date: 20-01-2022

1 ETNIA

1.1 Leemos las bases de datos para construir etnia

casen_2006 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2006_c.rds")
casen_2006 <- mutate_if(casen_2006, is.factor, as.character)
casen_2009 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2009_c.rds")
casen_2009 <- mutate_if(casen_2009, is.factor, as.character)
casen_2011 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2011_c.rds")
casen_2011 <- mutate_if(casen_2011, is.factor, as.character)
casen_2013 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2013_c.rds")
casen_2013 <- mutate_if(casen_2013, is.factor, as.character)
casen_2015 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2015_c.rds")
casen_2015 <- mutate_if(casen_2015, is.factor, as.character)
casen_2017 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2017_c.rds")
casen_2017 <- mutate_if(casen_2017, is.factor, as.character)
casen_2020 <- readRDS(file = "C:/Users/chris/Desktop/archivos grandes/casen_2020_c.rds")
casen_2020 <- mutate_if(casen_2020, is.factor, as.character)

1.2 Extraccion

Extraemos un subset solo con los campos que nos interesan:

casen_2006 <- casen_2006[,c("EXPC", "COMUNA","Z","T4","SEXO","E1")]
casen_2009 <- casen_2009[,c("EXPC", "COMUNA","ZONA","T5","SEXO","E1")]
casen_2011 <- casen_2011[,c("expc_full", "comuna","zona","r6","sexo","e1","r2p_cod")]
casen_2013 <- casen_2013[,c("expc", "comuna","zona","r6","sexo","e1","r2_p_cod")]
casen_2015 <- casen_2015[,c("expc_todas", "comuna","zona","r3","sexo","e1","r2espp_cod")]
casen_2017 <- casen_2017[,c("expc", "comuna","zona","r3","sexo","e1","r2_p_cod")]
casen_2020 <- casen_2020[,c("expc", "comuna","zona","r3","sexo","e1","r2_pais_esp")]

Rapa Nui == Pascuense

# 2006 x
unique(casen_2006$T4)

##  [1] "No pertenece a ningún pueblo indígena"
##  [2] "Mapuche"                              
##  [3] "Kawaskar"                             
##  [4] "Atacameño"                            
##  [5] "Diaguita"                             
##  [6] "Aymara"                               
##  [7] "Sin dato"                             
##  [8] "Quechua"                              
##  [9] "Yagan"                                
## [10] "Coya"                                 
## [11] "Rapa Nui"

# 2009
unique(casen_2009$T5)

##  [1] "No pertenece a ningún pueblo indígena"
##  [2] "Mapuche"                              
##  [3] "Aymara"                               
##  [4] "Quechua"                              
##  [5] "Diaguita"                             
##  [6] "Kawésqar"                             
##  [7] "Rapa Nui"                             
##  [8] "Atacameño"                            
##  [9] "Coya"                                 
## [10] "Yagán"

# 2011
unique(casen_2011$r6)

##  [1] "No pertenece a ningún pueblo indígena"
##  [2] "Mapuche"                              
##  [3] "Diaguita"                             
##  [4] "Coya"                                 
##  [5] "Quechua"                              
##  [6] "Yagán (Yámana)"                       
##  [7] "Aymara"                               
##  [8] "Rapa Nui (Pascuense)"                 
##  [9] "Atacameño (Likán Antai)"              
## [10] "Kawésqar (Alacalufes)"

# 2013
unique(casen_2013$r6)

##  [1] "No pertenece a ningún pueblo indígena"
##  [2] "Mapuche"                              
##  [3] "Aymara"                               
##  [4] "NS/NR"                                
##  [5] "Diaguita"                             
##  [6] "Quechua"                              
##  [7] "Atacameño (Likán Antai)"              
##  [8] "Coya"                                 
##  [9] "Rapa Nui (Pascuense)"                 
## [10] "Kawésqar (Alacalufes)"                
## [11] "Yagán (Yámana)"

# 2015
unique(casen_2015$r3)

##  [1] "No pertenece a ningún pueblo indígena"
##  [2] "Aimara"                               
##  [3] "Mapuche"                              
##  [4] "Diaguita"                             
##  [5] "Quechua"                              
##  [6] "Atacameño (Likán-Antai)"              
##  [7] "Sin dato"                             
##  [8] "Collas"                               
##  [9] "Kawashkar o Alacalufes"               
## [10] "Yámana o Yagán"                       
## [11] "Rapa-Nui o Pascuenses"

