#%>%
#mutate(p133 = ifelse(p133==1,2,p133)-1)# juntamos el cohort 1 y 2
hijos_para_sal <- hijos_para_sal%>%
mutate(estrato = ifelse(p133 %in% c(1,2),1,
ifelse(p133 %in% c(3),2,
ifelse(p133 %in% c(4,5),3,4)))
)
ajustamos region
# ajsutajos a 4 regiones
hijos_para_sal <- hijos_para_sal%>%
mutate(
region = ifelse(Estado %in% as.integer(c("02","26","08","05","19","28")),1,
ifelse(Estado %in%as.integer(c("12","20","07","30","27","04","31","23")),4,
ifelse(Estado %in% as.integer(c("03","25","18","10","32","16","06","14","01","24")),2,3)))
)
#sum(hijos_para_sal$p133==1)
unique(hijos_para_sal$region)
nrow(hijos_para_sal)
Hijos fijos
base_hijos_fija <- hijos_para_sal%>%filter(p133==2)
#ingreso_hijo_e18_emovi
base_hijos_fija_f<-base_hijos_fija%>%
summarise(
id_hijo = "fijo",
id_ho = id_ho,
mov= mov,
regionh = region,
edad = p05,
cohort = 100,# paa que sepueda hacer la resta pero no significa nada
ing_men_p_h = list(2400),
estrato = estrato,
estado = Estado,
sexo = p06
)
base_hijos_fija_f
saveRDS(base_hijos_fija_f, file="Datos/base_hijos_fija_f_cri3.Rda")
Hijos para estimar
hijos_para_sal_1<-hijos_para_sal%>%
mutate(p133 = ifelse(p133==1,2,p133)-1)# transformamos
unique(hijos_para_sal_1$p133)
saveRDS(hijos_para_sal_1, file="Datos/hijos_emovi2017_p1.Rda")
unique(hijos_para_sal_1$p133)
Hijos construcción ingresos 16
poblacion enigh 16
poblacion_e16<-cargar_enigh_sav("poblacion")
poblacion_e16f<- poblacion_e16%>%
#filter(asis_esc == "2")%>% # ya no asiste a la escuela
select(folioviv,foliohog,numren,sexo,edad,nivelaprob)%>%
filter(between(as.integer(edad),23,52))#criterio 1 entre 25-40, criterio 3 entre 25-50
# no filtramos por jefe de hogar
nrow(poblacion_e16f)
sum(is.na(poblacion_e16f))
creamos region 16
poblacion_e16f<- poblacion_e16f%>%
mutate(
region = ifelse(substr(folioviv,1,2) %in% c("02","26","08","05","19","28"),1,
ifelse(substr(folioviv,1,2) %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(substr(folioviv,1,2) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
unique(poblacion_e16f$region)
nrow(poblacion_e16f)
sum(is.na(poblacion_e16f))
trabajos enigh 16
Filtrams los trabajos
trabajos_e16<-cargar_enigh_sav("trabajos")
trabajos_e16f<-trabajos_e16%>%
select(folioviv,foliohog,numren,sinco)%>%
filter(!is.na(sinco))
NROW(trabajos_e16f)
#NROW(trabajos)
sum(is.na(trabajos_e16f))
Ingresos enigh 2016
ingresos_e16<-cargar_enigh_sav("ingresos")
familias_integrantes_e16 <- function(df,folhog,nr){
dfn <- df %>%
filter(foliohog %in% folhog)%>%
filter(numren %in% nr)
return(dfn)
}
ingresos_acu_e16 <- function(df,clavs=get_labels(ingresos_e16$clave)){
dfn <- df %>%
group_by(folioviv, foliohog, numren) %>%
filter(clave %in% clavs)%>%
summarise(
claves_a = paste(clave,collapse = ","),
ing_tri_t = sum(ing_tri),
ing_men_p = ing_tri_t/3,
ling_men_p = log(ing_men_p)
) %>% ungroup()
return(dfn)
}
hogares <- c("1","2","3","4","5")
personas_h <- c('01','02','03','04','05')
a<-familias_integrantes_e16(ingresos_e16,hogares,personas_h)
head(a)
### criterio 1 algunos ingresos no todos los de abajo
#claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", "P016","P017","P018","P020","P021","P026","P027","P035","P040","P041","P042","P044","P045")
## criterio 2 flexibilidad en los ingresos, criterio 3
claves<-c("P001","P002","P003","P011","P13","P023","P024","P025","P026","P027","P028","P029","P030","P031","P032","P033","P054","P055","P056", "P059","P060","P061","P062","P063","P068","P069","P070","P071","P072","P073","P074","P075","P076","P077","P078","P079","P080")
ingresos_e16a<-ingresos_acu_e16(a,claves)
ingresos_e16a<- ingresos_e16a%>%
mutate(coh_ing = ifelse(ing_men_p<=2400,1,
ifelse(ing_men_p<=4800,2,
ifelse(ing_men_p<=7200,3,
ifelse(ing_men_p<=12000,4,
ifelse(ing_men_p<=24000,5,6))))))
ingresos_e16a<- ingresos_e16a%>%mutate(
region = ifelse(substr(folioviv,1,2) %in% c("02","26","08","05","19","28"),1,
ifelse(substr(folioviv,1,2) %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(substr(folioviv,1,2) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
ingresos_e16fc<-ingresos_e16a%>%
select(folioviv,foliohog,numren,coh_ing)
sort(unique(as.character(ingresos_e16$clave)))[]
rango percentil 16
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e16a$ling_men_pd <- ingresos_e16a$ling_men_p + log(IN)
rp_e16 <- rank(ingresos_e16a$ling_men_pd)/length(ingresos_e16a$ling_men_pd)
ingresos_e16a1 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==1)%>%pull(ling_men_p) + log(IN))
rp_e161 <- rank(ingresos_e16a1$ling_men_pd)/length(ingresos_e16a1$ling_men_pd)
ingresos_e16a2 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==2)%>%pull(ling_men_p) + log(IN))
rp_e162 <- rank(ingresos_e16a2$ling_men_pd)/length(ingresos_e16a2$ling_men_pd)
ingresos_e16a3 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==3)%>%pull(ling_men_p) + log(IN))
rp_e163 <- rank(ingresos_e16a3$ling_men_pd)/length(ingresos_e16a3$ling_men_pd)
ingresos_e16a4 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==4)%>%pull(ling_men_p) + log(IN))
rp_e164 <- rank(ingresos_e16a4$ling_men_pd)/length(ingresos_e16a4$ling_men_pd)
hist(rp_e16)
Base cruzada pob-tra 16
enigh_16<- full_join(poblacion_e16f,trabajos_e16f)%>%
drop_na()
#enigh<-full_join(enigh,concentrado)%>%
# drop_na()
nrow(enigh_16)
sum(is.na(enigh_16))
Cambio etiquetado educacion 16
Pasamos las etiqeuteas de educación de emovi a
hijos_emovi <- readRDS(file="Datos/hijos_emovi2017_p1.Rda")#criterio 1 y 3
hijos_emovi<- hijos_emovi %>%
mutate(p13_m = ifelse(p13 %in% c(1),"1",
ifelse( p13 == 2,"2",
ifelse(p13 %in% c(3,4),"3",
ifelse( p13 %in% c(5,6),"4",
ifelse(p13 %in% c(7,8),"6",
ifelse(p13 %in% c(9,10),"5",
ifelse(p13==11,"7",
ifelse(p13==12,"8","0")))))))))
unique(hijos_emovi$p13_m)
enigh_16<-enigh_16%>%
mutate(nivelaprob=ifelse(nivelaprob=="9","8",nivelaprob))
unique(enigh_16$nivelaprob)
enigh_16f<-enigh_16%>%
filter(nivelaprob %in% unique(hijos_emovi$p13_m))
nrow(enigh_16f)
Buscar folios con caracteristicas 16
busca<-function(i){
return(enigh_16f%>%
filter(
#as.integer(substr(folioviv,1,2))==hijos_emovi[i,]$Estado &
region == hijos_emovi[i,]$region &
sexo == hijos_emovi[i,]$p06 &
((edad-2) <= hijos_emovi[i,]$p05) &
(hijos_emovi[i,]$p05 <= (edad+2)) &
#as.character(sinco) == hijos_emovi[i,]$SINCO3 &
substr(as.character(sinco),1,1)== substr(hijos_emovi[i,]$SINCO3,1,1)&
nivelaprob == hijos_emovi[i,]$p13_m)%>%
select(folioviv,foliohog))
}
a<-lapply(1:nrow(hijos_emovi), busca)
contador<-0
for(i in 1:nrow(hijos_emovi))
{if(nrow(a[[i]])>0)
contador<-contador +1
}
contador
#saveRDS(a, file="Datos/folhijos_enigh16_emovi_re_ch.Rda")
#saveRDS(a, file="Datos/folhijos_enigh16_emovi_re_ch_cri3.Rda")
saveRDS(a, file="Datos/folhijos_enigh16_emovi_sinm1_re_p1.Rda")
#saveRDS(a, file="Datos/folhijos_enigh16_emovi_est_ch_cri3.Rda")
- Con ocupacion y region del total, 668 personas tienen gemelos
- Con region,
Criterio 3 - ocupacion y region y, 979 - estado, mas al rato cohort en el ingreso ### filtamos solo los hijos encontrados 16
# etiquetamos para no perder la numeracion respecto a los hijos originales
folhijos_e16_emovi <- readRDS(file="Datos/folhijos_enigh16_emovi_sinm1_re_p1.Rda")
names(folhijos_e16_emovi) <- as.character(1:nrow(hijos_emovi))
length(folhijos_e16_emovi)
L <- c()
for(i in 1:length(folhijos_e16_emovi)){
if(!(nrow(folhijos_e16_emovi[[i]])>0)){
L<-c(L,i)}
}
# Solo hijos que se les pudo encontrar en enigh-2016 1077/1131
folhijos_e16_emovif<-folhijos_e16_emovi[-L]
length(folhijos_e16_emovif)
#names(folhijos_emovif)
#names(folhijos_emovi)
Unicos folios, diferentes trabajos 16
unicos_folios<-function(i){
folhijos_e16_emovif[[i]]%>%
distinct()
}
# filtramos unicos folios por diferentes trabajos de la misma persona
a<-lapply(1:length(folhijos_e16_emovif), unicos_folios)
names(a)<-names(folhijos_e16_emovif)
length(a)
folhijos_e16_emovif<-a
Observamos las edades de los encontrados 16
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
hijos_emovi%>%
slice(as.integer(names(folhijos_e16_emovif)))%>%
select(p05)%>%
count(p05)%>%
hchart('column',
hcaes( y = 'n'))%>%
hc_add_theme(hc_theme_ffx())%>%
hc_title(
text = ""
) %>%
#hc_subtitle(text = "Las edades están agrupadas en intervalos
# de 10 años (da clic sobre F o M)") %>%
# hc_credits(
# enabled = TRUE, text = "Source: SSS",
# style = list(fontSize = "12px"))%>%
hc_yAxis(title = list(text = "Número de participantes"))%>%
hc_xAxis(title=list(text="Número de personas"),
categories = as.character(25:50)) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
Observamos las estados de los encontrados 16
hijos_emovi%>%
slice(as.integer(names(folhijos_e16_emovif)))%>%
count(Estado)%>%
hchart('column',
hcaes( y = 'n'))%>%
hc_add_theme(hc_theme_ffx())%>%
hc_title(
text = ""
) %>%
#hc_subtitle(text = "Las edades están agrupadas en intervalos
# de 10 años (da clic sobre F o M)") %>%
# hc_credits(
# enabled = TRUE, text = "Source: SSS",
# style = list(fontSize = "12px"))%>%
hc_yAxis(title = list(text = "Número de participantes"))%>%
hc_xAxis(title=list(text="Número de personas"),
categories = get_labels(hijos_emovi$Estado)) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
Ingreso hijo enigh_16 - emovi
ingreso_hijo_e16_emovi<-function(i){
ingresos_e16a%>%
filter(folioviv %in% folhijos_e16_emovif[[i]]$folioviv &
foliohog %in% folhijos_e16_emovif[[i]]$foliohog)%>%
#filter(coh_ing==hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p133"])%>%
summarise(
id_hijo = names(folhijos_e16_emovif[i]),
id_ho = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"id_ho"],
mov= hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"mov"],
regionh = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"region"],
edad = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p05"],
cohort = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p133"],
ing_men_p_h =list(ing_men_p),
estrato = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"estrato"],
estado = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"Estado"],
sexo = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p06"]
)
}
#273,332 son vacios
# ingreso hijo pero ualgunos no podrian estar por no tener registro de salario
a<-lapply(1:length(folhijos_e16_emovif), ingreso_hijo_e16_emovi)
ingreso_hijog_e16_emovi<-a
length(ingreso_hijog_e16_emovi)
#ingreso_hijog_e16_emovi[[332]]
#x<-ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[1]
#sum(ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[[1]],na.rm = TRUE)
Filtramos sobre ingresos encontrados 16
L <- c()
for(i in 1:length(ingreso_hijog_e16_emovi)){
if(sum(ingreso_hijog_e16_emovi[[i]]$ing_men_p_h[[1]],na.rm = TRUE)==0){
L<-c(L,i)}
}
length(L)
ingreso_hijog_e16_emovi<-ingreso_hijog_e16_emovi[-L]
length(ingreso_hijog_e16_emovi)
ingreso_hijog_e16_emovi[1]
ingreso_hijog_e16_emovi[2]
ingreso_hijog_e16_emovif<-bind_rows(ingreso_hijog_e16_emovi[1:length(ingreso_hijog_e16_emovi)])
nrow(ingreso_hijog_e16_emovif)
saveRDS(ingreso_hijog_e16_emovif, file="Datos/ingreso_hij_f_emovi_sinm1_re_p1.Rda")
gemelos<-c()
for( i in 1:nrow(ingreso_hijog_e16_emovif)){
gemelos<-c(gemelos,length(ingreso_hijog_e16_emovif$ing_men_p_h[[i]]))
}
#gemelos
#min(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])
#max(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])
ingreso_hijog_e16_emovif%>%select(id_ho)
Promedio de gemelos por individuo
len<-0
for(i in 1:nrow(ingreso_hijog_e16_emovif)){
len<- len + length(ingreso_hijog_e16_emovif$ing_men_p_h[[i]])
}
len/nrow(ingreso_hijog_e16_emovif)
CRITERIO 1 - con region 1117 tienen salario - con region y en cohort de ingreso tienen salario 1100 - con ocupacion y region 659 tienen salario - con ocupacion y region y cohort de ingreso 436 tienen salario
CRITERIO 2 (ingresoso aumentamos las claves) - con region y en cohort de ingreso tienen salario 1103
CRITERIO 3 (ingresoso aumentamos las claves, y los rangos de edad) - con region y en cohort de ingreso tienen salario 1658 - con region, ocupación y en el cohort de ingreso tienen 683 - cone estado, cohort en el ingreso, 1473
Hijos construcción ingresos 18
poblacion enigh 18
poblacion_e18<-cargar_enigh_sav_18("poblacion")
poblacion_e18f<- poblacion_e18%>%
#filter(asis_esc == "2")%>% # ya no asiste a la escuela
select(folioviv,foliohog,numren,sexo,edad,nivelaprob)%>%
filter(between(as.integer(edad),23,52))#criterio 1 entre 25-40, criterio 3 entre 25-50
# no filtramos por jefe de hogar
nrow(poblacion_e18f)
sum(is.na(poblacion_e18f))
creamos region 18
poblacion_e18f<- poblacion_e18f%>%
mutate(
region = ifelse(substr(folioviv,1,2) %in% c("02","26","08","05","19","28"),1,
ifelse(substr(folioviv,1,2) %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(substr(folioviv,1,2) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
unique(poblacion_e18f$region)
nrow(poblacion_e18f)
sum(is.na(poblacion_e18f))
trabajos enigh 18
Filtrams los trabajos
trabajos_e18<-cargar_enigh_sav_18("trabajos")
trabajos_e18f<-trabajos_e18%>%
select(folioviv,foliohog,numren,sinco)%>%
filter(!is.na(sinco))
NROW(trabajos_e18f)
#NROW(trabajos)
sum(is.na(trabajos_e18f))
Ingresos enigh 2018
ingresos_e18<-cargar_enigh_sav_18("ingresos")
familias_integrantes_e18 <- function(df,folhog,nr){
dfn <- df %>%
filter(foliohog %in% folhog)%>%
filter(numren %in% nr)
return(dfn)
}
ingresos_acu_e18 <- function(df,clavs=get_labels(ingresos_e18$clave)){
dfn <- df %>%
group_by(folioviv, foliohog, numren) %>%
filter(clave %in% clavs)%>%
summarise(
claves_a = paste(clave,collapse = ","),
ing_tri_t = sum(ing_tri),
ing_men_p = ing_tri_t/3,
ling_men_p = log(ing_men_p)
) %>% ungroup()
return(dfn)
}
hogares <- c("1","2","3","4","5")
personas_h <- c('01','02','03','04','05')
a<-familias_integrantes_e18(ingresos_e18,hogares,personas_h)
head(a)
### criterio 1 algunos ingresos no todos los de abajo
#claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", "P016","P017","P018","P020","P021","P026","P027","P035","P040","P041","P042","P044","P045")
## criterio 2 flexibilidad en los ingresos, criterio 3
claves<-c("P001","P002","P003","P011","P13","P023","P024","P025","P026","P027","P028","P029","P030","P031","P032","P033","P054","P055","P056", "P059","P060","P061","P062","P063","P068","P069","P070","P071","P072","P073","P074","P075","P076","P077","P078","P079","P080")
ingresos_e18a<-ingresos_acu_e18(a,claves)
ingresos_e18a<- ingresos_e18a%>%
mutate(coh_ing = ifelse(ing_men_p<=2400,1,
ifelse(ing_men_p<=4800,2,
ifelse(ing_men_p<=7200,3,
ifelse(ing_men_p<=12000,4,
ifelse(ing_men_p<=24000,5,6))))))
ingresos_e18a
ingresos_e18fc<-ingresos_e18a%>%
select(folioviv,foliohog,numren,coh_ing)
rango percentil 18
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e18a$ling_men_pd <- ingresos_e18a$ling_men_p + log(IN)
rp_e18 <- rank(ingresos_e18a$ling_men_pd)/length(ingresos_e18a$ling_men_pd)
hist(rp_e18)
Base cruzada pob-tra 18
enigh_18<- full_join(poblacion_e18f,trabajos_e18f)%>%
drop_na()
#enigh<-full_join(enigh,concentrado)%>%
# drop_na()
nrow(enigh_18)
sum(is.na(enigh_18))
Cambio etiquetado educacion 18
Pasamos las etiqeuteas de educación de emovi a
hijos_emovi <- readRDS(file="Datos/hijos_emovi2017_p1.Rda")#criterio 1 y 3
hijos_emovi<- hijos_emovi %>%
mutate(p13_m = ifelse(p13 %in% c(1),"1",
ifelse( p13 == 2,"2",
ifelse(p13 %in% c(3,4),"3",
ifelse( p13 %in% c(5,6),"4",
ifelse(p13 %in% c(7,8),"6",
ifelse(p13 %in% c(9,10),"5",
ifelse(p13==11,"7",
ifelse(p13==12,"8","0")))))))))
unique(hijos_emovi$p13_m)
enigh_18<-enigh_18%>%
mutate(nivelaprob=ifelse(nivelaprob=="9","8",nivelaprob))
unique(enigh_18$nivelaprob)
enigh_18f<-enigh_18%>%
filter(nivelaprob %in% unique(hijos_emovi$p13_m))
nrow(enigh_18f)
Buscar folios con caracteristicas 18
busca<-function(i){
return(enigh_18f%>%
filter(
#as.integer(substr(folioviv,1,2))==hijos_emovi[i,]$Estado &
region == hijos_emovi[i,]$region &
sexo == hijos_emovi[i,]$p06 &
((edad-2) <= hijos_emovi[i,]$p05) &
(hijos_emovi[i,]$p05 <= (edad+2)) &
#as.character(sinco) == hijos_emovi[i,]$SINCO3 &
substr(as.character(sinco),1,1) == substr(hijos_emovi[i,]$SINCO3,1,1)&
nivelaprob == hijos_emovi[i,]$p13_m)%>%
select(folioviv,foliohog))
}
a<-lapply(1:nrow(hijos_emovi), busca)
contador<-0
for(i in 1:nrow(hijos_emovi))
{if(nrow(a[[i]])>0)
contador<-contador +1
}
contador
saveRDS(a, file="Datos/folhijos_enigh18_emovi_sinm1_re_p1.Rda")
filtamos solo los hijos encontrados 18
# etiquetamos para no perder la numeracion respecto a los hijos originales
folhijos_e18_emovi <- readRDS(file="Datos/folhijos_enigh18_emovi_sinm1_re_p1.Rda")
names(folhijos_e18_emovi) <- as.character(1:nrow(hijos_emovi))
length(folhijos_e18_emovi)
L <- c()
for(i in 1:length(folhijos_e18_emovi)){
if(!(nrow(folhijos_e18_emovi[[i]])>0)){
L<-c(L,i)}
}
# Solo hijos que se les pudo encontrar en enigh-2016 1077/1131
folhijos_e18_emovif<-folhijos_e18_emovi[-L]
length(folhijos_e18_emovif)
#names(folhijos_emovif)
#names(folhijos_emovi)
Unicos folios, diferentes trabajos 18
unicos_folios<-function(i){
folhijos_e18_emovif[[i]]%>%
distinct()
}
# filtramos unicos folios por diferentes trabajos de la misma persona
a<-lapply(1:length(folhijos_e18_emovif), unicos_folios)
names(a)<-names(folhijos_e18_emovif)
length(a)
folhijos_e18_emovif<-a
Observamos las edades de los encontrados 18
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
hijos_emovi%>%
slice(as.integer(names(folhijos_e18_emovif)))%>%
select(p05)%>%
count(p05)%>%
hchart('column',
hcaes( y = 'n'))%>%
hc_add_theme(hc_theme_ffx())%>%
hc_title(
text = ""
) %>%
#hc_subtitle(text = "Las edades están agrupadas en intervalos
# de 10 años (da clic sobre F o M)") %>%
# hc_credits(
# enabled = TRUE, text = "Source: SSS",
# style = list(fontSize = "12px"))%>%
hc_yAxis(title = list(text = "Número de participantes"))%>%
hc_xAxis(title=list(text="Número de personas"),
categories = as.character(25:50)) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
Observamos las estados de los encontrados 18
hijos_emovi%>%
slice(as.integer(names(folhijos_e18_emovif)))%>%
count(Estado)%>%
hchart('column',
hcaes( y = 'n'))%>%
hc_add_theme(hc_theme_ffx())%>%
hc_title(
text = ""
) %>%
#hc_subtitle(text = "Las edades están agrupadas en intervalos
# de 10 años (da clic sobre F o M)") %>%
# hc_credits(
# enabled = TRUE, text = "Source: SSS",
# style = list(fontSize = "12px"))%>%
hc_yAxis(title = list(text = "Número de participantes"))%>%
hc_xAxis(title=list(text="Número de personas"),
categories = get_labels(hijos_emovi$Estado)) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
Ingreso hijo enigh 18 - emovi
ingreso_hijo_e18_emovi<-function(i){
ingresos_e18a%>%
filter(folioviv %in% folhijos_e18_emovif[[i]]$folioviv &
foliohog %in% folhijos_e18_emovif[[i]]$foliohog)%>%
# filter(coh_ing==hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p133"])%>%
summarise(
id_hijo = names(folhijos_e18_emovif[i]),
id_ho = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"id_ho"],
mov= hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"mov"],
regionh = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"region"],
edad = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p05"],
cohort = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p133"],
ing_men_p_h =list(ing_men_p),
estrato = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"estrato"],
estado = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"Estado"],
sexo = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p06"]
)
}
#273,332 son vacios
# ingreso hijo pero ualgunos no podrian estar por no tener registro de salario
a<-lapply(1:length(folhijos_e18_emovif), ingreso_hijo_e18_emovi)
ingreso_hijog_e18_emovi<-a
length(ingreso_hijog_e18_emovi)
#ingreso_hijog_e16_emovi[[332]]
#x<-ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[1]
#sum(ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[[1]],na.rm = TRUE)
Filtramos sobre ingresos encontrados 18
L <- c()
for(i in 1:length(ingreso_hijog_e18_emovi)){
if(sum(ingreso_hijog_e18_emovi[[i]]$ing_men_p_h[[1]],na.rm = TRUE)==0){
L<-c(L,i)}
}
length(L)
ingreso_hijog_e18_emovi<-ingreso_hijog_e18_emovi[-L]
length(ingreso_hijog_e18_emovi)
ingreso_hijog_e18_emovi[1]
ingreso_hijog_e18_emovi[2]
ingreso_hijog_e18_emovif<-bind_rows(ingreso_hijog_e18_emovi[1:length(ingreso_hijog_e18_emovi)])
nrow(ingreso_hijog_e18_emovif)
saveRDS(ingreso_hijog_e18_emovif, file="Datos/ingreso18_hij_f_emovi_sinm1_re_p1.Rda")
gemelos<-c()
for( i in 1:nrow(ingreso_hijog_e18_emovif)){
gemelos<-c(gemelos,length(ingreso_hijog_e18_emovif$ing_men_p_h[[i]]))
}
#gemelos
#min(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])
#max(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])
CRITERIO 3 (ingresoso aumentamos las claves, y los rangos de edad) - con region, ocupación y en el cohort de ingreso tienen 717
ingreso_hijog_e18_emovif%>%select(id_hijo,id_ho)
len<-0
for(i in 1:nrow(ingreso_hijog_e18_emovif)){
len<- len + length(ingreso_hijog_e18_emovif$ing_men_p_h[[i]])
}
len/nrow(ingreso_hijog_e18_emovif)
Base de ingresos hijos final
base1<- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_p1.Rda")
nrow(base1)#1148 +89 = 1237
base2<- readRDS(file="Datos/ingreso18_hij_f_emovi_sin_re_p1.Rda")
nrow(base2)#1148 + 89 = 1237
#base1%>%filter(!(id_ho %in% base2$id_ho))
basep<-base2%>%filter(!(id_ho %in% base1$id_ho))
nrow(basep)
basep2<-full_join(base1, basep)
nrow(basep2) # 1237 + 62 = 1299
#_______________________________________
base3<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm3_re_p1.Rda")
nrow(base3) # 1266 + 33 = 1299
#basep2%>%filter(!(id_ho %in% base3$id_ho))
basep3<- base3%>%filter(!(id_ho %in% basep2$id_ho))
nrow(basep3)
basep4<- full_join(basep3,basep2)
nrow(basep4)# 1299
#____________________________________________
base4<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm2_re_p1.Rda")
nrow(base4) # 1346
#basep4%>%filter(!(id_ho %in% base4$id_ho))
basep5<-base4%>%filter(!(id_ho %in% basep4$id_ho))
nrow(basep5)#59
basep6<-full_join(basep5,basep4)
nrow(basep6)# 1358
#______________________________
base5<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm1_re_p1.Rda")
nrow(base5)# 1473
#basep6%>%filter(!(id_ho %in% base5$id_ho))
basep7<-base5%>%filter(!(id_ho %in% basep6$id_ho))
nrow(basep7)# 119
basep8<-full_join(basep7,basep6)
nrow(basep8)# 1477
#______________________________________________
base6<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm3_re_p1.Rda")
nrow(base6)#1270
#basep8%>%filter(!(id_ho %in% base6$id_ho))
basep9<-base6%>%filter(!(id_ho %in% basep8$id_ho))
nrow(basep9)# 1
basep10<-full_join(basep9,basep8)
nrow(basep10) # 1478
#__________________________________________________________________
base7<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm2_re_p1.Rda")
nrow(base7)#1342
#basep10%>%filter(!(id_ho %in% base7$id_ho))
basep11<-base7%>%filter(!(id_ho %in% basep10$id_ho))
nrow(basep11)# 2
basep12<-full_join(basep11,basep10)
nrow(basep12)# 1480
#__________________________________________________________________
base8<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm1_re_p1.Rda")
nrow(base8) # 1476
#basep12%>%filter(!(id_ho %in% base8$id_ho))
basep13<-base8%>%filter(!(id_ho %in% basep12$id_ho))
nrow(basep13) # 6
basep14<-full_join(basep13,basep12)
nrow(basep14)# 1486
base_cf<-basep14
saveRDS(base_cf,file="Datos/ingreso_hij_f_emovi_combinada_p1.Rda")
base1<- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_ch_cri3.Rda")
nrow(base1) # 504 + 158 = 662
base2<- readRDS(file="Datos/ingreso18_hij_f_emovi_sin_re_ch_cri3.Rda")
nrow(base2) # 526 + 136 = 662
#base1%>%filter(!(id_ho %in% base2$id_ho))
basep<-base2%>%filter(!(id_ho %in% base1$id_ho))
basep2<-full_join(base1, basep)
nrow(basep2) # 662 + 106 = 768
#_______________________________________
base3<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm3_re_ch_cri3.Rda")
nrow(base3) # 648 + 120 = 768
#basep2%>%filter(!(id_ho %in% base3$id_ho))
basep3<- base3%>%filter(!(id_ho %in% basep2$id_ho))
nrow(basep3)
basep4<- full_join(basep3,basep2)
nrow(basep4)# 768 + 48 = 816
#____________________________________________
base4<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm3_re_ch_cri3.Rda")
nrow(base4)# 671 + 145 = 816
#basep4%>%filter(!(id_ho %in% base4$id_ho))
basep5<-base4%>%filter(!(id_ho %in% basep4$id_ho))
nrow(basep5)# 48
basep6<-full_join(basep5,basep4)
nrow(basep6)# 816 + 111 = 927
#______________________________
base5<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm2_re_ch_cri3.Rda")
nrow(base5)# 840 + 87 = 927
#basep6%>%filter(!(id_ho %in% base5$id_ho))
basep7<-base5%>%filter(!(id_ho %in% basep6$id_ho))
nrow(basep7)# 111
basep8<-full_join(basep7,basep6)
nrow(basep8)# 927 + 46 = 973
#______________________________________________
base6<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm2_re_ch_cri3.Rda")
nrow(base6)# 841 + 132 = 973
#basep8%>%filter(!(id_ho %in% base6$id_ho))
basep9<-base6%>%filter(!(id_ho %in% basep8$id_ho))
nrow(basep9)# 46
basep10<-full_join(basep9,basep8)
nrow(basep10) # 973 + 197 = 1170
#__________________________________________________________________
base7<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm1_re_ch_cri3.Rda")
nrow(base7)# 1127 + 43 = 1170
#basep10%>%filter(!(id_ho %in% base7$id_ho))
basep11<-base7%>%filter(!(id_ho %in% basep10$id_ho))
nrow(basep11)# 197
basep12<-full_join(basep11,basep10)
nrow(basep12)# 1170 + 41 = 1211
#___________________________________________________________________
base8<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm1_re_ch_cri3.Rda")
nrow(base8) # 1140 + 71 = 1211
#basep12%>%filter(!(id_ho %in% base8$id_ho))
basep13<-base8%>%filter(!(id_ho %in% basep12$id_ho))
nrow(basep13) # 41
basep14<-full_join(basep13,basep12)
nrow(basep14)# 1211
base_cf<-full_join(base_hijos_fija_f, basep14)
str(unique(base_cf$id_ho))
Ingreso hijos directo
hijos_para_sal_2 <- hijos_para_sal%>%filter(!(id_ho %in%base_cf$id_ho ))
nrow(hijos_para_sal_2)
hijos_para_sal_2%>%count(p133)
hacemos su intervalo
hijos_para_sal_2<-hijos_para_sal_2%>%mutate(
low=ifelse(p133==1,0,
ifelse(p133==3, 2401.0,
ifelse(p133==4,4801.0,
ifelse(p133==5,7201.0,
ifelse(p133==6,12001.0,24001))) )),
upper = ifelse(p133==1,2399.0,
ifelse(p133==3, 4800.0,
ifelse(p133==4,7200.0,
ifelse(p133==5,12000.0,
ifelse(p133==6,24000,40000))) ))
#type = ifelse(p133!=7,"interval","left"),
#event = ifelse(p133!=7,3,2)
)
hings<-hijos_para_sal_2%>%mutate(p052=p05*p05)
Z <-with(hings, Surv(low,
upper,
event = rep(3,nrow(hings)),
type = "interval"))
modelo<-survreg(Z~ factor(p133) + p05 + p052 + p06 + p13 + factor(substr(SINCO3,1,1))-1, data=hings,
dist = "gaussian")
summary(modelo)
newd<- data.frame(#region = factor(hings$region),
p133 = factor(hings$p133),
p05=hings$p05,
p052=hings$p052,
p06=hings$p06,
p13=hings$p13,
SINCO3=factor(substr(hings$SINCO3,1,1)))
hings$ingress<-predict(modelo,newd, type="response")
hings%>%select(-ingreso)
pro<-full_join(hings,hijos_para_sal2%>%select(-ingreso))
pro%>%mutate(ingress=ifelse(p133==2,2400,ingress))%>%
mutate(cohort_h = ifelse(ingress<=2400,1,
ifelse(ingress<=4800,2,
ifelse(ingress<=7200,3,
ifelse(ingress<=12000,4,
ifelse(ingress<=24000,5,6))))),
cohort_dif= as.integer(p133)-cohort_h)%>%filter(p133==2)%>%nrow()
saveRDS(base_cf,file="Datos/ingreso_hij_f_emovi_combinada_cri3.Rda")
rango percentil 16-18
#IN<-(100/110.907)
IN<-(100/93.6)
#
ingre1618<-bind_rows(ingresos_e16a%>%select(ling_men_pd),ingresos_e18a%>%select(ling_men_pd))
rp_e1618 <- rank(ingre1618$ling_men_pd)/length(ingre1618$ling_men_pd)
#rp_e1618<-percent_rank(ingre1618$ling_men_pd)
hist(rp_e1618)
Hijos con ingreso
hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_combinada_p1.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_combinada_cri3.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_ch_cri3.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_re_ch_cri3.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_re_ch_cri2.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_re.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_ch.Rda")
nrow(hijos_emovi_hp)
hijos_emovi_hp
# p05 edad act
# p06 sexo act
# p13 educ hijo
# SINCO3, ocupación hijo
# p23 en qeu estado vivia a los 14
# p26 principal sosten ecómico
# p38_11 edad padre 2016
# p38m_11 edad madre 2016
# p43 nivel educativo padre
#p43m nivel educativo madre
# cmo1_2 ocupacion del padre
# cmo2_2 ocupacion de la madre
varp<-c("Estado","p05","p06","p13","SINCO3","p23","p26","p38_11","p38m_11","p43","p44",
"p43m","p44m","cmo1_2","cmo2_2","mov",
"region","p133","id_ho")
padres_h<-data_emovi2017%>%
# # filter(p02 == 1) %>% #compartir gasot para comer
# filter(between(p05,25,50)) %>% # edad <-----------------criterio 1 y 3
# #filter(p08 == 1) %>%
# filter(p12 == 2) %>%# ya no estudia
# filter(p26 == 1 | p26==2 ) %>% # sosten principal papa, mama,
# filter((!is.na(p43) & p43!=98 & p26 == 1 & cmo1_2!="." & !is.na(p38_11))|#<----------ojo ocupacion ns
# (!is.na(p43m) & p43m!=98 & p26 == 2 & cmo2_2!="." & !is.na(p38m_11))) %>% # reporta educación de pp
# filter(!is.na(p13))%>% # reporta educación
# filter(SINCO3 !="") %>%# reporta oficio actual
# filter(p68 == 1 | p69 == 1) %>% # si trabaja
# filter(p132 %in% c(1)) %>%# persona que aportan ingreso
# filter(!p133 %in% c(8,9,NA) )%>%
select(varp)%>%
slice(as.integer(unlist(hijos_emovi_hp%>%select(id_ho))))%>%#<---------oojooo ers id_hijo
# filter(id_ho %in% hijos_emovi_hp$id_ho )%>%
mutate(
pp_id_hijo = hijos_emovi_hp%>%pull(id_ho),# era id_hijo
ing_h = hijos_emovi_hp%>%pull(ing_men_p_h),
cohort = ifelse(p133==1,2,p133)-1,
region = hijos_emovi_hp%>%pull(regionh)
)%>%
rename(
edad_h = p05,
sexo_h = p06,
edu_h = p13,
ocu_h = SINCO3,
pp_estado = p23,
pp = p26,
p_edad_2016= p38_11,
m_edad_2016 = p38m_11,
p_edu = p43,
p_edug = p44,
m_edu = p43m,
m_edug = p44m,
p_ocu = cmo1_2,
m_ocu = cmo2_2
)%>%
mutate(
p_edu = as.integer(p_edu),
m_edu = as.integer(m_edu)
)
length(padres_h)
# las ocupaciones no encontradas las sustituimos por un NA
padres_h<-padres_h%>%
mutate(p_ocu = ifelse(p_ocu==".",NA,p_ocu),
m_ocu = ifelse(m_ocu==".",NA,m_ocu))
unique(padres_h$p_ocu)
unique(padres_h$m_ocu)
head(padres_h)
Crear base limpia
filtro_pp_c<-function(df_1,general = 0)
{
#recibe un hijo de un dataframe, para polimorfismo
# general = 0y regresa un data frame con los datos
# ordenados del hijo
if(df_1["pp"]==1)
{
dfn <- df_1%>%
filter(pp==1)%>%
select(starts_with(c("pp","p_")),
ing_h,edu_h,ocu_h,edad_h,p_edad_2016,
region,cohort,sexo_h)%>%
mutate(pp_edad2016 = p_edad_2016)%>%
rename(
id_ho = pp_id_hijo,
pp_sexo = pp,
pp_edad = p_edad_2016,
pp_educ = p_edu,
pp_educg = p_edug,
pp_ocup = p_ocu
)%>%
mutate(pp_edad = pp_edad + (14-edad_h))
}
if(df_1["pp"]==2)
{
dfn<-df_1%>%
filter(pp==2)%>%
select(starts_with(c("pp","m_")),ing_h,edu_h,
ocu_h,edad_h,m_edad_2016,
region,cohort,sexo_h)%>%
mutate(pp_edad2016 = m_edad_2016)%>%
rename(
id_ho = pp_id_hijo,
pp_sexo = pp,
pp_edad = m_edad_2016,
pp_educ = m_edu,
pp_educg = m_edug,
pp_ocup = m_ocu
)%>%
mutate(pp_edad = pp_edad + (14-edad_h))
}
if(is.na(dfn$pp_edad) & general){
return("ND")
}
return(dfn)
}
#comporbamos qeu funcione bien
filtro_pp_c(padres_h[1,])
acomoda_df_c<-function(df){
#damos un df de una edad, y
# nos regresa un data frame acomodado
aux<-function(i){
return(df[i,])
}
a<-lapply(1:nrow(df),aux)
L<-lapply(a,filtro_pp_c)
dfn <-bind_rows(L[1:length(L)])
return(dfn)
}
dfh<-acomoda_df_c(padres_h)
pro_edad_c <- function(df){
#le damos el df y nos regresa el promedio de la edade los padres
df%>%summarise(
promedi_edad_padres = mean(pp_edad2016,na.rm=TRUE))
}
edades_pro<- pro_edad_c(dfh)
mean(dfh$edad_h)
edades_pro
