1 Introducción
El procedimiento de generación de tablas de contingencia trae problemas si se consideran varias tablas referidas por ejemplo a varios años, cuyas categorías de divergen.
Ésta pregunta sólo se comenzó a aplicar en la Casen del 2011 y hasta la versión 2017
casen_2006 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2006_c.rds")
casen_2006 <- mutate_if(casen_2006, is.factor, as.character)
casen_2009 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2009_c.rds")
casen_2009 <- mutate_if(casen_2009, is.factor, as.character)
casen_2011 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2011_c.rds")
casen_2011 <- mutate_if(casen_2011, is.factor, as.character)
casen_2013 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2013_c.rds")
casen_2013 <- mutate_if(casen_2013, is.factor, as.character)
casen_2015 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2015_c.rds")
casen_2015 <- mutate_if(casen_2015, is.factor, as.character)
casen_2017 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2017_c.rds")
casen_2017 <- mutate_if(casen_2017, is.factor, as.character)
casen_2020 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2020_c.rds")
casen_2020 <- mutate_if(casen_2020, is.factor, as.character)
cod_com <- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/codigos_comunales_2006-2020.rds")
names(cod_com)[2] <- "comuna"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){
union$Etnia[union$Etnia == "Aimara" ] <- "Aymara"
union$Etnia[union$Etnia == "No pertenece a ninguno de estos pueblos indígenas" ] <- "No pertenece a ningún pueblo indígena"
union$Etnia[union$Etnia == "Mapuche"] <- "Mapuche"
union$Etnia[union$Etnia == "Diaguita"] <- "Diaguita"
union$Etnia[union$Etnia == "Atacameño" ] <- "Atacameño"
union$Etnia[union$Etnia == "Atacameño (Likan-Antai)" ] <- "Atacameño"
union$Etnia[union$Etnia == "Atacameño (Likán Antai)" ] <- "Atacameño"
union$Etnia[union$Etnia == "Atacameño (Likán-Antai)" ] <- "Atacameño"
union$Etnia[union$Etnia == "Quechua" ] <- "Quechua"
union$Etnia[union$Etnia == "Yámana o Yagán" ] <- "Yagán"
union$Etnia[union$Etnia == "Yagan" ] <- "Yagán"
union$Etnia[union$Etnia == "Yagán (Yámana)" ] <- "Yagán"
union$Etnia[union$Etnia == "Rapa-Nui o Pascuenses"] <- "Pascuense"
union$Etnia[union$Etnia == "Rapa-Nui"] <- "Pascuense"
union$Etnia[union$Etnia == "Rapa Nui (Pascuense)"] <- "Pascuense"
union$Etnia[union$Etnia == "Rapa Nui"] <- "Pascuense"
union$Etnia[union$Etnia == "Collas"] <- "Coya"
union$Etnia[union$Etnia == "Kawashkar o Alacalufes" ] <- "Alacalufe"
union$Etnia[union$Etnia == "Kawashkar" ] <- "Alacalufe"
union$Etnia[union$Etnia == "Kawésqar (Alacalufes)" ] <- "Alacalufe"
union$Etnia[union$Etnia == "Kawésqar" ] <- "Alacalufe"
union$Etnia[union$Etnia == "Kawaskar" ] <- "Alacalufe"
union$Etnia[union$Etnia == "Chango" ] <- "Chango"
union$Etnia[union$Etnia == "Sin dato"] <- NA
union$Etnia[union$Etnia == "NS/NR" ] <- NA
union$Etnia[union$Etnia == "No sabe/no responde" ] <- NA
union <<- union
}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 o3 cada año
ab <- casen_2006
unique_d_2006 <- unique(ab$O3)
ab <- casen_2009
unique_d_2009 <- unique(ab$O3)
ab <- casen_2011
unique_d_2011 <- unique(ab$o3)
ab <- casen_2013
unique_d_2013 <- unique(ab$o3)
ab <- casen_2015
unique_d_2015 <- unique(ab$o3)
ab <- casen_2017
unique_d_2017 <- unique(ab$o3)
ab <- casen_2020
unique_d_2020 <- unique(ab$o3)2 Diccionario
Se unen todas las categorías de respuesta, se excluyen las repetidas y se les asocia un código:
unique_d_2006 <- as.data.frame(unique_d_2006)
colnames(unique_d_2006)[1] <- "superduper"
unique_d_2009 <- as.data.frame(unique_d_2009)
colnames(unique_d_2009)[1] <- "superduper"
unique_d_2011 <- as.data.frame(unique_d_2011)
colnames(unique_d_2011)[1] <- "superduper"
unique_d_2013 <- as.data.frame(unique_d_2013)
colnames(unique_d_2013)[1] <- "superduper"
unique_d_2015 <- as.data.frame(unique_d_2015)
colnames(unique_d_2015)[1] <- "superduper"
unique_d_2017 <- as.data.frame(unique_d_2017)
colnames(unique_d_2017)[1] <- "superduper"
unique_d_2020 <- as.data.frame(unique_d_2020)
colnames(unique_d_2020)[1] <- "superduper"
el_total <- rbind(unique_d_2006, unique_d_2009, unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017 , unique_d_2020)
el_total_final <- unique(el_total)
el_total_final$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_Diccionario'),
list(extend='pdf',
filename= 'tabla_Diccionario')),
text = 'Descargar')), scrollX = TRUE))dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['o3'][m['o3'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion <- function(m) {
m['o3'][m['o3'] == 'NA'] <- '1'
m['o3'][m['o3'] == 'No'] <- '2'
m['o3'][m['o3'] == 'Sí'] <- '3'
mm <<- m
}3 Etnia
3.0.0.1 2006
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2006
b <- ab$COMUNA
c <- ab$O3
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$anio <- "2006"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2006 <- d
d_2006 <- mutate_if(d_2006, is.factor, as.character)
la_correccion(d_2006)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_06 <- mm3.0.0.2 2009
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2009
b <- ab$COMUNA
c <- ab$O3
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$anio <- "2009"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2009 <- d
d_2009 <- mutate_if(d_2009, is.factor, as.character)
la_correccion(d_2009)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_09 <- mm3.0.0.3 2011
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2011
