1 Las variables de ingreso a utilizar serán:
| Tipo | ||||
|---|---|---|---|---|
| Ingreso total | Ingreso autónomo | Ingreso del trabajo | ||
| año | ||||
| 2017 | ytotcor | yautcor | ytrabajocor | |
| 2015 | ytotcor | yautcor | ytrabajocor | |
| 2013 | ytotcor | yautcor | ytrabajocor | |
| 2011 | ytrabaj | yautaj | ytrabaj | |
| 2009 | ytrabaj | yautaj | ytrabaj | |
| 2006 | ytrabaj | yautaj | ytrabaj |
1.1 Lectura de bases de datos Casen
dataset_06 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2006_c.rds")
dataset_06 <- mutate_if(dataset_06, is.factor, as.character)
dataset_09 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2009_c.rds")
dataset_09 <- mutate_if(dataset_09, is.factor, as.character)
dataset_11 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2011_c.rds")
dataset_11 <- mutate_if(dataset_11, is.factor, as.character)
dataset_13 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2013_c.rds")
dataset_13 <- mutate_if(dataset_13, is.factor, as.character)
dataset_15 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2015_c.rds")
dataset_15 <- mutate_if(dataset_15, is.factor, as.character)
dataset_17 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2017_c.rds")
dataset_17 <- mutate_if(dataset_17, is.factor, as.character)
dataset_20 <<- readRDS("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/casen_2020.rds")
dataset_20 <- mutate_if(dataset_20, is.factor, as.character)1.1.1 Homologación de alfabetismo
dataset_06$E1[dataset_06$E1 == "No sabe /Sin dato"] <- NA
dataset_11$e1[dataset_11$e1 == "Sí, lee y escribe"] <- "Sí"
dataset_11$e1[dataset_11$e1 == "No, sólo lee"] <- "No"
dataset_11$e1[dataset_11$e1 == "No, ninguno"] <- "No"
dataset_11$e1[dataset_11$e1 == "No, sólo escribe"] <- "No"
dataset_13$e1[dataset_13$e1 == "Sí, lee y escribe"] <- "Sí"
dataset_13$e1[dataset_13$e1 == "No, ninguno"] <- "No"
dataset_13$e1[dataset_13$e1 == "No, sólo lee"] <- "No"
dataset_13$e1[dataset_13$e1 == "No, sólo escribe"] <- "No"
dataset_13$e1[dataset_13$e1 == "NS/NR"] <- NA
dataset_15$e1[dataset_15$e1 == "Sí, lee y escribe"] <- "Sí"
dataset_15$e1[dataset_15$e1 == "No, ninguno"] <- "No"
dataset_15$e1[dataset_15$e1 == "No, sólo lee"] <- "No"
dataset_15$e1[dataset_15$e1 == "No, sólo escribe"] <- "No"
dataset_17$e1[dataset_17$e1 == "Sí, lee y escribe"] <- "Sí"
dataset_17$e1[dataset_17$e1 == "No, sólo lee"] <- "No"
dataset_17$e1[dataset_17$e1 == "No, ninguno"] <- "No"
dataset_17$e1[dataset_17$e1 == "No sabe/responde"] <- NA
dataset_17$e1[dataset_17$e1 == "No, sólo escribe"] <- "No"1.1.2 Homologación de etnia
fn_etnia <- function(data_df3){
data_df3$Etnia[data_df3$Etnia == "Aimara" ] <- "Aymara"
data_df3$Etnia[data_df3$Etnia == "No pertenece a ninguno de estos pueblos indígenas" ] <- "No pertenece a ningún pueblo indígena"
data_df3$Etnia[data_df3$Etnia == "Mapuche"] <- "Mapuche"
data_df3$Etnia[data_df3$Etnia == "Diaguita"] <- "Diaguita"