#edades de padres faltantes
sum(is.na(dfh$pp_edad2016))
#
head(dfh)
# 2016-21
Codificacion eduación
dfh_c<- dfh%>%
mutate(
pp_educ_a = ifelse(pp_educ==1,1, #sin instruccion(1)
ifelse(pp_educ==2,1+pp_educg,
ifelse(pp_educ %in% c(3,4),7+pp_educg,
ifelse(pp_educ %in% c(5,6,7,8,9),10+pp_educg,
ifelse(pp_educ %in% c(10,11),13+pp_educg,17+pp_educg)))))
)
unique(dfh_c$pp_educ_a)
dfh_c<-dfh_c%>%
mutate(
pp_educ_ac = ifelse(pp_educ_a %in% 0:5,"C1",
ifelse(pp_educ_a %in% 6:10 ,"C2",
ifelse(pp_educ_a %in% 11:12,"C3",
ifelse(pp_educ_a %in% 13:14,"C4","C5"))))
)
### Guardamos base de datos de hijos final
saveRDS(dfh_c, file="Datos/dfh_c_combinada_p1.Rda")
#saveRDS(dfh_c, file="Datos/dfh_c_sin_re_ch_cri3.Rda")
#saveRDS(dfh_c, file="Datos/dfh_c_re_ch_cri3.Rda")
#saveRDS(dfh_c, file="Datos/dfh_c_re_ch_cri2.Rda")
#saveRDS(dfh_c, file="Datos/dfh_c_sin_re_ch.Rda")
Padres 1998
criterio 1, Seleccionamos de la ENIGH 1997, no hay entonces 1998
criterio 2 NO tenemos enigh 93 agarramos enigh 94 (cuando la tengamos)
Poblacion enigh 98
poblacion<-read.dbf(
file="Datos/ENIGH-Historica/1998/POBLA98.dbf"
)
poblacion_e98<-poblacion%>%
filter(
parentesco == "01",
between(edad,25,60),#<------------------ criterio 1 y 3 cambiar
!is.na(ed_formal),
!is.na(ed_tecnica),
!is.na(ocupacion),
n_empleos==1, # el entrevistado solo proporciona una ocupación
)%>%
select(
folio,
num_ren,
edad,
sexo,
ed_formal,
ed_tecnica,
ocupacion
)%>%
mutate(
ed_formal = as.integer(as.character(ed_formal)),
ed_tecnica= as.integer(as.character(ed_tecnica)),
estado = substr(folio,5,6)
)
#unique(pobla_enigh1998$ed_formal)
#pobla_enigh1998$ed_formal
nrow(poblacion_e98)
head(poblacion_e98$ed_formal)
str(poblacion_e98$ed_formal)#caracter 2 espacios
unique(poblacion_e98$ed_formal)
#pobla_enigh1998$ed_formal=="03"
head(poblacion_e98$ed_tecnica)
str(poblacion_e98$ed_tecnica)#caracter 1 espacio
unique(poblacion_e98$ed_tecnica)
#pobla_enigh1998%>%count(n_empleos), se exploro para quitar una ocupacion
names(poblacion)
str(poblacion$per_ing)
unique(poblacion_e98$estado)
folios de gente con hijos menores de 14 años
folios98<-as.character(poblacion%>%filter(parentesco %in% c("04","05"),edad<=18)%>%select(folio)%>%pull())
poblacion_e98<-poblacion_e98%>%filter(folio %in% folios98 )
Codificación educación enigh 1998
# hacemos la codificación de ENIGH2018 a ESRU
#1 Preescolar o kínder
# 2 Primaria
# 3 Secundaria técnica
# 4 Secundaria general
# 5 Preparatoria técnica
# 6 Preparatoria general
# 7 Técnica o comercial con secundaria
# 8 Técnica o comercial con preparatoria
# 9 Normal básica (con primaria o secundari
# 10 Normal de licenciatura
# 11 Profesional (licenciatura o ingeniería)
# 12 Postgrado (maestría o doctorado)
#_____________________________________________________
# 98 NS, no aplica porque pedimos la edución del pp como instrumento
# vemso las combinaciones de educación para codificar
comb<-poblacion_e98%>%
mutate( combi = paste(as.character(ed_formal),as.character(ed_tecnica)))%>%
select(ed_formal,ed_tecnica,combi)
sort(unique(comb$combi))
# [1] "01 1" "02 1" "03 1" "03 2" "04 1" "05 1" "06 1" "07 1" "07 2" "08 1"
# [11] "08 2" "08 3" "08 4" "08 5" "09 1" "09 4" "09 5" "10 1" "10 2" "10 4"
# [21] "10 5" "11 1" "11 3" "11 4" "11 5" "11 6" "11 7" "12 1" "12 3" "12 5"
# [31] "12 6" "12 7" "13 1" "13 5" "13 6" "13 7" "13 8" "13 9" "14 1" "14 3"
# [41] "14 5" "14 6" "14 7" "14 9" "15 1" "15 3" "15 4" "15 5" "15 6" "15 7"
# [51] "15 9" "16 1" "16 5" "16 7" "16 9"
# "16 1" "16 5" "16 7" "16 9"
#codificamos a a años de educación
poblacion_e98c<-poblacion_e98%>%
mutate(
edu_a = ifelse(ed_formal==1 & ed_tecnica==1,0, #sin instruccion(1)
ifelse((ed_formal %in% c(2,3,4,5,6,7,8,9,10,11,12,13))
& (ed_tecnica %in% c(1,2)),ed_formal-1,
ifelse(ed_formal==8 & (ed_tecnica %in% c(3,4,5)),9,
ifelse(ed_formal==9 & (ed_tecnica %in% c(4,5)),10,
ifelse(ed_formal==10 & (ed_tecnica %in% c(2,4,5)),11,
ifelse(ed_formal==11 & (ed_tecnica %in% c(3,4,5,5,7)),12,
ifelse((ed_formal %in%c(12)) & (ed_tecnica %in% c(3,5,6,7)),12,
ifelse(ed_formal==13 & (ed_tecnica %in% c(5,6,7,8,9)),13,
ifelse(ed_formal==14 & (ed_tecnica %in% c(1,3,5,6,7,9)),14,
ifelse(ed_formal==15 & (ed_tecnica %in% c(1,3,4,5,6,7,9)),17,19))))))))))
)
unique(poblacion_e98c$edu_a)
#codificamos a coghort de educación
poblacion_e98c<-poblacion_e98c%>%
mutate(
edu_ac = ifelse(edu_a %in% 0:5,"C1",
ifelse(edu_a %in% 6:10 ,"C2",
ifelse(edu_a %in% 11:12,"C3",
ifelse(edu_a %in% 13:14,"C4","C5"))))
)
unique(poblacion_e98c$edu_ac )
Creacion de region
poblacion_e98c<-poblacion_e98c%>%
mutate(
region = ifelse(estado %in% c("02","26","08","05","19","28"),1,
ifelse(estado %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(estado %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
Ingresos enigh 98
ingresos<-read.dbf(
file="Datos/ENIGH-Historica/1998/ingresos.dbf"
)
familias_integrantes <- function(df,nr){
dfn <- df %>%
filter(NUM_REN %in% nr)
return(dfn)
}
ingresos_acu <- function(df,clavs){
dfn <- df %>%
group_by(FOLIO, NUM_REN) %>%
filter(CLAVE %in% clavs)%>%
summarise(
claves_a = paste(CLAVE,collapse = ","),
ing_tri_t = sum(ING_TRI),
ing_men_p = ing_tri_t/3,
ling_men_p = log(ing_men_p)
) %>% ungroup()
return(dfn)
}
#hogares <- c("1","2","3","4","5")
personas_h <- c('01')
a<-familias_integrantes(ingresos,personas_h)
head(a)
## criterio 1
#claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", #"P016","P017","P018","P020","P021","P026","P027","P035","P040","P041","P042","P044","P045")
## criterio 2 flexibilida de los ingresos y criterio 3
claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", "P016","P017","P018","P020","P021","P022","P023","P024","P025","P026","P027","P028","P035","P036","P037","P040","P041","P042","P044","P045")
ingresos_e98<-ingresos_acu(a,claves)
ingresos_e98<-ingresos_e98%>%
select(FOLIO,NUM_REN,claves_a,ing_men_p,ling_men_p)%>%
rename(
folio = FOLIO,
num_ren = NUM_REN
)
head(ingresos_e98)
ingresos_e98<-ingresos_e98%>%
mutate(
region = ifelse(substr(folio,5,6) %in% c("02","26","08","05","19","28"),1,
ifelse(substr(folio,5,6) %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(substr(folio,5,6) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
rango percentil 98
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e98$ling_men_pd<-ingresos_e98$ling_men_p + log(IN)
rp_e98 <- rank(ingresos_e98$ling_men_pd)/length(ingresos_e98$ling_men_pd)
#rp_e98<-percent_rank(ingresos_e98$ling_men_pd)
ingresos_e981 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==1)%>%pull(ling_men_p) + log(IN))
rp_e981 <- rank(ingresos_e981$ling_men_pd)/length(ingresos_e981$ling_men_pd)
ingresos_e982 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==2)%>%pull(ling_men_p) + log(IN))
rp_e982 <- rank(ingresos_e982$ling_men_pd)/length(ingresos_e982$ling_men_pd)
ingresos_e983 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==3)%>%pull(ling_men_p) + log(IN))
rp_e983 <- rank(ingresos_e983$ling_men_pd)/length(ingresos_e983$ling_men_pd)
ingresos_e984 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==4)%>%pull(ling_men_p) + log(IN))
rp_e984 <- rank(ingresos_e984$ling_men_pd)/length(ingresos_e984$ling_men_pd)
Base cruzada pobla-ing
enigh_98<-full_join(poblacion_e98c,ingresos_e98)%>%
drop_na()%>%
mutate(
ocupacion = substr(ocupacion,1,2)
)#%>%
# rename(
# EDU=N_INSTR161,
# OCU = CMO121
# )%>%
# mutate(
# EDU = as.character(EDU)
# )
head(enigh_98)
unique(enigh_98$edad)
# ajuste para nivel de ecucación ESRU
#data_padres2005$EDU <- factor(data_padres2005$EDU)
#data_padres2005$OCU <- factor(data_padres2005$OCU)
#unique(data_padres2005$OCU)
sum(is.na(enigh_98))
##### Guardamos base de padres final
saveRDS(enigh_98, file="Datos/enigh_98_cri3.Rda")
enigh_98%>%count(sexo)
Grafica de ingresos y edad
ed_ear<-full_join(poblacion_e98c,ingresos_e98)%>%
select(edad, ing_men_p)%>%drop_na()%>%
group_by(edad)%>%
summarise(
pro_ing = mean(ing_men_p)
)
ed_ear%>%
hchart( "scatter",
hcaes(x = edad, y = pro_ing),
name="ingreso promedio",
regression = TRUE,
regressionSettings = list(
type = "polynomial",
dashStyle = "ShortDash",
color = "skyblue",
order = 2,
lineWidth = 5,
name = "%eq | $r^2$: %r",
hideInLegend = FALSE)
)%>%
hc_add_dependency("plugins/highcharts-regression.js")
Esdística descriptiva 98
summary(enigh_98$edad)
hist(enigh_98$edad)
papas<- enigh_98%>%group_by(edad)%>%summarise(msal = mean(ing_men_p))
plot(papas$edad,papas$msal)
Padres 1996
poblacion eningh 1996
poblacion<-read.dbf(
file="Datos/ENIGH-1996/POBLA96.dbf"
)
poblacion <- poblacion %>%
rename(
parentesco = PARENTESCO,
folio = FOLIO,
num_ren = NUM_REN,
sexo = SEXO,
edad = EDAD,
per_ing=PER_ING,
ed_formal=ED_FORMAL,
ed_tecnica=ED_TECNICA,
ocupacion=OCUPACION
)
poblacion
poblacion_e96<-poblacion%>%
filter(
parentesco == "01",#en el 98 era "01"
between(edad,25,60),
!is.na(ed_formal),
!is.na(ed_tecnica),
!is.na(ocupacion),
N_EMPLEOS==1, # el entrevistado solo proporciona una ocupación
)%>%
select(
folio,
num_ren,
edad,
sexo,
ed_formal,
ed_tecnica,
ocupacion
)%>%
mutate(
ed_formal = as.integer(as.character(ed_formal)),
ed_tecnica= as.integer(as.character(ed_tecnica)),
estado = substr(folio,5,6)
)
#En 1998 para hijos es "04" y "05". En 1996 "4" abarca todos los hijos
#En 1998 se considera edad<=18, en 1996 se considera edad<=14
folios96<-as.character(poblacion%>%filter(parentesco %in% c("04","05"),edad<=16)%>%select(folio)%>%pull())
poblacion_e96<-poblacion_e96%>%filter(folio %in% folios96 )
nrow(poblacion_e96)
head(poblacion_e96$ed_formal)
str(poblacion_e96$ed_formal)#caracter 2 espacios
unique(poblacion_e96$ed_formal)
head(poblacion_e96$ed_tecnica)
str(poblacion_e96$ed_tecnica)#caracter 1 espacio
unique(poblacion_e96$ed_tecnica)
names(poblacion_e96)
str(poblacion_e96$per_ing)
# hacemos la codificación de ENIGH2018 a ESRU
# 1 Preescolar o kínder
# 2 Primaria
# 3 Secundaria técnica
# 4 Secundaria general
# 5 Preparatoria técnica
# 6 Preparatoria general
# 7 Técnica o comercial con secundaria
# 8 Técnica o comercial con preparatoria
# 9 Normal básica (con primaria o secundari
# 10 Normal de licenciatura
# 11 Profesional (licenciatura o ingeniería)
# 12 Postgrado (maestría o doctorado)
#_____________________________________________________
# 96 NS, no aplica porque pedimos la edución del pp como instrumento
comb<-poblacion_e96%>%
mutate( combi = paste(as.character(ed_formal),as.character(ed_tecnica)))%>%
select(ed_formal,ed_tecnica,combi)
comb
sort(unique(comb$combi))
poblacion_e96c<-poblacion_e96%>%
mutate(
edu_a = ifelse(ed_formal==0 & ed_tecnica==0,0, #sin instruccion(1)
ifelse((ed_formal %in% c(2:13)) & (ed_tecnica %in% c(0,2,3)),ed_formal,
ifelse((ed_formal %in% c(7:13)) & (ed_tecnica %in% c(4,5,6,8)),ed_formal+1,
ifelse(ed_formal==14 & (ed_tecnica %in% c(0,2,4,5,6,8)),17,19))))
)
poblacion_e96c<-poblacion_e96c%>%
mutate(
edu_ac = ifelse(edu_a %in% 0:5,"C1",
ifelse(edu_a %in% 6:10 ,"C2",
ifelse(edu_a %in% 11:12,"C3",
ifelse(edu_a %in% 13:14,"C4","C5"))))
)
poblacion_e96c<-poblacion_e96c%>%
mutate(
region = ifelse(estado %in% c("02","26","08","05","19","28"),1,
ifelse(estado %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(estado %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
Ingresos 96
ingresos<-read.dbf(
file="Datos/ENIGH-1996/ingresos.dbf"
)
familias_integrantes <- function(df,nr){
dfn <- df %>%
filter(NUM_REN %in% nr)
return(dfn)
}
ingresos_acu <- function(df,clavs){
dfn <- df %>%
group_by(FOLIO, NUM_REN) %>%
filter(CLAVE %in% clavs)%>%
summarise(
claves_a = paste(CLAVE,collapse = ","),
ing_tri_t = sum(ING_TRI),
ing_men_p = ing_tri_t/3,
ling_men_p = log(ing_men_p)
) %>% ungroup()
return(dfn)
}
#hogares <- c("1","2","3","4","5")
personas_h <- c('01')
a<-familias_integrantes(ingresos,personas_h)
head(a)
#P001 Sueldos, salarios, jornal y horas extras
#P002 Comisiones, propinas y destajo
#P010:P018 de 1998 son las mismas que P006:P014 de 1996
#P020:P021 de 1998 son las mismas que P016:P017 de 1996
#P026 de 1998 es la misma que P022 de 1996, aunque no hay equivalente en 1996 a la P027 de 1998
#P035 de 1998 es la misma que P030 de 1996
#P040:P042 de 1998 son las mismas que P035:P037 de 1996
#P044:P045 de 1998 son las mismas que P039:P040 de 1996
claves<-c("P001","P002","P006","P007","P008","P009","P010","P011", "P012","P013","P014","P016","P017","P022","P030","P035","P036","P037","P039","P040")
ingresos_e96<-ingresos_acu(a,claves)
ingresos_e96<-ingresos_e96%>%
select(FOLIO,NUM_REN,claves_a,ing_men_p,ling_men_p)%>%
rename(
folio = FOLIO,
num_ren = NUM_REN
)
head(ingresos_e96)
ingresos_e96<- ingresos_e96%>%
mutate(
region = ifelse(substr(folio,5,6) %in% c("02","26","08","05","19","28"),1,
ifelse(substr(folio,5,6) %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(substr(folio,5,6) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
rango percentil 96
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e96$ling_men_pd<-ingresos_e96$ling_men_p + log(IN)
rp_e96 <- rank(ingresos_e96$ling_men_pd)/length(ingresos_e96$ling_men_pd)
#rp_e96<-percent_rank(ingresos_e96$ling_men_pd)
Base cruzada pobla-ing 96
enigh_96<-full_join(poblacion_e96c,ingresos_e96)%>%
drop_na()%>%
mutate(
ocupacion = substr(ocupacion,1,2)
)#%>%
# rename(
# EDU=N_INSTR161,
# OCU = CMO121
# )%>%
# mutate(
# EDU = as.character(EDU)
# )
head(enigh_96)
unique(enigh_96$edad)
# ajuste para nivel de ecucación ESRU
#data_padres2005$EDU <- factor(data_padres2005$EDU)
#data_padres2005$OCU <- factor(data_padres2005$OCU)
#unique(data_padres2005$OCU)
sum(is.na(enigh_96))
names(enigh_96)
##### Guardamos base de padres final
saveRDS(enigh_96, file="Datos/enigh_96_cri3.Rda")
Esdística descriptiva 96
summary(enigh_96$edad)
hist(enigh_96$edad)
papas<- enigh_96%>%group_by(edad)%>%summarise(msal = mean(ing_men_p))
plot(papas$edad,papas$msal)
Padres 1994
poblacion enigh 1994
poblacion<-read.dbf(
file="Datos/ENIGH-Historica/1994/POBLA94.dbf"
)
poblacion <- poblacion %>%
rename(
parentesco = PARENTESCO,
folio = FOLIO,
num_ren = NUM_REN,
sexo = SEXO,
edad = EDAD,
per_ing=PER_ING,
ed_formal=ED_FORMAL,
ed_tecnica=ED_TECNICA,
ocupacion=OCUPACION
)
poblacion
poblacion_e94<-poblacion%>%
filter(
parentesco == "1",#en el 98 era "01"
between(edad,25,60),
!is.na(ed_formal),
!is.na(ed_tecnica),
!is.na(ocupacion),
N_EMPLEOS==1, # el entrevistado solo proporciona una ocupación
)%>%
select(
folio,
num_ren,
edad,
sexo,
ed_formal,
ed_tecnica,
ocupacion
)%>%
mutate(
ed_formal = as.integer(as.character(ed_formal)),
ed_tecnica= as.integer(as.character(ed_tecnica)),
estado = substr(folio,5,6)
)
#En 1998 para hijos es "04" y "05". En 1994 "4" abarca todos los hijos
#En 1998 se considera edad<=18, en 1994 se considera edad<=14
folios94<-as.character(poblacion%>%filter(parentesco %in% c("4"),edad<=13)%>%select(folio)%>%pull())
poblacion_e94<-poblacion_e94%>%filter(folio %in% folios94 )
nrow(poblacion_e94)
head(poblacion_e94$ed_formal)
str(poblacion_e94$ed_formal)#caracter 2 espacios
unique(poblacion_e94$ed_formal)
head(poblacion_e94$ed_tecnica)
str(poblacion_e94$ed_tecnica)#caracter 1 espacio
unique(poblacion_e94$ed_tecnica)
names(poblacion_e94)
str(poblacion_e94$per_ing)
# hacemos la codificación de ENIGH2018 a ESRU
# 1 Preescolar o kínder
# 2 Primaria
# 3 Secundaria técnica
# 4 Secundaria general
# 5 Preparatoria técnica
# 6 Preparatoria general
# 7 Técnica o comercial con secundaria
# 8 Técnica o comercial con preparatoria
# 9 Normal básica (con primaria o secundari
# 10 Normal de licenciatura
# 11 Profesional (licenciatura o ingeniería)
# 12 Postgrado (maestría o doctorado)
#_____________________________________________________
# 94 NS, no aplica porque pedimos la edución del pp como instrumento
comb<-poblacion_e94%>%
mutate( combi = paste(as.character(ed_formal),as.character(ed_tecnica)))%>%
select(ed_formal,ed_tecnica,combi)
comb
sort(unique(comb$combi))
poblacion_e94c<-poblacion_e94%>%
mutate(
edu_a = ifelse(ed_formal==0 & ed_tecnica==0,0, #sin instruccion(1)
ifelse(ed_formal==1 & (ed_tecnica %in% c(0,1)),3,
ifelse(ed_formal==1 & ed_tecnica==2,4,
ifelse(ed_formal==2 & (ed_tecnica %in% c(0,1)),6,
ifelse(ed_formal==2 & (ed_tecnica %in% c(2,3)),7,
ifelse(ed_formal==3 & (ed_tecnica %in% c(0,1)),7,
ifelse(ed_formal==3 & (ed_tecnica %in% c(2,3)),8,
ifelse(ed_formal==4 & ed_tecnica==0,9,
ifelse(ed_formal==4 & ed_tecnica==2,10,
ifelse(ed_formal==4 & (ed_tecnica %in% c(4,5)),11,
ifelse(ed_formal==4 & ed_tecnica==6,13,
ifelse(ed_formal==5 & ed_tecnica==0,11,
ifelse(ed_formal==5 & (ed_tecnica %in% c(4,5)),13,
ifelse(ed_formal==5 & ed_tecnica==6,14,
ifelse(ed_formal==6 & ed_tecnica==0,12,
ifelse(ed_formal==6 & ed_tecnica==2,13,
ifelse(ed_formal==6 & (ed_tecnica %in% c(4,5)),14,
ifelse(ed_formal==6 & (ed_tecnica %in% c(6,7,8)),15,
ifelse(ed_formal==7 & (ed_tecnica %in% c(0,1)),15,
ifelse(ed_formal==7 & (ed_tecnica %in% c(2,3)),16,
ifelse(ed_formal==7 & (ed_tecnica %in% c(4,5,6,8)),17,
ifelse(ed_formal==8 & ed_tecnica==0,17,
ifelse(ed_formal==8 & (ed_tecnica %in% c(2,4,5,6,8)),18,19)))))))))))))))))))))))
)
poblacion_e94c<-poblacion_e94c%>%
mutate(
edu_ac = ifelse(edu_a %in% 0:5,"C1",
ifelse(edu_a %in% 6:10 ,"C2",
ifelse(edu_a %in% 11:12,"C3",
ifelse(edu_a %in% 13:14,"C4","C5"))))
)
poblacion_e94c<-poblacion_e94c%>%
mutate(
region = ifelse(estado %in% c("02","26","08","05","19","28"),1,
ifelse(estado %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(estado %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
Ingresos engih 94
ingresos<-read.dbf(
file="Datos/ENIGH-Historica/1994/ingresos.dbf"
)
familias_integrantes <- function(df,nr){
dfn <- df %>%
filter(NUM_REN %in% nr)
return(dfn)
}
ingresos_acu <- function(df,clavs){
dfn <- df %>%
group_by(FOLIO, NUM_REN) %>%
filter(CLAVE %in% clavs)%>%
summarise(
claves_a = paste(CLAVE,collapse = ","),
ing_tri_t = sum(ING_TRI),
ing_men_p = ing_tri_t/3,
ling_men_p = log(ing_men_p)
) %>% ungroup()
return(dfn)
}
#hogares <- c("1","2","3","4","5")
personas_h <- c('01')
a<-familias_integrantes(ingresos,personas_h)
head(a)
#P001 Sueldos, salarios, jornal y horas extras
#P002 Comisiones, propinas y destajo
#P010:P018 de 1998 son las mismas que P006:P014 de 1994
#P020:P021 de 1998 son las mismas que P016:P017 de 1994
#P026 de 1998 es la misma que P022 de 1994, aunque no hay equivalente en 1994 a la P027 de 1998
#P035 de 1998 es la misma que P029 de 1994
#P040:P042 de 1998 son las mismas que P034:P036 de 1994
#P044:P045 de 1998 son las mismas que P038:P039 de 1994
claves<-c("P001","P002","P006","P007","P008","P009","P010","P011", "P012","P013","P014","P016","P017","P022","P029","P034","P035","P036","P038","P039")
ingresos_e94<-ingresos_acu(a,claves)
ingresos_e94<-ingresos_e94%>%
select(FOLIO,NUM_REN,claves_a,ing_men_p,ling_men_p)%>%
rename(
folio = FOLIO,
num_ren = NUM_REN
)
head(ingresos_e94)
ingresos_e94<- ingresos_e94%>%
mutate(
region = ifelse(substr(folio,5,6) %in% c("02","26","08","05","19","28"),1,
ifelse(substr(folio,5,6) %in% c("12","20","07","30","27","04","31","23"),4,
ifelse(substr(folio,5,6) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)
rango percentil 94
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e94$ling_men_pd<-ingresos_e94$ling_men_p + log(IN)
rp_e94 <- rank(ingresos_e94$ling_men_pd)/length(ingresos_e94$ling_men_pd)
#rp_e94<- percent_rank(ingresos_e94$ling_men_pd)
Bae cruzada pobla-ingre 94
enigh_94<-full_join(poblacion_e94c,ingresos_e94)%>%
drop_na()%>%
mutate(
ocupacion = substr(ocupacion,1,2)
)#%>%
# rename(
# EDU=N_INSTR161,
# OCU = CMO121
# )%>%
# mutate(
# EDU = as.character(EDU)
# )
head(enigh_94)
unique(enigh_94$edad)
# ajuste para nivel de ecucación ESRU
#data_padres2005$EDU <- factor(data_padres2005$EDU)
#data_padres2005$OCU <- factor(data_padres2005$OCU)
#unique(data_padres2005$OCU)
sum(is.na(enigh_94))
names(enigh_94)
##### Guardamos base de padres final
saveRDS(enigh_94, file="Datos/enigh_94_cri3.Rda")
Esdística descriptiva 94
summary(enigh_94$edad)
hist(enigh_94$edad)
papas<- enigh_94%>%group_by(edad)%>%summarise(msal = mean(ing_men_p))
plot(papas$edad,papas$msal)
Eslaticidad integeneracional de ingresos
base_logsing <- list(ingresos_e94,ingresos_e96,ingresos_e98)
base_rp <- list(rp_e94,rp_e96,rp_e98)
Deflactamos ingresos
INPC Feb 2021 = 110.907 Consulta
enigh_94 <- readRDS(file = "Datos/enigh_94_cri3.Rda")
enigh_96 <- readRDS(file = "Datos/enigh_96_cri3.Rda")
enigh_98 <- readRDS(file = "Datos/enigh_98_cri3.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_sin_re.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_sin_re_ch.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_re.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_re_ch_cri3.Rda")
dfh_c <- readRDS(file = "Datos/dfh_c_combinada_ch_cri3.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_combinada_p1.Rda")
enigh_94<-enigh_94%>%mutate(
edad_c = ifelse(25<=edad & edad<=29,"Edad1",
ifelse(30<=edad & edad<=34,"Edad2",
ifelse(35<=edad & edad<=39,"Edad3",
ifelse(40<=edad & edad<=44,"Edad4",
ifelse(45<=edad & edad<=49,"Edad5",
ifelse(50<=edad & edad<=54,"Edad6","Edad7"))))))
)
enigh_96<-enigh_96%>%mutate(
edad_c = ifelse(25<=edad & edad<=29,"Edad1",
ifelse(30<=edad & edad<=34,"Edad2",
ifelse(35<=edad & edad<=39,"Edad3",
ifelse(40<=edad & edad<=44,"Edad4",
ifelse(45<=edad & edad<=49,"Edad5",
ifelse(50<=edad & edad<=54,"Edad6","Edad7"))))))
)
enigh_98<-enigh_98%>%mutate(
edad_c = ifelse(25<=edad & edad<=29,"Edad1",
ifelse(30<=edad & edad<=34,"Edad2",
ifelse(35<=edad & edad<=39,"Edad3",
ifelse(40<=edad & edad<=44,"Edad4",
ifelse(45<=edad & edad<=49,"Edad5",
ifelse(50<=edad & edad<=54,"Edad6","Edad7"))))))
)
dfh_c <- dfh_c %>%mutate(
pp_edad_c = ifelse( pp_edad<=29,"Edad1",
ifelse(30<=pp_edad & pp_edad<=34,"Edad2",
ifelse(35<=pp_edad & pp_edad<=39,"Edad3",
ifelse(40<=pp_edad & pp_edad<=44,"Edad4",
ifelse(45<=pp_edad & pp_edad<=49,"Edad5",
ifelse(50<=pp_edad & pp_edad<=54,"Edad6","Edad7"))))))
)
dfh_c<-dfh_c%>%
mutate(
region= as.integer(region),
pp_estado = as.integer(pp_estado),
pp_region = ifelse(pp_estado %in% as.integer(c("02","26","08","05","19","28")),1,
ifelse(pp_estado %in% as.integer(c("12","20","07","30","27","04","31","23")),4,
ifelse(pp_estado %in% as.integer(c("03","25","18","10","32","16","06","14","01","24")),2,3)))
)
#IN<-(100/110.907)
IN<-(100/93.6)# enero 2017
enigh_94<-enigh_94%>%
mutate(
ling_men_p=ling_men_p+log(IN)
)
enigh_96<-enigh_96%>%
mutate(
ling_men_p=ling_men_p+log(IN)
)
enigh_98<-enigh_98%>%
mutate(
ling_men_p=ling_men_p+log(IN)
)
unique(dfh_c$region)
unique(dfh_c$pp_region)
#id_c_re<-dfh_c$id_hijo
#id_c_combinada<-dfh_c$id_ho
#sum(id_c_re %in% id_c_combinada)
nrow(dfh_c)
provi<-dfh_c%>%filter((pp_ocup %in% enigh_94$ocupacion) &
(pp_ocup %in% enigh_96$ocupacion) &
(pp_ocup %in% enigh_98$ocupacion))
nrow(provi)
Por la porgramación, para los hijos, se hace dentro del sigueinte programa
# considerando la migracion
dfh_c<- dfh_c %>%
filter(pp_region==region)
nrow(dfh_c)
dfh_cna<-dfh_c%>%drop_na(pp_ocup)%>%drop_na(pp_edad)
nrow(dfh_cna)
Criterio 1 - por region, solo los que no migran 1025 - por region y cohort de ingreso, solo los que no migran 1009 - con ocupacion y por region los ingresos, solo los que no migran 603 - con ocupacion y por region y por cohort de los ingresos, solo los que no migran 405
Criterio 2 - por region y cohort de ingreso, solo los que no migran 1012
criterio 3
- por region y cohort de ingreso 1527
- con ocupacion y por region y por cohort de los ingresos, solo los que no migran 630
Combinada
- no migran 1464 ## Estimación
eii<-function(i,fp,fhp,code=0,reg=0){
#enigh_98 <- readRDS(file = "Datos/enigh_98.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c.Rda")
#fp = ling_men_p ~ edu_ac + ocupacion + edad + I(edad*edad)
#fhp = ling_h ~ ling_p + edad_h+ I(edad_h*edad_h)
#reg<-1
#code<-1
#print(reg)
# BOOSTRAP
# Regresión de ingresos de padres
#summary(reg_padres)
# numero de padres sin ocupación reportada de hijos
#p_socu<-sum(is.na(dfh_c$pp_ocup))
regresion_padres<-function(enigh,dfh_cna,code=code,reg=reg){
if(code==1){
# regresion padres por region
enigh<-enigh%>%filter(region==reg)
enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]
reg_padres<-lm(
formula = fp,
data = enigh_r
)
# data frame region
dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
data<- data.frame(
edu_ac= dfh_cna$pp_educ_ac,
ocupacion= dfh_cna$pp_ocup,
sexo = dfh_cna$pp_sexo,
#edad_c = "Edad4"
edad = 0#dfh_cna$pp_edad
)
}
else{
# regresion padres completa
enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]
reg_padres<-lm(
formula = fp,
data = enigh_r
)
# data frame completo
dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
data<- data.frame(
edu_ac= dfh_cna$pp_educ_ac,
ocupacion= dfh_cna$pp_ocup,
sexo = dfh_cna$pp_sexo,
#edad_c = "Edad4"
edad = 0#dfh_cna$pp_edad
)
}
return(list(reg_padres,data,dfh_cna))
}
if(code==1){
dfh_cna <- dfh_cna%>%filter(region==reg)
}
p94<-regresion_padres(enigh_94,dfh_cna,code,reg)
p96<-regresion_padres(enigh_96,dfh_cna,code,reg)
p98<-regresion_padres(enigh_98,dfh_cna,code,reg)
reg_p94<-p94[[1]]
data_94<-p94[[2]]
#enigr_p4<-p94[[3]]
dfh_94<-p94[[3]]
reg_p96<-p96[[1]]
data_96<-p96[[2]]
#enigr_p6<-p96[[3]]
dfh_96<-p96[[3]]
reg_p98<-p98[[1]]
data_98<-p98[[2]]
#enigr_p8<-p98[[3]]
dfh_98<-p98[[3]]
dfh_cna <- dfh_cna%>%filter((pp_ocup %in% dfh_94$pp_ocup) &
(pp_ocup %in% dfh_96$pp_ocup) &
(pp_ocup %in% dfh_98$pp_ocup) )
data_94<- data_94%>%filter(ocupacion %in% dfh_cna$pp_ocup)
data_96<- data_96%>%filter(ocupacion %in% dfh_cna$pp_ocup)
data_98<- data_98%>%filter(ocupacion %in% dfh_cna$pp_ocup)
#print(nrow(dfh_cna))
# BOOSTRAP ingreso HIJOS
# calculamos el ingreso de los hijos con los gemelos
# re-sampleados, ademas codificamos la ocupación del hijo a dos caracteres
auxs<-function(k){
sample(k,size=length(k),replace=TRUE)
}
aux_hrr<-function(ing){
#error_e<- abs(ingresos_e16a$ling_men_pd - ing)
error_e<- abs(ingre1618$ling_men_pd - ing)
indice_e<-match(min(error_e),error_e)
indice_e<-indice_e[1]
#return(rp_e16[indice_e16]*100)
return(rp_e1618[indice_e]*100)
}
dfh_cna <- dfh_cna %>%
mutate(
ocu_hc= substring(ocu_h,1,2),
ing_h=unlist(lapply(
lapply(ing_h,auxs),
mean
)
),
ling_h = log(ing_h)+log(IN),
pling_h = unlist(lapply(ling_h,aux_hrr))
)
#dfh_f<-dfh_cna%>%
# mutate(cohort_e = ifelse(ing_h<=2400,1,
# ifelse(ing_h<=4800,2,
# ifelse(ing_h<=7200,3,
# ifelse(ing_h<=12000,4,ifelse(ing_h<=24000,5,6))))),
# cohort_dif=cohort-cohort_e)
#dfh_cna
#dfh_cna$ocu_hijo
#dfh_cna$ling_h
#str(substr(dfh_cna$ocu_h,1,2))
# estimación ingreso padres
# inputamos al hijo correspondiente
estimacion_padres<-function(reg_padres,data,num_base){
aux_prr<-function(ing,num_base){
error_e<- abs(base_logsing[[num_base]]$ling_men_pd - ing)
indice_e<-match(min(error_e),error_e)
indice_e<-indice_e[1]
return(base_rp[[num_base]][indice_e]*100)
}
ling_pf <- predict(
object = reg_padres,
newdata = data
)
pling_pf <- unlist(lapply(ling_pf,aux_prr,num_base))
return(list(ling_pf,pling_pf))
}
est1 <- estimacion_padres(reg_p94,data_94,1)
est2 <- estimacion_padres(reg_p96,data_96,2)
est3 <- estimacion_padres(reg_p98,data_98,3)
mling<-(est1[[1]] + est2[[1]] + est3[[1]])/3
#mling1<-est1[[1]]
#mling2<-est2[[1]]
#mling3<-est3[[1]]
mpling<-(est1[[2]] + est2[[2]]+ est3[[2]])/3
#print("----")
#print(length(est1[[1]]))
#print(length(est2[[1]]))
#print(length(est3[[1]]))
dfhpf<- dfh_cna%>%
mutate(
ling_p = mling,
pling_p = mpling
)
# Boostrap hijos
dfhpf<-dfhpf[sample(nrow(dfhpf),nrow(dfhpf), replace = TRUE),]
reg_hp <- lm(
formula =fhp ,#+ ocu_hc,
data = dfhpf
)
#print(dfhpf$pling_h)
#print(dfhpf$pling_p)
#data.frame(dfhpf$pling_h,dfhpf$pling_p)
reg_hpr <- lm(
formula = pling_h ~ pling_p,
data = dfhpf
)
# plot(dfhpf$pling_p,dfhpf$pling_h,
# xlim=c(0, 100))
#lines(1:100,1:100)
#summary(reg_hp)
beta0<- reg_hp$coefficients[1]
beta <- reg_hp$coefficients[2]
sexoh<- reg_hp$coefficients[3]
edadh<- reg_hp$coefficients[4]
edad2h<-reg_hp$coefficients[5]
betar <- reg_hpr$coefficients[2]
alfar <- reg_hpr$coefficients[1]
nhijos<-nrow(dfh_cna)
# cohort_dif<-as.integer(dfhpf%>%count(cohort_dif)%>%
# filter(cohort_dif==0)%>%
# select(n))
return(data.frame()%>%
summarise(
#cohort_dif=list(dfh_f$cohort_dif),
beta0 = beta0,
beta = beta,
sexoh = sexoh,
edadh = edadh,
edad2h = edad2h,
betar = betar,
alfar = alfar,
nhijos = nhijos,
coeficiente = list(reg_hp$coefficients),
coeficienter = list(reg_hpr$coefficients),
#p_socu = p_socu,
regresionp94 = list(reg_p94),
regresionp96 = list(reg_p96),
regresionp98 = list(reg_p98),
regresion = list(reg_hp),
regresionr = list(reg_hpr)
)
)
}
Estimaciòn individual
eiii<-function(i,fp,fhp,eni,code=0,reg=0){
#enigh_98 <- readRDS(file = "Datos/enigh_98.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c.Rda")
#fp = ling_men_p ~ edu_ac + ocupacion + edad + I(edad*edad)
#fhp = ling_h ~ ling_p + edad_h+ I(edad_h*edad_h)
#reg<-1
#code<-1
#print(reg)
# BOOSTRAP
# Regresión de ingresos de padres
#summary(reg_padres)
# numero de padres sin ocupación reportada de hijos
#p_socu<-sum(is.na(dfh_c$pp_ocup))
regresion_padres<-function(enigh,dfh_cna,code=code,reg=reg){
if(code==1){
# regresion padres por region
enigh<-enigh%>%filter(region==reg)
enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]
reg_padres<-lm(
formula = fp,
data = enigh_r
)
# data frame region
dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
data<- data.frame(
edu_ac= dfh_cna$pp_educ_ac,
ocupacion= dfh_cna$pp_ocup,
sexo = dfh_cna$pp_sexo,
edad = dfh_cna$pp_edad
)
}
else{
# regresion padres completa
enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]
reg_padres<-lm(
formula = fp,
data = enigh_r
)
# data grame completo
dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
data<- data.frame(
edu_ac= dfh_cna$pp_educ_ac,
ocupacion= dfh_cna$pp_ocup,
sexo = dfh_cna$pp_sexo,
edad = dfh_cna$pp_edad
)
}
return(list(reg_padres,data,dfh_cna))
}
if(code==1){
dfh_cna <- dfh_cna%>%filter(region==reg)
}
if(eni==94){
enigh <- enigh_94
num_base <- 1
}
if(eni==96){
enigh <- enigh_96
num_base <- 2
}
if(eni==98){
enigh <- enigh_98
num_base <- 3
}
p<-regresion_padres(enigh,dfh_cna,code,reg)
reg_p<-p[[1]]
data<-p[[2]]
dfh_cna<-p[[3]]
auxs<-function(k){
sample(k,size=length(k),replace=TRUE)
}
aux_hrr<-function(ing){
#error_e<- abs(ingresos_e16a$ling_men_pd - ing)
error_e<- abs(ingre1618$ling_men_pd - ing)
indice_e<-match(min(error_e),error_e)
indice_e<-indice_e[1]
#return(rp_e16[indice_e]*100)
return(rp_e1618[indice_e]*100)
}
# BOOSTRAP ingreso HIJOS
# calculamos el ingreso de los hijos con los gemelos
# re-sampleados, ademas codificamos la ocupación del hijo a dos caracteres
dfh_cna <- dfh_cna %>%
mutate(
ocu_hc= substring(ocu_h,1,2),
ing_h=unlist(lapply(
lapply(ing_h,auxs),
mean
)
),
ling_h = log(ing_h)+log(IN),
pling_h = unlist(lapply(ling_h,aux_hrr))
)
#dfh_f<-dfh_cna%>%
# mutate(cohort_e = ifelse(ing_h<=2400,1,
# ifelse(ing_h<=4800,2,
# ifelse(ing_h<=7200,3,
# ifelse(ing_h<=12000,4,ifelse(ing_h<=24000,5,6))))),
# cohort_dif=cohort-cohort_e)
#dfh_cna
#dfh_cna$ocu_hijo
#dfh_cna$ling_h
#str(substr(dfh_cna$ocu_h,1,2))
# estimación ingreso padres
# inputamos al hijo correspondiente
estimacion_padres<-function(reg_padres,data,num_base){
aux_prr<-function(ing,num_base){
error_e<- abs(base_logsing[[num_base]]$ling_men_pd - ing)
indice_e<-match(min(error_e),error_e)
indice_e<-indice_e[1]
return(base_rp[[num_base]][indice_e]*100)
}
ling_pf <- predict(
object = reg_padres,
newdata = data
)
pling_pf <- unlist(lapply(ling_pf,aux_prr,num_base))
return(list(ling_pf,pling_pf))
}
est <- estimacion_padres(reg_p,data,num_base)
mling<-est[[1]]
mpling<-est[[2]]
dfhpf<- dfh_cna%>%
mutate(
ling_p = mling,
pling_p = mpling