b <- ab$comuna
c <- ab$o3
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$anio <- "2011"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2011 <- d
d_2011 <- mutate_if(d_2011, is.factor, as.character)
la_correccion(d_2011)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_11 <- mm3.0.0.4 2013
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2013
b <- ab$comuna
c <- ab$o3
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$anio <- "2013"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2013 <- d
d_2013 <- mutate_if(d_2013, is.factor, as.character)
la_correccion(d_2013)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_13 <- mm3.0.0.5 2015
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2015
b <- ab$comuna
c <- ab$o3
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$anio <- "2015"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2015 <- d
d_2015 <- mutate_if(d_2015, is.factor, as.character)
la_correccion(d_2015)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_15 <- mm3.0.0.6 2017
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2017
b <- ab$comuna
c <- ab$o3
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$anio <- "2017"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2017 <- d
d_2017 <- mutate_if(d_2017, is.factor, as.character)
la_correccion(d_2017)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_17 <- mm3.0.0.7 2020
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2020
b <- ab$comuna
c <- ab$o3
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$anio <- "2020"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Etnia"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2020 <- d
d_2020 <- mutate_if(d_2020, is.factor, as.character)
la_correccion(d_2020)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_20 <- mm4 Tabla final etnia
union <- rbind(mm_06,mm_09,mm_11,mm_13,mm_15,mm_17,mm_20)
fn_etnia(union)
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"
Etnia <- c(sort(unique(union$Etnia)[-6]),"No pertenece a ningún pueblo indígena",NA)
Etnia<- as.data.frame(Etnia)
Etnia$cod_etnia <- paste("00",seq(1:nrow(Etnia)), sep = "")
codigos <- Etnia$cod_etnia
rango <- seq(1:nrow(Etnia))
cadena <- paste("",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(1),4)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
codigos <- cbind(Etnia,cadena)
colnames(codigos) <- c("Etnia","cadena","cod_etnia")
tab_f <- merge(x=union, y=codigos, by="Etnia")
tab_f <- merge(x = tab_f, y = cod_com, by = "comuna")
tab_f <- tab_f[,c(1,13,3,4,2,12,5,9,6,10,7,8)]
datatable(tab_f, extensions = 'Buttons', escape = FALSE, rownames = FALSE,
options = list(dom = 'Bfrtip',
buttons = list('colvis', list(extend = 'collection',
buttons = list(
list(extend='copy'),
list(extend='excel',
filename = 'tabla_ytotcor_e5a'),
list(extend='pdf',
filename= 'tabla_ytotcor_e5a')),
text = 'Descargar')), scrollX = TRUE))## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
5 Migración
5.0.0.1 2011
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2011
b <- ab$comuna
c <- ab$o3
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$anio <- "2011"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Migracion"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2011 <- d
d_2011 <- mutate_if(d_2011, is.factor, as.character)
la_correccion(d_2011)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_11 <- mm5.0.0.2 2013
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2013
b <- ab$comuna
c <- ab$o3
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$anio <- "2013"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Migracion"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2013 <- d
d_2013 <- mutate_if(d_2013, is.factor, as.character)
la_correccion(d_2013)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_13 <- mm5.0.0.3 2015
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2015
b <- ab$comuna
c <- ab$o3
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$anio <- "2015"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Migracion"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2015 <- d
d_2015 <- mutate_if(d_2015, is.factor, as.character)
la_correccion(d_2015)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_15 <- mm5.0.0.4 2017
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2017
b <- ab$comuna
c <- ab$o3
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$anio <- "2017"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Migracion"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2017 <- d
d_2017 <- mutate_if(d_2017, is.factor, as.character)
la_correccion(d_2017)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_17 <- mm5.0.0.5 2020
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2020
b <- ab$comuna
c <- ab$o3
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$anio <- "2020"
names(d)[1] <- "comuna"
names(d)[2] <- "o3"
names(d)[3] <- "Migracion"
names(d)[4] <- "Sexo"
names(d)[5] <- "Sabe leer?"
names(d)[6] <- "Frecuencia"
names(d)[7] <- "Año"
d$cod <- d[,2]
d <- d[,c(1,8,2,3,4,5,6,7)]
d_2020 <- d
d_2020 <- mutate_if(d_2020, is.factor, as.character)
la_correccion(d_2020)
names(mm)[2] <- paste0(colnames(mm)[3])
names(mm)[3] <- paste0("cod_",colnames(mm)[3])
mm_20 <- mm6 Tabla final migración
union <- rbind(mm_11,mm_13,mm_15,mm_17,mm_20)
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_ytotcor_e5a'),
list(extend='pdf',
filename= 'tabla_ytotcor_e5a')),
text = 'Descargar')), scrollX = TRUE))