data_df3$Etnia[data_df3$Etnia == "Atacameño" ] <- "Atacameño"
data_df3$Etnia[data_df3$Etnia == "Atacameño (Likan-Antai)" ] <- "Atacameño"
data_df3$Etnia[data_df3$Etnia == "Atacameño (Likán Antai)" ] <- "Atacameño"
data_df3$Etnia[data_df3$Etnia == "Atacameño (Likán-Antai)" ] <- "Atacameño"
data_df3$Etnia[data_df3$Etnia == "Quechua" ] <- "Quechua"
data_df3$Etnia[data_df3$Etnia == "Yámana o Yagán" ] <- "Yagán"
data_df3$Etnia[data_df3$Etnia == "Yagan" ] <- "Yagán"
data_df3$Etnia[data_df3$Etnia == "Yagán (Yámana)" ] <- "Yagán"
data_df3$Etnia[data_df3$Etnia == "Rapa-Nui o Pascuenses"] <- "Pascuense"
data_df3$Etnia[data_df3$Etnia == "Rapa-Nui"] <- "Pascuense"
data_df3$Etnia[data_df3$Etnia == "Rapa Nui (Pascuense)"] <- "Pascuense"
data_df3$Etnia[data_df3$Etnia == "Rapa Nui"] <- "Pascuense"
data_df3$Etnia[data_df3$Etnia == "Collas"] <- "Coya"
data_df3$Etnia[data_df3$Etnia == "Kawashkar o Alacalufes" ] <- "Alacalufe"
data_df3$Etnia[data_df3$Etnia == "Kawashkar" ] <- "Alacalufe"
data_df3$Etnia[data_df3$Etnia == "Kawésqar (Alacalufes)" ] <- "Alacalufe"
data_df3$Etnia[data_df3$Etnia == "Kawésqar" ] <- "Alacalufe"
data_df3$Etnia[data_df3$Etnia == "Kawaskar" ] <- "Alacalufe"
data_df3$Etnia[data_df3$Etnia == "Chango" ] <- "Chango"
data_df3$Etnia[data_df3$Etnia == "Sin dato"] <- NA
data_df3$Etnia[data_df3$Etnia == "NS/NR" ] <- NA
data_df3$Etnia[data_df3$Etnia == "No sabe/no responde" ] <- NA
data_df3 <<- data_df3
}1.1.3 Homologación de migración
for (i in unique(dataset_20$r2_pais_esp)) {
pais <- gsub("(^[[:space:]]+|[[:space:]]+$)", "", i)
pais <- tolower(pais)
dataset_20$r2_pais_esp[dataset_20$r2_pais_esp == i] <- str_to_title(pais)
}
dataset_11$r2p_cod[dataset_11$r2p_cod == "No contesta"] <- "NS/NR"
dataset_13$r2_p_cod[dataset_13$r2_p_cod == "No contesta"] <- "NS/NR"
dataset_15$r2espp_cod[dataset_15$r2espp_cod == "No contesta"] <- "NS/NR"
dataset_17$r2_p_cod[dataset_17$r2_p_cod == "No Bien Especificado"] <- "NS/NR"
dataset_17$r2_p_cod[dataset_17$r2_p_cod == "No Responde"] <- "NS/NR"
dataset_20$r2_pais_esp[dataset_20$r2_pais_esp == "No Bien Especificado"] <- "NS/NR"
dataset_20$r2_pais_esp[dataset_20$r2_pais_esp == ""] <- NACorrección s4
ab <- dataset_11
unique_d_2011 <- unique(ab$s7)
ab <- dataset_13
unique_d_2013 <- unique(ab$s5)
ab <- dataset_15
unique_d_2015 <- unique(ab$s4)
ab <- dataset_17
unique_d_2017 <- unique(ab$s4)
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"
el_total <- rbind( unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017)
el_total_final <- unique(el_total)
el_total_final$observation <- 1:nrow(el_total_final)
dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['Variable'][m['Variable'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion_1 <- function(m) {
m['Variable'][m['Variable'] == 'NA'] <- '1'
m['Variable'][m['Variable'] == '2'] <- '2'
m['Variable'][m['Variable'] == '3'] <- '3'