)
# Boostrap hijos
dfhpf<-dfhpf[sample(nrow(dfhpf),nrow(dfhpf), replace = TRUE),]
reg_hp <- lm(
formula =fhp ,#+ ocu_hc,
data = dfhpf
)
#print(dfhpf$pling_h)
#print(dfhpf$pling_p)
#data.frame(dfhpf$pling_h,dfhpf$pling_p)
reg_hpr <- lm(
formula = pling_h ~ pling_p,
data = dfhpf
)
# plot(dfhpf$pling_p,dfhpf$pling_h,
# xlim=c(0, 100))
# lines(1:100,1:100)
#summary(reg_hp)
beta <- reg_hp$coefficients[2]
betar <- reg_hpr$coefficients[2]
alfar <- reg_hpr$coefficients[1]
# cohort_dif<-as.integer(dfhpf%>%count(cohort_dif)%>%
# filter(cohort_dif==0)%>%
# select(n))
return(data.frame()%>%
summarise(
#cohort_dif=list(dfh_f$cohort_dif),
beta=beta,
betar = betar,
alfar = alfar,
coeficiente = list(reg_hp$coefficients),
coeficienter = list(reg_hpr$coefficients),
#p_socu = p_socu,
regresionp = list(reg_p),
regresionhp = list(reg_hp),
regresionhpr = list(reg_hpr)
)
)
}
Funciones ara regresion
regre_completa<-function(boot=1000){
fp <- ling_men_p ~ edu_ac + ocupacion + sexo + edad + I(edad*edad)
fhp<- ling_h ~ ling_p + sexo_h #+ edad_h + I(edad_h*edad_h)-1
set.seed(123)
a<-lapply(1:boot,eii,fp,fhp,code=0)
resul<-bind_rows(a[1:length(a)])
print("beta cero media")
print(mean(resul$beta0))
print("beta cero sd")
print(sd(resul$beta0))
print("beta media")
print(mean(resul$beta))
print("beta sd")
print(sd(resul$beta))
print("sexo media")
print(mean(resul$sexoh))
print("sexo sd")
print(sd(resul$sexoh))
print("edad media")
print(mean(resul$edadh))
print("edad sd")
print(sd(resul$edadh))
print("edad-2 media")
print(mean(resul$edad2h))
print("edad-2 sd")
print(sd(resul$edad2h))
print("betar media")
print(mean(resul$betar))
print("betar sd")
print(sd(resul$betar))
print("alfar media")
print(mean(resul$alfar))
print("alfar sd")
print(sd(resul$alfar))
hist(resul$beta)
hist(resul$betar)
return(resul)
}
regre_completa_ind<-function(eni,boot=1000){
fp <- ling_men_p ~ edu_ac + ocupacion + sexo + edad + I(edad*edad)
fhp<- ling_h ~ ling_p + sexo_h #+ edad_h +I(edad_h*edad_h)
set.seed(123)
a<-lapply(1:boot,eiii,fp,fhp,eni,code=0)
resul<-bind_rows(a[1:length(a)])
print("beta media")
print(mean(resul$beta))
print("beta sd")
print(sd(resul$beta))
print("betar media")
print(mean(resul$betar))
print("betar sd")
print(sd(resul$betar))
print("alfar media")
print(mean(resul$alfar))
print("alfar sd")
print(sd(resul$alfar))
hist(resul$beta)
hist(resul$betar)
return(resul)
}
regre_region<-function(r, boot = 1000){
fpr <- ling_men_p ~ edu_ac + ocupacion + sexo + edad + I(edad*edad)
fhpr<- ling_h ~ ling_p + sexo_h + edad_h #+ I(edad_h*edad_h)
set.seed(123)
a<-lapply(1:boot,eii,fpr,fhpr,code=1, reg=r)
resul<-bind_rows(a[1:length(a)])
print("beta media")
print(mean(resul$beta))
print("beta sd")
print(sd(resul$beta))
print("betar media")
print(mean(resul$betar))
print("betar sd")
print(sd(resul$betar))
print("alfar media")
print(mean(resul$alfar))
print("alfar sd")
print(sd(resul$alfar))
hist(resul$beta)
hist(resul$betar)
return(resul)
}
regre_region_ind<-function(r,eni, boot = 1000){
fpr <- ling_men_p ~ edu_ac + ocupacion + sexo + edad + I(edad*edad)
fhpr<- ling_h ~ ling_p + sexo_h #+ edad_h + I(edad_h*edad_h)
set.seed(123)
a<-lapply(1:boot,eiii,fpr,fhpr,eni,code=1, reg=r)
resul<-bind_rows(a[1:length(a)])
print("beta media")
print(mean(resul$beta))
print("beta sd")
print(sd(resul$beta))
print("betar media")
print(mean(resul$betar))
print("betar sd")
print(sd(resul$betar))
print("alfar media")
print(mean(resul$alfar))
print("alfar sd")
print(sd(resul$alfar))
hist(resul$beta)
hist(resul$betar)
return(resul)
}
regre_region_rel<-function(regi=0,boot = 1000){
fpr <- ling_men_p ~ edu_ac + ocupacion + sexo + poly((edad-30),2)-1
fhpr<- ling_h ~ ling_p + poly((edad_h-25),2)
set.seed(123)
a<-lapply(1:boot,eii,fpr,fhpr,code=1, reg=regi, rel=1)
resul<-bind_rows(a[1:length(a)])
print("beta media")
print(mean(resul$beta))
print("beta sd")
print(sd(resul$beta))
print("betar media")
print(mean(resul$betar))
print("betar sd")
print(sd(resul$betar))
hist(resul$beta)
hist(resul$betar)
return(resul)
}
# Rango percentil: el porcentaje de puntuaciones en una distribución de puntuación especificada que están por debajo de una puntuación determinada.
# https://www.math.fsu.edu/~wooland/hm2ed/Part3Module1/prVsNth.html
Criterios
Total promedio, criterio principal
reg_cp_100 <- regre_completa(20)
saveRDS(reg_cp_100, file="Datos/Regresiones/reg_cp_100.Rda")
Total promedio, criterio dos
reg_cd_100 <- regre_completa(100)
#reg_cd_100p <- regre_completa(100)
saveRDS(reg_cd_100, file="Datos/Regresiones/reg_cd_100.Rda")
Total individual, criterio principal
reg_cp_98100<-regre_completa_ind(98,100)
saveRDS(reg_cp_94100, file="Datos/Regresiones/reg_cp100_94.Rda")
saveRDS(reg_cp_96100, file="Datos/Regresiones/reg_cp100_96.Rda")
saveRDS(reg_cp_98100, file="Datos/Regresiones/reg_cp100_98.Rda")
#obtener los datoso de los modelos ya establecidos
z<-reg_cp_96$regresionhpr[1][[1]]
class(z)
augment(z)
Total individual, criterio Dos
reg_cd_94100<-regre_completa_ind(100)
reg_cd_96100<-regre_completa_ind(96,100)
reg_cd_98100<-regre_completa_ind(100)
saveRDS(reg_cd_94100, file="Datos/Regresiones/reg_cd100_94.Rda")
saveRDS(reg_cd_96100, file="Datos/Regresiones/reg_cd100_96.Rda")
saveRDS(reg_cd_98100, file="Datos/Regresiones/reg_cd100_98.Rda")
#obtener los datoso de los modelos ya establecidos
z<-reg_cp_96$regresionhpr[1][[1]]
class(z)
augment(z)
Region promedio, criterio principal
reg_reg1_cp_100<-regre_region(1,100)
reg_reg2_cp_100<-regre_region(2,100)
reg_reg3_cp_100<-regre_region(3,100)
reg_reg4_cp_100<-regre_region(4,100)
#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])
saveRDS(reg_reg1_cp_100, file="Datos/Regresiones/reg_reg1_cp_100.Rda")
saveRDS(reg_reg2_cp_100, file="Datos/Regresiones/reg_reg2_cp_100.Rda")
saveRDS(reg_reg3_cp_100, file="Datos/Regresiones/reg_reg3_cp_100.Rda")
saveRDS(reg_reg4_cp_100, file="Datos/Regresiones/reg_reg4_cp_100.Rda")
df<-reg_reg1_cp_100
r1<- data.frame(Norte=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg2_cp_100
r2<- data.frame(Norte_Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg3_cp_100
r3<- data.frame(Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg4_cp_100
r4<- data.frame(Sur=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
regionesdf<-bind_cols(r1,r2,r3,r4)
stargazer(regionesdf,summary = FALSE, digits = 3)
Region promedio, criterio dos
reg_reg1_cdp_100<-regre_region(1,100)
reg_reg2_cdp_100<-regre_region(2,100)
reg_reg3_cdp_100<-regre_region(3,100)
reg_reg4_cdp_100<-regre_region(4,100)
#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])
saveRDS(reg_reg1_cdp_100, file="Datos/Regresiones/reg_reg1_cdp_100.Rda")
saveRDS(reg_reg2_cdp_100, file="Datos/Regresiones/reg_reg2_cdp_100.Rda")
saveRDS(reg_reg3_cdp_100, file="Datos/Regresiones/reg_reg3_cdp_100.Rda")
saveRDS(reg_reg4_cdp_100, file="Datos/Regresiones/reg_reg4_cdp_100.Rda")
df<-reg_reg1_cd_100
r1<- data.frame(Norte=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg2_cd_100
r2<- data.frame(Norte_Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg3_cd_100
r3<- data.frame(Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg4_cd_100
r4<- data.frame(Sur=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
regionesdf<-bind_cols(r1,r2,r3,r4)
stargazer(regionesdf,summary = FALSE, digits = 3)
Region individual, criterio principal
reg_reg1_cp_98100<-regre_region_ind(1,98,100)
reg_reg2_cp_98100<-regre_region_ind(2,98,100)
reg_reg3_cp_98100<-regre_region_ind(3,98,100)
reg_reg4_cp_98100<-regre_region_ind(4,98,100)
#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])
saveRDS(reg_reg1_cp_98100, file="Datos/Regresiones/reg_reg1_cp_98100.Rda")
saveRDS(reg_reg2_cp_98100, file="Datos/Regresiones/reg_reg2_cp_98100.Rda")
saveRDS(reg_reg3_cp_98100, file="Datos/Regresiones/reg_reg3_cp_98100.Rda")
saveRDS(reg_reg4_cp_98100, file="Datos/Regresiones/reg_reg4_cp_98100.Rda")
df<-reg_reg1_cp_98100
r1<- data.frame(Norte=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg2_cp_98100
r2<- data.frame(Norte_Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg3_cp_98100
r3<- data.frame(Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg4_cp_98100
r4<- data.frame(Sur=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
regionesdf<-bind_cols(r1,r2,r3,r4)
stargazer(regionesdf,summary = FALSE, digits = 3)
resul %>%
ggplot(aes(x=beta)) +
geom_histogram(aes(y=..density.. ), bins=25, alpha=0.6,color="blue")+
geom_density() + theme_gray()
Region individual, criterio dos
reg_reg1_cd_98100<-regre_region_ind(1,100)
reg_reg2_cd_98100<-regre_region_ind(2,100)
reg_reg3_cd_98100<-regre_region_ind(3,100)
reg_reg4_cd_98100<-regre_region_ind(4,100)
#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])
saveRDS(reg_reg1_cd_98100, file="Datos/Regresiones/reg_reg1_cd_98100.Rda")
saveRDS(reg_reg2_cd_98100, file="Datos/Regresiones/reg_reg2_cd_98100.Rda")
saveRDS(reg_reg3_cd_98100, file="Datos/Regresiones/reg_reg3_cd_98100.Rda")
saveRDS(reg_reg4_cd_98100, file="Datos/Regresiones/reg_reg4_cd_98100.Rda")
df<-reg_reg1_cd_98100
r1<- data.frame(Norte=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg2_cd_98100
r2<- data.frame(Norte_Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg3_cd_98100
r3<- data.frame(Centro=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
df<-reg_reg4_cd_98100
r4<- data.frame(Sur=c("",
substr(as.character(mean(df$beta)),1,5),
paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
"",
substr(as.character(mean(df$betar)),1,5),
paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
"",
substr(as.character(mean(df$alfar)),1,6),
paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
""
),
row.names=c(" ",
"beta_r",
"sd br",
" ",
"hat rho_r$",
"sd rho",
" ",
"alpha_r",
"sd alphar",
" "
)
)
regionesdf<-bind_cols(r1,r2,r3,r4)
stargazer(regionesdf,summary = FALSE, digits = 3)
#Graficas
a<- readRDS("Datos/Regresiones/reg_reg1_cp_98100.Rda")
#grafica rango rango regional
graficarr<-function(df,a = "", region= "")
{
la<-df%>%pull(regresionhpr)
la1<-lapply(la[1:length(la)],function(x) x$model)
la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )
la2<-bind_rows(la1[1:length(la1)])
print(nrow(la2))
print(sum(is.na(la2)))
la2$pling_p<-la2%>%pull(pling_p)%>%as.integer()
la3<-la2%>%group_by(pling_p)%>%summarise(pling_pm=mean(pling_h))
alfar <- mean(df$alfar)
betar <- mean(df$betar)
ggplot(la3,aes(x=pling_p, y = pling_pm)) +
geom_point(col="gray")+
labs(title = paste(region," ",a),
subtitle = "Rango percentil intergeneracional") +
xlab("Rango pasado") +
ylab("Rango actual") +
xlim(0,100) +
ylim(0,100) +
theme_bw() +
theme(plot.title = element_text(size = 20),
plot.subtitle = element_text(size = 15),
axis.title = element_text(size = 15),
axis.text = element_text(size = 10),
legend.position="top",
legend.title = element_text(size=8, face="bold"),
legend.text = element_text(size=6, face="bold")) +
geom_function(fun = function(x)+ alfar + betar*x,
colour = "red") +
annotate("text",
x=70,
y=25,
label = paste(substr(as.character(alfar),1,6),
" + ",substr(as.character(betar),1,5)
,"x"),
colour = "red",
size = 5)
#parse = TRUE)
}
graficarr(a, "2017 - 1998", "Región Norte:")
grafica rango rango
graficarr_promedio<-function(df,region= "",a = "")
{
la<-df%>%pull(regresionr)
la1<-lapply(la[1:length(la)],function(x) x$model)
la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )
la2<-bind_rows(la1[1:length(la1)])
#print(nrow(la2))
#print(sum(is.na(la2)))
la3<-la2%>%mutate(cohortp = cut(pling_p,breaks=seq(0,100,2)))
la3<-la3%>%group_by(cohortp)%>%summarise(pling_pm=mean(pling_p),
pling_hm=mean(pling_h))
alfar <- mean(df$alfar)
betar <- mean(df$betar)
g<-ggplot(la3,aes(x=pling_pm, y = pling_hm)) +
geom_point(col="gray")+
labs(title = paste(region," ",a))+
# subtitle = "Rango percentil intergeneracional") +
xlab("Rango percentil pseudo PP") +
ylab("Rango percentil actual") +
xlim(0,100) +
ylim(0,100) +
# theme_classic() +
theme(
plot.title = element_text(size = 15),
plot.subtitle = element_text(size = 15),
axis.title = element_text(size = 15),
axis.text = element_text(size = 15),
legend.position="top",
legend.title = element_text(size=8, face="bold"),
legend.text = element_text(size=6, face="bold")) +
geom_function(fun = function(x)+ alfar + betar*x,
colour = "red") +
geom_function(fun = function(x) x,colour = "blue")+
theme_update(plot.title = element_text(hjust = 0.5))#+
# annotate("text",
# x=70,
# y=25,
# label = paste("R(y^a)=",substr(as.character(alfar),1,6),
# " + ",substr(as.character(betar),1,5)
# ,"R(y^p)"),
# colour = "red",
# size = 5, parser=TRUE)
#
#parse = TRUE)
return(g)
}
r1<-graficarr_promedio(reg_reg1_cd_100,"Norte (región 1):","Relación de estimaciones de rango percentil")
r2<-graficarr_promedio(reg_reg2_cd_100,"Centro-Norte (región 2):","Relación de estimaciones de rango percentil")
r3<-graficarr_promedio(reg_reg3_cd_100,"Centro (región 3):","Relación de estimaciones de rango percentil")
r4<-graficarr_promedio(reg_reg4_cd_100,"Sur (región 4):","Relación de estimaciones de rango percentil")
r4
regiones conjuntas
alfar1 <- mean(reg_reg1_cd_100$alfar)
betar1 <- mean(reg_reg1_cd_100$betar)
alfar2 <- mean(reg_reg2_cd_100$alfar)
betar2 <- mean(reg_reg2_cd_100$betar)
alfar3 <- mean(reg_reg3_cd_100$alfar)
betar3 <- mean(reg_reg3_cd_100$betar)
alfar4 <- mean(reg_reg4_cd_100$alfar)
betar4 <- mean(reg_reg4_cd_100$betar)
ggplot() +
labs(title = "AIR estimado por regiones")+
xlab("Rango percentil pseudo PP") +
ylab("Rango percentil actual") +
xlim(0,100) +
ylim(0,100) +
# theme_classic() +
theme(
plot.title = element_text(size = 15),
plot.subtitle = element_text(size = 15),
axis.title = element_text(size = 15),
axis.text = element_text(size = 15),
legend.position="top",
legend.title = element_text(size=8, face="bold"),
legend.text = element_text(size=6, face="bold")) +
geom_function(fun = function(x)+ alfar1 + betar1*x,aes(linetype="1"),
colour = "red", alpha=1) +
geom_function(fun = function(x)+ alfar2 + betar2*x,aes(linetype="2"),
colour = "red", alpha=0.8) +
geom_function(fun = function(x)+ alfar3 + betar3*x,aes(linetype="5"),
colour = "red", alpha=0.6) +
geom_function(fun = function(x)+ alfar4 + betar4*x,aes(linetype="6"),
colour = "red", alpha=1) +
scale_linetype_discrete("Región", breaks=c("1", "2","5", "6"), labels=c("Norte", "Centro-Norte","Centro","Sur"))+
geom_function(fun = function(x) x,colour = "blue")+
theme_update(plot.title = element_text(hjust = 0.5))#+
Matriz de transición
transicion<-function(df,region= "",a = "")
{
la<-df%>%pull(regresionr)
la1<-lapply(la[1:length(la)],function(x) x$model)
la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )
la2<-bind_rows(la1[1:length(la1)])
#print(nrow(la2))
#print(sum(is.na(la2)))
la3<-la2%>%mutate(cohortp = cut(pling_p,breaks=seq(0,100,20),labels=c("Q1","Q2","Q3","Q4","Q5")),
cohorth = cut(pling_h,breaks=seq(0,100,20),labels=c("q1","q2","q3","q4","q5")))
la3<-la3%>%group_by(cohortp,cohorth)%>%
count()%>%ungroup()
la3<-la3%>%group_by(cohortp)%>%
summarise(p1=n*100/sum(n),
cohortp=factor(cohortp, levels=c("Q1","Q2","Q3","Q4","Q5")),
cohorth=factor(cohorth, levels=c("q5","q4","q3","q2","q1")))%>%ungroup()
la3<-la3%>%arrange(cohortp)#,decreasing = TRUE)
#print(la3%>%group_by(cohortp)%>%summarise(sum(p1)))
la3<-la3%>% pivot_wider(names_from = cohorth, values_from = p1)
return(la3)
}
tranreg1<-transicion(reg_cd_100)
#row.names(tranreg1)<-tranreg1$cohortp
stargazer(as.data.frame(tranreg1),rownames= FALSE, summary = FALSE, digits= 1)
# bar_order <- list(
# "(0,20]" = 4,
# "(20,40]" = 3,
# "(40,60]" = 2,
# "(60,80]" = 1,
# "(80,100]" = 0
# )
# bar_order_v <- as.numeric(bar_order)
tranreg1 %>%
hchart(
'column', hcaes(x = cohortp, y = p1, group = cohorth),
stacking = "normal"
# explicit_order = c("q5","q2","q3","q4","q1")
)
ggsave("region1rr.png", plot = r1, path = "imagenes/")
ggsave("region2rr.png", plot = r2, path = "imagenes/")
ggsave("region3rr.png", plot = r3, path = "imagenes/")
ggsave("region4rr.png", plot = r4, path = "imagenes/")
Graficas EII
graficareii_promedio<-function(df,nl=FALSE,region= "",a = "")
{
la<-df%>%pull(regresion)
la1<-lapply(la[1:length(la)],function(x) x$model)
la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )
la2<-bind_rows(la1[1:length(la1)])
#print(nrow(la2))
#print(sum(is.na(la2)))
la2<-la2%>%drop_na()
if(nl==TRUE){
la2<-la2%>%mutate(ingh = exp(ling_h),
ingp = exp(ling_p)
)%>%select(ingh,ingp)
#head(la2)
la2<-la2%>%mutate(peringp=rank(ingp)/length(ingp))
la2<-la2%>%mutate(cohop = cut(peringp, breaks = seq(0,1,0.01), include.lowest=TRUE))
la3<-la2%>%group_by(cohop)%>%summarise(ing_hm=mean(ingh),
ing_pm=mean(ingp))
}else{
la2<-la2%>%mutate(peringp=rank(ling_p)/length(ling_p))
la2 <-la2 %>% mutate(cohop = cut(peringp, breaks = seq(0,1,0.02), include.lowest=TRUE))
la3<-la2%>%group_by(cohop)%>%summarise(ing_hm=mean(ling_h),
ing_pm=mean(ling_p))
}
beta0 <- mean(df$beta0)
beta <- mean(df$beta)
sex<- mean(df$sexoh)
g<-ggplot(la3,aes(x=ing_pm, y = ing_hm)) +
geom_point(col="gray")+
labs(title = paste(region," ",a))+
# subtitle = "Estimación lineal") +
xlab("Log ingreso pseudo PP") +
#xlab("Ingreso pseudo PP") +
ylab("Log ingreso actual") +
#ylab("Ingreso actual") +
#xlim(0,max(la3$ing_pm)) +
#ylim(0,max(la3$ing_hm)) +
xlim(min(la3$ing_pm),max(la3$ing_pm)) +
ylim(6,max(la3$ing_hm)) +
theme_classic() +
theme(
plot.title = element_text(size = 15),
plot.subtitle = element_text(size = 10),
axis.title = element_text(size = 15),
axis.text = element_text(size = 10),
legend.position="top",
legend.title = element_text(size=8, face="bold"),
legend.text = element_text(size=6, face="bold")) +
geom_function(fun = function(x) beta0 + x*beta, colour = "red") +
#geom_function(fun = function(x) exp(beta0) * x**beta, colour = "red") +
geom_function(fun = function(x) beta0 + x*beta + sex, colour = "orange")+
#geom_function(fun = function(x) exp(beta0) * x**beta *exp(sex), colour = "orange")+
#geom_function(fun = function(x) x,colour = "blue")+
theme_update(plot.title = element_text(hjust = 0.5))
# annotate("text",
# x=70,
# y=25,
# label = paste(substr(as.character(alfar),1,6),
# " + ",substr(as.character(betar),1,5)
# ,"x"),
# colour = "red",
# size = 5)
#
#parse = TRUE)
}
#graficareii_promedio<-function(df,nl=FALSE,a = "", region= "")
#region<-"Relación de estimaciones: ingresos actuales vs ingresos pseudo PP"
a<-"log ingresos actuales vs log ingresos pseudo PP"
r1<-graficareii_promedio(reg_reg1_cd_100,nl=FALSE,"Norte (region 1):",a)
r2<-graficareii_promedio(reg_reg2_cd_100,nl=FALSE,"Centro-Norte (region 2):",a)
#r3<-graficareii_promedio(reg_reg3_cd_100,nl=FALSE,"Centro (region 3):",a)
#r4<-graficareii_promedio(reg_reg4_cd_100,nl=FALSE,"Sur (region 4):",a)
r1
r2
r3
r4
ggsave("region1eeil.png", plot = r1, path = "imagenes/")
ggsave("region2eeil.png", plot = r2, path = "imagenes/")
ggsave("region3eeil.png", plot = r3, path = "imagenes/")
ggsave("region4eeil.png", plot = r4, path = "imagenes/")
graficarr_promedio(reg_cp_500,"Actual-Pasado promedio","México, Base A:")
Estad des A
resu<-dfh_c%>%summarise(Sexo_Masculino = filter(dfh_c,sexo_h==1)%>%count()%>%pull(),
Sexo_Femenino = filter(dfh_c,sexo_h==2)%>%count()%>%pull(),
Promedio_Edad_Entrevistado = mean(edad_h),
Sexo_Masculino_PP = filter(dfh_c,pp_sexo==1)%>%count()%>%pull(),
Sexo_Femenino_PP = filter(dfh_c,pp_sexo==2)%>%count()%>%pull(),
Edad_Mínima_PP = min(pp_edad),
Edad_Máxima_PP = max(pp_edad),
Promedio_Edad_PP_2017 = mean(pp_edad)+22)
stargazer(resu,summary = FALSE, digits = 1, flip = TRUE)
Esta des B
resu<-dfh_c%>%summarise(Sexo_Masculino = filter(dfh_c,sexo_h==1)%>%count()%>%pull(),
Sexo_Femenino = filter(dfh_c,sexo_h==2)%>%count()%>%pull(),
Promedio_Edad_Entrevistado = mean(edad_h),
Sexo_Masculino_PP = filter(dfh_c,pp_sexo==1)%>%count()%>%pull(),
Sexo_Femenino_PP = filter(dfh_c,pp_sexo==2)%>%count()%>%pull(),
Edad_Mínima_PP = min(pp_edad),
Edad_Máxima_PP = max(pp_edad),
Promedio_Edad_PP_2017 = mean(pp_edad)+22)
stargazer(resu,summary = FALSE, digits = 1, flip = TRUE)
Esta des bases ENIGH
resuen<- data.frame()%>%summarise(
Tamano_de_muestra = c(nrow(enigh_94),nrow(enigh_96),nrow(enigh_98)),
Hombres = c(nrow(enigh_94%>%filter(sexo==1)),nrow(enigh_96%>%filter(sexo==1)),
nrow(enigh_98%>%filter(sexo==1))),
Mujeres = c(nrow(enigh_94%>%filter(sexo==2)),nrow(enigh_96%>%filter(sexo==2)),
nrow(enigh_98%>%filter(sexo==2))),
Promedio_Ingresos = c(enigh_94%>%select(ing_men_p)%>%pull()%>%mean()*IN,
enigh_96%>%select(ing_men_p)%>%pull()%>%mean()*IN,
enigh_98%>%select(ing_men_p)%>%pull()%>%mean()*IN),
Promedio_logIngresos = log(Promedio_Ingresos)
)
row.names(resuen) = c("E94","E96","E98")
resuen
stargazer(resuen,summary = FALSE)
promedio de ingreso por edad
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
ingreso_edad<-function(df){
df %>%
group_by(edad)%>%
arrange(edad)%>%
summarise(
pro_ing = mean(ing_men_p)*IN
)%>%
hchart( "scatter",
hcaes(x = edad, y = pro_ing),
name="Ingreso promedio",
regression = TRUE,
regressionSettings = list(
type = "polynomial",
dashStyle = "ShortDash",
color = "skyblue",
order = 2,
lineWidth = 5,
name = "%eq | $r^2$: %r",
hideInLegend = FALSE)
)%>%
hc_add_dependency("plugins/highcharts-regression.js")%>%
hc_yAxis(title = list(text = "Promedio de ingreso por edad"))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Promedio de ingreso por edad E98"
) %>%
hc_xAxis(title=list(text="Edad")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
}
ingreso_edad(enigh_98)
histo ingreso
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
histo_ingreso<-function(df){
a<-sort(df$ling_men_pd)
#a<-a[1:(length(a)-5)]
#a<-a[5:(length(a))]
hchart(a, name= "Frecuencia")%>%
hc_yAxis(title = list(text ="Número de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Distribución de ingreso E98"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
}
histo_ingreso(enigh_98)
revisión rango percentil
d<- enigh_98p$ling_men_pd
rd<-rank(d)
nd<-length(d)
rangop<- rd/nd
min(rangop)
max(rangop)
d1<- ingresos_e98$ling_men_p
d1
d2<- ingresos_e98$ling_men_pd
d2
dfe<-data.frame(d,d1,d2)
tail(dfe)
aux_prr<-function(ing,num_base){
error_e<- abs(base_logsing[[num_base]]$ling_men_pd - ing)
#print(error_e)
indice_e<-match(min(error_e),error_e)
print(indice_e)
indice_e<-indice_e[1]
return(base_rp[[num_base]][indice_e]*100)
}
aux_prr(7,3)
base_logsing[[3]]$ling_men_pd[4167]
f<-base_logsing[[3]]$ling_men_pd - 7
f[4167]
dfe[4167,]
d[4167]
rangop[4167]
densidad ingre selecionados
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(enigh_94$ling_men_pd)
a2<-sort(enigh_96$ling_men_pd)
a3<-sort(enigh_98$ling_men_pd)
hchart(
density(a1),
type = "area",
name = "E94"
)%>%
hc_add_series(
density(a2), type = "area",
name = "E96"
)%>%
hc_add_series(
density(a3), type = "area",
name = "E98"
)%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Densidad de ingreso E94, E96, y E98"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
densidad ingre total viejas
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(ingresos_e94$ling_men_pd)
a2<-sort(ingresos_e96$ling_men_pd)
a3<-sort(ingresos_e98$ling_men_pd)
hchart(
density(a1),
type = "area",
name = "E94"
)%>%
hc_add_series(
density(a2), type = "area",
name = "E96"
)%>%
hc_add_series(
density(a3), type = "area",
name = "E98"
)%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Densidad de ingreso E94, E96, y E98"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
densidad ingre total viejas
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(ingresos_e16a$ling_men_pd)
a2<-sort(ingresos_e18a$ling_men_pd)
hchart(
density(a1),
type = "area",
name = "E16"
)%>%
hc_add_series(
density(a2), type = "area",
name = "E18"
)%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Densidad de ingreso E16 y E18"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
densidad ingre conjuay total viejas
ingre1618_gra<-bind_rows(ingresos_e16a,ingresos_e18a)
nrow(ingre1618_gra)
ingre949698_gra<-bind_rows(ingresos_e94,ingresos_e96,ingresos_e98)
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(ingre1618_gra$ling_men_pd)
hchart(
density(a1),
type = "area",
name = "E16-E18"
)%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Densidad de ingreso union E16-E18"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
Total viejeas y nuevas
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a2<-exp(ingre1618_gra%>%select(ling_men_pd)%>%pull())
a1<-exp(ingre949698_gra%>%select(ling_men_pd)%>%pull())
x <- hist(a1, plot = FALSE)
y <- hist(a2, plot = FALSE)
a2p<-percent_rank(a2)
a1p<-percent_rank(a1)
#a1<-percent_rank(a1)*100
#a2<-percent_rank(a2)*100
hchart(
a2/length(a2),
breaks = 5000,
#type = "area",
name = "E94-E96-E98"
)%>%
hc_add_series(
density(a1),
breaks = 20,
#type = "area",
name = "E16-E18"
)%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Densidad de ingreso union, E94-E96-E98, E16-E18"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)"),#)%>%
min = 0,
max = 30000) %>%
hc_xAxis(plotLines = list(list(value=median(a1),color="red",width=2 ),
list(value = median(a2),color="blue",width=2)))%>%
#hc_xAxis(plotLines = list(list(value=median(a2),color = "red" )))%>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
total hisotgrama completo
a2<-ingre1618_gra%>%select(ling_men_pd)%>%pull()
a1<-ingre949698_gra%>%select(ling_men_pd)%>%pull()
dats <- rbind(data.frame(pred = a1, Encuesta = 'E94-E96-E98'),
data.frame(pred = a2, Encuesta = 'E16-E18'))
# here the plot
ggplot(dats, aes(pred, fill = Encuesta)) +
#geom_density(alpha = 0.5, position = "identity", bins = 60)+
geom_density(alpha = 0.5, position = "identity")+
#xlim(0,30000)+
geom_vline(xintercept=median(a1),color = "blue") +
geom_vline(xintercept=median(a2),color = "red")+
labs(title = "Densidad de ingreso")+
xlab("Logaritmo Ingresos") +
ylab("Densidad")
reginales nuevas y viejas
highchart(type = "chart")%>%
hc_yAxis_multiples(
create_yaxis(4, height = c(5,5,5,5), sep=0.1, offset=0.5, turnopposite = FALSE,
title = list(text = rep("Número de casos confirmados",4)))
) %>%
hc_add_series(name="región 1 E94-E96-E98",density(ingre949698_gra%>%
filter(region==1)%>%
select(ling_men_pd)%>%
pull()),
yAxis=0)%>%
hc_add_series(name="región 1 E16-E18",density(ingre1618_gra%>%
filter(region==1)%>%
select(ling_men_pd)%>%
pull()),
yAxis=0)%>%
hc_add_series(name="región 2 E94-E96-E98",density(ingre949698_gra%>%
filter(region==2)%>%
select(ling_men_pd)%>%
pull()),
yAxis=1)%>%
hc_add_series(name="región 2 E16-E18",density(ingre1618_gra%>%
filter(region==2)%>%
select(ling_men_pd)%>%
pull()),
yAxis=1)%>%
hc_add_series(name="región 3 E94-E96-E98",density(ingre949698_gra%>%
filter(region==3)%>%
select(ling_men_pd)%>%
pull()),
yAxis=2)%>%
hc_add_series(name="región 3 E16-E18",density(ingre1618_gra%>%
filter(region==3)%>%
select(ling_men_pd)%>%
pull()),
yAxis=2)%>%
hc_add_series(name="región 4 E94-E96-E98",density(ingre949698_gra%>%
filter(region==4)%>%
select(ling_men_pd)%>%
pull()),
yAxis=3)%>%
hc_add_series(name="región 4 E16-E18",density(ingre1618_gra%>%
filter(region==4)%>%
select(ling_men_pd)%>%
pull()),
yAxis=3)%>%
#hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(text = "Densidad por región E94-E96-E98, E16-E18 ")%>%
#margin = 20, align = "left",
#style = list(color = "#90ed7d", useHTML = TRUE)) %>%
# hc_subtitle(text = "Utilice la herramienta de zoom en la
# parte inferior")%>%
# hc_tooltip(crosshairs = TRUE, backgroundColor = "#FCFFC5",
# shared = TRUE, borderWidth = 5) %>%
#hc_yAxis(title = list(text = rep("Número de casos confirmados",4))%>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
densidad por región
den_region<-function(df){
a1<-df%>%filter(region==1)%>%select(ling_men_pd)%>%pull()
a2<-df%>%filter(region==2)%>%select(ling_men_pd)%>%pull()
a3<-df%>%filter(region==3)%>%select(ling_men_pd)%>%pull()
a4<- df%>%filter(region==4)%>%select(ling_men_pd)%>%pull()
hchart(
density(a1),
type = "area",
name = "región 1"
)%>%
hc_add_series(
density(a2), type = "area",
name = "región 2"
)%>%
hc_add_series(
density(a3), type = "area",
name = "región 3"
)%>%
hc_add_series(
density(a4), type = "area",
name = "región 4"
)%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Densidad de ingreso por región, E16-E18"
) %>%
hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
}
den_region(ingre1618_gra)
edad region ingreso
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
ingreso_edad_region<-function(df){
df %>%
group_by(region,edad)%>%
summarise(
pro_ing = mean(ing_men_p)*IN
)%>%
hchart( "line",
hcaes(x = edad, y = pro_ing, group = region ))%>%
hc_yAxis(title = list(text = "Promedio de ingreso"))%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
text = "Promedio de ingreso por edad y región E98") %>%
hc_xAxis(title=list(text="Edad")) %>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
}
ingreso_edad_region(enigh_98)
Mapa regional
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
mapdata <- get_data_from_map(download_map_data("countries/mx/mx-all"))
mapdata
mapa<-mapdata %>%
select(code = `woe-name`) %>%
arrange(code)%>%
mutate(tz = ifelse(code %in% c("Baja California", "Chihuahua", "Coahuila", "Nuevo León", "Sonora", "Tamaulipas"),"región 1",
ifelse(code %in% c("Aguascalientes","Baja California Sur", "Colima", "Durango", "Jalisco", "Michoacán", "Nayarit", "San Luis Potosí", "Sinaloa", "Zacatecas"),"región 2",
ifelse(code %in% c("Campeche", "Chiapas", "Guerrero", "Oaxaca", "Quintana Roo", "Tabasco", "Veracruz","Yucatán"),"región 4", "región 3")
) ),
value = as.integer(factor(tz)))
dta_clss <- mapa %>%
group_by(tz) %>%
summarise(value = unique(value)) %>%
arrange(value) %>%
rename(name = tz, from = value) %>%
mutate(to = from + 1) %>%
list_parse()
hcmap("countries/mx/mx-all", data = mapa, value = "value",
joinBy = c("woe-name","code"), name = "Estado",
tooltip = list(pointFormat = "{point.name} {point.tz}"))%>%
hc_title(
text = "Regiones de México"
)%>%
hc_add_theme(hc_theme_ggplot2())%>%
hc_colorAxis(dataClassColor = "category",
dataClasses=dta_clss)%>%
hc_exporting(enabled = TRUE,
filename = "datos",
buttons = list(contextButton = list(menuItems = myMenuItems)))
data <- tibble(
country =
c("PT", "IE", "GB", "IS",
"NO", "SE", "DK", "DE", "NL", "BE", "LU", "ES", "FR", "PL", "CZ", "AT",
"CH", "LI", "SK", "HU", "SI", "IT", "SM", "HR", "BA", "YF", "ME", "AL", "MK",
"FI", "EE", "LV", "LT", "BY", "UA", "MD", "RO", "BG", "GR", "TR", "CY",
"RU"),
tz = c(rep("UTC", 4), rep("UTC + 1",25), rep("UCT + 2",12), "UTC + 3")
)
data <- data %>%
mutate(value = cumsum(!duplicated(tz)))
individuos region
nrow(dfh_c%>%filter(region==4))
caluclo de bcero
promedio_beta0<-function(df,i){
return(df$regresion[[i]]$coefficients[3])
}
mean(unlist(lapply(1:100,promedio_beta0,df=reg_cd_100)))
sd(unlist(lapply(1:100,promedio_beta0,df=reg_cd_100)))
promedio_papas<-function(df,i){
return(df$regresionp98[[i]]$coefficients[27])
}
mean(unlist(lapply(1:10,promedio_papas,df=reg_cd_100)))
sd(unlist(lapply(1:10,promedio_papas,df=reg_cd_100)))
percentilerank<-function(x){
rx<-rle(sort(x))
smaller<-cumsum(c(0, rx$lengths))[seq(length(rx$lengths))]
larger<-rev(cumsum(c(0, rev(rx$lengths))))[-1]
rxpr<-smaller/(smaller+larger)
rxpr[match(x, rx$values)]
}
a<-c(0,2,3,4,5,1,1,1)
rank(a)/length(a)
percentilerank(a)
percent_rank(a)
---
title: "Estiamcion"
author: "Rafael Martínez Martínez"
output: 
  html_document:
    toc: true
    toc_float: true
    toc_depth: 3
    number_sections: true
    code_download: true
    code_folding: show
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, eval = FALSE)
```


