1 ETNIA
casen_2006 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2006_c.rds")
casen_2006 <- mutate_if(casen_2006, is.factor, as.character)
casen_2009 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2009_c.rds")
casen_2009 <- mutate_if(casen_2009, is.factor, as.character)
casen_2011 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2011_c.rds")
casen_2011 <- mutate_if(casen_2011, is.factor, as.character)
casen_2013 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2013_c.rds")
casen_2013 <- mutate_if(casen_2013, is.factor, as.character)
casen_2015 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2015_c.rds")
casen_2015 <- mutate_if(casen_2015, is.factor, as.character)
casen_2017 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2017_c.rds")
casen_2017 <- mutate_if(casen_2017, is.factor, as.character)
casen_2020 <- readRDS(file = "C:/Users/chris/OneDrive/Documentos/archivos_grandes/casen_2020_e1.rds")
casen_2020 <- mutate_if(casen_2020, is.factor, as.character)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 etnia
fn_etnia <- function(union_etnia){
union_etnia$Etnia[union_etnia$Etnia == "Aimara" ] <- "Aymara"
union_etnia$Etnia[union_etnia$Etnia == "No pertenece a ninguno de estos pueblos indígenas" ] <- "No pertenece a ningún pueblo indígena"
union_etnia$Etnia[union_etnia$Etnia == "Mapuche"] <- "Mapuche"
union_etnia$Etnia[union_etnia$Etnia == "Diaguita"] <- "Diaguita"
union_etnia$Etnia[union_etnia$Etnia == "Atacameño" ] <- "Atacameño"
union_etnia$Etnia[union_etnia$Etnia == "Atacameño (Likan-Antai)" ] <- "Atacameño"
union_etnia$Etnia[union_etnia$Etnia == "Atacameño (Likán Antai)" ] <- "Atacameño"
union_etnia$Etnia[union_etnia$Etnia == "Atacameño (Likán-Antai)" ] <- "Atacameño"
union_etnia$Etnia[union_etnia$Etnia == "Quechua" ] <- "Quechua"
union_etnia$Etnia[union_etnia$Etnia == "Yámana o Yagán" ] <- "Yagán"
union_etnia$Etnia[union_etnia$Etnia == "Yagan" ] <- "Yagán"
union_etnia$Etnia[union_etnia$Etnia == "Yagán (Yámana)" ] <- "Yagán"
union_etnia$Etnia[union_etnia$Etnia == "Rapa-Nui o Pascuenses"] <- "Pascuense"
union_etnia$Etnia[union_etnia$Etnia == "Rapa-Nui"] <- "Pascuense"
union_etnia$Etnia[union_etnia$Etnia == "Rapa Nui (Pascuense)"] <- "Pascuense"
union_etnia$Etnia[union_etnia$Etnia == "Rapa Nui"] <- "Pascuense"
union_etnia$Etnia[union_etnia$Etnia == "Collas"] <- "Coya"
union_etnia$Etnia[union_etnia$Etnia == "Kawashkar o Alacalufes" ] <- "Alacalufe"
union_etnia$Etnia[union_etnia$Etnia == "Kawashkar" ] <- "Alacalufe"
union_etnia$Etnia[union_etnia$Etnia == "Kawésqar (Alacalufes)" ] <- "Alacalufe"
union_etnia$Etnia[union_etnia$Etnia == "Kawésqar" ] <- "Alacalufe"
union_etnia$Etnia[union_etnia$Etnia == "Kawaskar" ] <- "Alacalufe"
union_etnia$Etnia[union_etnia$Etnia == "Chango" ] <- "Chango"
union_etnia$Etnia[union_etnia$Etnia == "Sin dato"] <- NA
union_etnia$Etnia[union_etnia$Etnia == "NS/NR" ] <- NA
union_etnia$Etnia[union_etnia$Etnia == "No sabe/no responde" ] <- NA
union_etnia <<- union_etnia
}Homologación de migración
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 == ""] <- NA1.1 Se obtiene el universo de categorías para cada año
2006
ab <- casen_2006
unique_d_2006 <- unique(ab$Z)2009
ab <- casen_2009
unique_d_2009 <- unique(ab$ZONA)ab <- casen_2011
unique_d_2011 <- unique(ab$z)2013
ab <- casen_2013
unique_d_2013 <- unique(ab$zona)2015
ab <- casen_2015
unique_d_2015 <- unique(ab$zona)2017
ab <- casen_2017
unique_d_2017 <- unique(ab$zona)2020:
ab <- casen_2020
unique_d_2020 <- unique(ab$zona)2 Diccionario PADRE
Se unen todas las categorías de respuesta, se excluyen las repetidas y se les asocia un código:
unique_d_2006 <- as.data.frame(unique_d_2006)
colnames(unique_d_2006)[1] <- "super"
unique_d_2009 <- as.data.frame(unique_d_2009)
colnames(unique_d_2009)[1] <- "super"
unique_d_2011 <- as.data.frame(unique_d_2011)
colnames(unique_d_2011)[1] <- "super"
unique_d_2013 <- as.data.frame(unique_d_2013)
colnames(unique_d_2013)[1] <- "super"
unique_d_2015 <- as.data.frame(unique_d_2015)
colnames(unique_d_2015)[1] <- "super"