m['Variable'][m['Variable'] == '0'] <- '4'
m['Variable'][m['Variable'] == '4'] <- '5'
m['Variable'][m['Variable'] == '5'] <- '6'
m['Variable'][m['Variable'] == '1'] <- '7'
m['Variable'][m['Variable'] == '7'] <- '8'
m['Variable'][m['Variable'] == '6'] <- '9'
m['Variable'][m['Variable'] == '8'] <- '10'
m['Variable'][m['Variable'] == '11'] <- '11'
m['Variable'][m['Variable'] == '10'] <- '12'
m['Variable'][m['Variable'] == '13'] <- '13'
m['Variable'][m['Variable'] == '16'] <- '14'
m['Variable'][m['Variable'] == '9'] <- '15'
m['Variable'][m['Variable'] == '12'] <- '16'
m['Variable'][m['Variable'] == '14'] <- '17'
m['Variable'][m['Variable'] == '99'] <- '18'
m['Variable'][m['Variable'] == '15'] <- '19'
m['Variable'][m['Variable'] == '20'] <- '20'
m['Variable'][m['Variable'] == '17'] <- '21'
m['Variable'][m['Variable'] == '18'] <- '22'
m['Variable'][m['Variable'] == '19'] <- '23'
m['Variable'][m['Variable'] == 'No sabe'] <- '24'
m['Variable'][m['Variable'] == 'No ha tenido hijos'] <- '25'
m['Variable'][m['Variable'] == '22'] <- '26'
data_df3 <<- m
}Corrección s5
ab <- dataset_11
unique_d_2011 <- unique(ab$s8)
ab <- dataset_13
unique_d_2013 <- unique(ab$s6)
ab <- dataset_15
unique_d_2015 <- unique(ab$s5)
ab <- dataset_17
unique_d_2017 <- unique(ab$s5)
unique_d_2011 <- as.data.frame(unique_d_2011)
colnames(unique_d_2011)[1] <- "superduper"
unique_d_2013 <- as.data.frame(unique_d_2013)
colnames(unique_d_2013)[1] <- "superduper"
unique_d_2015 <- as.data.frame(unique_d_2015)
colnames(unique_d_2015)[1] <- "superduper"
unique_d_2017 <- as.data.frame(unique_d_2017)
colnames(unique_d_2017)[1] <- "superduper"
el_total <- rbind( unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017)
el_total_final <- unique(el_total)
el_total_final$observation <- 1:nrow(el_total_final)
dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['Variable'][m['Variable'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion_2 <- function(m) {
m['Variable'][m['Variable'] == 'NA'] <- '1'
m['Variable'][m['Variable'] == '18'] <- '2'
m['Variable'][m['Variable'] == '21'] <- '3'
m['Variable'][m['Variable'] == '24'] <- '4'
m['Variable'][m['Variable'] == '25'] <- '5'
m['Variable'][m['Variable'] == '17'] <- '6'
m['Variable'][m['Variable'] == '19'] <- '7'
m['Variable'][m['Variable'] == '32'] <- '8'
m['Variable'][m['Variable'] == '26'] <- '9'
m['Variable'][m['Variable'] == '22'] <- '10'
m['Variable'][m['Variable'] == '14'] <- '11'
m['Variable'][m['Variable'] == '23'] <- '12'
m['Variable'][m['Variable'] == '29'] <- '13'
m['Variable'][m['Variable'] == '42'] <- '14'
m['Variable'][m['Variable'] == '27'] <- '15'
m['Variable'][m['Variable'] == '28'] <- '16'
m['Variable'][m['Variable'] == '16'] <- '17'
m['Variable'][m['Variable'] == '15'] <- '18'
m['Variable'][m['Variable'] == '30'] <- '19'
m['Variable'][m['Variable'] == '20'] <- '20'