```{r cargar_biliotecas}
library(tidyverse)
library(haven) # importa, entre otros software, de Stata
library(sjlabelled) # manejar labels de las variables 
library(openxlsx) # leer archivos de excel
library(DT) # visualizar a tablas
library(data.table) # convertir a tablas
library(highcharter) # gráficas interactivas
library(foreign)
library(stargazer)
library(ggplot2)
library(survival)
library(broom)
library(gridExtra)
```


# Funciones de carga

```{r fun_carg_ESRU}

cargar_esru<- function(){

return(read_stata(
                     path = "Datos/ESRU-EMOVI-2017/ESRU-EMOVI-2017-Entrevistado.dta",
                     atomic.to.fac = TRUE,
                     enc = NULL,
                     ))
}
```


```{r caragar_enigh}
cargar_enigh<- function(docu){

return(read_stata(
                     path = paste("Datos/ENIGH-2016/",docu,".dta",sep=""),
                     atomic.to.fac = TRUE,
                     enc = NULL,
                     ))
}

cargar_enigh_sav<- function(docu){

return(read_spss(
                     path = paste("Datos/ENIGH-2016/",docu,".sav",sep=""),
                     atomic.to.fac = TRUE,
                     enc = NULL,
                     ))
}
```



```{r caragar_enigh_18}
cargar_enigh_18<- function(docu){

return(read_stata(
                     path = paste("Datos/ENIGH-2018/",docu,".dta",sep=""),
                     atomic.to.fac = TRUE,
                     enc = NULL,
                     ))
}

cargar_enigh_sav_18<- function(docu){

return(read_spss(
                     path = paste("Datos/ENIGH-2018/",docu,".sav",sep=""),
                     atomic.to.fac = TRUE,
                     enc = NULL,
                     ))
}
```



# Filtrar hijos ESRU

## Creamos  per movilidad

```{r carga_esru_crea_mov} 

data_emovi2017<-cargar_esru()

data_emovi2017<-data_emovi2017%>%
  mutate(mov = ifelse(p147>p148,"mejor",
                      ifelse(p147<p148,"peor","igual")),
         id_ho = as.integer(row.names(data_emovi2017)))
data_emovi2017$mov<-set_labels(
                          data_emovi2017$mov,
                          labels = c("mejor" = "mejor",
                                     "peor" = "peor",
                                     "igual" = "igual"))

```




## seleccion de hijos

Criterios de filtrado
- pregunta p2, comparten el mimso gasto para comer: 1 ) Si 2 ) No
- pregunta 5 Edades entre 25 y 45 años
- pregunta p08, es el jefe del hogar: 1 ) jefe 2) cónyuge NO IMPLEMENTADO
- pregunta p12, actualmente estudia, 1)Si 2)No
- p26 Había un jefe de hogar en la infancia (1 padre 2 madre)
- p43 y p43m Reporta educación del padre o madre priciapl
- p68, Personas que trabajan, 1. Si, 2. No
- p69 si trabaja pero la semana pasada no 
- p132 numero de personas que aportan ingreso
- p133 cohort de todas las personas y todos los ingresos (1 ingreso)


Variables de selección para estimar ingreso

- Estado

- pregunta 5, edad

- pregunta 6, sexo

- Pregunta 13, nivel de escuela

- p76 ocupación
--SINCO3 codificación ocupación entrevistado

- p133 cohort de todas las personas y todos los ingresos


```{r hijos_sel_esru}
# pensamos que los datos se leeen como factores cando corresponda, si se leen como enteros no debe hacerse el casting
#data_emovi2017<-datos_e
hijos <- data_emovi2017 %>%
  filter(p02 == 1)  %>% #compartir gasot para comer
  filter(between(p05,25,50)) %>% # edad, criterio 1 de 25 a 40,criterio2 aumentamos los ingreos, criterio 3 de 25 a 50
  #filter(p08 == 1)  %>% 
  filter(p12 == 2)  %>% # ya no estudia
  filter(p26 == 1 | p26==2 ) %>% # sosten principal papa, mama, 
   filter((!is.na(p43) & p43!=98 & p26 == 1 & cmo1_2!="." & !is.na(p38_11)) |
            (!is.na(p43m) & p43m!=98  & p26 == 2 & cmo2_2!="." & !is.na(p38m_11) )) %>% # reporta educación de pp
   filter(!is.na(p13))%>% #  reporta educación
  filter(SINCO3 !="") %>%# reporta oficio actual
   filter(p68 == 1 | p69 == 1)  %>% # si trabaja
   filter(p132 %in% c(1)) %>%# persona que aportan ingreso
  filter(!p133 %in% c(8,9,NA) ) # reporta ingresos
                                # no estan los ingresos  hogar aislados. 