# 2017
unique(casen_2017$r3)

##  [1] "No pertenece a ningún pueblo indígena"
##  [2] "Mapuche"                              
##  [3] "No sabe/no responde"                  
##  [4] "Aimara"                               
##  [5] "Quechua"                              
##  [6] "Diaguita"                             
##  [7] "Kawashkar o Alacalufes"               
##  [8] "Atacameño (Likan-Antai)"              
##  [9] "Rapa-Nui o Pascuenses"                
## [10] "Collas"                               
## [11] "Yámana o Yagán"

# 2020
unique(casen_2020$r3)

##  [1] "Aimara"                                           
##  [2] "No pertenece a ninguno de estos pueblos indígenas"
##  [3] "Mapuche"                                          
##  [4] "Diaguita"                                         
##  [5] "Atacameño (Likan-Antai)"                          
##  [6] "Quechua"                                          
##  [7] "Yámana o Yagán"                                   
##  [8] "Rapa-Nui o Pascuenses"                            
##  [9] "Collas"                                           
## [10] "Kawashkar o Alacalufes"                           
## [11] "Chango"

variable_etnia <- function(dataset){
  
variable <- switch(i,"T4","T5","r6","r6","r3","r3","r3")
  
dataset[,variable][dataset[,variable] == "Aimara" ]  <- "Aymara"
dataset[,variable][dataset[,variable] == "No pertenece a ninguno de estos pueblos indígenas" ]  <-  "No pertenece a ningún pueblo indígena" 
dataset[,variable][dataset[,variable] == "Mapuche"]  <- "Mapuche"
dataset[,variable][dataset[,variable] == "Diaguita"]  <- "Diaguita"
dataset[,variable][dataset[,variable] == "Atacameño (Likan-Antai)" ]  <- "Atacameño"
dataset[,variable][dataset[,variable] == "Atacameño (Likán Antai)" ]  <- "Atacameño"
dataset[,variable][dataset[,variable] == "Atacameño (Likán-Antai)" ]  <- "Atacameño"
dataset[,variable][dataset[,variable] == "Yámana o Yagán" ]  <- "Yagán"
dataset[,variable][dataset[,variable] == "Yagan" ]  <- "Yagán"
dataset[,variable][dataset[,variable] == "Yagán (Yámana)" ]  <- "Yagán"
dataset[,variable][dataset[,variable] == "Rapa-Nui o Pascuenses"]  <- "Pascuense"
dataset[,variable][dataset[,variable] == "Rapa-Nui"]  <- "Pascuense"
dataset[,variable][dataset[,variable] == "Rapa Nui"]  <- "Pascuense"
dataset[,variable][dataset[,variable] == "Rapa Nui (Pascuense)"]  <- "Pascuense"
dataset[,variable][dataset[,variable] == "Collas"]  <- "Coya"
dataset[,variable][dataset[,variable] == "Kawashkar o Alacalufes" ]  <- "Alacalufe"
dataset[,variable][dataset[,variable] == "Kawashkar" ]  <- "Alacalufe"
dataset[,variable][dataset[,variable] == "Kawésqar (Alacalufes)" ]  <- "Alacalufe"
dataset[,variable][dataset[,variable] == "Kawésqar" ]  <- "Alacalufe"
dataset[,variable][dataset[,variable] == "Kawaskar" ]  <- "Alacalufe"
dataset[,variable][dataset[,variable] ==  "Sin dato"]  <- NA
dataset[,variable][dataset[,variable] ==  "NS/NR"   ]  <- NA
dataset[,variable][dataset[,variable] == "No sabe/no responde" ]  <- NA

    switch(i,
        case =  casen_2006 <<- dataset,
        case =  casen_2009 <<- dataset,
        case =  casen_2011 <<- dataset,
        case =  casen_2013 <<- dataset,
        case =  casen_2015 <<- dataset,
        case =  casen_2017 <<- dataset,
        case =  casen_2020 <<- dataset 
)
}

for (i in 1:7) {
  
  switch(i,
        case = casen <- casen_2006,
        case = casen <- casen_2009,
        case = casen <- casen_2011,
        case = casen <- casen_2013,
        case = casen <- casen_2015,
        case = casen <- casen_2017,
        case = casen <- casen_2020
)
  
  variable_etnia(casen)
  