m['Variable'][m['Variable'] == '38'] <- '21'
m['Variable'][m['Variable'] == '37'] <- '22'
m['Variable'][m['Variable'] == '99'] <- '23'
m['Variable'][m['Variable'] == '31'] <- '24'
m['Variable'][m['Variable'] == '13'] <- '25'
m['Variable'][m['Variable'] == '35'] <- '26'
m['Variable'][m['Variable'] == '12'] <- '27'
m['Variable'][m['Variable'] == '45'] <- '28'
m['Variable'][m['Variable'] == '33'] <- '29'
m['Variable'][m['Variable'] == '36'] <- '30'
m['Variable'][m['Variable'] == '43'] <- '31'
m['Variable'][m['Variable'] == '41'] <- '32'
m['Variable'][m['Variable'] == '34'] <- '33'
m['Variable'][m['Variable'] == '39'] <- '34'
m['Variable'][m['Variable'] == '48'] <- '35'
m['Variable'][m['Variable'] == '40'] <- '36'
m['Variable'][m['Variable'] == '49'] <- '37'
m['Variable'][m['Variable'] == '44'] <- '38'
m['Variable'][m['Variable'] == '47'] <- '39'
m['Variable'][m['Variable'] == '50'] <- '40'
m['Variable'][m['Variable'] == '46'] <- '41'
m['Variable'][m['Variable'] == 'No sabe / No recuerda'] <- '42'
m['Variable'][m['Variable'] == '10'] <- '43'
m['Variable'][m['Variable'] == '11'] <- '44'
m['Variable'][m['Variable'] == 'Ns/Nr'] <- '45'
m['Variable'][m['Variable'] == 'No sabe/No recuerda'] <- '46'
m['Variable'][m['Variable'] == '53'] <- '47'
m['Variable'][m['Variable'] == '52'] <- '48'
m['Variable'][m['Variable'] == '55'] <- '49'
m['Variable'][m['Variable'] == '51'] <- '50'
m['Variable'][m['Variable'] == '56'] <- '51'
m['Variable'][m['Variable'] == '62'] <- '52'
m['Variable'][m['Variable'] == '57'] <- '53'
m['Variable'][m['Variable'] == '58'] <- '54'
m['Variable'][m['Variable'] == '54'] <- '55'
m['Variable'][m['Variable'] == '60'] <- '56'
m['Variable'][m['Variable'] == '61'] <- '57'
m['Variable'][m['Variable'] == '65'] <- '58'
data_df3 <<- m
}Corrección s6
ab <- dataset_11
unique_d_2011 <- unique(ab$s9)
ab <- dataset_13
unique_d_2013 <- unique(ab$s7)
ab <- dataset_15
unique_d_2015 <- unique(ab$s6)
ab <- dataset_17
unique_d_2017 <- unique(ab$s6)
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"
el_total <- rbind( unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017)
el_total_final <- unique(el_total)
el_total_final$observation <- 1:nrow(el_total_final)
dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['Variable'][m['Variable'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion_3 <- function(m) {
m['Variable'][m['Variable'] == 'NA'] <- '1'
m['Variable'][m['Variable'] == 'No'] <- '2'
m['Variable'][m['Variable'] == 'Sí, amamantando'] <- '3'
m['Variable'][m['Variable'] == 'Sí, embarazada'] <- '4'
m['Variable'][m['Variable'] == 'NS/NR'] <- '5'
m['Variable'][m['Variable'] == 'Sin dato'] <- '6'
data_df3 <<- m
}Corrección s7
ab <- dataset_11
unique_d_2011 <- unique(ab$s11)
ab <- dataset_13
unique_d_2013 <- unique(ab$s9)
ab <- dataset_15
unique_d_2015 <- unique(ab$s7)
ab <- dataset_17
unique_d_2017 <- unique(ab$s7)