#edad_promedio_padres <- mean(hijos$p38_11, na.rm = TRUE)
#edad_promedio_padres-21

unique(hijos$region)

unique(hijos$p43)
nrow(hijos)
```
## hijos seleccionados

Hacemos un cohort del 1 y el 2 

```{r hijos_sel_esru_mod}
var<-c("Estado","p05","p06","p13","SINCO3","p133","mov","id_ho","region")
hijos_para_sal<-hijos%>%
  select(var)#%>%
  #mutate(p133 = ifelse(p133==1,2,p133)-1)# juntamos el cohort 1 y 2 

hijos_para_sal <- hijos_para_sal%>%
            mutate(estrato = ifelse(p133 %in% c(1,2),1,
                                    ifelse(p133 %in% c(3),2,
                                           ifelse(p133 %in% c(4,5),3,4)))
                   )

```


## ajustamos region

```{r hijos_sel_esru_mod_1}
# ajsutajos a 4 regiones

hijos_para_sal <- hijos_para_sal%>%
          mutate(
            region = ifelse(Estado %in% as.integer(c("02","26","08","05","19","28")),1,
                ifelse(Estado %in%as.integer(c("12","20","07","30","27","04","31","23")),4,
          ifelse(Estado %in% as.integer(c("03","25","18","10","32","16","06","14","01","24")),2,3)))
          )
          


#sum(hijos_para_sal$p133==1)
unique(hijos_para_sal$region)
nrow(hijos_para_sal)
```
## Hijos fijos

```{r}
base_hijos_fija <- hijos_para_sal%>%filter(p133==2)

```

```{r}
#ingreso_hijo_e18_emovi

base_hijos_fija_f<-base_hijos_fija%>%
    summarise(
     id_hijo = "fijo",
     id_ho = id_ho,
     mov= mov,
     regionh = region,
     edad = p05,
     cohort = 100,# paa que sepueda hacer la resta pero no significa nada
    ing_men_p_h = list(2400),
     estrato = estrato,
    estado = Estado,
    sexo = p06
     )

base_hijos_fija_f
```

```{r}
saveRDS(base_hijos_fija_f, file="Datos/base_hijos_fija_f_cri3.Rda")
```


## Hijos para estimar

```{r hijos_sel_esru_mod_estimar}
hijos_para_sal_1<-hijos_para_sal%>%
       mutate(p133 = ifelse(p133==1,2,p133)-1)# transformamos

unique(hijos_para_sal_1$p133)
```


```{r hijos_sel_esru_mod_guar}
saveRDS(hijos_para_sal_1, file="Datos/hijos_emovi2017_p1.Rda")
unique(hijos_para_sal_1$p133)

```


# Hijos construcción ingresos 16


## poblacion enigh 16
```{r poblacion_e16}
poblacion_e16<-cargar_enigh_sav("poblacion") 
poblacion_e16f<- poblacion_e16%>%
  #filter(asis_esc == "2")%>% # ya no asiste a la escuela
  select(folioviv,foliohog,numren,sexo,edad,nivelaprob)%>%
  filter(between(as.integer(edad),23,52))#criterio 1 entre 25-40, criterio 3 entre 25-50
# no filtramos por jefe de hogar

nrow(poblacion_e16f)
sum(is.na(poblacion_e16f))
```

## creamos region 16
```{r poblacion_region_e16}


poblacion_e16f<- poblacion_e16f%>%
          mutate(
            region = ifelse(substr(folioviv,1,2) %in% c("02","26","08","05","19","28"),1,
                ifelse(substr(folioviv,1,2) %in% c("12","20","07","30","27","04","31","23"),4,
            ifelse(substr(folioviv,1,2) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
          )
unique(poblacion_e16f$region)
nrow(poblacion_e16f)
sum(is.na(poblacion_e16f))
```

## trabajos enigh 16

Filtrams los trabajos

```{r trabajos_e16}
trabajos_e16<-cargar_enigh_sav("trabajos")
trabajos_e16f<-trabajos_e16%>%
select(folioviv,foliohog,numren,sinco)%>%
 filter(!is.na(sinco))
NROW(trabajos_e16f)
#NROW(trabajos)
sum(is.na(trabajos_e16f))

```

## Ingresos enigh 2016

```{r ingresos_e16}
ingresos_e16<-cargar_enigh_sav("ingresos")
familias_integrantes_e16 <- function(df,folhog,nr){
  dfn <- df %>%
    filter(foliohog %in% folhog)%>%
    filter(numren %in% nr)
  return(dfn)
} 



ingresos_acu_e16 <- function(df,clavs=get_labels(ingresos_e16$clave)){
 dfn <- df %>%
   group_by(folioviv, foliohog, numren) %>%
   filter(clave %in% clavs)%>%
   summarise(
   claves_a = paste(clave,collapse = ","),
  ing_tri_t = sum(ing_tri),
  ing_men_p = ing_tri_t/3,
  ling_men_p = log(ing_men_p)
  ) %>% ungroup()
 
 
 return(dfn)
}

hogares <- c("1","2","3","4","5")
personas_h <- c('01','02','03','04','05')

a<-familias_integrantes_e16(ingresos_e16,hogares,personas_h)
head(a)

### criterio 1 algunos ingresos no todos los de abajo
#claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", "P016","P017","P018","P020","P021","P026","P027","P035","P040","P041","P042","P044","P045")

## criterio 2 flexibilidad en los ingresos, criterio 3
claves<-c("P001","P002","P003","P011","P13","P023","P024","P025","P026","P027","P028","P029","P030","P031","P032","P033","P054","P055","P056", "P059","P060","P061","P062","P063","P068","P069","P070","P071","P072","P073","P074","P075","P076","P077","P078","P079","P080")

ingresos_e16a<-ingresos_acu_e16(a,claves) 
ingresos_e16a<- ingresos_e16a%>%
          mutate(coh_ing = ifelse(ing_men_p<=2400,1,
                          ifelse(ing_men_p<=4800,2,
                          ifelse(ing_men_p<=7200,3,
                          ifelse(ing_men_p<=12000,4,
                          ifelse(ing_men_p<=24000,5,6))))))

ingresos_e16a<- ingresos_e16a%>%mutate(
   region = ifelse(substr(folioviv,1,2) %in% c("02","26","08","05","19","28"),1,
                ifelse(substr(folioviv,1,2) %in% c("12","20","07","30","27","04","31","23"),4,
            ifelse(substr(folioviv,1,2) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
)

ingresos_e16fc<-ingresos_e16a%>% 
 
  select(folioviv,foliohog,numren,coh_ing)
```


```{r}
sort(unique(as.character(ingresos_e16$clave)))[]
```



## rango percentil 16

```{r}
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e16a$ling_men_pd <- ingresos_e16a$ling_men_p + log(IN)
rp_e16 <- rank(ingresos_e16a$ling_men_pd)/length(ingresos_e16a$ling_men_pd)
```



```{r}
ingresos_e16a1 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==1)%>%pull(ling_men_p) + log(IN))
rp_e161 <- rank(ingresos_e16a1$ling_men_pd)/length(ingresos_e16a1$ling_men_pd)
```

```{r}
ingresos_e16a2 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==2)%>%pull(ling_men_p) + log(IN))
rp_e162 <- rank(ingresos_e16a2$ling_men_pd)/length(ingresos_e16a2$ling_men_pd)
```


```{r}
ingresos_e16a3 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==3)%>%pull(ling_men_p) + log(IN))
rp_e163 <- rank(ingresos_e16a3$ling_men_pd)/length(ingresos_e16a3$ling_men_pd)
```

```{r}
ingresos_e16a4 <- data.frame(ling_men_pd=ingresos_e16a%>%filter(region==4)%>%pull(ling_men_p) + log(IN))
rp_e164 <- rank(ingresos_e16a4$ling_men_pd)/length(ingresos_e16a4$ling_men_pd)
```




```{r}
hist(rp_e16)
```


## Base cruzada pob-tra 16

```{r base_cruzada}
enigh_16<- full_join(poblacion_e16f,trabajos_e16f)%>%
   drop_na()
#enigh<-full_join(enigh,concentrado)%>%
 # drop_na()
nrow(enigh_16)
sum(is.na(enigh_16))
```

## Cambio etiquetado educacion 16
Pasamos las etiqeuteas de educación de emovi a 

```{r cambia_e_esru_ocu}

hijos_emovi <- readRDS(file="Datos/hijos_emovi2017_p1.Rda")#criterio 1 y 3
hijos_emovi<- hijos_emovi %>%
  mutate(p13_m = ifelse(p13 %in% c(1),"1",
                      ifelse( p13 == 2,"2",
                      ifelse(p13 %in% c(3,4),"3",
                      ifelse( p13 %in% c(5,6),"4",
                      ifelse(p13 %in% c(7,8),"6",
                      ifelse(p13 %in% c(9,10),"5",
                      ifelse(p13==11,"7",
                      ifelse(p13==12,"8","0")))))))))
unique(hijos_emovi$p13_m)

enigh_16<-enigh_16%>%
  mutate(nivelaprob=ifelse(nivelaprob=="9","8",nivelaprob))

unique(enigh_16$nivelaprob)

enigh_16f<-enigh_16%>%
  filter(nivelaprob %in% unique(hijos_emovi$p13_m))

nrow(enigh_16f)
```


## Buscar folios con caracteristicas 16

```{r busca_folios_e16}
busca<-function(i){
return(enigh_16f%>%
  filter(
    #as.integer(substr(folioviv,1,2))==hijos_emovi[i,]$Estado &
    region == hijos_emovi[i,]$region &
    sexo == hijos_emovi[i,]$p06 &
    ((edad-2) <= hijos_emovi[i,]$p05) &
    (hijos_emovi[i,]$p05 <= (edad+2)) &
    #as.character(sinco) == hijos_emovi[i,]$SINCO3 &
    substr(as.character(sinco),1,1)== substr(hijos_emovi[i,]$SINCO3,1,1)&
    nivelaprob == hijos_emovi[i,]$p13_m)%>%
    
  select(folioviv,foliohog))
}

  
a<-lapply(1:nrow(hijos_emovi), busca)

contador<-0
for(i in 1:nrow(hijos_emovi))
{if(nrow(a[[i]])>0)
  contador<-contador +1
}
contador

#saveRDS(a, file="Datos/folhijos_enigh16_emovi_re_ch.Rda")
#saveRDS(a, file="Datos/folhijos_enigh16_emovi_re_ch_cri3.Rda")
saveRDS(a, file="Datos/folhijos_enigh16_emovi_sinm1_re_p1.Rda")
#saveRDS(a, file="Datos/folhijos_enigh16_emovi_est_ch_cri3.Rda")

```

- Con ocupacion y region del total, 668 personas tienen gemelos
- Con region, 

Criterio 3
- ocupacion y region y, 979
- estado, mas al rato cohort en el ingreso
### filtamos solo los hijos encontrados 16


```{r etique_hijos_enc_} 
# etiquetamos para no perder la numeracion respecto a los hijos originales
folhijos_e16_emovi <- readRDS(file="Datos/folhijos_enigh16_emovi_sinm1_re_p1.Rda")
names(folhijos_e16_emovi) <- as.character(1:nrow(hijos_emovi))
length(folhijos_e16_emovi)
```

```{r filtra_hijos_encon}
L <- c()
for(i in 1:length(folhijos_e16_emovi)){
  if(!(nrow(folhijos_e16_emovi[[i]])>0)){
    L<-c(L,i)}
}
# Solo hijos que se les pudo encontrar en enigh-2016 1077/1131
folhijos_e16_emovif<-folhijos_e16_emovi[-L]
    

length(folhijos_e16_emovif)

#names(folhijos_emovif)
#names(folhijos_emovi)
```

### Unicos folios, diferentes trabajos 16



```{r filtra_fil_hij_uni}
unicos_folios<-function(i){
folhijos_e16_emovif[[i]]%>%
  distinct()
}

# filtramos unicos folios por diferentes trabajos de la misma persona
a<-lapply(1:length(folhijos_e16_emovif), unicos_folios)
names(a)<-names(folhijos_e16_emovif)
length(a)

folhijos_e16_emovif<-a
```


### Observamos las edades de los encontrados 16

```{r gra_edades_h_enc}

myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')

hijos_emovi%>%
  slice(as.integer(names(folhijos_e16_emovif)))%>%
  select(p05)%>%
  count(p05)%>%
    hchart('column',
         hcaes( y = 'n'))%>%
  hc_add_theme(hc_theme_ffx())%>%
  hc_title(
    text = ""
  ) %>%
  #hc_subtitle(text = "Las edades están agrupadas en intervalos 
   #           de 10 años (da clic sobre F o M)") %>%
  # hc_credits(
  #  enabled = TRUE, text = "Source: SSS",
  #  style = list(fontSize = "12px"))%>%
  hc_yAxis(title = list(text = "Número de participantes"))%>%
  hc_xAxis(title=list(text="Número de personas"),
           categories = as.character(25:50)) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))


```

### Observamos las estados de los encontrados 16

```{r gra_estados_h_e16}
hijos_emovi%>%
  slice(as.integer(names(folhijos_e16_emovif)))%>%
  count(Estado)%>%
    hchart('column',
         hcaes( y = 'n'))%>%
  hc_add_theme(hc_theme_ffx())%>%
  hc_title(
    text = ""
  ) %>%
  #hc_subtitle(text = "Las edades están agrupadas en intervalos 
   #           de 10 años (da clic sobre F o M)") %>%
  # hc_credits(
  #  enabled = TRUE, text = "Source: SSS",
  #  style = list(fontSize = "12px"))%>%
  hc_yAxis(title = list(text = "Número de participantes"))%>%
  hc_xAxis(title=list(text="Número de personas"),
           categories = get_labels(hijos_emovi$Estado)) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))

```


## Ingreso hijo enigh_16 - emovi

```{r ingre_hijo_e16_emovi}

ingreso_hijo_e16_emovi<-function(i){
  ingresos_e16a%>%
  filter(folioviv %in% folhijos_e16_emovif[[i]]$folioviv &
    foliohog %in% folhijos_e16_emovif[[i]]$foliohog)%>%
   #filter(coh_ing==hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p133"])%>%
    summarise(
     id_hijo = names(folhijos_e16_emovif[i]),
     id_ho = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"id_ho"],
     mov= hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"mov"],
     regionh = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"region"],
     edad = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p05"],
     cohort = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p133"],
    ing_men_p_h =list(ing_men_p),
     estrato = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"estrato"],
    estado = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"Estado"],
    sexo = hijos_emovi[as.integer(names(folhijos_e16_emovif[i])),"p06"]
     )
}
#273,332 son vacios
# ingreso hijo pero ualgunos no podrian estar por no tener registro de salario

a<-lapply(1:length(folhijos_e16_emovif), ingreso_hijo_e16_emovi)
ingreso_hijog_e16_emovi<-a
length(ingreso_hijog_e16_emovi)
#ingreso_hijog_e16_emovi[[332]]
#x<-ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[1]
#sum(ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[[1]],na.rm = TRUE)


```
## Filtramos sobre ingresos encontrados 16

```{r fil_ingre_enc_e16_emovi}

L <- c()
for(i in 1:length(ingreso_hijog_e16_emovi)){
  if(sum(ingreso_hijog_e16_emovi[[i]]$ing_men_p_h[[1]],na.rm = TRUE)==0){
    L<-c(L,i)}
}
length(L)

ingreso_hijog_e16_emovi<-ingreso_hijog_e16_emovi[-L]

length(ingreso_hijog_e16_emovi)
ingreso_hijog_e16_emovi[1]
ingreso_hijog_e16_emovi[2]


ingreso_hijog_e16_emovif<-bind_rows(ingreso_hijog_e16_emovi[1:length(ingreso_hijog_e16_emovi)])


nrow(ingreso_hijog_e16_emovif)

saveRDS(ingreso_hijog_e16_emovif, file="Datos/ingreso_hij_f_emovi_sinm1_re_p1.Rda")

gemelos<-c()
for( i in 1:nrow(ingreso_hijog_e16_emovif)){
gemelos<-c(gemelos,length(ingreso_hijog_e16_emovif$ing_men_p_h[[i]]))
}
#gemelos
#min(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])
#max(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])

ingreso_hijog_e16_emovif%>%select(id_ho)
```
Promedio de gemelos por individuo

```{r}

len<-0
for(i in 1:nrow(ingreso_hijog_e16_emovif)){
len<- len + length(ingreso_hijog_e16_emovif$ing_men_p_h[[i]])
}

len/nrow(ingreso_hijog_e16_emovif)
```


CRITERIO 1
- con  region 1117 tienen salario
- con region y en cohort de ingreso tienen salario 1100
- con ocupacion y region 659 tienen salario
- con ocupacion y region y cohort de ingreso 436 tienen salario


CRITERIO 2 (ingresoso aumentamos las claves)
- con region y en cohort de ingreso tienen salario 1103

CRITERIO 3 (ingresoso aumentamos las claves, y los rangos de edad)
- con region y en cohort de ingreso tienen salario 1658
- con region, ocupación y en el cohort de ingreso tienen 683
- cone estado, cohort en el ingreso, 1473




# Hijos construcción ingresos 18


## poblacion enigh 18
```{r poblacion_e18}
poblacion_e18<-cargar_enigh_sav_18("poblacion") 
poblacion_e18f<- poblacion_e18%>%
  #filter(asis_esc == "2")%>% # ya no asiste a la escuela
  select(folioviv,foliohog,numren,sexo,edad,nivelaprob)%>%
  filter(between(as.integer(edad),23,52))#criterio 1 entre 25-40, criterio 3 entre 25-50
# no filtramos por jefe de hogar

nrow(poblacion_e18f)
sum(is.na(poblacion_e18f))
```


## creamos region 18
```{r poblacion_region_e18}


poblacion_e18f<- poblacion_e18f%>%
          mutate(
            region = ifelse(substr(folioviv,1,2) %in% c("02","26","08","05","19","28"),1,
                ifelse(substr(folioviv,1,2) %in% c("12","20","07","30","27","04","31","23"),4,
            ifelse(substr(folioviv,1,2) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
          )
unique(poblacion_e18f$region)
nrow(poblacion_e18f)
sum(is.na(poblacion_e18f))
```

## trabajos enigh 18

Filtrams los trabajos

```{r trabajos_e18}
trabajos_e18<-cargar_enigh_sav_18("trabajos")
trabajos_e18f<-trabajos_e18%>%
select(folioviv,foliohog,numren,sinco)%>%
 filter(!is.na(sinco))
NROW(trabajos_e18f)
#NROW(trabajos)
sum(is.na(trabajos_e18f))

```
## Ingresos enigh 2018

```{r ingresos_e18}
ingresos_e18<-cargar_enigh_sav_18("ingresos")
familias_integrantes_e18 <- function(df,folhog,nr){
  dfn <- df %>%
    filter(foliohog %in% folhog)%>%
    filter(numren %in% nr)
  return(dfn)
} 



ingresos_acu_e18 <- function(df,clavs=get_labels(ingresos_e18$clave)){
 dfn <- df %>%
   group_by(folioviv, foliohog, numren) %>%
   filter(clave %in% clavs)%>%
   summarise(
   claves_a = paste(clave,collapse = ","),
  ing_tri_t = sum(ing_tri),
  ing_men_p = ing_tri_t/3,
  ling_men_p = log(ing_men_p)
  ) %>% ungroup()
 
 
 return(dfn)
}

hogares <- c("1","2","3","4","5")
personas_h <- c('01','02','03','04','05')

a<-familias_integrantes_e18(ingresos_e18,hogares,personas_h)
head(a)

### criterio 1 algunos ingresos no todos los de abajo
#claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", "P016","P017","P018","P020","P021","P026","P027","P035","P040","P041","P042","P044","P045")

## criterio 2 flexibilidad en los ingresos, criterio 3
claves<-c("P001","P002","P003","P011","P13","P023","P024","P025","P026","P027","P028","P029","P030","P031","P032","P033","P054","P055","P056", "P059","P060","P061","P062","P063","P068","P069","P070","P071","P072","P073","P074","P075","P076","P077","P078","P079","P080")

ingresos_e18a<-ingresos_acu_e18(a,claves) 
ingresos_e18a<- ingresos_e18a%>%
          mutate(coh_ing = ifelse(ing_men_p<=2400,1,
                          ifelse(ing_men_p<=4800,2,
                          ifelse(ing_men_p<=7200,3,
                          ifelse(ing_men_p<=12000,4,
                          ifelse(ing_men_p<=24000,5,6))))))

ingresos_e18a
ingresos_e18fc<-ingresos_e18a%>%
  select(folioviv,foliohog,numren,coh_ing)
```

## rango percentil 18

```{r}
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e18a$ling_men_pd <- ingresos_e18a$ling_men_p + log(IN)
rp_e18 <- rank(ingresos_e18a$ling_men_pd)/length(ingresos_e18a$ling_men_pd)
```



```{r}
hist(rp_e18)
```




## Base cruzada pob-tra 18

```{r base_cruzada_18}
enigh_18<- full_join(poblacion_e18f,trabajos_e18f)%>%
   drop_na()
#enigh<-full_join(enigh,concentrado)%>%
 # drop_na()
nrow(enigh_18)
sum(is.na(enigh_18))
```

## Cambio etiquetado educacion 18
Pasamos las etiqeuteas de educación de emovi a 

```{r cambia_e_esru_ocu_18}

hijos_emovi <- readRDS(file="Datos/hijos_emovi2017_p1.Rda")#criterio 1 y 3
hijos_emovi<- hijos_emovi %>%
  mutate(p13_m = ifelse(p13 %in% c(1),"1",
                      ifelse( p13 == 2,"2",
                      ifelse(p13 %in% c(3,4),"3",
                      ifelse( p13 %in% c(5,6),"4",
                      ifelse(p13 %in% c(7,8),"6",
                      ifelse(p13 %in% c(9,10),"5",
                      ifelse(p13==11,"7",
                      ifelse(p13==12,"8","0")))))))))
unique(hijos_emovi$p13_m)

enigh_18<-enigh_18%>%
  mutate(nivelaprob=ifelse(nivelaprob=="9","8",nivelaprob))

unique(enigh_18$nivelaprob)

enigh_18f<-enigh_18%>%
  filter(nivelaprob %in% unique(hijos_emovi$p13_m))

nrow(enigh_18f)
```

## Buscar folios con caracteristicas 18

```{r busca_folios_e18}
busca<-function(i){
return(enigh_18f%>%
  filter(
    #as.integer(substr(folioviv,1,2))==hijos_emovi[i,]$Estado &
    region == hijos_emovi[i,]$region &
    sexo == hijos_emovi[i,]$p06 &
    ((edad-2) <= hijos_emovi[i,]$p05) &
    (hijos_emovi[i,]$p05 <= (edad+2)) &
    #as.character(sinco) == hijos_emovi[i,]$SINCO3 &
    substr(as.character(sinco),1,1) == substr(hijos_emovi[i,]$SINCO3,1,1)&
    nivelaprob == hijos_emovi[i,]$p13_m)%>%
    
  select(folioviv,foliohog))
}

  
a<-lapply(1:nrow(hijos_emovi), busca)

contador<-0
for(i in 1:nrow(hijos_emovi))
{if(nrow(a[[i]])>0)
  contador<-contador +1
}
contador

saveRDS(a, file="Datos/folhijos_enigh18_emovi_sinm1_re_p1.Rda")

```
### filtamos solo los hijos encontrados 18

```{r etique_hijos_enc_18} 
# etiquetamos para no perder la numeracion respecto a los hijos originales
folhijos_e18_emovi <- readRDS(file="Datos/folhijos_enigh18_emovi_sinm1_re_p1.Rda")
names(folhijos_e18_emovi) <- as.character(1:nrow(hijos_emovi))
length(folhijos_e18_emovi)
```

```{r filtra_hijos_encon_18}
L <- c()
for(i in 1:length(folhijos_e18_emovi)){
  if(!(nrow(folhijos_e18_emovi[[i]])>0)){
    L<-c(L,i)}
}
# Solo hijos que se les pudo encontrar en enigh-2016 1077/1131
folhijos_e18_emovif<-folhijos_e18_emovi[-L]
    

length(folhijos_e18_emovif)

#names(folhijos_emovif)
#names(folhijos_emovi)
```

### Unicos folios, diferentes trabajos 18



```{r filtra_fil_hij_uni_18}
unicos_folios<-function(i){
folhijos_e18_emovif[[i]]%>%
  distinct()
}

# filtramos unicos folios por diferentes trabajos de la misma persona
a<-lapply(1:length(folhijos_e18_emovif), unicos_folios)
names(a)<-names(folhijos_e18_emovif)
length(a)

folhijos_e18_emovif<-a
```

### Observamos las edades de los encontrados 18

```{r gra_edades_h_enc_18}

myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')

hijos_emovi%>%
  slice(as.integer(names(folhijos_e18_emovif)))%>%
  select(p05)%>%
  count(p05)%>%
    hchart('column',
         hcaes( y = 'n'))%>%
  hc_add_theme(hc_theme_ffx())%>%
  hc_title(
    text = ""
  ) %>%
  #hc_subtitle(text = "Las edades están agrupadas en intervalos 
   #           de 10 años (da clic sobre F o M)") %>%
  # hc_credits(
  #  enabled = TRUE, text = "Source: SSS",
  #  style = list(fontSize = "12px"))%>%
  hc_yAxis(title = list(text = "Número de participantes"))%>%
  hc_xAxis(title=list(text="Número de personas"),
           categories = as.character(25:50)) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))


```


### Observamos las estados de los encontrados 18

```{r gra_estados_h_e18}
hijos_emovi%>%
  slice(as.integer(names(folhijos_e18_emovif)))%>%
  count(Estado)%>%
    hchart('column',
         hcaes( y = 'n'))%>%
  hc_add_theme(hc_theme_ffx())%>%
  hc_title(
    text = ""
  ) %>%
  #hc_subtitle(text = "Las edades están agrupadas en intervalos 
   #           de 10 años (da clic sobre F o M)") %>%
  # hc_credits(
  #  enabled = TRUE, text = "Source: SSS",
  #  style = list(fontSize = "12px"))%>%
  hc_yAxis(title = list(text = "Número de participantes"))%>%
  hc_xAxis(title=list(text="Número de personas"),
           categories = get_labels(hijos_emovi$Estado)) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))

```

## Ingreso hijo enigh 18 - emovi

```{r ingre_hijo_e18_emovi}

ingreso_hijo_e18_emovi<-function(i){
  ingresos_e18a%>%
  filter(folioviv %in% folhijos_e18_emovif[[i]]$folioviv &
    foliohog %in% folhijos_e18_emovif[[i]]$foliohog)%>%
  # filter(coh_ing==hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p133"])%>%
    summarise(
     id_hijo = names(folhijos_e18_emovif[i]),
     id_ho = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"id_ho"],
     mov= hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"mov"],
     regionh = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"region"],
     edad = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p05"],
     cohort = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p133"],
    ing_men_p_h =list(ing_men_p),
     estrato = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"estrato"],
    estado = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"Estado"],
    sexo = hijos_emovi[as.integer(names(folhijos_e18_emovif[i])),"p06"]
     )
}
#273,332 son vacios
# ingreso hijo pero ualgunos no podrian estar por no tener registro de salario

a<-lapply(1:length(folhijos_e18_emovif), ingreso_hijo_e18_emovi)
ingreso_hijog_e18_emovi<-a
length(ingreso_hijog_e18_emovi)
#ingreso_hijog_e16_emovi[[332]]
#x<-ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[1]
#sum(ingreso_hijog_e16_emovi[[332]]$ing_men_p_h[[1]],na.rm = TRUE)


```
## Filtramos sobre ingresos encontrados 18

```{r fil_ingre_enc_e18_emovi}

L <- c()
for(i in 1:length(ingreso_hijog_e18_emovi)){
  if(sum(ingreso_hijog_e18_emovi[[i]]$ing_men_p_h[[1]],na.rm = TRUE)==0){
    L<-c(L,i)}
}
length(L)

ingreso_hijog_e18_emovi<-ingreso_hijog_e18_emovi[-L]

length(ingreso_hijog_e18_emovi)
ingreso_hijog_e18_emovi[1]
ingreso_hijog_e18_emovi[2]


ingreso_hijog_e18_emovif<-bind_rows(ingreso_hijog_e18_emovi[1:length(ingreso_hijog_e18_emovi)])


nrow(ingreso_hijog_e18_emovif)

saveRDS(ingreso_hijog_e18_emovif, file="Datos/ingreso18_hij_f_emovi_sinm1_re_p1.Rda")

gemelos<-c()
for( i in 1:nrow(ingreso_hijog_e18_emovif)){
gemelos<-c(gemelos,length(ingreso_hijog_e18_emovif$ing_men_p_h[[i]]))
}
#gemelos
#min(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])
#max(ingreso_hijog_e16_emovif$ing_men_p_h[[459]])


```





CRITERIO 3 (ingresoso aumentamos las claves, y los rangos de edad)
- con region, ocupación y en el cohort de ingreso tienen 717


```{r}
ingreso_hijog_e18_emovif%>%select(id_hijo,id_ho)
```



```{r}

len<-0
for(i in 1:nrow(ingreso_hijog_e18_emovif)){
len<- len + length(ingreso_hijog_e18_emovif$ing_men_p_h[[i]])
}

len/nrow(ingreso_hijog_e18_emovif)
```


# Base de ingresos hijos final

```{r}
base1<- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_p1.Rda")
nrow(base1)#1148 +89 = 1237
base2<- readRDS(file="Datos/ingreso18_hij_f_emovi_sin_re_p1.Rda")
nrow(base2)#1148 + 89 = 1237

#base1%>%filter(!(id_ho %in% base2$id_ho))

basep<-base2%>%filter(!(id_ho %in% base1$id_ho))
nrow(basep)

basep2<-full_join(base1, basep)
nrow(basep2) # 1237 + 62 = 1299

#_______________________________________
base3<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm3_re_p1.Rda")
nrow(base3) # 1266 + 33 = 1299

#basep2%>%filter(!(id_ho %in% base3$id_ho))
basep3<- base3%>%filter(!(id_ho %in% basep2$id_ho)) 
nrow(basep3)  

basep4<- full_join(basep3,basep2)
nrow(basep4)# 1299

#____________________________________________
base4<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm2_re_p1.Rda")
nrow(base4) # 1346

#basep4%>%filter(!(id_ho %in% base4$id_ho))

basep5<-base4%>%filter(!(id_ho %in% basep4$id_ho))
nrow(basep5)#59

basep6<-full_join(basep5,basep4)
nrow(basep6)# 1358


#______________________________
base5<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm1_re_p1.Rda")
nrow(base5)# 1473

#basep6%>%filter(!(id_ho %in% base5$id_ho))

basep7<-base5%>%filter(!(id_ho %in% basep6$id_ho))
nrow(basep7)# 119

basep8<-full_join(basep7,basep6)
nrow(basep8)# 1477

#______________________________________________
base6<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm3_re_p1.Rda")
nrow(base6)#1270

#basep8%>%filter(!(id_ho %in% base6$id_ho))

basep9<-base6%>%filter(!(id_ho %in% basep8$id_ho))
nrow(basep9)# 1

basep10<-full_join(basep9,basep8)
nrow(basep10) # 1478 

#__________________________________________________________________
base7<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm2_re_p1.Rda")
nrow(base7)#1342

#basep10%>%filter(!(id_ho %in% base7$id_ho))

basep11<-base7%>%filter(!(id_ho %in% basep10$id_ho))
nrow(basep11)# 2

basep12<-full_join(basep11,basep10)
nrow(basep12)# 1480

#__________________________________________________________________
base8<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm1_re_p1.Rda")
nrow(base8) # 1476

#basep12%>%filter(!(id_ho %in% base8$id_ho))

basep13<-base8%>%filter(!(id_ho %in% basep12$id_ho))
nrow(basep13) # 6


basep14<-full_join(basep13,basep12)
nrow(basep14)# 1486

base_cf<-basep14


```


```{r}
saveRDS(base_cf,file="Datos/ingreso_hij_f_emovi_combinada_p1.Rda")

```



______________________________________

```{r}
base1<- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_ch_cri3.Rda")
nrow(base1) # 504 + 158 = 662
base2<- readRDS(file="Datos/ingreso18_hij_f_emovi_sin_re_ch_cri3.Rda")
nrow(base2) # 526 + 136 =  662

#base1%>%filter(!(id_ho %in% base2$id_ho))

basep<-base2%>%filter(!(id_ho %in% base1$id_ho))

basep2<-full_join(base1, basep)
nrow(basep2) # 662 + 106 =  768

#_______________________________________
base3<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm3_re_ch_cri3.Rda")
nrow(base3) # 648 + 120 =  768

#basep2%>%filter(!(id_ho %in% base3$id_ho))
basep3<- base3%>%filter(!(id_ho %in% basep2$id_ho)) 
nrow(basep3)  

basep4<- full_join(basep3,basep2)
nrow(basep4)# 768 + 48 = 816


#____________________________________________
base4<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm3_re_ch_cri3.Rda")
nrow(base4)# 671 + 145 = 816

#basep4%>%filter(!(id_ho %in% base4$id_ho))

basep5<-base4%>%filter(!(id_ho %in% basep4$id_ho))
nrow(basep5)# 48

basep6<-full_join(basep5,basep4)
nrow(basep6)# 816 + 111 = 927

#______________________________
base5<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm2_re_ch_cri3.Rda")
nrow(base5)# 840 + 87 =  927

#basep6%>%filter(!(id_ho %in% base5$id_ho))

basep7<-base5%>%filter(!(id_ho %in% basep6$id_ho))
nrow(basep7)# 111 

basep8<-full_join(basep7,basep6)
nrow(basep8)# 927 + 46 = 973

#______________________________________________
base6<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm2_re_ch_cri3.Rda")
nrow(base6)# 841 + 132 = 973

#basep8%>%filter(!(id_ho %in% base6$id_ho))

basep9<-base6%>%filter(!(id_ho %in% basep8$id_ho))
nrow(basep9)# 46

basep10<-full_join(basep9,basep8)
nrow(basep10) # 973 + 197 = 1170

#__________________________________________________________________
base7<- readRDS(file="Datos/ingreso_hij_f_emovi_sinm1_re_ch_cri3.Rda")
nrow(base7)# 1127 + 43  = 1170

#basep10%>%filter(!(id_ho %in% base7$id_ho))

basep11<-base7%>%filter(!(id_ho %in% basep10$id_ho))
nrow(basep11)# 197

basep12<-full_join(basep11,basep10)
nrow(basep12)# 1170 + 41 = 1211

#___________________________________________________________________
base8<- readRDS(file="Datos/ingreso18_hij_f_emovi_sinm1_re_ch_cri3.Rda")
nrow(base8) # 1140 + 71 = 1211

#basep12%>%filter(!(id_ho %in% base8$id_ho))

basep13<-base8%>%filter(!(id_ho %in% basep12$id_ho))
nrow(basep13) # 41


basep14<-full_join(basep13,basep12)
nrow(basep14)# 1211

base_cf<-full_join(base_hijos_fija_f, basep14)
str(unique(base_cf$id_ho))
```




## Ingreso hijos directo 


```{r}
hijos_para_sal_2 <- hijos_para_sal%>%filter(!(id_ho %in%base_cf$id_ho ))
nrow(hijos_para_sal_2)
hijos_para_sal_2%>%count(p133)
```
 
hacemos su intervalo

```{r}





hijos_para_sal_2<-hijos_para_sal_2%>%mutate(
  low=ifelse(p133==1,0,
             ifelse(p133==3, 2401.0, 
                    ifelse(p133==4,4801.0,
                           ifelse(p133==5,7201.0,
                                  ifelse(p133==6,12001.0,24001))) )),
  
  upper = ifelse(p133==1,2399.0,
             ifelse(p133==3, 4800.0, 
                    ifelse(p133==4,7200.0,
                           ifelse(p133==5,12000.0,
                                  ifelse(p133==6,24000,40000))) ))
  #type = ifelse(p133!=7,"interval","left"),
  #event = ifelse(p133!=7,3,2)
  
  )


hings<-hijos_para_sal_2%>%mutate(p052=p05*p05)