}

Homologación de alfabetismo

casen_2006$E1[casen_2006$E1 == "No sabe /Sin dato"] <- NA
casen_2011$e1[casen_2011$e1 == "Sí, lee y escribe"] <- "Sí"
casen_2011$e1[casen_2011$e1 == "No, sólo lee"] <- "No"
casen_2011$e1[casen_2011$e1 == "No, ninguno"] <- "No"
casen_2011$e1[casen_2011$e1 == "No, sólo escribe"] <- "No"
casen_2013$e1[casen_2013$e1 == "Sí, lee y escribe"] <- "Sí"
casen_2013$e1[casen_2013$e1 == "No, ninguno"] <- "No"
casen_2013$e1[casen_2013$e1 == "No, sólo lee"] <- "No"
casen_2013$e1[casen_2013$e1 == "No, sólo escribe"] <- "No"
casen_2013$e1[casen_2013$e1 == "NS/NR"] <- NA
casen_2015$e1[casen_2015$e1 == "Sí, lee y escribe"] <- "Sí"
casen_2015$e1[casen_2015$e1 == "No, ninguno"] <- "No"
casen_2015$e1[casen_2015$e1 == "No, sólo lee"] <- "No"
casen_2015$e1[casen_2015$e1 == "No, sólo escribe"] <- "No"
casen_2017$e1[casen_2017$e1 == "Sí, lee y escribe"] <- "Sí"
casen_2017$e1[casen_2017$e1 == "No, sólo lee"] <- "No"
casen_2017$e1[casen_2017$e1 == "No, ninguno"] <- "No"
casen_2017$e1[casen_2017$e1 == "No sabe/responde"] <- NA
casen_2017$e1[casen_2017$e1 == "No, sólo escribe"] <- "No"
casen_2020$e1[casen_2020$e1 == 1] <- "Sí"
casen_2020$e1[casen_2020$e1 == 0] <- "No"

Homologación de variable

variable_en_estudio <- function(dataset){
    
variable <- switch(i,"Z","ZONA","zona","zona","zona","zona","zona")       
    
dataset[,variable][dataset[,variable]== "Rural"] <- "Rural"
dataset[,variable][dataset[,variable]== "Urbano"] <-"Urbano"
dataset[,variable][dataset[,variable]== "Urbana"] <-"Urbano"

  switch(i,
        case =  casen_2006 <<- dataset,
        case =  casen_2009 <<- dataset,
        case =  casen_2011 <<- dataset,
        case =  casen_2013 <<- dataset,
        case =  casen_2015 <<- dataset,
        case =  casen_2017 <<- dataset,
        case =  casen_2020 <<- dataset 
)
}

for (i in 1:7) {
  
  switch(i,
        case = casen <- casen_2006,
        case = casen <- casen_2009,
        case = casen <- casen_2011,
        case = casen <- casen_2013,
        case = casen <- casen_2015,
        case = casen <- casen_2017,
        case = casen <- casen_2020
)
 variable_en_estudio(casen)
}

for (i in unique(casen_2020$r2_pais_esp)) {
  pais <- gsub("(^[[:space:]]+|[[:space:]]+$)", "", i)
  pais <- tolower(pais)
  casen_2020$r2_pais_esp[casen_2020$r2_pais_esp == i] <- str_to_title(pais) 
} 

casen_2011$r2p_cod[casen_2011$r2p_cod == "No contesta"] <- "NS/NR"
casen_2013$r2_p_cod[casen_2013$r2_p_cod == "No contesta"] <- "NS/NR"
casen_2015$r2espp_cod[casen_2015$r2espp_cod == "No contesta"] <- "NS/NR"
casen_2017$r2_p_cod[casen_2017$r2_p_cod == "No Bien Especificado"] <- "NS/NR"
casen_2017$r2_p_cod[casen_2017$r2_p_cod == "No Responde"] <- "NS/NR"
casen_2020$r2_pais_esp[casen_2020$r2_pais_esp == "No Bien Especificado"] <- "NS/NR"
casen_2020$r2_pais_esp[casen_2020$r2_pais_esp == ""] <- NA
casen_2020$r2_pais_esp[casen_2020$r2_pais_esp == "No Responde"] <- "NS/NR"

1.2.1 2006

ab <- casen_2006

b <- ab$COMUNA
c <- ab$Z
d <- ab$T4
e <- ab$SEXO
f <- ab$E1

cross_tab =  xtabs(ab$EXPC ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$EXPC ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2006"
      