unique_d_2017 <- as.data.frame(unique_d_2017)
colnames(unique_d_2017)[1] <- "super"
unique_d_2020 <- as.data.frame(unique_d_2020)
colnames(unique_d_2020)[1] <- "super"el_total <- rbind(unique_d_2006, unique_d_2009, unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017, unique_d_2020)el_total_final <- unique(el_total)2.1 Diccionario
el_total_final$observation <- 1:nrow(el_total_final)
datatable(el_total_final, 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_ytotcor_e5a'),
list(extend='pdf',
filename= 'tabla_ytotcor_e5a')),
text = 'Download')), scrollX = TRUE))2.1.1 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$anio <- "2006"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2006 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2006['z'][d_2006['z'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2006 <- data.frame(lapply(d_2006 , as.character), stringsAsFactors=FALSE)
d_2006$string_z <- d_2006$z
d_2006['z'][d_2006['z'] == 'Rural'] <- '1'
d_2006['z'][d_2006['z'] == 'Urbano'] <- '2'
d_2006['z'][d_2006['z'] == 'Urbana'] <- '3'
d <- d_2006
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2006 <- d
d_2006 <- d_2006[c(1,8,2,3,4,5,6,7)]2.1.2 2009
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2009
b <- ab$COMUNA
c <- ab$Z
d <- ab$T5
e <- ab$SEXO
f <- ab$E1cross_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] <- "z"
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$string_z <- d_2009$zdataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2009['z'][d_2009['z'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2009 <- data.frame(lapply(d_2009 , as.character), stringsAsFactors=FALSE)
#d_2009$string_z <- d_2009$z
d_2009['z'][d_2009['z'] == 'Rural'] <- '1'
d_2009['z'][d_2009['z'] == 'Urbano'] <- '2'
d_2009['z'][d_2009['z'] == 'Urbana'] <- '3'
d <- d_2009
names(d)[1] <- "comuna"
names(d)[2] <- "z"
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 <- d_2009[c(1,8,2,3,4,5,6,7)]2.1.3 2011
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2011
b <- ab$comuna
c <- ab$z
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$anio <- "2011"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2011 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2011['z'][d_2011['z'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2011 <- data.frame(lapply(d_2011 , as.character), stringsAsFactors=FALSE)
d_2011$string_z <- d_2011$z
d_2011['z'][d_2011['z'] == 'Rural'] <- '1'
d_2011['z'][d_2011['z'] == 'Urbano'] <- '2'
d_2011['z'][d_2011['z'] == 'Urbana'] <- '3'
d <- d_2011
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2011 <- d
d_2011 <- d_2011[c(1,8,2,3,4,5,6,7)]2.1.4 2013
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2013
b <- ab$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] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2013 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2013['z'][d_2013['z'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2013 <- data.frame(lapply(d_2013 , as.character), stringsAsFactors=FALSE)
d_2013$string_z <- d_2013$z
d_2013['z'][d_2013['z'] == 'Rural'] <- '1'
d_2013['z'][d_2013['z'] == 'Urbano'] <- '2'
d_2013['z'][d_2013['z'] == 'Urbana'] <- '3'
d <- d_2013
names(d)[1] <- "comuna"
names(d)[2] <- "z"
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 <- d_2013[c(1,8,2,3,4,5,6,7)]2.1.5 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$anio <- "2015"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2015 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2015['o15'][d_2015['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2015 <- data.frame(lapply(d_2015 , as.character), stringsAsFactors=FALSE)
d_2015$string_z <- d_2015$z
d_2015['z'][d_2015['z'] == 'Rural'] <- '1'
d_2015['z'][d_2015['z'] == 'Urbano'] <- '2'
d_2015['z'][d_2015['z'] == 'Urbana'] <- '3'
d <- d_2015
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2015 <- d
d_2015 <- d_2015[c(1,8,2,3,4,5,6,7)]2.1.6 2017
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2017