Z <-with(hings, Surv(low,
                     upper,
                     event = rep(3,nrow(hings)),
                     type = "interval")) 

modelo<-survreg(Z~  factor(p133) + p05 + p052 + p06  + p13 + factor(substr(SINCO3,1,1))-1, data=hings,
                dist = "gaussian")

summary(modelo)

newd<- data.frame(#region = factor(hings$region),
                  p133 = factor(hings$p133),
                  p05=hings$p05,
                  p052=hings$p052,
                  p06=hings$p06,
                  p13=hings$p13,
                  SINCO3=factor(substr(hings$SINCO3,1,1)))

hings$ingress<-predict(modelo,newd, type="response")

hings%>%select(-ingreso)
pro<-full_join(hings,hijos_para_sal2%>%select(-ingreso))
pro%>%mutate(ingress=ifelse(p133==2,2400,ingress))%>%
  mutate(cohort_h = ifelse(ingress<=2400,1,
                          ifelse(ingress<=4800,2,
                          ifelse(ingress<=7200,3,
                          ifelse(ingress<=12000,4,
                          ifelse(ingress<=24000,5,6))))),
         cohort_dif= as.integer(p133)-cohort_h)%>%filter(p133==2)%>%nrow()
```





```{r}
saveRDS(base_cf,file="Datos/ingreso_hij_f_emovi_combinada_cri3.Rda")

```


## rango percentil 16-18

```{r}
#IN<-(100/110.907)
IN<-(100/93.6)
#
ingre1618<-bind_rows(ingresos_e16a%>%select(ling_men_pd),ingresos_e18a%>%select(ling_men_pd))
rp_e1618 <- rank(ingre1618$ling_men_pd)/length(ingre1618$ling_men_pd)
#rp_e1618<-percent_rank(ingre1618$ling_men_pd)
```


```{r}
hist(rp_e1618)
```


# Hijos con ingreso

```{r filtro_hijos_ya_ingreso}
hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_combinada_p1.Rda")

#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_combinada_cri3.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_ch_cri3.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_re_ch_cri3.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_re_ch_cri2.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_re.Rda")
#hijos_emovi_hp <- readRDS(file="Datos/ingreso_hij_f_emovi_sin_re_ch.Rda")

nrow(hijos_emovi_hp)
hijos_emovi_hp 
# p05 edad act
# p06 sexo act
# p13 educ hijo
# SINCO3, ocupación hijo
# p23 en qeu estado vivia a los 14
# p26 principal sosten ecómico
# p38_11 edad padre 2016
# p38m_11 edad madre 2016
# p43 nivel educativo padre
#p43m nivel educativo madre
# cmo1_2 ocupacion del padre
# cmo2_2 ocupacion de la madre 


varp<-c("Estado","p05","p06","p13","SINCO3","p23","p26","p38_11","p38m_11","p43","p44",
        "p43m","p44m","cmo1_2","cmo2_2","mov",
        "region","p133","id_ho")
padres_h<-data_emovi2017%>%
# #    filter(p02 == 1)  %>% #compartir gasot para comer
#   filter(between(p05,25,50)) %>% # edad <-----------------criterio 1 y 3
#   #filter(p08 == 1)  %>% 
#   filter(p12 == 2)  %>%# ya no estudia
#   filter(p26 == 1 | p26==2 ) %>% # sosten principal papa, mama, 
#    filter((!is.na(p43) & p43!=98 & p26 == 1 & cmo1_2!="." & !is.na(p38_11))|#<----------ojo ocupacion ns
#             (!is.na(p43m) & p43m!=98  & p26 == 2 & cmo2_2!="." & !is.na(p38m_11))) %>% # reporta educación de pp
#    filter(!is.na(p13))%>% #  reporta educación
#   filter(SINCO3 !="") %>%# reporta oficio actual
#    filter(p68 == 1 | p69 == 1)  %>% # si trabaja
#    filter(p132 %in% c(1)) %>%# persona que aportan ingreso
#   filter(!p133 %in% c(8,9,NA) )%>%
   select(varp)%>%
   slice(as.integer(unlist(hijos_emovi_hp%>%select(id_ho))))%>%#<---------oojooo ers id_hijo
#   filter(id_ho %in% hijos_emovi_hp$id_ho )%>%
  mutate(
         pp_id_hijo = hijos_emovi_hp%>%pull(id_ho),# era id_hijo
         ing_h = hijos_emovi_hp%>%pull(ing_men_p_h),
         cohort = ifelse(p133==1,2,p133)-1,
         region = hijos_emovi_hp%>%pull(regionh)
         )%>%
  rename(
          edad_h = p05,
          sexo_h = p06,
          edu_h = p13,
          ocu_h = SINCO3,
          pp_estado = p23,
          pp = p26,
          p_edad_2016= p38_11,
          m_edad_2016 = p38m_11,
          p_edu = p43,
          p_edug = p44,
          m_edu = p43m,
          m_edug = p44m,
          p_ocu = cmo1_2,
          m_ocu = cmo2_2
         )%>%
  mutate(
        p_edu = as.integer(p_edu),
        m_edu = as.integer(m_edu)
  )
  
length(padres_h)
# las ocupaciones no encontradas las sustituimos por un NA
padres_h<-padres_h%>%
  mutate(p_ocu = ifelse(p_ocu==".",NA,p_ocu),
         m_ocu = ifelse(m_ocu==".",NA,m_ocu))

unique(padres_h$p_ocu)
unique(padres_h$m_ocu)
head(padres_h)
```

## Crear base limpia

```{r base_lim_h_ing}


filtro_pp_c<-function(df_1,general = 0)
{
  #recibe un hijo de un dataframe, para polimorfismo
  # general = 0y regresa un data frame con los datos
  # ordenados del hijo
  if(df_1["pp"]==1)
  {
    dfn <- df_1%>%
          filter(pp==1)%>%
          select(starts_with(c("pp","p_")),
             ing_h,edu_h,ocu_h,edad_h,p_edad_2016,
             region,cohort,sexo_h)%>%
          mutate(pp_edad2016 = p_edad_2016)%>%
          rename(
            id_ho = pp_id_hijo,
            pp_sexo = pp,
            pp_edad = p_edad_2016,
            pp_educ = p_edu,
            pp_educg = p_edug,
            pp_ocup = p_ocu
          )%>%
        mutate(pp_edad = pp_edad + (14-edad_h))
  }
  if(df_1["pp"]==2)
    {
    dfn<-df_1%>%
        filter(pp==2)%>%
        select(starts_with(c("pp","m_")),ing_h,edu_h,
            ocu_h,edad_h,m_edad_2016,
            region,cohort,sexo_h)%>%
       mutate(pp_edad2016 = m_edad_2016)%>%
        rename(
          id_ho = pp_id_hijo,
          pp_sexo = pp,
          pp_edad = m_edad_2016,
          pp_educ = m_edu,
          pp_educg = m_edug,
          pp_ocup = m_ocu
        )%>%
        mutate(pp_edad = pp_edad + (14-edad_h))
  }
  if(is.na(dfn$pp_edad) & general){
    return("ND")
  }
  return(dfn)
}

#comporbamos qeu funcione bien
filtro_pp_c(padres_h[1,])


acomoda_df_c<-function(df){
  #damos un df de una edad,  y
  # nos regresa un data frame acomodado
  aux<-function(i){
    return(df[i,])
  }
  a<-lapply(1:nrow(df),aux)
  L<-lapply(a,filtro_pp_c)
  dfn <-bind_rows(L[1:length(L)])
  return(dfn)
}

dfh<-acomoda_df_c(padres_h)



pro_edad_c <- function(df){
  #le damos el df y nos regresa el promedio de la edade  los padres
  df%>%summarise(
    promedi_edad_padres = mean(pp_edad2016,na.rm=TRUE))
  }

edades_pro<- pro_edad_c(dfh)
mean(dfh$edad_h)
edades_pro
#edades de padres faltantes
sum(is.na(dfh$pp_edad2016))
# 
head(dfh)

# 2016-21
```
## Codificacion eduación


```{r cod_edu_hijo}

dfh_c<- dfh%>%
   mutate(
   pp_educ_a = ifelse(pp_educ==1,1,    #sin instruccion(1)
          ifelse(pp_educ==2,1+pp_educg,
          ifelse(pp_educ %in% c(3,4),7+pp_educg,
          ifelse(pp_educ %in% c(5,6,7,8,9),10+pp_educg,
          ifelse(pp_educ %in% c(10,11),13+pp_educg,17+pp_educg)))))
  )

  

unique(dfh_c$pp_educ_a)

dfh_c<-dfh_c%>%
  mutate(
   pp_educ_ac = ifelse(pp_educ_a %in% 0:5,"C1",    
          ifelse(pp_educ_a %in% 6:10 ,"C2",
          ifelse(pp_educ_a %in% 11:12,"C3",
          ifelse(pp_educ_a %in% 13:14,"C4","C5"))))
  )

### Guardamos base de datos de hijos final

saveRDS(dfh_c, file="Datos/dfh_c_combinada_p1.Rda")


#saveRDS(dfh_c, file="Datos/dfh_c_sin_re_ch_cri3.Rda")

#saveRDS(dfh_c, file="Datos/dfh_c_re_ch_cri3.Rda")

#saveRDS(dfh_c, file="Datos/dfh_c_re_ch_cri2.Rda")

#saveRDS(dfh_c, file="Datos/dfh_c_sin_re_ch.Rda")




```

# Padres 1998

criterio 1,
Seleccionamos de la ENIGH 1997, no hay entonces 1998

criterio 2
NO tenemos enigh 93 agarramos enigh 94 (cuando la tengamos)

## Poblacion enigh 98

```{r poblacion_e98}

poblacion<-read.dbf(
              file="Datos/ENIGH-Historica/1998/POBLA98.dbf"
              )



poblacion_e98<-poblacion%>%
  filter(
    parentesco == "01",
    between(edad,25,60),#<------------------ criterio 1 y 3 cambiar 
    !is.na(ed_formal),
    !is.na(ed_tecnica),
    !is.na(ocupacion),
    n_empleos==1, # el entrevistado solo proporciona una ocupación
  )%>%
  select(
    folio,
    num_ren,
    edad,
    sexo,
    ed_formal,
    ed_tecnica,
    ocupacion
  )%>%
 mutate(
  ed_formal = as.integer(as.character(ed_formal)),
   ed_tecnica= as.integer(as.character(ed_tecnica)),
  estado = substr(folio,5,6)
 )

#unique(pobla_enigh1998$ed_formal)
#pobla_enigh1998$ed_formal
nrow(poblacion_e98)
head(poblacion_e98$ed_formal)
str(poblacion_e98$ed_formal)#caracter 2 espacios
unique(poblacion_e98$ed_formal)
#pobla_enigh1998$ed_formal=="03"
head(poblacion_e98$ed_tecnica)
str(poblacion_e98$ed_tecnica)#caracter 1 espacio
unique(poblacion_e98$ed_tecnica)
#pobla_enigh1998%>%count(n_empleos), se exploro para quitar una ocupacion
names(poblacion)
str(poblacion$per_ing)
unique(poblacion_e98$estado)
```
folios de gente con hijos menores de 14 años
```{r}
folios98<-as.character(poblacion%>%filter(parentesco %in% c("04","05"),edad<=18)%>%select(folio)%>%pull())
poblacion_e98<-poblacion_e98%>%filter(folio %in%  folios98 )
```



### Codificación educación enigh 1998

```{r cod_ed_e98}
# hacemos la codificación de ENIGH2018 a ESRU
#1	Preescolar o kínder
# 2	Primaria
# 3	Secundaria técnica
# 4	Secundaria general
# 5	Preparatoria técnica
# 6	Preparatoria general
# 7	Técnica o comercial con secundaria
# 8	Técnica o comercial con preparatoria
# 9	Normal básica (con primaria o secundari
# 10 Normal de licenciatura
# 11 Profesional (licenciatura o ingeniería)
# 12 Postgrado (maestría o doctorado)
#_____________________________________________________
# 98 NS, no aplica porque pedimos la edución del pp como instrumento


# vemso las combinaciones de educación para codificar

comb<-poblacion_e98%>%
   mutate( combi = paste(as.character(ed_formal),as.character(ed_tecnica)))%>%
  select(ed_formal,ed_tecnica,combi)

  
sort(unique(comb$combi))
# [1] "01 1" "02 1" "03 1" "03 2" "04 1" "05 1" "06 1" "07 1" "07 2" "08 1"
# [11] "08 2" "08 3" "08 4" "08 5" "09 1" "09 4" "09 5" "10 1" "10 2" "10 4"
# [21] "10 5" "11 1" "11 3" "11 4" "11 5" "11 6" "11 7" "12 1" "12 3" "12 5"
# [31] "12 6" "12 7" "13 1" "13 5" "13 6" "13 7" "13 8" "13 9" "14 1" "14 3"
# [41] "14 5" "14 6" "14 7" "14 9" "15 1" "15 3" "15 4" "15 5" "15 6" "15 7"
# [51] "15 9" "16 1" "16 5" "16 7" "16 9"




# "16 1" "16 5" "16 7" "16 9"

#codificamos a a años de educación
poblacion_e98c<-poblacion_e98%>%
   mutate(
   edu_a = ifelse(ed_formal==1 & ed_tecnica==1,0,    #sin instruccion(1)
          ifelse((ed_formal %in% c(2,3,4,5,6,7,8,9,10,11,12,13))
                 & (ed_tecnica %in% c(1,2)),ed_formal-1,
          ifelse(ed_formal==8 & (ed_tecnica %in% c(3,4,5)),9,
          ifelse(ed_formal==9 & (ed_tecnica %in% c(4,5)),10,
          ifelse(ed_formal==10 & (ed_tecnica %in% c(2,4,5)),11,
          ifelse(ed_formal==11 & (ed_tecnica %in% c(3,4,5,5,7)),12,
          ifelse((ed_formal %in%c(12)) & (ed_tecnica %in% c(3,5,6,7)),12,
          ifelse(ed_formal==13 & (ed_tecnica %in% c(5,6,7,8,9)),13,
          ifelse(ed_formal==14 & (ed_tecnica %in% c(1,3,5,6,7,9)),14,
          ifelse(ed_formal==15 & (ed_tecnica %in% c(1,3,4,5,6,7,9)),17,19))))))))))
  )

unique(poblacion_e98c$edu_a)

#codificamos a coghort de educación

poblacion_e98c<-poblacion_e98c%>%
   mutate(
   edu_ac = ifelse(edu_a %in% 0:5,"C1",    
          ifelse(edu_a %in% 6:10 ,"C2",
          ifelse(edu_a %in% 11:12,"C3",
          ifelse(edu_a %in% 13:14,"C4","C5"))))
  )

unique(poblacion_e98c$edu_ac )


```

### Creacion de region
```{r crea_region_e98}

poblacion_e98c<-poblacion_e98c%>%
          mutate(
            region = ifelse(estado %in% c("02","26","08","05","19","28"),1,
                ifelse(estado %in% c("12","20","07","30","27","04","31","23"),4,
                ifelse(estado %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
          )

```


## Ingresos enigh 98


```{r ingresos_e98}
ingresos<-read.dbf(
              file="Datos/ENIGH-Historica/1998/ingresos.dbf"
              )

familias_integrantes <- function(df,nr){
  dfn <- df %>%
    filter(NUM_REN %in% nr)
  return(dfn)
} 



ingresos_acu <- function(df,clavs){
 dfn <- df %>%
   group_by(FOLIO, NUM_REN) %>%
   filter(CLAVE %in% clavs)%>%
   summarise(
   claves_a = paste(CLAVE,collapse = ","),
  ing_tri_t = sum(ING_TRI),
  ing_men_p = ing_tri_t/3,
  ling_men_p = log(ing_men_p)
  ) %>% ungroup()
 
 
 return(dfn)
}

#hogares <- c("1","2","3","4","5")
personas_h <- c('01')

a<-familias_integrantes(ingresos,personas_h)
head(a)
## criterio 1
#claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", #"P016","P017","P018","P020","P021","P026","P027","P035","P040","P041","P042","P044","P045")

## criterio 2 flexibilida de los ingresos y criterio 3
claves<-c("P001","P002","P003","P010","P011","P012","P013","P014","P015", "P016","P017","P018","P020","P021","P022","P023","P024","P025","P026","P027","P028","P035","P036","P037","P040","P041","P042","P044","P045")


ingresos_e98<-ingresos_acu(a,claves) 
ingresos_e98<-ingresos_e98%>%
  select(FOLIO,NUM_REN,claves_a,ing_men_p,ling_men_p)%>%
  rename(
    folio = FOLIO,
    num_ren = NUM_REN
  )
head(ingresos_e98)

ingresos_e98<-ingresos_e98%>%
  mutate(
     region = ifelse(substr(folio,5,6) %in% c("02","26","08","05","19","28"),1,
                ifelse(substr(folio,5,6) %in% c("12","20","07","30","27","04","31","23"),4,
                ifelse(substr(folio,5,6) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
  )

```

## rango percentil 98

```{r}
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e98$ling_men_pd<-ingresos_e98$ling_men_p + log(IN)
rp_e98 <- rank(ingresos_e98$ling_men_pd)/length(ingresos_e98$ling_men_pd)
#rp_e98<-percent_rank(ingresos_e98$ling_men_pd)
```



```{r}
ingresos_e981 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==1)%>%pull(ling_men_p) + log(IN))
rp_e981 <- rank(ingresos_e981$ling_men_pd)/length(ingresos_e981$ling_men_pd)
```


```{r}
ingresos_e982 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==2)%>%pull(ling_men_p) + log(IN))
rp_e982 <- rank(ingresos_e982$ling_men_pd)/length(ingresos_e982$ling_men_pd)
```


```{r}
ingresos_e983 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==3)%>%pull(ling_men_p) + log(IN))
rp_e983 <- rank(ingresos_e983$ling_men_pd)/length(ingresos_e983$ling_men_pd)
```

```{r}
ingresos_e984 <- data.frame(ling_men_pd=ingresos_e98%>%filter(region==4)%>%pull(ling_men_p) + log(IN))
rp_e984 <- rank(ingresos_e984$ling_men_pd)/length(ingresos_e984$ling_men_pd)
```


## Base cruzada pobla-ing

```{r base_cruz_e98}
enigh_98<-full_join(poblacion_e98c,ingresos_e98)%>%
  drop_na()%>%
   mutate(
     ocupacion = substr(ocupacion,1,2)
   )#%>%
  # rename(
  #   EDU=N_INSTR161,
  #   OCU = CMO121
  # )%>%
  # mutate(
  #   EDU = as.character(EDU)
  # )
head(enigh_98)

unique(enigh_98$edad)

# ajuste para nivel de ecucación ESRU

#data_padres2005$EDU <- factor(data_padres2005$EDU)
#data_padres2005$OCU <- factor(data_padres2005$OCU)

#unique(data_padres2005$OCU)
sum(is.na(enigh_98))
```



```{r base_cruz_e98 guarda}
##### Guardamos base de padres final
saveRDS(enigh_98, file="Datos/enigh_98_cri3.Rda")
enigh_98%>%count(sexo)
```


## Grafica de ingresos y edad

```{r}
ed_ear<-full_join(poblacion_e98c,ingresos_e98)%>%
  select(edad, ing_men_p)%>%drop_na()%>%
  group_by(edad)%>%
  summarise(
   pro_ing = mean(ing_men_p)
  )

ed_ear%>%
hchart( "scatter", 
        hcaes(x = edad, y = pro_ing),
        name="ingreso promedio",
        regression = TRUE,
          regressionSettings = list(
    type = "polynomial",
    dashStyle = "ShortDash",
    color = "skyblue",
    order = 2,
    lineWidth = 5,
    name = "%eq | $r^2$: %r",
    hideInLegend = FALSE)
        )%>%
  hc_add_dependency("plugins/highcharts-regression.js")
  

  
```




## Esdística descriptiva 98

```{r}
summary(enigh_98$edad)
hist(enigh_98$edad)
papas<- enigh_98%>%group_by(edad)%>%summarise(msal = mean(ing_men_p))
plot(papas$edad,papas$msal)
```


# Padres  1996

## poblacion eningh 1996

```{r}
poblacion<-read.dbf(
              file="Datos/ENIGH-1996/POBLA96.dbf"
              )
poblacion <- poblacion %>% 
                rename(
                  parentesco = PARENTESCO,
                  folio = FOLIO,
                  num_ren = NUM_REN,
                  sexo = SEXO,
                  edad = EDAD,
                  per_ing=PER_ING,
                  ed_formal=ED_FORMAL,
                  ed_tecnica=ED_TECNICA,
                  ocupacion=OCUPACION
                  )
poblacion
```


```{r}
poblacion_e96<-poblacion%>%
  filter(
    parentesco == "01",#en el 98 era "01"
    between(edad,25,60),
    !is.na(ed_formal),
    !is.na(ed_tecnica),
    !is.na(ocupacion),
    N_EMPLEOS==1, # el entrevistado solo proporciona una ocupación
  )%>%
  select(
    folio,
    num_ren,
    edad,
    sexo,
    ed_formal,
    ed_tecnica,
    ocupacion
  )%>%
 mutate(
  ed_formal = as.integer(as.character(ed_formal)),
   ed_tecnica= as.integer(as.character(ed_tecnica)),
  estado = substr(folio,5,6)
 )
```


```{r}
#En 1998 para hijos es "04" y "05". En 1996 "4" abarca todos los hijos
#En 1998 se considera edad<=18, en 1996 se considera edad<=14
folios96<-as.character(poblacion%>%filter(parentesco %in% c("04","05"),edad<=16)%>%select(folio)%>%pull())
poblacion_e96<-poblacion_e96%>%filter(folio %in%  folios96 )
```

```{r}
nrow(poblacion_e96)
head(poblacion_e96$ed_formal)
str(poblacion_e96$ed_formal)#caracter 2 espacios
unique(poblacion_e96$ed_formal)
```

```{r}
head(poblacion_e96$ed_tecnica)
str(poblacion_e96$ed_tecnica)#caracter 1 espacio
unique(poblacion_e96$ed_tecnica)

names(poblacion_e96)
str(poblacion_e96$per_ing)
```

```{r}
# hacemos la codificación de ENIGH2018 a ESRU
# 1	Preescolar o kínder
# 2	Primaria
# 3	Secundaria técnica
# 4	Secundaria general
# 5	Preparatoria técnica
# 6	Preparatoria general
# 7	Técnica o comercial con secundaria
# 8	Técnica o comercial con preparatoria
# 9	Normal básica (con primaria o secundari
# 10 Normal de licenciatura
# 11 Profesional (licenciatura o ingeniería)
# 12 Postgrado (maestría o doctorado)
#_____________________________________________________
# 96 NS, no aplica porque pedimos la edución del pp como instrumento
comb<-poblacion_e96%>%
   mutate( combi = paste(as.character(ed_formal),as.character(ed_tecnica)))%>%
  select(ed_formal,ed_tecnica,combi)
comb
sort(unique(comb$combi))
```

```{r}
poblacion_e96c<-poblacion_e96%>%
   mutate(
   edu_a = ifelse(ed_formal==0 & ed_tecnica==0,0,    #sin instruccion(1)
          ifelse((ed_formal %in% c(2:13)) & (ed_tecnica %in% c(0,2,3)),ed_formal,
          ifelse((ed_formal %in% c(7:13)) & (ed_tecnica %in% c(4,5,6,8)),ed_formal+1,
          ifelse(ed_formal==14 & (ed_tecnica %in% c(0,2,4,5,6,8)),17,19))))
)
```

```{r}
poblacion_e96c<-poblacion_e96c%>%
   mutate(
   edu_ac = ifelse(edu_a %in% 0:5,"C1",    
          ifelse(edu_a %in% 6:10 ,"C2",
          ifelse(edu_a %in% 11:12,"C3",
          ifelse(edu_a %in% 13:14,"C4","C5"))))
  )
```


```{r}
poblacion_e96c<-poblacion_e96c%>%
          mutate(
            region = ifelse(estado %in% c("02","26","08","05","19","28"),1,
                ifelse(estado %in% c("12","20","07","30","27","04","31","23"),4,
                ifelse(estado %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
          )
```


## Ingresos 96

```{r}
ingresos<-read.dbf(
              file="Datos/ENIGH-1996/ingresos.dbf"
              )

```

```{r}
familias_integrantes <- function(df,nr){
  dfn <- df %>%
    filter(NUM_REN %in% nr)
  return(dfn)
}
```

```{r}
ingresos_acu <- function(df,clavs){
 dfn <- df %>%
   group_by(FOLIO, NUM_REN) %>%
   filter(CLAVE %in% clavs)%>%
   summarise(
   claves_a = paste(CLAVE,collapse = ","),
  ing_tri_t = sum(ING_TRI),
  ing_men_p = ing_tri_t/3,
  ling_men_p = log(ing_men_p)
  ) %>% ungroup()
 
 
 return(dfn)
}
```

```{r}
#hogares <- c("1","2","3","4","5")
personas_h <- c('01')

a<-familias_integrantes(ingresos,personas_h)
head(a)
#P001 Sueldos, salarios, jornal y horas extras 
#P002 Comisiones, propinas y destajo 
#P010:P018 de 1998 son las mismas que P006:P014 de 1996
#P020:P021 de 1998 son las mismas que P016:P017 de 1996
#P026 de 1998 es la misma que P022 de 1996, aunque no hay equivalente en 1996 a la P027 de 1998
#P035 de 1998 es la misma que P030 de 1996
#P040:P042 de 1998 son las mismas que P035:P037 de 1996
#P044:P045 de 1998 son las mismas que P039:P040 de 1996
claves<-c("P001","P002","P006","P007","P008","P009","P010","P011", "P012","P013","P014","P016","P017","P022","P030","P035","P036","P037","P039","P040")
ingresos_e96<-ingresos_acu(a,claves) 
ingresos_e96<-ingresos_e96%>%
  select(FOLIO,NUM_REN,claves_a,ing_men_p,ling_men_p)%>%
  rename(
    folio = FOLIO,
    num_ren = NUM_REN
  )
head(ingresos_e96)

ingresos_e96<- ingresos_e96%>%
  mutate(
     region = ifelse(substr(folio,5,6) %in% c("02","26","08","05","19","28"),1,
                ifelse(substr(folio,5,6) %in% c("12","20","07","30","27","04","31","23"),4,
                ifelse(substr(folio,5,6) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
  )

```

## rango percentil 96

```{r}
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e96$ling_men_pd<-ingresos_e96$ling_men_p + log(IN)
rp_e96 <- rank(ingresos_e96$ling_men_pd)/length(ingresos_e96$ling_men_pd)
#rp_e96<-percent_rank(ingresos_e96$ling_men_pd)
```



## Base cruzada pobla-ing 96

```{r base_cruz_e96}
enigh_96<-full_join(poblacion_e96c,ingresos_e96)%>%
  drop_na()%>%
   mutate(
     ocupacion = substr(ocupacion,1,2)
   )#%>%
  # rename(
  #   EDU=N_INSTR161,
  #   OCU = CMO121
  # )%>%
  # mutate(
  #   EDU = as.character(EDU)
  # )
head(enigh_96)

unique(enigh_96$edad)

# ajuste para nivel de ecucación ESRU

#data_padres2005$EDU <- factor(data_padres2005$EDU)
#data_padres2005$OCU <- factor(data_padres2005$OCU)

#unique(data_padres2005$OCU)
sum(is.na(enigh_96))
names(enigh_96)
```


```{r base_cruz_e96 guarda}
##### Guardamos base de padres final
saveRDS(enigh_96, file="Datos/enigh_96_cri3.Rda")

```

## Esdística descriptiva 96

```{r}
summary(enigh_96$edad)
hist(enigh_96$edad)
papas<- enigh_96%>%group_by(edad)%>%summarise(msal = mean(ing_men_p))
plot(papas$edad,papas$msal)
```





# Padres 1994

## poblacion enigh 1994

```{r}
poblacion<-read.dbf(
              file="Datos/ENIGH-Historica/1994/POBLA94.dbf"
              )
poblacion <- poblacion %>% 
                rename(
                  parentesco = PARENTESCO,
                  folio = FOLIO,
                  num_ren = NUM_REN,
                  sexo = SEXO,
                  edad = EDAD,
                  per_ing=PER_ING,
                  ed_formal=ED_FORMAL,
                  ed_tecnica=ED_TECNICA,
                  ocupacion=OCUPACION
                  )
poblacion
```

```{r}
poblacion_e94<-poblacion%>%
  filter(
    parentesco == "1",#en el 98 era "01"
    between(edad,25,60),
    !is.na(ed_formal),
    !is.na(ed_tecnica),
    !is.na(ocupacion),
    N_EMPLEOS==1, # el entrevistado solo proporciona una ocupación
  )%>%
  select(
    folio,
    num_ren,
    edad,
    sexo,
    ed_formal,
    ed_tecnica,
    ocupacion
  )%>%
 mutate(
  ed_formal = as.integer(as.character(ed_formal)),
   ed_tecnica= as.integer(as.character(ed_tecnica)),
  estado = substr(folio,5,6)
 )
```


```{r}
#En 1998 para hijos es "04" y "05". En 1994 "4" abarca todos los hijos
#En 1998 se considera edad<=18, en 1994 se considera edad<=14
folios94<-as.character(poblacion%>%filter(parentesco %in% c("4"),edad<=13)%>%select(folio)%>%pull())
poblacion_e94<-poblacion_e94%>%filter(folio %in%  folios94 )
```

```{r}
nrow(poblacion_e94)
head(poblacion_e94$ed_formal)
str(poblacion_e94$ed_formal)#caracter 2 espacios
unique(poblacion_e94$ed_formal)
```
```{r}
head(poblacion_e94$ed_tecnica)
str(poblacion_e94$ed_tecnica)#caracter 1 espacio
unique(poblacion_e94$ed_tecnica)

names(poblacion_e94)
str(poblacion_e94$per_ing)
```
```{r}
# hacemos la codificación de ENIGH2018 a ESRU
# 1	Preescolar o kínder
# 2	Primaria
# 3	Secundaria técnica
# 4	Secundaria general
# 5	Preparatoria técnica
# 6	Preparatoria general
# 7	Técnica o comercial con secundaria
# 8	Técnica o comercial con preparatoria
# 9	Normal básica (con primaria o secundari
# 10 Normal de licenciatura
# 11 Profesional (licenciatura o ingeniería)
# 12 Postgrado (maestría o doctorado)
#_____________________________________________________
# 94 NS, no aplica porque pedimos la edución del pp como instrumento
comb<-poblacion_e94%>%
   mutate( combi = paste(as.character(ed_formal),as.character(ed_tecnica)))%>%
  select(ed_formal,ed_tecnica,combi)
comb
sort(unique(comb$combi))
```

```{r}
poblacion_e94c<-poblacion_e94%>%
   mutate(
   edu_a = ifelse(ed_formal==0 & ed_tecnica==0,0,    #sin instruccion(1)
          ifelse(ed_formal==1 & (ed_tecnica %in% c(0,1)),3,
          ifelse(ed_formal==1 & ed_tecnica==2,4,
          ifelse(ed_formal==2 & (ed_tecnica %in% c(0,1)),6,
          ifelse(ed_formal==2 & (ed_tecnica %in% c(2,3)),7,
          ifelse(ed_formal==3 & (ed_tecnica %in% c(0,1)),7,
          ifelse(ed_formal==3 & (ed_tecnica %in% c(2,3)),8,
          ifelse(ed_formal==4 & ed_tecnica==0,9,
          ifelse(ed_formal==4 & ed_tecnica==2,10,
          ifelse(ed_formal==4 & (ed_tecnica %in% c(4,5)),11,
          ifelse(ed_formal==4 & ed_tecnica==6,13,
          ifelse(ed_formal==5 & ed_tecnica==0,11,
          ifelse(ed_formal==5 & (ed_tecnica %in% c(4,5)),13,
          ifelse(ed_formal==5 & ed_tecnica==6,14,
          ifelse(ed_formal==6 & ed_tecnica==0,12,
          ifelse(ed_formal==6 & ed_tecnica==2,13,
          ifelse(ed_formal==6 & (ed_tecnica %in% c(4,5)),14,
          ifelse(ed_formal==6 & (ed_tecnica %in% c(6,7,8)),15,
          ifelse(ed_formal==7 & (ed_tecnica %in% c(0,1)),15,
          ifelse(ed_formal==7 & (ed_tecnica %in% c(2,3)),16,
          ifelse(ed_formal==7 & (ed_tecnica %in% c(4,5,6,8)),17,
          ifelse(ed_formal==8 & ed_tecnica==0,17,
          ifelse(ed_formal==8 & (ed_tecnica %in% c(2,4,5,6,8)),18,19)))))))))))))))))))))))
  )
```