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2006 <- d
d_2006 <- mutate_if(d_2006, is.factor, as.character)

1.2.2 2009

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2009

b <- ab$COMUNA
c <- ab$ZONA
d <- ab$T5
e <- ab$SEXO
f <- ab$E1

cross_tab =  xtabs(ab$EXPC~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$EXPC ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))

tabla <- as.data.frame(cross_tab)

d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2009"

names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2009 <- d
d_2009 <- mutate_if(d_2009, is.factor, as.character)

1.2.3 2011

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2011

b <- ab$comuna
c <- ab$zona
d <- ab$r6
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc_full ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc_full ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2011"
      
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2011 <- d
d_2011 <- mutate_if(d_2011, is.factor, as.character)

1.2.4 2013

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2013

b <- ab$comuna
c <- ab$zona
d <- ab$r6
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2013"
      
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2013 <- d
d_2013 <- mutate_if(d_2013, is.factor, as.character)

1.2.5 2015

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2015

b <- ab$comuna
c <- ab$zona
d <- ab$r3
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc_todas ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc_todas ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2015"
      
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2015 <- d
d_2015 <- mutate_if(d_2015, is.factor, as.character)

1.2.6 2017

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2017

b <- ab$comuna
c <- ab$zona
d <- ab$r3
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"
      
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2017 <- d
d_2017 <- mutate_if(d_2017, is.factor, as.character)

2 2020

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2020

b <- ab$comuna
c <- ab$zona
d <- ab$r3
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e) + unlist(f) ,aggregate(ab$expc ~  unlist(b) + unlist(c) + unlist(d) + unlist(e)  + unlist(f),ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2020"
      
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2020 <- d

d_2020 <- mutate_if(d_2020, is.factor, as.character)
d_2020$`Sabe leer?`[d_2020$`Sabe leer?` == 0] <- "Sí"
d_2020$`Sabe leer?`[d_2020$`Sabe leer?` == 1] <- "No"

d_2020 <- mutate_if(d_2020, is.factor, as.character)

Unimos y desplegamos la tabla corregida:

3 Tabla final etnia homologada

union_etnia <- rbind(d_2006,d_2009, d_2011, d_2013, d_2015, d_2017)
union_etnia <- mutate_if(union_etnia, is.factor, as.character)
union <-union_etnia

#fn_etnia(union)

cod_com <- readRDS("C:/Users/chris/Desktop/archivos grandes/codigos_comunales_2006-2020.rds") 
names(cod_com)[2] <- "comuna"
tab_f <- merge(x=union_etnia, y=cod_com, by="comuna") 


tab_f$cod_etnia[tab_f$Etnia == "Alacalufe" ]  <- "01"
tab_f$cod_etnia[tab_f$Etnia == "Atacameño" ]  <- "02"
tab_f$cod_etnia[tab_f$Etnia == "Aymara" ]  <- "03"
tab_f$cod_etnia[tab_f$Etnia == "Chango" ]  <- "04"
tab_f$cod_etnia[tab_f$Etnia == "Coya" ]  <- "05"
tab_f$cod_etnia[tab_f$Etnia == "Diaguita" ]  <- "06"
tab_f$cod_etnia[tab_f$Etnia == "Mapuche" ]  <- "07"
tab_f$cod_etnia[tab_f$Etnia == "Pascuense" ]  <- "08"
tab_f$cod_etnia[tab_f$Etnia == "Quechua" ]  <- "09" 
tab_f$cod_etnia[tab_f$Etnia == "Yagán" ]  <- "10"
tab_f$cod_etnia[tab_f$Etnia == "No pertenece a ningún pueblo indígena" ]  <- "11"
tab_f$cod_etnia[tab_f$Etnia == NA ]  <- "12"






tab_f$cod_sexo <- tab_f$Sexo
tab_f$cod_sexo[tab_f$cod_sexo == "Hombre"] <- "01"
tab_f$cod_sexo[tab_f$cod_sexo == "Mujer"] <- "02"

tab_f$cod_alfa <- tab_f$`Sabe leer?`
tab_f$cod_alfa[tab_f$cod_alfa == "Sí"] <- "01"
tab_f$cod_alfa[tab_f$cod_alfa == "No"] <- "02"

tab_f$cod_variable <- tab_f$Zona
tab_f$cod_variable[tab_f$cod_variable == "Rural"    ] <-    '1'
tab_f$cod_variable[tab_f$cod_variable == "Urbano"   ] <-    '2'

datatable(tab_f, extensions = 'Buttons', escape = FALSE, rownames = FALSE,
          options = list(dom = 'Bfrtip',
          buttons = list('colvis', list(extend = 'collection',
          buttons = list(
          list(extend='copy'),
          list(extend='excel',
            filename = 'tabla'),
          list(extend='pdf',
            filename= 'tabla')),
          text = 'Download')), scrollX = TRUE))

writexl::write_xlsx(tab_f, "tabla01_etnia.xlsx")

