1 Introducción
<- switch(3,"C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/","C:/Users/chris/OneDrive/Documentos/archivos_grandes/", "C:/Users/Ian/Documents/Casen/")
direccion
<<- readRDS(paste0(direccion,"casen_2006_c.rds"))
casen_2006 <- mutate_if(casen_2006, is.factor, as.character)
casen_2006 <<- readRDS(paste0(direccion,"casen_2009_c.rds"))
casen_2009 <- mutate_if(casen_2009, is.factor, as.character)
casen_2009 <<- readRDS(paste0(direccion,"casen_2011_c.rds"))
casen_2011 <- mutate_if(casen_2011, is.factor, as.character)
casen_2011 <<- readRDS(paste0(direccion,"casen_2013_c.rds"))
casen_2013 <- mutate_if(casen_2013, is.factor, as.character)
casen_2013 <<- readRDS(paste0(direccion,"casen_2015_c.rds"))
casen_2015 <- mutate_if(casen_2015, is.factor, as.character)
casen_2015 <<- readRDS(paste0(direccion,"casen_2017_c.rds"))
casen_2017 <- mutate_if(casen_2017, is.factor, as.character)
casen_2017 <<- readRDS(paste0(direccion,"casen_2020_e1.rds"))
casen_2020 <- mutate_if(casen_2020, is.factor, as.character) casen_2020
2 Categorías de respuesta
Obtenemos las frecuencias de respuestas ya expandidas a la población, por categoría. , include = FALSE
## # A tibble: 3 x 2
## Variable Homologacion_043
## <chr> <chr>
## 1 Sí Sí
## 2 <NA> No Aplica
## 3 No No
<- function(df){
diccionario
#entre 2006 y 2020: 7
<- switch(i,"O1","O1","o1","o1","o1","o1","o1")
variable
names(carrera)[1] <- variable
<- merge(df, carrera, by = variable)
df
switch(i,
case = casen_2006 <<- df,
case = casen_2009 <<- df,
case = casen_2011 <<- df,
case = casen_2013 <<- df,
case = casen_2015 <<- df,
case = casen_2017 <<- df,
case = casen_2020 <<- df
)
}
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
)
diccionario(casen)
}
<- data.frame()
df_tablas
<- function(n){
funcion1
<-switch(n,"2006","2009","2011","2013" ,"2015","2017","2020")
xx
<<- xx
tanio
<- switch(n, "Homologacion_043","Homologacion_043", "Homologacion_043","Homologacion_043", "Homologacion_043","Homologacion_043","Homologacion_043")
v1
if(xx==2006) {
<- casen_2006
eliminated <- eliminated[,c(v1)]
c <- 2006
anio
}
if(xx==2009) {
<- casen_2009
eliminated <- eliminated[,c(v1)]
c <- 2009
anio
}
if(xx==2011) {
<- casen_2011
eliminated <- eliminated[,c(v1)]
c <- 2011
anio
}
if(xx==2013) {
<- casen_2013
eliminated <- eliminated[,c(v1)]
c <- 2013
anio
}
if(xx==2015) {
<- casen_2015
eliminated <- eliminated[,c(v1)]
c <- 2015
anio
}
if(xx==2017) {
<- casen_2017
eliminated <- eliminated[,c(v1)]
c <- 2017
anio
}
if(xx==2020) {
<- casen_2020
eliminated <- eliminated[,c(v1)]
c <- 2020
anio
}
################ -- frecuencia
<-switch(n, "EXPC","EXPC", "expc_full","expc", "expc_todas","expc", "expc")
expan<-xtabs(eliminated[,(expan)]~c, data = eliminated)
tabla_matp <- as.data.frame(tabla_matp)
tabla_matp names(tabla_matp)[1] <- "Homologación"
<<- tabla_matp
data_df1 ################
}
for (n in 1:7){
funcion1(n)
assign(paste0("tabla_",tanio),data_df1)
}
<- merge(tabla_2006, tabla_2009, by= "Homologación", all.x = T, all.y = T)
tabla_f <- merge(tabla_f, tabla_2011, by= "Homologación", all.x = T, all.y = T)
tabla_f <- merge(tabla_f, tabla_2013, by= "Homologación", all.x = T, all.y = T)
tabla_f <- merge(tabla_f, tabla_2015, by= "Homologación", all.x = T, all.y = T)
tabla_f <- merge(tabla_f, tabla_2017, by= "Homologación", all.x = T, all.y = T)
tabla_f <- merge(tabla_f, tabla_2020, by= "Homologación", all.x = T, all.y = T)
tabla_f tabla_f
## Homologación Freq.x Freq.y Freq.x Freq.y Freq.x Freq.y Freq
## 1 No 6919794 7592401 7448251 7421997 7418946 6850751 8969394
## 2 No Aplica 2874718 2810456 2838630 2866610 2865563 3428834 3682288
## 3 Sí 6358229 6204194 6636867 6946690 7231065 7489581 6848780
colnames(tabla_f) <- c("variable", "2006","2009","2011","2013","2015","2017", "2020")
<- mutate_all(tabla_f, ~replace(., is.na(.), 0))
tabla_f
<- tabla_f
tabla_t $a2007 <- NA
tabla_t$a2008 <- NA
tabla_t$a2010 <- NA
tabla_t$a2012 <- NA
tabla_t$a2014 <- NA
tabla_t$a2016 <- NA
tabla_t$a2018 <- NA
tabla_t$a2019 <- NA
tabla_t
<- tabla_t[,c("variable","2006","a2007","a2008","2009","a2010","2011","a2012","2013","a2014","2015","a2016","2017","a2018","a2019","2020")]
tabla_t
<- data.frame()
receptaculo for (n in 1:nrow(tabla_t)) {
<- na.approx(c(tabla_t[n,c(2:ncol(tabla_t))]))
calculado <- rbind(receptaculo,calculado)
receptaculo
}<- cbind(tabla_t$variable,receptaculo)
receptaculo colnames(receptaculo) <- c("Homologación",paste0(seq(2006,2020,1)))
# receptaculo$categorias<- as.character(receptaculo$categorias)
################
<- sapply(receptaculo, is.numeric)
is.num <- lapply(receptaculo [is.num], round, 2) receptaculo [is.num]
datatable(receptaculo, extensions = 'Buttons', escape = FALSE, rownames = TRUE,
options = list(dom = 'Bfrtip',
buttons = list('colvis', list(extend = 'collection',
buttons = list(
list(extend='copy'),
list(extend='excel',
filename = 'tabla_genero'),
list(extend='pdf',
filename= 'tabla_genero')),
text = 'Download')), scrollX = TRUE))%>%
formatRound(columns=c(paste0(seq(2006,2020,1))) ,mark = "", digits=0)
3 Análisis