unique_d_2011 <- as.data.frame(unique_d_2011)
colnames(unique_d_2011)[1] <- "superduper"
unique_d_2013 <- as.data.frame(unique_d_2013)
colnames(unique_d_2013)[1] <- "superduper"
unique_d_2015 <- as.data.frame(unique_d_2015)
colnames(unique_d_2015)[1] <- "superduper"
unique_d_2017 <- as.data.frame(unique_d_2017)
colnames(unique_d_2017)[1] <- "superduper"
el_total <- rbind( unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017)
el_total_final <- unique(el_total)
el_total_final$observation <- 1:nrow(el_total_final)
dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['Variable'][m['Variable'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion_4 <- function(m) {
m['Variable'][m['Variable'] == 'NA'] <- '1'
m['Variable'][m['Variable'] == 'No sabe, no recuerda'] <- '2'
m['Variable'][m['Variable'] == 'No retiró alimentos'] <- '3'
m['Variable'][m['Variable'] == 'Sí, leche Purita Mamá'] <- '4'
m['Variable'][m['Variable'] == 'Sí, ambos alimentos'] <- '5'
m['Variable'][m['Variable'] == 'Sí, leche Purita Fortificada (26% MG)'] <- '6'
m['Variable'][m['Variable'] == 'No sabe / No recuerda'] <- '7'
m['Variable'][m['Variable'] == 'Sí, leche Purita Fortificada'] <- '8'
m['Variable'][m['Variable'] == 'Ns/Nr'] <- '9'
m['Variable'][m['Variable'] == 'Sí, Leche Purita Mamá'] <- '10'
m['Variable'][m['Variable'] == 'No retiró alimento'] <- '11'
m['Variable'][m['Variable'] == 'No sabe/No recuerda'] <- '12'
m['Variable'][m['Variable'] == 'Sí, Leche Purita Fortificada'] <- '13'
data_df3 <<- m
}Corrección s8
ab <- dataset_11
unique_d_2011 <- unique(ab$s13)
ab <- dataset_13
unique_d_2013 <- unique(ab$s10)
ab <- dataset_15
unique_d_2015 <- unique(ab$s8)
ab <- dataset_17
unique_d_2017 <- unique(ab$s8)
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"
el_total <- rbind( unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017)
el_total_final <- unique(el_total)
el_total_final$observation <- 1:nrow(el_total_final)
dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['Variable'][m['Variable'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion_5 <- function(m) {
m['Variable'][m['Variable'] == 'NA'] <- '1'
m['Variable'][m['Variable'] == 'No'] <- '2'
m['Variable'][m['Variable'] == 'No sabe, no recuerda'] <- '3'
m['Variable'][m['Variable'] == 'Sí, durante el último año'] <- '4'
m['Variable'][m['Variable'] == 'Sí, hace más de 2 años hasta 3 años'] <- '5'
m['Variable'][m['Variable'] == 'Sí, hace más de un año hasta 2 años'] <- '6'
m['Variable'][m['Variable'] == 'No sabe / No recuerda'] <- '7'
m['Variable'][m['Variable'] == 'Ns/Nr'] <- '8'
m['Variable'][m['Variable'] == 'Sí, hace más de un año y hasta 2 años'] <- '9'
m['Variable'][m['Variable'] == 'Sí, hace más de 2 años y hasta 3 años'] <- '10'
m['Variable'][m['Variable'] == 'No sabe/No recuerda'] <- '11'
data_df3 <<- m
}Corrección s9
ab <- dataset_11
unique_d_2011 <- unique(ab$s14)
ab <- dataset_13