```{r}
poblacion_e94c<-poblacion_e94c%>%
   mutate(
   edu_ac = ifelse(edu_a %in% 0:5,"C1",    
          ifelse(edu_a %in% 6:10 ,"C2",
          ifelse(edu_a %in% 11:12,"C3",
          ifelse(edu_a %in% 13:14,"C4","C5"))))
  )
```


```{r}
poblacion_e94c<-poblacion_e94c%>%
          mutate(
            region = ifelse(estado %in% c("02","26","08","05","19","28"),1,
                ifelse(estado %in% c("12","20","07","30","27","04","31","23"),4,
                ifelse(estado %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
          )
```


## Ingresos engih 94

```{r}
ingresos<-read.dbf(
              file="Datos/ENIGH-Historica/1994/ingresos.dbf"
              )

```

```{r}
familias_integrantes <- function(df,nr){
  dfn <- df %>%
    filter(NUM_REN %in% nr)
  return(dfn)
}
```

```{r}
ingresos_acu <- function(df,clavs){
 dfn <- df %>%
   group_by(FOLIO, NUM_REN) %>%
   filter(CLAVE %in% clavs)%>%
   summarise(
   claves_a = paste(CLAVE,collapse = ","),
  ing_tri_t = sum(ING_TRI),
  ing_men_p = ing_tri_t/3,
  ling_men_p = log(ing_men_p)
  ) %>% ungroup()
 
 
 return(dfn)
}
```

```{r}
#hogares <- c("1","2","3","4","5")
personas_h <- c('01')

a<-familias_integrantes(ingresos,personas_h)
head(a)
#P001 Sueldos, salarios, jornal y horas extras 
#P002 Comisiones, propinas y destajo 
#P010:P018 de 1998 son las mismas que P006:P014 de 1994
#P020:P021 de 1998 son las mismas que P016:P017 de 1994
#P026 de 1998 es la misma que P022 de 1994, aunque no hay equivalente en 1994 a la P027 de 1998
#P035 de 1998 es la misma que P029 de 1994
#P040:P042 de 1998 son las mismas que P034:P036 de 1994
#P044:P045 de 1998 son las mismas que P038:P039 de 1994
claves<-c("P001","P002","P006","P007","P008","P009","P010","P011", "P012","P013","P014","P016","P017","P022","P029","P034","P035","P036","P038","P039")
ingresos_e94<-ingresos_acu(a,claves) 
ingresos_e94<-ingresos_e94%>%
  select(FOLIO,NUM_REN,claves_a,ing_men_p,ling_men_p)%>%
  rename(
    folio = FOLIO,
    num_ren = NUM_REN
  )
head(ingresos_e94)


ingresos_e94<- ingresos_e94%>%
  mutate(
     region = ifelse(substr(folio,5,6) %in% c("02","26","08","05","19","28"),1,
                ifelse(substr(folio,5,6) %in% c("12","20","07","30","27","04","31","23"),4,
                ifelse(substr(folio,5,6) %in% c("03","25","18","10","32","16","06","14","01","24"),2,3)))
  )


```

## rango percentil 94

```{r}
#IN<-(100/110.907)
IN<-(100/93.6)
ingresos_e94$ling_men_pd<-ingresos_e94$ling_men_p + log(IN)
rp_e94 <- rank(ingresos_e94$ling_men_pd)/length(ingresos_e94$ling_men_pd)
#rp_e94<- percent_rank(ingresos_e94$ling_men_pd)
```



## Bae cruzada pobla-ingre 94

```{r base_cruz_e94}
enigh_94<-full_join(poblacion_e94c,ingresos_e94)%>%
  drop_na()%>%
   mutate(
     ocupacion = substr(ocupacion,1,2)
   )#%>%
  # rename(
  #   EDU=N_INSTR161,
  #   OCU = CMO121
  # )%>%
  # mutate(
  #   EDU = as.character(EDU)
  # )
head(enigh_94)

unique(enigh_94$edad)

# ajuste para nivel de ecucación ESRU

#data_padres2005$EDU <- factor(data_padres2005$EDU)
#data_padres2005$OCU <- factor(data_padres2005$OCU)

#unique(data_padres2005$OCU)
sum(is.na(enigh_94))
names(enigh_94)
```


```{r base_cruz_e94 guarda}
##### Guardamos base de padres final
saveRDS(enigh_94, file="Datos/enigh_94_cri3.Rda")

```

## Esdística descriptiva 94

```{r}
summary(enigh_94$edad)
hist(enigh_94$edad)
papas<- enigh_94%>%group_by(edad)%>%summarise(msal = mean(ing_men_p))
plot(papas$edad,papas$msal)
```







# Eslaticidad integeneracional de ingresos


```{r}
base_logsing <- list(ingresos_e94,ingresos_e96,ingresos_e98)
base_rp <- list(rp_e94,rp_e96,rp_e98)
```



## Deflactamos ingresos

INPC Feb 2021 = 110.907 [Consulta][]









```{r}
enigh_94 <- readRDS(file = "Datos/enigh_94_cri3.Rda")
enigh_96 <- readRDS(file = "Datos/enigh_96_cri3.Rda")
enigh_98 <- readRDS(file = "Datos/enigh_98_cri3.Rda")

#dfh_c <- readRDS(file = "Datos/dfh_c_sin_re.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_sin_re_ch.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_re.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_re_ch_cri3.Rda")
dfh_c <- readRDS(file = "Datos/dfh_c_combinada_ch_cri3.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c_combinada_p1.Rda")


enigh_94<-enigh_94%>%mutate(
  edad_c = ifelse(25<=edad & edad<=29,"Edad1",
                  ifelse(30<=edad & edad<=34,"Edad2",
                         ifelse(35<=edad & edad<=39,"Edad3",
                        ifelse(40<=edad & edad<=44,"Edad4",
                        ifelse(45<=edad & edad<=49,"Edad5",
              ifelse(50<=edad & edad<=54,"Edad6","Edad7"))))))
)

enigh_96<-enigh_96%>%mutate(
  edad_c = ifelse(25<=edad & edad<=29,"Edad1",
                  ifelse(30<=edad & edad<=34,"Edad2",
                         ifelse(35<=edad & edad<=39,"Edad3",
                        ifelse(40<=edad & edad<=44,"Edad4",
                        ifelse(45<=edad & edad<=49,"Edad5",
              ifelse(50<=edad & edad<=54,"Edad6","Edad7"))))))
)

enigh_98<-enigh_98%>%mutate(
  edad_c = ifelse(25<=edad & edad<=29,"Edad1",
                  ifelse(30<=edad & edad<=34,"Edad2",
                         ifelse(35<=edad & edad<=39,"Edad3",
                        ifelse(40<=edad & edad<=44,"Edad4",
                        ifelse(45<=edad & edad<=49,"Edad5",
              ifelse(50<=edad & edad<=54,"Edad6","Edad7"))))))
)

dfh_c <- dfh_c %>%mutate(
  pp_edad_c = ifelse( pp_edad<=29,"Edad1",
                  ifelse(30<=pp_edad & pp_edad<=34,"Edad2",
                ifelse(35<=pp_edad & pp_edad<=39,"Edad3",
                ifelse(40<=pp_edad & pp_edad<=44,"Edad4",
                ifelse(45<=pp_edad & pp_edad<=49,"Edad5",
          ifelse(50<=pp_edad & pp_edad<=54,"Edad6","Edad7"))))))
)

dfh_c<-dfh_c%>%
          mutate(
            region= as.integer(region),
            pp_estado = as.integer(pp_estado),
            pp_region = ifelse(pp_estado %in% as.integer(c("02","26","08","05","19","28")),1,
                ifelse(pp_estado %in% as.integer(c("12","20","07","30","27","04","31","23")),4,
                ifelse(pp_estado %in% as.integer(c("03","25","18","10","32","16","06","14","01","24")),2,3)))
          )

#IN<-(100/110.907)
IN<-(100/93.6)# enero 2017

enigh_94<-enigh_94%>%
  mutate(
  ling_men_p=ling_men_p+log(IN)
  )

enigh_96<-enigh_96%>%
  mutate(
  ling_men_p=ling_men_p+log(IN)
  )

enigh_98<-enigh_98%>%
  mutate(
  ling_men_p=ling_men_p+log(IN)
  )


unique(dfh_c$region)
unique(dfh_c$pp_region)


#id_c_re<-dfh_c$id_hijo
#id_c_combinada<-dfh_c$id_ho

#sum(id_c_re %in% id_c_combinada)
nrow(dfh_c)
```


```{r}
provi<-dfh_c%>%filter((pp_ocup %in% enigh_94$ocupacion) &
                              (pp_ocup %in% enigh_96$ocupacion) &
                              (pp_ocup %in% enigh_98$ocupacion))
nrow(provi)
```


Por la porgramación, para los hijos, se hace dentro del sigueinte programa 

  [Consulta]: https://www.banxico.org.mx/SieInternet/consultarDirectorioInternetAction.do?accion=consultarCuadro&idCuadro=CP154&locale=es






```{r migra}
# considerando la migracion

dfh_c<- dfh_c %>%
  filter(pp_region==region)

nrow(dfh_c)
```

```{r}
dfh_cna<-dfh_c%>%drop_na(pp_ocup)%>%drop_na(pp_edad)
nrow(dfh_cna)
```



Criterio 1
- por region, solo los que no migran 1025
-  por region y cohort de ingreso, solo los que no migran 1009
- con ocupacion y por region los ingresos, solo los que no migran 603
- con ocupacion y por region y por cohort de los ingresos, solo los que no migran 405

Criterio 2
-  por region y cohort de ingreso, solo los que no migran 1012


criterio 3

- por region y cohort de ingreso 1527
- con ocupacion y por region y por cohort de los ingresos, solo los que no migran 630

Combinada

- no migran 1464
## Estimación

```{r fun_prin_eii}
eii<-function(i,fp,fhp,code=0,reg=0){
#enigh_98 <- readRDS(file = "Datos/enigh_98.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c.Rda")
#fp = ling_men_p ~ edu_ac + ocupacion + edad + I(edad*edad)
#fhp = ling_h ~ ling_p + edad_h+ I(edad_h*edad_h)
#reg<-1
#code<-1
#print(reg)
# BOOSTRAP  
# Regresión de ingresos de padres  
#summary(reg_padres)

# numero de padres sin ocupación reportada de hijos
#p_socu<-sum(is.na(dfh_c$pp_ocup))

regresion_padres<-function(enigh,dfh_cna,code=code,reg=reg){
  if(code==1){

    # regresion padres  por region
  
    enigh<-enigh%>%filter(region==reg)
    enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]

    reg_padres<-lm(
    formula = fp,
    data = enigh_r
    )
    
    # data frame region
    
    dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
  
    data<- data.frame(
          edu_ac= dfh_cna$pp_educ_ac,
          ocupacion= dfh_cna$pp_ocup,
          sexo = dfh_cna$pp_sexo,
          #edad_c = "Edad4"
          edad = 0#dfh_cna$pp_edad
        )
  }
  else{
  
    # regresion padres completa
  
    enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]
  
    reg_padres<-lm(
    formula = fp,
    data = enigh_r
    )
    
    # data frame completo
    
    dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
  
    data<- data.frame(
          edu_ac= dfh_cna$pp_educ_ac,
          ocupacion= dfh_cna$pp_ocup,
          sexo = dfh_cna$pp_sexo,
          #edad_c = "Edad4"
          edad = 0#dfh_cna$pp_edad
        )
  }

  return(list(reg_padres,data,dfh_cna))
  
}

if(code==1){
dfh_cna <- dfh_cna%>%filter(region==reg)
}



p94<-regresion_padres(enigh_94,dfh_cna,code,reg)
p96<-regresion_padres(enigh_96,dfh_cna,code,reg)
p98<-regresion_padres(enigh_98,dfh_cna,code,reg)

reg_p94<-p94[[1]]
data_94<-p94[[2]]
#enigr_p4<-p94[[3]]
dfh_94<-p94[[3]]

reg_p96<-p96[[1]]
data_96<-p96[[2]]
#enigr_p6<-p96[[3]]
dfh_96<-p96[[3]]

reg_p98<-p98[[1]]
data_98<-p98[[2]]
#enigr_p8<-p98[[3]]
dfh_98<-p98[[3]]

dfh_cna <- dfh_cna%>%filter((pp_ocup %in% dfh_94$pp_ocup) &
                              (pp_ocup %in% dfh_96$pp_ocup) &
                              (pp_ocup %in% dfh_98$pp_ocup) )


data_94<- data_94%>%filter(ocupacion %in% dfh_cna$pp_ocup)
data_96<- data_96%>%filter(ocupacion %in% dfh_cna$pp_ocup)
data_98<- data_98%>%filter(ocupacion %in% dfh_cna$pp_ocup)

#print(nrow(dfh_cna))

# BOOSTRAP ingreso HIJOS

# calculamos el ingreso de los hijos con los gemelos
# re-sampleados, ademas codificamos la ocupación del hijo a dos caracteres

auxs<-function(k){
  sample(k,size=length(k),replace=TRUE)
}



aux_hrr<-function(ing){
  #error_e<- abs(ingresos_e16a$ling_men_pd - ing)
  error_e<- abs(ingre1618$ling_men_pd - ing)
  indice_e<-match(min(error_e),error_e)
  indice_e<-indice_e[1]
  #return(rp_e16[indice_e16]*100)
  return(rp_e1618[indice_e]*100)
}



dfh_cna <- dfh_cna %>%
  mutate(
   ocu_hc= substring(ocu_h,1,2),
   ing_h=unlist(lapply(
                      lapply(ing_h,auxs),
                      mean
                      )
                ),
   ling_h = log(ing_h)+log(IN),
   pling_h = unlist(lapply(ling_h,aux_hrr))
  )


     #dfh_f<-dfh_cna%>%
      #  mutate(cohort_e = ifelse(ing_h<=2400,1,
         #                  ifelse(ing_h<=4800,2,
         #                  ifelse(ing_h<=7200,3,
         #                  ifelse(ing_h<=12000,4,ifelse(ing_h<=24000,5,6))))),
         # cohort_dif=cohort-cohort_e)

  
      #dfh_cna   
     #dfh_cna$ocu_hijo 
      #dfh_cna$ling_h

      #str(substr(dfh_cna$ocu_h,1,2))

      # estimación ingreso padres
      # inputamos al hijo correspondiente


estimacion_padres<-function(reg_padres,data,num_base){

  aux_prr<-function(ing,num_base){
    error_e<- abs(base_logsing[[num_base]]$ling_men_pd - ing)
    indice_e<-match(min(error_e),error_e)
    indice_e<-indice_e[1]
    return(base_rp[[num_base]][indice_e]*100)
  }
  
  ling_pf <- predict(
            object = reg_padres,   
            newdata = data
          )
  
  pling_pf <- unlist(lapply(ling_pf,aux_prr,num_base))
  
  return(list(ling_pf,pling_pf))
}

est1 <- estimacion_padres(reg_p94,data_94,1)
est2 <- estimacion_padres(reg_p96,data_96,2)
est3 <- estimacion_padres(reg_p98,data_98,3)


mling<-(est1[[1]] + est2[[1]] + est3[[1]])/3
#mling1<-est1[[1]]
#mling2<-est2[[1]]
#mling3<-est3[[1]]
  
mpling<-(est1[[2]] + est2[[2]]+ est3[[2]])/3

#print("----")
#print(length(est1[[1]]))
#print(length(est2[[1]]))
#print(length(est3[[1]]))

dfhpf<- dfh_cna%>%
        mutate(
          ling_p = mling,
          pling_p = mpling
        )


# Boostrap hijos
dfhpf<-dfhpf[sample(nrow(dfhpf),nrow(dfhpf), replace = TRUE),]
  
  
reg_hp <- lm(
            formula =fhp ,#+ ocu_hc,
            data = dfhpf
          ) 
                #print(dfhpf$pling_h)
                #print(dfhpf$pling_p)
                #data.frame(dfhpf$pling_h,dfhpf$pling_p)
 
reg_hpr <- lm(
            formula = pling_h ~ pling_p,
            data = dfhpf
          )
               # plot(dfhpf$pling_p,dfhpf$pling_h,
                #     xlim=c(0, 100)) 
                #lines(1:100,1:100)



                #summary(reg_hp)
beta0<- reg_hp$coefficients[1]
beta <- reg_hp$coefficients[2]
sexoh<- reg_hp$coefficients[3]
edadh<- reg_hp$coefficients[4]
edad2h<-reg_hp$coefficients[5]
betar <- reg_hpr$coefficients[2]
alfar <- reg_hpr$coefficients[1]
nhijos<-nrow(dfh_cna)
          # cohort_dif<-as.integer(dfhpf%>%count(cohort_dif)%>%
          #                         filter(cohort_dif==0)%>%
          #                         select(n))


return(data.frame()%>%
         summarise(
            #cohort_dif=list(dfh_f$cohort_dif),
            beta0 = beta0,
            beta = beta,
            sexoh = sexoh,
            edadh = edadh,
            edad2h = edad2h,
            betar = betar,
            alfar = alfar,
            nhijos = nhijos,
            coeficiente = list(reg_hp$coefficients),
            coeficienter = list(reg_hpr$coefficients),
            #p_socu = p_socu,
            regresionp94 = list(reg_p94),
            regresionp96 = list(reg_p96),
            regresionp98 = list(reg_p98),
            regresion = list(reg_hp),
            regresionr = list(reg_hpr)
        )
      )

}


```



## Estimaciòn individual

```{r fun_prin_eiii}
eiii<-function(i,fp,fhp,eni,code=0,reg=0){
#enigh_98 <- readRDS(file = "Datos/enigh_98.Rda")
#dfh_c <- readRDS(file = "Datos/dfh_c.Rda")
#fp = ling_men_p ~ edu_ac + ocupacion + edad + I(edad*edad)
#fhp = ling_h ~ ling_p + edad_h+ I(edad_h*edad_h)
#reg<-1
#code<-1
#print(reg)
# BOOSTRAP  
# Regresión de ingresos de padres  
#summary(reg_padres)

# numero de padres sin ocupación reportada de hijos
#p_socu<-sum(is.na(dfh_c$pp_ocup))

regresion_padres<-function(enigh,dfh_cna,code=code,reg=reg){
  if(code==1){

    # regresion padres  por region
  
    enigh<-enigh%>%filter(region==reg)
    enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]

    reg_padres<-lm(
    formula = fp,
    data = enigh_r
    )
    
    # data frame region
    
    dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
  
    data<- data.frame(
          edu_ac= dfh_cna$pp_educ_ac,
          ocupacion= dfh_cna$pp_ocup,
          sexo = dfh_cna$pp_sexo,
          edad = dfh_cna$pp_edad 
        )
  }
  else{
  
    # regresion padres completa
  
    enigh_r <-enigh[sample(nrow(enigh),nrow(enigh), replace = TRUE),]
  
    reg_padres<-lm(
    formula = fp,
    data = enigh_r
    )
    
    # data grame completo
    
    dfh_cna<-dfh_cna%>%filter(pp_ocup %in% enigh_r$ocupacion)
  
    data<- data.frame(
          edu_ac= dfh_cna$pp_educ_ac,
          ocupacion= dfh_cna$pp_ocup,
          sexo = dfh_cna$pp_sexo,
          edad = dfh_cna$pp_edad 
        )
  }

  return(list(reg_padres,data,dfh_cna))
  
}

if(code==1){
dfh_cna <- dfh_cna%>%filter(region==reg)
}


if(eni==94){
enigh <- enigh_94
num_base <- 1
}

if(eni==96){
enigh <- enigh_96
num_base <- 2
}

if(eni==98){
enigh <- enigh_98
num_base <- 3
}

  
p<-regresion_padres(enigh,dfh_cna,code,reg)

reg_p<-p[[1]]
data<-p[[2]]
dfh_cna<-p[[3]]


auxs<-function(k){
  sample(k,size=length(k),replace=TRUE)
}



aux_hrr<-function(ing){
  #error_e<- abs(ingresos_e16a$ling_men_pd - ing)
  error_e<- abs(ingre1618$ling_men_pd - ing)
  indice_e<-match(min(error_e),error_e)
  indice_e<-indice_e[1]
  #return(rp_e16[indice_e]*100)
  return(rp_e1618[indice_e]*100)
}


# BOOSTRAP ingreso HIJOS

# calculamos el ingreso de los hijos con los gemelos
# re-sampleados, ademas codificamos la ocupación del hijo a dos caracteres

dfh_cna <- dfh_cna %>%
  mutate(
   ocu_hc= substring(ocu_h,1,2),
   ing_h=unlist(lapply(
                      lapply(ing_h,auxs),
                      mean
                      )
                ),
   ling_h = log(ing_h)+log(IN),
   pling_h = unlist(lapply(ling_h,aux_hrr))
  )


     #dfh_f<-dfh_cna%>%
      #  mutate(cohort_e = ifelse(ing_h<=2400,1,
         #                  ifelse(ing_h<=4800,2,
         #                  ifelse(ing_h<=7200,3,
         #                  ifelse(ing_h<=12000,4,ifelse(ing_h<=24000,5,6))))),
         # cohort_dif=cohort-cohort_e)

  
      #dfh_cna   
     #dfh_cna$ocu_hijo 
      #dfh_cna$ling_h

      #str(substr(dfh_cna$ocu_h,1,2))

      # estimación ingreso padres
      # inputamos al hijo correspondiente


estimacion_padres<-function(reg_padres,data,num_base){

  aux_prr<-function(ing,num_base){
    error_e<- abs(base_logsing[[num_base]]$ling_men_pd - ing)
    indice_e<-match(min(error_e),error_e)
    indice_e<-indice_e[1]
    return(base_rp[[num_base]][indice_e]*100)
  }
  
  ling_pf <- predict(
            object = reg_padres,   
            newdata = data
          )
  
  pling_pf <- unlist(lapply(ling_pf,aux_prr,num_base))
  
  return(list(ling_pf,pling_pf))
}

est <- estimacion_padres(reg_p,data,num_base)


mling<-est[[1]]

mpling<-est[[2]] 


dfhpf<- dfh_cna%>%
        mutate(
          ling_p = mling,
          pling_p = mpling
        )


# Boostrap hijos
dfhpf<-dfhpf[sample(nrow(dfhpf),nrow(dfhpf), replace = TRUE),]
  
  
reg_hp <- lm(
            formula =fhp ,#+ ocu_hc,
            data = dfhpf
          ) 
                #print(dfhpf$pling_h)
                #print(dfhpf$pling_p)
                #data.frame(dfhpf$pling_h,dfhpf$pling_p)
 
reg_hpr <- lm(
            formula = pling_h ~ pling_p,
            data = dfhpf
          )
                # plot(dfhpf$pling_p,dfhpf$pling_h,
                #      xlim=c(0, 100)) 
                #  lines(1:100,1:100)



                #summary(reg_hp)

beta <- reg_hp$coefficients[2]
betar <- reg_hpr$coefficients[2]
alfar <- reg_hpr$coefficients[1]
          # cohort_dif<-as.integer(dfhpf%>%count(cohort_dif)%>%
          #                         filter(cohort_dif==0)%>%
          #                         select(n))


return(data.frame()%>%
         summarise(
            #cohort_dif=list(dfh_f$cohort_dif),
            beta=beta,
            betar = betar,
            alfar = alfar,
            coeficiente = list(reg_hp$coefficients),
            coeficienter = list(reg_hpr$coefficients),
            #p_socu = p_socu,
            regresionp = list(reg_p),
            regresionhp = list(reg_hp),
            regresionhpr = list(reg_hpr)
        )
      )

}


```



## Funciones ara regresion

```{r fun_prin_eii fncones}

regre_completa<-function(boot=1000){
  fp <- ling_men_p ~ edu_ac + ocupacion + sexo + edad + I(edad*edad)
  fhp<- ling_h ~ ling_p  + sexo_h   #+ edad_h + I(edad_h*edad_h)-1
  set.seed(123)
  a<-lapply(1:boot,eii,fp,fhp,code=0)
  resul<-bind_rows(a[1:length(a)])
  
  print("beta cero media")
  print(mean(resul$beta0))
  print("beta cero sd")
  print(sd(resul$beta0))
  
  print("beta media")
  print(mean(resul$beta))
  print("beta sd")
  print(sd(resul$beta))
  
  print("sexo media")
  print(mean(resul$sexoh))
  print("sexo sd")
  print(sd(resul$sexoh))
  
  print("edad media")
  print(mean(resul$edadh))
  print("edad sd")
  print(sd(resul$edadh))
  
  print("edad-2 media")
  print(mean(resul$edad2h))
  print("edad-2 sd")
  print(sd(resul$edad2h))
  
  print("betar media")
  print(mean(resul$betar))
  print("betar sd")
  print(sd(resul$betar))
  
  print("alfar media")
  print(mean(resul$alfar))
  print("alfar sd")
  print(sd(resul$alfar))
  
  hist(resul$beta)
  hist(resul$betar)
  return(resul)
}

regre_completa_ind<-function(eni,boot=1000){
  fp <- ling_men_p ~ edu_ac + ocupacion + sexo + edad + I(edad*edad)
  fhp<- ling_h ~ ling_p  + sexo_h #+ edad_h +I(edad_h*edad_h)
  set.seed(123)
  a<-lapply(1:boot,eiii,fp,fhp,eni,code=0)
  resul<-bind_rows(a[1:length(a)])
  print("beta media")
  print(mean(resul$beta))
  print("beta sd")
  print(sd(resul$beta))
  print("betar media")
  print(mean(resul$betar))
  print("betar sd")
  print(sd(resul$betar))
  print("alfar media")
  print(mean(resul$alfar))
  print("alfar sd")
  print(sd(resul$alfar))
  hist(resul$beta)
  hist(resul$betar)
  return(resul)
}


regre_region<-function(r, boot = 1000){
  fpr <- ling_men_p ~ edu_ac + ocupacion + sexo  + edad + I(edad*edad)
  fhpr<- ling_h ~ ling_p   + sexo_h + edad_h #+ I(edad_h*edad_h)
  set.seed(123)
  a<-lapply(1:boot,eii,fpr,fhpr,code=1, reg=r)
  resul<-bind_rows(a[1:length(a)])
  print("beta media")
  print(mean(resul$beta))
  print("beta sd")
  print(sd(resul$beta))
  print("betar media")
  print(mean(resul$betar))
  print("betar sd")
  print(sd(resul$betar))
  print("alfar media")
  print(mean(resul$alfar))
  print("alfar sd")
  print(sd(resul$alfar))

  hist(resul$beta)
  hist(resul$betar)
  return(resul)
}

regre_region_ind<-function(r,eni, boot = 1000){
  fpr <- ling_men_p ~ edu_ac + ocupacion + sexo  + edad + I(edad*edad)
  fhpr<- ling_h ~ ling_p   + sexo_h #+ edad_h + I(edad_h*edad_h)
  set.seed(123)
  a<-lapply(1:boot,eiii,fpr,fhpr,eni,code=1, reg=r)
  resul<-bind_rows(a[1:length(a)])
  print("beta media")
  print(mean(resul$beta))
  print("beta sd")
  print(sd(resul$beta))
  print("betar media")
  print(mean(resul$betar))
  print("betar sd")
  print(sd(resul$betar))
  print("alfar media")
  print(mean(resul$alfar))
  print("alfar sd")
  print(sd(resul$alfar))

  hist(resul$beta)
  hist(resul$betar)
  return(resul)
}

regre_region_rel<-function(regi=0,boot = 1000){
  fpr <- ling_men_p ~ edu_ac + ocupacion + sexo + poly((edad-30),2)-1
  fhpr<- ling_h ~ ling_p   + poly((edad_h-25),2)
  set.seed(123)
  a<-lapply(1:boot,eii,fpr,fhpr,code=1, reg=regi, rel=1)
  resul<-bind_rows(a[1:length(a)])
  print("beta media")
  print(mean(resul$beta))
  print("beta sd")
  print(sd(resul$beta))
  print("betar media")
  print(mean(resul$betar))
  print("betar sd")
  print(sd(resul$betar))
  hist(resul$beta)
  hist(resul$betar)
  return(resul)
}

# Rango percentil: el porcentaje de puntuaciones en una distribución de puntuación especificada que están por debajo de una puntuación determinada.
# https://www.math.fsu.edu/~wooland/hm2ed/Part3Module1/prVsNth.html


```



#  Criterios

# Total promedio, criterio principal

```{r}
reg_cp_100 <- regre_completa(20)
```

```{r}
saveRDS(reg_cp_100, file="Datos/Regresiones/reg_cp_100.Rda")
```


# Total promedio, criterio dos

```{r}
reg_cd_100 <- regre_completa(100)
#reg_cd_100p <- regre_completa(100)
```


```{r}
saveRDS(reg_cd_100, file="Datos/Regresiones/reg_cd_100.Rda")
```

# Total individual, criterio principal


```{r}
reg_cp_98100<-regre_completa_ind(98,100)
```

```{r}
saveRDS(reg_cp_94100, file="Datos/Regresiones/reg_cp100_94.Rda")
saveRDS(reg_cp_96100, file="Datos/Regresiones/reg_cp100_96.Rda")
saveRDS(reg_cp_98100, file="Datos/Regresiones/reg_cp100_98.Rda")
```


```{r}
#obtener los datoso de los modelos ya establecidos 
z<-reg_cp_96$regresionhpr[1][[1]]
class(z)

augment(z)

```

# Total individual, criterio Dos


```{r}
reg_cd_94100<-regre_completa_ind(100)

reg_cd_96100<-regre_completa_ind(96,100)

reg_cd_98100<-regre_completa_ind(100)


```

```{r}
saveRDS(reg_cd_94100, file="Datos/Regresiones/reg_cd100_94.Rda")
saveRDS(reg_cd_96100, file="Datos/Regresiones/reg_cd100_96.Rda")
saveRDS(reg_cd_98100, file="Datos/Regresiones/reg_cd100_98.Rda")
```


```{r}
#obtener los datoso de los modelos ya establecidos 
z<-reg_cp_96$regresionhpr[1][[1]]
class(z)

augment(z)

```


# Region promedio,  criterio principal



```{r}

reg_reg1_cp_100<-regre_region(1,100)
reg_reg2_cp_100<-regre_region(2,100)
reg_reg3_cp_100<-regre_region(3,100)
reg_reg4_cp_100<-regre_region(4,100)

#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])


```



```{r}
saveRDS(reg_reg1_cp_100, file="Datos/Regresiones/reg_reg1_cp_100.Rda")
saveRDS(reg_reg2_cp_100, file="Datos/Regresiones/reg_reg2_cp_100.Rda")
saveRDS(reg_reg3_cp_100, file="Datos/Regresiones/reg_reg3_cp_100.Rda")
saveRDS(reg_reg4_cp_100, file="Datos/Regresiones/reg_reg4_cp_100.Rda")

```



```{r}
df<-reg_reg1_cp_100
r1<- data.frame(Norte=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )

df<-reg_reg2_cp_100
r2<- data.frame(Norte_Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )
df<-reg_reg3_cp_100
r3<- data.frame(Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


df<-reg_reg4_cp_100
r4<- data.frame(Sur=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


regionesdf<-bind_cols(r1,r2,r3,r4)

stargazer(regionesdf,summary = FALSE, digits = 3)

```

# Region promedio,  criterio dos



```{r}

reg_reg1_cdp_100<-regre_region(1,100)
reg_reg2_cdp_100<-regre_region(2,100)
reg_reg3_cdp_100<-regre_region(3,100)
reg_reg4_cdp_100<-regre_region(4,100)

#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])


```



```{r}
saveRDS(reg_reg1_cdp_100, file="Datos/Regresiones/reg_reg1_cdp_100.Rda")
saveRDS(reg_reg2_cdp_100, file="Datos/Regresiones/reg_reg2_cdp_100.Rda")
saveRDS(reg_reg3_cdp_100, file="Datos/Regresiones/reg_reg3_cdp_100.Rda")
saveRDS(reg_reg4_cdp_100, file="Datos/Regresiones/reg_reg4_cdp_100.Rda")

```



```{r}
df<-reg_reg1_cd_100
r1<- data.frame(Norte=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )

df<-reg_reg2_cd_100
r2<- data.frame(Norte_Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )
df<-reg_reg3_cd_100
r3<- data.frame(Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


df<-reg_reg4_cd_100
r4<- data.frame(Sur=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


regionesdf<-bind_cols(r1,r2,r3,r4)

stargazer(regionesdf,summary = FALSE, digits = 3)

```

# Region individual,  criterio principal



```{r}

reg_reg1_cp_98100<-regre_region_ind(1,98,100)
reg_reg2_cp_98100<-regre_region_ind(2,98,100)
reg_reg3_cp_98100<-regre_region_ind(3,98,100)
reg_reg4_cp_98100<-regre_region_ind(4,98,100)