4 MIGRA

4.0.1 2011

Generamos las tablas de contingencia tal como acostumbramos:

ab <- casen_2011

b <- ab$comuna
c <- ab$z
d <- ab$r2p_cod
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc_full ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc_full ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2011"

names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2011 <- d

4.0.2 2013

ab <- casen_2013

b <- ab$comuna
c <- ab$zona
d <- ab$r2_p_cod
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2013"

names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2013 <- d

4.0.3 2015

 ab <- casen_2015

b <- ab$comuna
c <- ab$zona
d <- ab$r2espp_cod
e <- ab$sexo
f <- ab$e1

 cross_tab =  xtabs(ab$expc_todas ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc_todas ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
 tabla <- as.data.frame(cross_tab)
 d <-tabla[!(tabla$Freq == 0),]
 d$anio <- "2015"

 names(d)[1] <- "comuna"
 names(d)[2] <- "Zona"
 names(d)[3] <- "Origen"
 names(d)[4] <- "Sexo"
 names(d)[5] <- "Sabe leer?"
 names(d)[6] <- "Frecuencia"
 names(d)[7] <- "Año"

 d_2015 <- d

4.0.4 2017

ab <- casen_2017

b <- ab$comuna
c <- ab$zona
d <- ab$r2_p_cod
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc ~  unlist(b) + unlist(c) + unlist(d) + unlist(e) + unlist(f) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"

names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"

d_2017 <- d

4.0.5 2020

ab <- casen_2020

b <- ab$comuna
c <- ab$zona
d <- ab$r2_pais_esp
e <- ab$sexo
f <- ab$e1

cross_tab =  xtabs(ab$expc ~   unlist(b) + unlist(c)  + unlist(d) + unlist(e)  + unlist(f),aggregate(ab$expc ~  unlist(b) + unlist(c) + unlist(d) + unlist(e)  + unlist(f),ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2020"
names(d)[1] <- "comuna"
names(d)[2] <- "Zona"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2020 <- d

Unimos y desplegamos la tabla corregida:

5 Tabla final inmigración homologada

union_etnia <- rbind(d_2011, d_2013, d_2015, d_2017, d_2020)
union_etnia <- mutate_if(union_etnia, is.factor, as.character)
union <-union_etnia

union$cod_sexo <- union$Sexo
union$cod_sexo[union$cod_sexo == "Hombre"] <- "01"
union$cod_sexo[union$cod_sexo == "Mujer"] <- "02"

union$cod_alfa <- union$`Sabe leer?`
union$cod_alfa[union$cod_alfa == "Sí"] <- "01"
union$cod_alfa[union$cod_alfa == "No"] <- "02"

union$cod_variable <- union$Zona
union$cod_variable[union$cod_variable == "Rural"    ] <-    '1'
union$cod_variable[union$cod_variable == "Urbano"   ] <-    '2'

datatable(union, extensions = 'Buttons', escape = FALSE, rownames = FALSE,
          options = list(dom = 'Bfrtip',
          buttons = list('colvis', list(extend = 'collection',
          buttons = list(
          list(extend='copy'),
          list(extend='excel',
            filename = 'tabla'),
          list(extend='pdf',
            filename= 'tabla')),
          text = 'Download')), scrollX = TRUE))

writexl::write_xlsx(tab_f, "tabla01_migra.xlsx")

Casens 2006-2020 Tabla 01

Usted habita un territorio urbano o rural?

VE-CC-AJ

1 ETNIA

1.1 Leemos las bases de datos para construir etnia

1.2 Extraccion

1.2.1 2006

1.2.2 2009

1.2.3 2011

1.2.4 2013

1.2.5 2015

1.2.6 2017

2 2020

3 Tabla final etnia homologada

4 MIGRA

4.0.1 2011

4.0.2 2013

4.0.3 2015

4.0.4 2017

4.0.5 2020

5 Tabla final inmigración homologada