unique_d_2013 <- unique(ab$s11)
ab <- dataset_15
unique_d_2015 <- unique(ab$s9)
ab <- dataset_17
unique_d_2017 <- unique(ab$s9)
unique_d_2011 <- as.data.frame(unique_d_2011)
colnames(unique_d_2011)[1] <- "superduper"
unique_d_2013 <- as.data.frame(unique_d_2013)
colnames(unique_d_2013)[1] <- "superduper"
unique_d_2015 <- as.data.frame(unique_d_2015)
colnames(unique_d_2015)[1] <- "superduper"
unique_d_2017 <- as.data.frame(unique_d_2017)
colnames(unique_d_2017)[1] <- "superduper"
el_total <- rbind( unique_d_2011, unique_d_2013, unique_d_2015, unique_d_2017)
el_total_final <- unique(el_total)
el_total_final$observation <- 1:nrow(el_total_final)
dataf1 <- data.frame()
for (n in 1:nrow(el_total_final)) {
dataf1 <- rbind(dataf1,paste0("m['Variable'][m['Variable'] == '",el_total_final[n,1],"']"," <- '",el_total_final[n,2],"'"))
}
dataf1 <- as.data.frame(dataf1)
write_xlsx(dataf1,"el_total_final.xlsx")
la_correccion_6 <- function(m) {
m['Variable'][m['Variable'] == 'NA'] <- '1'
m['Variable'][m['Variable'] == 'No cree que lo necesite'] <- '2'
m['Variable'][m['Variable'] == 'No le corresponde'] <- '3'
m['Variable'][m['Variable'] == 'Le da miedo o le disgusta'] <- '4'
m['Variable'][m['Variable'] == 'Otra razón'] <- '5'
m['Variable'][m['Variable'] == 'No tiene tiempo'] <- '6'
m['Variable'][m['Variable'] == 'Se le olvida hacérselo'] <- '7'
m['Variable'][m['Variable'] == 'No conoce el examen'] <- '8'
m['Variable'][m['Variable'] == 'El horario del consultorio no le sirve'] <- '9'
m['Variable'][m['Variable'] == 'No sabe'] <- '10'
m['Variable'][m['Variable'] == 'No ha podido conseguir hora'] <- '11'
m['Variable'][m['Variable'] == 'No sabía que tenía que hacerse examen'] <- '12'
m['Variable'][m['Variable'] == 'No sabe dónde hacérselo'] <- '13'
m['Variable'][m['Variable'] == 'No tiene dinero'] <- '14'
m['Variable'][m['Variable'] == 'No conoce ese examen'] <- '15'
m['Variable'][m['Variable'] == 'No sabía que tenía que hacerse ese examen'] <- '16'
data_df3 <<- m
}2 Generación de tablas de contingencia Etnia
df_tablas <- data.frame()
for (var1 in 1:6) {
funcion1 <- function(n){
xx<-switch(n, "2011","2013","2015","2017","2020")
tanio <<- xx
switch (var1,
case = v1 <- switch(n,"s7","s5","s4","s4"),
case = v1 <- switch(n,"s8","s6","s5","s5"),
case = v1 <- switch(n,"s9","s7","s6","s6"),
case = v1 <- switch(n,"s11","s9","s7","s7"),
case = v1 <- switch(n,"s13","s10","s8","s8"),
case = v1 <- switch(n,"s14","s11","s9","s9")
)
if(xx==2011) {
eliminated <- dataset_11
b <- eliminated$comuna
c <- eliminated[,c(v1)]
d <- eliminated$e1 #alfabetismo
e <- eliminated$r6 #etnia
f <- eliminated$sexo
anio <- 2011
}
if(xx==2013) {
eliminated <- dataset_13
b <- eliminated$comuna
c <- eliminated[,c(v1)]
d <- eliminated$e1 #alfabetismo
e <- eliminated$r6 #etnia
f <- eliminated$sexo
anio <- 2013
}
if(xx==2015) {
eliminated <- dataset_15
b <- eliminated$comuna