b <- ab$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] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2017 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2017['o15'][d_2017['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2017 <- data.frame(lapply(d_2017 , as.character), stringsAsFactors=FALSE)
d_2017$string_z <- d_2017$z
d_2017['z'][d_2017['z'] == 'Rural'] <- '1'
d_2017['z'][d_2017['z'] == 'Urbano'] <- '2'
d_2017['z'][d_2017['z'] == 'Urbana'] <- '3'
d <- d_2017
names(d)[1] <- "comuna"
names(d)[2] <- "z"
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 <- d_2017[c(1,8,2,3,4,5,6,7)]3 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$anio <- "2020"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
d_2020 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2020['o15'][d_2020['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")d_2020 <- data.frame(lapply(d_2020 , as.character), stringsAsFactors=FALSE)
d_2020$string_z <- d_2020$z
d_2020['z'][d_2020['z'] == 'Rural'] <- '1'
d_2020['z'][d_2020['z'] == 'Urbano'] <- '2'
d_2020['z'][d_2020['z'] == 'Urbana'] <- '3'
d <- d_2020
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2020 <- d
d_2020 <- d_2020[c(1,8,2,3,4,5,6,7)]Unimos y desplegamos lña tabla corregida:
4 Tabla final etnia
union_etnia <- rbind(d_2006,d_2009, d_2011, d_2013, d_2015, d_2017,d_2020)
union <-union_etnia
fn_etnia(union)
cod_com <- readRDS("C:/Users/chris/OneDrive/Documentos/archivos_grandes/codigos_comunales_2006-2020.rds")
names(cod_com)[2] <- "comuna"
tab_f <- merge(x=union_etnia, y=cod_com, by="comuna")
Etnia <- c(sort(unique(tab_f$Etnia)[-6]),"No pertenece a ningún pueblo indígena",NA)
Etnia<- as.data.frame(Etnia)
Etnia$cod_etnia <- paste("00",seq(1:nrow(Etnia)), sep = "")
codigos <- Etnia$cod_etnia
rango <- seq(1:nrow(Etnia))
cadena <- paste("",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(1),4)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
codigos <- cbind(Etnia,cadena)
colnames(codigos) <- c("Etnia","cadena","cod_etnia")
tab_f <- merge(x=tab_f, y=codigos, by="Etnia")
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"
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))5 MIGRA
5.0.0.1 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$anio <- "2011"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2011 <- d6 2013
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2013
b <- ab$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] <- "z"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2013 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2013['o15'][d_2013['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")6.0.0.1 2015
Generamos las tablas de contingencia tal como acostumbramos:
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$anio <- "2015"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2015 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2015['o15'][d_2015['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")6.0.0.2 2017
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2017
b <- ab$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] <- "z"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2017 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2017['o15'][d_2017['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")6.0.0.3 2020
Generamos las tablas de contingencia tal como acostumbramos:
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$anio <- "2020"
names(d)[1] <- "comuna"
names(d)[2] <- "z"
names(d)[3] <- "Origen"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d_2020 <- ddataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("d_2020['o15'][d_2020['o15'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")UNIMOS MIGRA
7 Tabla final migración
union <- rbind(d_2011,d_2013,d_2015,d_2017,d_2020)
union <- mutate_if(union, is.factor, as.character)
union$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"tab_f <- merge(x = union, y = cod_com, by = "comuna")
tab_f <- tab_f[,c(1,2,3,4,5,9,6,10,7,8)]
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))