#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])


```



```{r}
saveRDS(reg_reg1_cp_98100, file="Datos/Regresiones/reg_reg1_cp_98100.Rda")
saveRDS(reg_reg2_cp_98100, file="Datos/Regresiones/reg_reg2_cp_98100.Rda")
saveRDS(reg_reg3_cp_98100, file="Datos/Regresiones/reg_reg3_cp_98100.Rda")
saveRDS(reg_reg4_cp_98100, file="Datos/Regresiones/reg_reg4_cp_98100.Rda")

```



```{r}
df<-reg_reg1_cp_98100
r1<- data.frame(Norte=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )

df<-reg_reg2_cp_98100
r2<- data.frame(Norte_Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )
df<-reg_reg3_cp_98100
r3<- data.frame(Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


df<-reg_reg4_cp_98100
r4<- data.frame(Sur=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


regionesdf<-bind_cols(r1,r2,r3,r4)

stargazer(regionesdf,summary = FALSE, digits = 3)

```


```{r}
resul %>% 
  ggplot(aes(x=beta)) +
    geom_histogram(aes(y=..density.. ), bins=25, alpha=0.6,color="blue")+
  geom_density() + theme_gray()
```



# Region individual,  criterio dos



```{r}

reg_reg1_cd_98100<-regre_region_ind(1,100)
reg_reg2_cd_98100<-regre_region_ind(2,100)
reg_reg3_cd_98100<-regre_region_ind(3,100)
reg_reg4_cd_98100<-regre_region_ind(4,100)

#summary(reg_reg1_combinada_sexo_cri3_r1617$regresion[[10]])# regresionp[[10]])


```



```{r}
saveRDS(reg_reg1_cd_98100, file="Datos/Regresiones/reg_reg1_cd_98100.Rda")
saveRDS(reg_reg2_cd_98100, file="Datos/Regresiones/reg_reg2_cd_98100.Rda")
saveRDS(reg_reg3_cd_98100, file="Datos/Regresiones/reg_reg3_cd_98100.Rda")
saveRDS(reg_reg4_cd_98100, file="Datos/Regresiones/reg_reg4_cd_98100.Rda")

```


```{r}
df<-reg_reg1_cd_98100
r1<- data.frame(Norte=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )

df<-reg_reg2_cd_98100
r2<- data.frame(Norte_Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )
df<-reg_reg3_cd_98100
r3<- data.frame(Centro=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


df<-reg_reg4_cd_98100
r4<- data.frame(Sur=c("",
                        substr(as.character(mean(df$beta)),1,5),
                        paste("(",substr(as.character(sd(df$beta)),1,5),")",sep="",collapse= ","),
                        "",
                        substr(as.character(mean(df$betar)),1,5),
                        paste("(",substr(as.character(sd(df$betar)),1,5),")",sep="",collapse = ","),
                        "",
                        substr(as.character(mean(df$alfar)),1,6),
                        paste("(",substr(as.character(sd(df$alfar)),1,5),")",sep="",collapse = ","),
                        ""
                        ), 
                row.names=c(" ",
                            "beta_r",
                            "sd br",
                            "  ",
                            "hat rho_r$",
                            "sd rho",
                            "   ",
                            "alpha_r",
                            "sd alphar",
                            "    "
                           )
                )


regionesdf<-bind_cols(r1,r2,r3,r4)

stargazer(regionesdf,summary = FALSE, digits = 3)

```

#Graficas

```{r}
a<- readRDS("Datos/Regresiones/reg_reg1_cp_98100.Rda")

```

#grafica rango rango  regional

```{r}
graficarr<-function(df,a = "", region= "")
{
  la<-df%>%pull(regresionhpr)

  la1<-lapply(la[1:length(la)],function(x) x$model)

  la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )

  la2<-bind_rows(la1[1:length(la1)])
  print(nrow(la2))
  print(sum(is.na(la2)))
  
  la2$pling_p<-la2%>%pull(pling_p)%>%as.integer()

  la3<-la2%>%group_by(pling_p)%>%summarise(pling_pm=mean(pling_h))
  
  alfar <-  mean(df$alfar)
  betar <- mean(df$betar)
  
  ggplot(la3,aes(x=pling_p, y = pling_pm)) + 
        geom_point(col="gray")+ 
        labs(title = paste(region," ",a),
             subtitle =  "Rango percentil intergeneracional") +
        xlab("Rango pasado") + 
        ylab("Rango actual") +
        xlim(0,100) + 
        ylim(0,100) +
        theme_bw() +
        theme(plot.title = element_text(size = 20),
            plot.subtitle = element_text(size = 15),
            axis.title = element_text(size = 15),
            axis.text = element_text(size = 10),
            legend.position="top",
            legend.title = element_text(size=8, face="bold"),
            legend.text = element_text(size=6, face="bold")) +
        geom_function(fun = function(x)+ alfar + betar*x,
                      colour = "red") +
        annotate("text",
                 x=70,
                 y=25,
                 label = paste(substr(as.character(alfar),1,6),
                               " + ",substr(as.character(betar),1,5)
                               ,"x"),
                 colour = "red",
                 size = 5)
                 
                 #parse = TRUE)
}

graficarr(a, "2017 - 1998", "Región Norte:")

```

## grafica rango rango

```{r}
graficarr_promedio<-function(df,region= "",a = "")
{
  la<-df%>%pull(regresionr)

  la1<-lapply(la[1:length(la)],function(x) x$model)

  la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )

  la2<-bind_rows(la1[1:length(la1)])
  #print(nrow(la2))
  #print(sum(is.na(la2)))
  
  la3<-la2%>%mutate(cohortp = cut(pling_p,breaks=seq(0,100,2)))
  
  la3<-la3%>%group_by(cohortp)%>%summarise(pling_pm=mean(pling_p),
                                           pling_hm=mean(pling_h))
  
  alfar <-  mean(df$alfar)
  betar <- mean(df$betar)
  
  g<-ggplot(la3,aes(x=pling_pm, y = pling_hm)) + 
        geom_point(col="gray")+ 
        labs(title = paste(region," ",a))+
            # subtitle =  "Rango percentil intergeneracional") +
        xlab("Rango percentil pseudo PP") + 
        ylab("Rango percentil actual") +
        xlim(0,100) + 
        ylim(0,100) +
       # theme_classic() +
        theme(
            plot.title = element_text(size = 15),
            plot.subtitle = element_text(size = 15),
            axis.title = element_text(size = 15),
            axis.text = element_text(size = 15),
            legend.position="top",
            legend.title = element_text(size=8, face="bold"),
            legend.text = element_text(size=6, face="bold")) +
        
    geom_function(fun = function(x)+ alfar + betar*x,
                      colour = "red") +
    
        geom_function(fun = function(x) x,colour = "blue")+
        
      theme_update(plot.title = element_text(hjust = 0.5))#+
        # annotate("text",
        #          x=70,
        #          y=25,
        #          label = paste("R(y^a)=",substr(as.character(alfar),1,6),
        #                        " + ",substr(as.character(betar),1,5)
        #                        ,"R(y^p)"),
        #          colour = "red",
        #          size = 5, parser=TRUE)
        #          
                 #parse = TRUE)
  return(g)
}

r1<-graficarr_promedio(reg_reg1_cd_100,"Norte (región 1):","Relación de estimaciones de rango percentil")
r2<-graficarr_promedio(reg_reg2_cd_100,"Centro-Norte (región 2):","Relación de estimaciones de rango percentil")
r3<-graficarr_promedio(reg_reg3_cd_100,"Centro (región 3):","Relación de estimaciones de rango percentil")
r4<-graficarr_promedio(reg_reg4_cd_100,"Sur (región 4):","Relación de estimaciones de rango percentil")

r4

```

# regiones conjuntas



```{r}
 alfar1 <-  mean(reg_reg1_cd_100$alfar)
  betar1 <- mean(reg_reg1_cd_100$betar)
  
   alfar2 <-  mean(reg_reg2_cd_100$alfar)
  betar2 <- mean(reg_reg2_cd_100$betar)
  
   alfar3 <-  mean(reg_reg3_cd_100$alfar)
  betar3 <- mean(reg_reg3_cd_100$betar)
  
   alfar4 <-  mean(reg_reg4_cd_100$alfar)
  betar4 <- mean(reg_reg4_cd_100$betar)
  
  ggplot() + 
         
        labs(title = "AIR estimado por regiones")+
          
        xlab("Rango percentil pseudo PP") + 
        ylab("Rango percentil actual") +
        xlim(0,100) + 
        ylim(0,100) +
       # theme_classic() +
        theme(
            plot.title = element_text(size = 15),
            plot.subtitle = element_text(size = 15),
            axis.title = element_text(size = 15),
            axis.text = element_text(size = 15),
            legend.position="top",
            legend.title = element_text(size=8, face="bold"),
            legend.text = element_text(size=6, face="bold")) +
        
    geom_function(fun = function(x)+ alfar1 + betar1*x,aes(linetype="1"),
                      colour = "red", alpha=1) +
  
    geom_function(fun = function(x)+ alfar2 + betar2*x,aes(linetype="2"),
                      colour = "red", alpha=0.8) +
    geom_function(fun = function(x)+ alfar3 + betar3*x,aes(linetype="5"),
                      colour = "red", alpha=0.6) +   
    geom_function(fun = function(x)+ alfar4 + betar4*x,aes(linetype="6"),
                      colour = "red", alpha=1) +
scale_linetype_discrete("Región", breaks=c("1", "2","5", "6"), labels=c("Norte", "Centro-Norte","Centro","Sur"))+
  
            geom_function(fun = function(x) x,colour = "blue")+
    

        
      theme_update(plot.title = element_text(hjust = 0.5))#+
```


# Matriz de transición


```{r}
transicion<-function(df,region= "",a = "")
{
  la<-df%>%pull(regresionr)

  la1<-lapply(la[1:length(la)],function(x) x$model)

  la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )

  la2<-bind_rows(la1[1:length(la1)])
  #print(nrow(la2))
  #print(sum(is.na(la2)))
  
  la3<-la2%>%mutate(cohortp = cut(pling_p,breaks=seq(0,100,20),labels=c("Q1","Q2","Q3","Q4","Q5")),
                    cohorth = cut(pling_h,breaks=seq(0,100,20),labels=c("q1","q2","q3","q4","q5")))
  
  la3<-la3%>%group_by(cohortp,cohorth)%>%
  count()%>%ungroup()
  
  la3<-la3%>%group_by(cohortp)%>% 
    summarise(p1=n*100/sum(n),
              cohortp=factor(cohortp, levels=c("Q1","Q2","Q3","Q4","Q5")),
              cohorth=factor(cohorth, levels=c("q5","q4","q3","q2","q1")))%>%ungroup()
   
  la3<-la3%>%arrange(cohortp)#,decreasing = TRUE)
   
  #print(la3%>%group_by(cohortp)%>%summarise(sum(p1)))
  
   la3<-la3%>% pivot_wider(names_from = cohorth, values_from = p1)
return(la3)
  
}

tranreg1<-transicion(reg_cd_100)
#row.names(tranreg1)<-tranreg1$cohortp

stargazer(as.data.frame(tranreg1),rownames= FALSE, summary = FALSE, digits= 1)
```

```{r}
# bar_order <- list(
#   "(0,20]" = 4,
#   "(20,40]" = 3,
#   "(40,60]" = 2,
#   "(60,80]" = 1,
#   "(80,100]" = 0
# )
# bar_order_v <- as.numeric(bar_order)

tranreg1 %>% 
  hchart(
    'column', hcaes(x = cohortp, y = p1, group = cohorth),
    stacking = "normal"
  #  explicit_order = c("q5","q2","q3","q4","q1")
    ) 
```




```{r}
ggsave("region1rr.png", plot = r1, path = "imagenes/")
ggsave("region2rr.png", plot = r2, path = "imagenes/")
ggsave("region3rr.png", plot = r3, path = "imagenes/")
ggsave("region4rr.png", plot = r4, path = "imagenes/")



```

# Graficas EII

```{r}
graficareii_promedio<-function(df,nl=FALSE,region= "",a = "")
{
  
  
  la<-df%>%pull(regresion)

  la1<-lapply(la[1:length(la)],function(x) x$model)

  la1<-lapply(la1[1:length(la1)],function(x){row.names(x)<-NULL; x } )

  la2<-bind_rows(la1[1:length(la1)])
  #print(nrow(la2))
  #print(sum(is.na(la2)))
  
  la2<-la2%>%drop_na()
  
  if(nl==TRUE){
  la2<-la2%>%mutate(ingh = exp(ling_h),
                     ingp = exp(ling_p)
                     )%>%select(ingh,ingp)
  #head(la2)
   la2<-la2%>%mutate(peringp=rank(ingp)/length(ingp))
   la2<-la2%>%mutate(cohop = cut(peringp, breaks = seq(0,1,0.01), include.lowest=TRUE))
   la3<-la2%>%group_by(cohop)%>%summarise(ing_hm=mean(ingh),
                                          ing_pm=mean(ingp))
   
  }else{
  la2<-la2%>%mutate(peringp=rank(ling_p)/length(ling_p))
  la2 <-la2 %>% mutate(cohop = cut(peringp, breaks = seq(0,1,0.02), include.lowest=TRUE))
  la3<-la2%>%group_by(cohop)%>%summarise(ing_hm=mean(ling_h),
                                         ing_pm=mean(ling_p))
  }
  
  
  
   
  
  beta0 <-  mean(df$beta0)
  beta <- mean(df$beta)
  sex<- mean(df$sexoh)
  
  g<-ggplot(la3,aes(x=ing_pm, y = ing_hm)) + 
        geom_point(col="gray")+ 
        labs(title = paste(region," ",a))+
            # subtitle =  "Estimación  lineal") +
        xlab("Log ingreso pseudo PP") + 
        #xlab("Ingreso pseudo PP") +
        
        ylab("Log ingreso actual") +
        #ylab("Ingreso actual") +
        
         #xlim(0,max(la3$ing_pm)) + 
         #ylim(0,max(la3$ing_hm)) +    
        
        xlim(min(la3$ing_pm),max(la3$ing_pm)) + 
        ylim(6,max(la3$ing_hm)) +
        
        theme_classic() +
        theme(
            plot.title = element_text(size = 15),
            plot.subtitle = element_text(size = 10),
            axis.title = element_text(size = 15),
            axis.text = element_text(size = 10),
            legend.position="top",
            legend.title = element_text(size=8, face="bold"),
            legend.text = element_text(size=6, face="bold")) +
        
    geom_function(fun = function(x) beta0 + x*beta, colour = "red") +
    #geom_function(fun = function(x) exp(beta0) * x**beta, colour = "red") +
    
    geom_function(fun = function(x) beta0 + x*beta + sex, colour = "orange")+
    #geom_function(fun = function(x) exp(beta0) * x**beta *exp(sex), colour = "orange")+
    
    #geom_function(fun = function(x) x,colour = "blue")+
        theme_update(plot.title = element_text(hjust = 0.5))
        # annotate("text",
        #          x=70,
        #          y=25,
        #          label = paste(substr(as.character(alfar),1,6),
        #                        " + ",substr(as.character(betar),1,5)
        #                        ,"x"),
        #          colour = "red",
        #          size = 5)
        #          
                 #parse = TRUE)
}

#graficareii_promedio<-function(df,nl=FALSE,a = "", region= "")
#region<-"Relación de estimaciones: ingresos actuales vs ingresos pseudo PP"

a<-"log ingresos actuales vs log ingresos pseudo PP"
r1<-graficareii_promedio(reg_reg1_cd_100,nl=FALSE,"Norte (region 1):",a)
r2<-graficareii_promedio(reg_reg2_cd_100,nl=FALSE,"Centro-Norte (region 2):",a)
#r3<-graficareii_promedio(reg_reg3_cd_100,nl=FALSE,"Centro (region 3):",a)
#r4<-graficareii_promedio(reg_reg4_cd_100,nl=FALSE,"Sur (region 4):",a)


r1
r2
r3
r4
```

```{r}
ggsave("region1eeil.png", plot = r1, path = "imagenes/")
ggsave("region2eeil.png", plot = r2, path = "imagenes/")
ggsave("region3eeil.png", plot = r3, path = "imagenes/")
ggsave("region4eeil.png", plot = r4, path = "imagenes/")
```











```{r}
graficarr_promedio(reg_cp_500,"Actual-Pasado promedio","México, Base A:")
```


# Estad des  A

```{r}
resu<-dfh_c%>%summarise(Sexo_Masculino = filter(dfh_c,sexo_h==1)%>%count()%>%pull(),
                  Sexo_Femenino = filter(dfh_c,sexo_h==2)%>%count()%>%pull(),
                 Promedio_Edad_Entrevistado = mean(edad_h),
                 Sexo_Masculino_PP = filter(dfh_c,pp_sexo==1)%>%count()%>%pull(),
                 Sexo_Femenino_PP = filter(dfh_c,pp_sexo==2)%>%count()%>%pull(),
                 Edad_Mínima_PP = min(pp_edad),
                 Edad_Máxima_PP = max(pp_edad),
                 Promedio_Edad_PP_2017 = mean(pp_edad)+22)


stargazer(resu,summary = FALSE, digits = 1, flip = TRUE)
```





# Esta des B 

```{r}
resu<-dfh_c%>%summarise(Sexo_Masculino = filter(dfh_c,sexo_h==1)%>%count()%>%pull(),
                  Sexo_Femenino = filter(dfh_c,sexo_h==2)%>%count()%>%pull(),
                 Promedio_Edad_Entrevistado = mean(edad_h),
                 Sexo_Masculino_PP = filter(dfh_c,pp_sexo==1)%>%count()%>%pull(),
                 Sexo_Femenino_PP = filter(dfh_c,pp_sexo==2)%>%count()%>%pull(),
                 Edad_Mínima_PP = min(pp_edad),
                 Edad_Máxima_PP = max(pp_edad),
                 Promedio_Edad_PP_2017 = mean(pp_edad)+22)


stargazer(resu,summary = FALSE, digits = 1, flip = TRUE)
```
# Esta des bases ENIGH

```{r}
resuen<- data.frame()%>%summarise(
  Tamano_de_muestra = c(nrow(enigh_94),nrow(enigh_96),nrow(enigh_98)),
  Hombres = c(nrow(enigh_94%>%filter(sexo==1)),nrow(enigh_96%>%filter(sexo==1)),
              nrow(enigh_98%>%filter(sexo==1))),
  Mujeres = c(nrow(enigh_94%>%filter(sexo==2)),nrow(enigh_96%>%filter(sexo==2)),
              nrow(enigh_98%>%filter(sexo==2))),
  Promedio_Ingresos = c(enigh_94%>%select(ing_men_p)%>%pull()%>%mean()*IN,
                        enigh_96%>%select(ing_men_p)%>%pull()%>%mean()*IN,
                        enigh_98%>%select(ing_men_p)%>%pull()%>%mean()*IN),
  Promedio_logIngresos = log(Promedio_Ingresos)
  )
row.names(resuen) = c("E94","E96","E98")
resuen

stargazer(resuen,summary = FALSE)
```

## promedio de ingreso por edad


```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
ingreso_edad<-function(df){
df %>%
  group_by(edad)%>%
    arrange(edad)%>%
  summarise(
   pro_ing = mean(ing_men_p)*IN
  )%>%
hchart( "scatter", 
        hcaes(x = edad, y = pro_ing),
        name="Ingreso promedio",
        regression = TRUE,
          regressionSettings = list(
    type = "polynomial",
    dashStyle = "ShortDash",
    color = "skyblue",
    order = 2,
    lineWidth = 5,
    name = "%eq | $r^2$: %r",
    hideInLegend = FALSE)
        )%>%
  hc_add_dependency("plugins/highcharts-regression.js")%>%
  hc_yAxis(title = list(text = "Promedio de ingreso por edad"))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Promedio de ingreso por edad E98"
  ) %>%
  hc_xAxis(title=list(text="Edad")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
}
ingreso_edad(enigh_98)
```

## histo ingreso


```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
histo_ingreso<-function(df){
a<-sort(df$ling_men_pd)
#a<-a[1:(length(a)-5)]
#a<-a[5:(length(a))]

hchart(a, name= "Frecuencia")%>%
  hc_yAxis(title = list(text ="Número de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Distribución de ingreso E98"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
}
histo_ingreso(enigh_98)
```

## revisión rango percentil

```{r}
d<- enigh_98p$ling_men_pd
rd<-rank(d)
nd<-length(d)
rangop<- rd/nd
min(rangop)
max(rangop)

d1<- ingresos_e98$ling_men_p
d1

d2<- ingresos_e98$ling_men_pd
d2

dfe<-data.frame(d,d1,d2)

tail(dfe)

aux_prr<-function(ing,num_base){
    error_e<- abs(base_logsing[[num_base]]$ling_men_pd - ing)
    #print(error_e)
    indice_e<-match(min(error_e),error_e)
    print(indice_e)
    indice_e<-indice_e[1]
    return(base_rp[[num_base]][indice_e]*100)
  }
aux_prr(7,3)

base_logsing[[3]]$ling_men_pd[4167]

f<-base_logsing[[3]]$ling_men_pd - 7
f[4167]

dfe[4167,]
d[4167]
rangop[4167]
```
## densidad ingre selecionados

```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(enigh_94$ling_men_pd)
a2<-sort(enigh_96$ling_men_pd)
a3<-sort(enigh_98$ling_men_pd)

hchart(
  density(a1), 
  type = "area", 
  name = "E94"
  )%>%
  hc_add_series(
    density(a2), type = "area",
    name = "E96"
    )%>%
  hc_add_series(
    density(a3), type = "area",
    name = "E98"
    )%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Densidad de ingreso E94, E96, y E98"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
```

## densidad ingre total viejas

```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(ingresos_e94$ling_men_pd)
a2<-sort(ingresos_e96$ling_men_pd)
a3<-sort(ingresos_e98$ling_men_pd)

hchart(
  density(a1), 
  type = "area", 
  name = "E94"
  )%>%
  hc_add_series(
    density(a2), type = "area",
    name = "E96"
    )%>%
  hc_add_series(
    density(a3), type = "area",
    name = "E98"
    )%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Densidad de ingreso E94, E96, y E98"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
```
## densidad ingre total viejas

```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(ingresos_e16a$ling_men_pd)
a2<-sort(ingresos_e18a$ling_men_pd)

hchart(
  density(a1), 
  type = "area", 
  name = "E16"
  )%>%
  hc_add_series(
    density(a2), type = "area",
    name = "E18"
    )%>%

hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Densidad de ingreso E16 y E18"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
```

## densidad ingre conjuay total viejas

```{r}
ingre1618_gra<-bind_rows(ingresos_e16a,ingresos_e18a)
nrow(ingre1618_gra)

ingre949698_gra<-bind_rows(ingresos_e94,ingresos_e96,ingresos_e98)
```



```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a1<-sort(ingre1618_gra$ling_men_pd)

hchart(
  density(a1), 
  type = "area", 
  name = "E16-E18"
  )%>%

hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
    text = "Densidad de ingreso union E16-E18"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
```

## Total viejeas y nuevas 

```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
a2<-exp(ingre1618_gra%>%select(ling_men_pd)%>%pull())
a1<-exp(ingre949698_gra%>%select(ling_men_pd)%>%pull())
x <- hist(a1, plot = FALSE)
y <- hist(a2, plot = FALSE)

a2p<-percent_rank(a2)
a1p<-percent_rank(a1)
#a1<-percent_rank(a1)*100
#a2<-percent_rank(a2)*100
hchart(
   a2/length(a2),
  breaks = 5000,
  #type = "area", 
  name = "E94-E96-E98"
  )%>%

hc_add_series(
  density(a1), 
  breaks = 20,
 #type = "area", 
  name = "E16-E18"
  )%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
hc_title(
    text = "Densidad de ingreso union, E94-E96-E98, E16-E18"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)"),#)%>%
          min = 0,
           max = 30000) %>%
  hc_xAxis(plotLines = list(list(value=median(a1),color="red",width=2 ),
                            list(value = median(a2),color="blue",width=2)))%>%
  #hc_xAxis(plotLines = list(list(value=median(a2),color = "red" )))%>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
```

## total hisotgrama completo

```{r}
a2<-ingre1618_gra%>%select(ling_men_pd)%>%pull()
a1<-ingre949698_gra%>%select(ling_men_pd)%>%pull()

dats <- rbind(data.frame(pred = a1, Encuesta = 'E94-E96-E98'),
              data.frame(pred = a2, Encuesta = 'E16-E18'))

# here the plot
ggplot(dats, aes(pred, fill = Encuesta)) + 
  #geom_density(alpha = 0.5, position = "identity", bins = 60)+
  geom_density(alpha = 0.5, position = "identity")+
    #xlim(0,30000)+
    geom_vline(xintercept=median(a1),color = "blue") +
  geom_vline(xintercept=median(a2),color = "red")+
  labs(title = "Densidad de ingreso")+
          
        xlab("Logaritmo Ingresos")  +
        ylab("Densidad") 





```



## reginales nuevas y viejas

```{r}
highchart(type = "chart")%>%
   hc_yAxis_multiples(
     create_yaxis(4, height = c(5,5,5,5), sep=0.1, offset=0.5, turnopposite = FALSE,
                  title = list(text = rep("Número de casos confirmados",4)))
     
     ) %>%
  
  hc_add_series(name="región 1 E94-E96-E98",density(ingre949698_gra%>%
                                filter(region==1)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=0)%>%

  hc_add_series(name="región 1 E16-E18",density(ingre1618_gra%>%
                                filter(region==1)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=0)%>%
  hc_add_series(name="región 2 E94-E96-E98",density(ingre949698_gra%>%
                                filter(region==2)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=1)%>%
  hc_add_series(name="región 2 E16-E18",density(ingre1618_gra%>%
                                filter(region==2)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=1)%>%
  hc_add_series(name="región 3 E94-E96-E98",density(ingre949698_gra%>%
                                filter(region==3)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=2)%>%
  hc_add_series(name="región 3 E16-E18",density(ingre1618_gra%>%
                                filter(region==3)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=2)%>%
  hc_add_series(name="región 4 E94-E96-E98",density(ingre949698_gra%>%
                                filter(region==4)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=3)%>%
  hc_add_series(name="región 4 E16-E18",density(ingre1618_gra%>%
                                filter(region==4)%>%
                                select(ling_men_pd)%>%
                                pull()),
                yAxis=3)%>%
  
#hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
  
hc_add_theme(hc_theme_ggplot2())%>%
  hc_title(text = "Densidad por región E94-E96-E98, E16-E18 ")%>%
           #margin = 20, align = "left",
      #style = list(color = "#90ed7d", useHTML = TRUE)) %>% 
 # hc_subtitle(text = "Utilice la herramienta de zoom en la
 #             parte inferior")%>%
 # hc_tooltip(crosshairs = TRUE, backgroundColor = "#FCFFC5",
  #           shared = TRUE, borderWidth = 5) %>% 
#hc_yAxis(title = list(text = rep("Número de casos confirmados",4))%>%
    hc_exporting(enabled = TRUE,
      filename = "datos",
      buttons = list(contextButton = list(menuItems = myMenuItems)))
```





# densidad por región

```{r}

den_region<-function(df){
a1<-df%>%filter(region==1)%>%select(ling_men_pd)%>%pull()
a2<-df%>%filter(region==2)%>%select(ling_men_pd)%>%pull()
a3<-df%>%filter(region==3)%>%select(ling_men_pd)%>%pull()
a4<- df%>%filter(region==4)%>%select(ling_men_pd)%>%pull()

hchart(
  density(a1), 
  type = "area", 
  name = "región 1"
  )%>%
  hc_add_series(
    density(a2), type = "area",
    name = "región 2"
    )%>%
  hc_add_series(
    density(a3), type = "area",
    name = "región 3"
    )%>%
  hc_add_series(
    density(a4), type = "area",
    name = "región 4"
    )%>%
hc_yAxis(title = list(text ="Proporción de seleccionados" ))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Densidad de ingreso por región, E16-E18"
  ) %>%
  hc_xAxis(title=list(text="Logaritmo del Ingreso (deflactado)")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
}


den_region(ingre1618_gra)
```

# edad region ingreso

```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
ingreso_edad_region<-function(df){
df %>%
  group_by(region,edad)%>%
  summarise(
   pro_ing = mean(ing_men_p)*IN
  )%>%
hchart( "line", 
        hcaes(x = edad, y = pro_ing, group = region ))%>%
  hc_yAxis(title = list(text = "Promedio de ingreso"))%>%
    hc_add_theme(hc_theme_ggplot2())%>%
    hc_title(
    text = "Promedio de ingreso por edad  y región E98") %>%
  hc_xAxis(title=list(text="Edad")) %>%
    hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))
}
ingreso_edad_region(enigh_98)
```




# Mapa regional


```{r}
myMenuItems <- c("downloadPNG", "downloadJPEG", "downloadPDF", 'downloadSVG', 'printChart')
mapdata <- get_data_from_map(download_map_data("countries/mx/mx-all"))

mapdata

mapa<-mapdata %>% 
  select(code = `woe-name`) %>%
  arrange(code)%>%
  mutate(tz = ifelse(code  %in% c("Baja California", "Chihuahua", "Coahuila", "Nuevo León", "Sonora", "Tamaulipas"),"región 1",
ifelse(code  %in% c("Aguascalientes","Baja California Sur", "Colima", "Durango", "Jalisco", "Michoacán", "Nayarit", "San Luis Potosí", "Sinaloa", "Zacatecas"),"región 2",
  ifelse(code  %in% c("Campeche", "Chiapas", "Guerrero", "Oaxaca", "Quintana Roo", "Tabasco", "Veracruz","Yucatán"),"región 4", "región 3")  
                        )  ),
value = as.integer(factor(tz)))

dta_clss <- mapa %>% 
  group_by(tz) %>% 
  summarise(value = unique(value)) %>% 
  arrange(value) %>% 
  rename(name = tz, from = value) %>% 
  mutate(to = from + 1) %>% 
  list_parse()


hcmap("countries/mx/mx-all", data = mapa, value = "value",
      joinBy = c("woe-name","code"), name = "Estado",
      tooltip = list(pointFormat = "{point.name} {point.tz}"))%>%
  hc_title(
    text = "Regiones de México"
  )%>%
  hc_add_theme(hc_theme_ggplot2())%>%
hc_colorAxis(dataClassColor = "category",
             dataClasses=dta_clss)%>%
hc_exporting(enabled = TRUE,
               filename = "datos",
               buttons = list(contextButton = list(menuItems = myMenuItems)))

```


```{r}
data <- tibble(
  country = 
    c("PT", "IE", "GB", "IS",
      
      "NO", "SE", "DK", "DE", "NL", "BE", "LU", "ES", "FR", "PL", "CZ", "AT",
      "CH", "LI", "SK", "HU", "SI", "IT", "SM", "HR", "BA", "YF", "ME", "AL", "MK",
      
      "FI", "EE", "LV", "LT", "BY", "UA", "MD", "RO", "BG", "GR", "TR", "CY",
      
      "RU"),  
  tz = c(rep("UTC", 4), rep("UTC + 1",25), rep("UCT + 2",12), "UTC + 3")
  )

data <- data %>% 
  mutate(value = cumsum(!duplicated(tz)))
```

## individuos region

```{r}
nrow(dfh_c%>%filter(region==4))
```

# caluclo de bcero


```{r}
promedio_beta0<-function(df,i){
return(df$regresion[[i]]$coefficients[3])
}

mean(unlist(lapply(1:100,promedio_beta0,df=reg_cd_100)))
sd(unlist(lapply(1:100,promedio_beta0,df=reg_cd_100)))


```

```{r}
promedio_papas<-function(df,i){
return(df$regresionp98[[i]]$coefficients[27])
}
mean(unlist(lapply(1:10,promedio_papas,df=reg_cd_100)))
sd(unlist(lapply(1:10,promedio_papas,df=reg_cd_100)))

```
```{r}
percentilerank<-function(x){
  rx<-rle(sort(x))
  smaller<-cumsum(c(0, rx$lengths))[seq(length(rx$lengths))]
  larger<-rev(cumsum(c(0, rev(rx$lengths))))[-1]
  rxpr<-smaller/(smaller+larger)
  rxpr[match(x, rx$values)]
}

a<-c(0,2,3,4,5,1,1,1)
rank(a)/length(a)

percentilerank(a)
percent_rank(a)
```