c <- eliminated[,c(v1)]
d <- eliminated$e1 #alfabetismo
e <- eliminated$r3 #etnia
f <- eliminated$sexo
anio <- 2015
}
if(xx==2017) {
eliminated <- dataset_17
b <- eliminated$comuna
c <- eliminated[,c(v1)]
d <- eliminated$e1 #alfabetismo
e <- eliminated$r3 #etnia
f <- eliminated$sexo
anio <- 2017
}
if(xx==2020) {
eliminated <- dataset_20
b <- eliminated$comuna
c <- eliminated[,c(v1)]
d <- eliminated$sexo #alfabetismo
e <- eliminated$r3 #etnia
f <- eliminated$sexo
anio <- 2020
}
################ -- frecuencia
expan<-switch(n, "expc_full","expc","expc_todas","expc","expc")
tabla_matp <-xtabs(eliminated[,(expan)]~b+c+d+e+f, data = eliminated)
tabla_matp <- as.data.frame(tabla_matp)
tabla_matp <-tabla_matp[!(tabla_matp$Freq == 0),]
################
tabla_matp$Año = xx
df <- tabla_matp
names(df)[1] <- "Comuna"
names(df)[2] <- "Variable"
names(df)[3] <- "Alfabetismo"
names(df)[4] <- "Etnia"
names(df)[5] <- "Sexo"
direc_cod_com <- paste0("C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/codigos_comunales_2006-2020.rds")
codigos_comunales <- readRDS(file = direc_cod_com)
names(codigos_comunales)[1] <- "Código"
tabla_df = merge( x = df, y = codigos_comunales, by = "Comuna", all.x = TRUE)
tabla_df2 <<- tabla_df
}
data_df3 <- data.frame()
for (n in 1:4){
funcion1(n)
data_df3 <- rbind(data_df3,tabla_df2)
}
variable_e <- switch(var1,"s4","s5","s6","s7","s8","s9")
data_df3 <- mutate_if(data_df3, is.factor, as.character)
fn_etnia(data_df3)
data_df3$cod_Variable <- data_df3$Variable
switch (var1,
case = la_correccion_1(data_df3),
case = la_correccion_2(data_df3),
case = la_correccion_3(data_df3),
case = la_correccion_4(data_df3),
case = la_correccion_5(data_df3)
)
data_df3$cod_sexo <- data_df3$Sexo
data_df3$cod_sexo[data_df3$cod_sexo == "Hombre"] <- "01"
data_df3$cod_sexo[data_df3$cod_sexo == "Mujer"] <- "02"
data_df3$cod_alfa <- data_df3$Alfabetismo
data_df3$cod_alfa[data_df3$cod_alfa == "Sí"] <- "01"
data_df3$cod_alfa[data_df3$cod_alfa == "No"] <- "02"
Etnia <- c(sort(unique(data_df3$Etnia)[-6]),"No pertenece a ningún pueblo indígena",NA)
Etnia<- as.data.frame(Etnia)
Etnia$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")
data_df3 <- merge(x=data_df3, y=codigos, by="Etnia")
data_df3 <- data_df3[,c(2,8,3,9,1,13,4,11,5,10,6,7)]
assign(paste0("tabla_",variable_e),data_df3)
print(paste0("tabla_",variable_e))
}## [1] "tabla_s4"
## [1] "tabla_s5"
## [1] "tabla_s6"
## [1] "tabla_s7"
## [1] "tabla_s8"
## [1] "tabla_s9"2.1 s4 ¿Cuántos hijos nacidos vivos ha tenido en su vida?
datatable(tabla_s4, 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_s4'),
list(extend='pdf',
filename= 'tabla_s4')),
text = 'Download')), scrollX = TRUE)) 2.2 s5 ¿Qué edad tenía cuando nació su primer hijo?
datatable(tabla_s5, 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_s4'),
list(extend='pdf',
filename= 'tabla_s4')),
text = 'Download')), scrollX = TRUE))