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.
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"vv <- c("T4","T5","r6","r6","r3","r3","r3")
casen_2006 <- casen_2006[,c("EXPC", "COMUNA" ,vv[1], "T4","SEXO","E1")]
casen_2009 <- casen_2009[,c("EXPC", "COMUNA" ,vv[2], "T5","SEXO","E1")]
casen_2011 <- casen_2011[,c("expc_full", "comuna" ,vv[3], "r6","sexo","e1","r2p_cod")]
casen_2013 <- casen_2013[,c("expc", "comuna" ,vv[4], "r6","sexo","e1","r2_p_cod")]
casen_2015 <- casen_2015[,c("expc_todas", "comuna" ,vv[5], "r3","sexo","e1","r2espp_cod")]
casen_2017 <- casen_2017[,c("expc", "comuna" ,vv[6], "r3","sexo","e1","r2_p_cod")]
casen_2020 <- casen_2020[,c("expc", "comuna" ,vv[7], "r3","sexo","e1","r2_pais_esp")]1.0.1 2006
ab <- casen_2006
c <- ab$T4
cross_tab = xtabs(ab$EXPC ~ unlist(c),aggregate(ab$EXPC ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2006"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2006 <- d
d_2006 <- mutate_if(d_2006, is.factor, as.character)1.0.2 2009
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2009
c <- ab$T5
cross_tab = xtabs(ab$EXPC~ unlist(c),aggregate(ab$EXPC ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2009"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2009 <- d1.0.3 2011
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2011
c <- ab$r6
cross_tab = xtabs(ab$expc_full ~ unlist(c),aggregate(ab$expc_full ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2011"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2011 <- d1.0.4 2013
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2013
c <- ab$r6
cross_tab = xtabs(ab$expc ~ unlist(c),aggregate(ab$expc ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2013"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2013 <- d1.0.5 2015
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2015
c <- ab$r3
cross_tab = xtabs(ab$expc_todas ~ unlist(c),aggregate(ab$expc_todas ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2015"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2015 <- d1.0.6 2017
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2017
c <- ab$r3
cross_tab = xtabs(ab$expc ~ unlist(c),aggregate(ab$expc ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2017 <- d1.0.7 2020
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2020
b <- ab$comuna
c <- ab$r3
d <- ab$r3
e <- ab$sexo
f <- ab$e1
cross_tab = xtabs(ab$expc ~ unlist(c),aggregate(ab$expc ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2020"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2020 <- dUnimos y desplegamos la tabla corregida:
2 Tabla final etnia homologada
union_etnia <- rbind(d_2006,d_2009, d_2011, d_2013, d_2015, d_2017, d_2020)
union_etnia <- mutate_if(union_etnia, is.factor, as.character)
datatable( union_etnia, 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))t1 <- union_etnia
p <- plot_ly(t1, width = 1200, x = ~Año, y = ~Frecuencia , color = ~variable, mode = 'markers') %>% add_lines()
pHomologación de etnia
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)"] <- "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
# df <<- dataset
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)
}2.0.1 2006
ab <- casen_2006
c <- ab$T4
cross_tab = xtabs(ab$EXPC ~ unlist(c),aggregate(ab$EXPC ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2006"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2006 <- d
d_2006 <- mutate_if(d_2006, is.factor, as.character)2.0.2 2009
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2009
c <- ab$T5
cross_tab = xtabs(ab$EXPC~ unlist(c),aggregate(ab$EXPC ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2009"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2009 <- d2.0.3 2011
Generamos las tablas de contingencia tal como acostumbramos:
ab <- casen_2011
c <- ab$r6
cross_tab = xtabs(ab$expc_full ~ unlist(c),aggregate(ab$expc_full ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2011"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2011 <- d2.0.4 2013
ab <- casen_2013
c <- ab$r6
cross_tab = xtabs(ab$expc ~ unlist(c),aggregate(ab$expc ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2013"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2013 <- d2.0.5 2015
ab <- casen_2015
c <- ab$r3
cross_tab = xtabs(ab$expc_todas ~ unlist(c),aggregate(ab$expc_todas ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2015"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2015 <- d 2.0.6 2017
ab <- casen_2017
c <- ab$r3
cross_tab = xtabs(ab$expc~ unlist(c),aggregate(ab$expc ~ unlist(c) ,ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2017"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2017 <- d2.0.7 2020
ab <- casen_2020
c <- ab$r3
cross_tab = xtabs(ab$expc ~ unlist(c),aggregate(ab$expc ~ unlist(c),ab,mean))
tabla <- as.data.frame(cross_tab)
d <-tabla[!(tabla$Freq == 0),]
d$anio <- "2020"
names(d)[1] <- "variable"
names(d)[2] <- "Frecuencia"
names(d)[3] <- "Año"
d_2020 <- dUnimos y desplegamos la tabla corregida:
3 Tabla final inmigración homologada
union_etnia <- rbind(d_2006,d_2009,d_2011, d_2013, d_2015, d_2017, d_2020)
union_etnia <- mutate_if(union_etnia, is.factor, as.character)
datatable(union_etnia, 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))Con homologación
t2 <- union_etnia
p <- plot_ly(t2, width = 1200, x = ~Año, y = ~Frecuencia , color = ~variable, mode = 'markers') %>% add_lines()
pSin homologación
p <- plot_ly(t1, width = 1200, x = ~Año, y = ~Frecuencia , color = ~variable, mode = 'markers') %>% add_lines()
p