1 Introduccion
<- switch(2,"C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/","C:/Users/chris/OneDrive/Documentos/archivos_grandes/")
direccion
<<- readRDS(paste0(direccion,"casen_2006_c.rds"))
dataset_06 <- mutate_if(dataset_06, is.factor, as.character)
dataset_06 <<- readRDS(paste0(direccion,"casen_2009_c.rds"))
dataset_09 <- mutate_if(dataset_09, is.factor, as.character)
dataset_09 <<- readRDS(paste0(direccion,"casen_2011_c.rds"))
dataset_11 <- mutate_if(dataset_11, is.factor, as.character)
dataset_11 <<- readRDS(paste0(direccion,"casen_2013_c.rds"))
dataset_13 <- mutate_if(dataset_13, is.factor, as.character)
dataset_13 <<- readRDS(paste0(direccion,"casen_2015_c.rds"))
dataset_15 <- mutate_if(dataset_15, is.factor, as.character)
dataset_15 <<- readRDS(paste0(direccion,"casen_2017_c.rds"))
dataset_17 <- mutate_if(dataset_17, is.factor, as.character)
dataset_17 <<- readRDS(paste0(direccion,"casen_2020_e1.rds"))
dataset_20 <- mutate_if(dataset_20, is.factor, as.character) dataset_20
<- dataset_06
casen_2006 <- dataset_09
casen_2009 <- dataset_11
casen_2011 <- dataset_13
casen_2013 <- dataset_15
casen_2015 <- dataset_17
casen_2017 <- dataset_20 casen_2020
2 Categorías de respuesta
Obtenemos las frecuencias de respuestas ya expandidas a la población, por categoría. , include = FALSE
## # A tibble: 54 x 2
## Variable Homologacion_041
## <chr> <chr>
## 1 1 De 1 a 5
## 2 3 De 1 a 5
## 3 5 De 1 a 5
## 4 2 De 1 a 5
## 5 4 De 1 a 5
## 6 12 De 11 a 20
## 7 20 De 11 a 20
## 8 15 De 11 a 20
## 9 11 De 11 a 20
## 10 14 De 11 a 20
## # ... with 44 more rows
La base de datos necesaria
<- function(df){
diccionario
#entre 2006 y 2020: 7
# 2006 2009 2011 2013 2015 2017 2020
<- switch(i,"S8A","S14A","s25a","s22a","s19a","s19a","")
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 1:6) {
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_041","Homologacion_041", "Homologacion_041","Homologacion_041", "Homologacion_041","Homologacion_041", "Homologacion_041")
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 ################
#print(data_df1)
}
for (n in 1:6){
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
tabla_f
## Homologación Freq.x Freq.y Freq.x Freq.y Freq.x Freq.y
## 1 De 1 a 5 2546022 2170843 2845289 2814456 3055148 2540924
## 2 De 11 a 20 25544 16918 18931 17230 16992 17113
## 3 De 21 a 30 618 361 1582 5602 1583 3242
## 4 De 31 a 40 475 NA NA 499 771 5181
## 5 De 41 a 50 177 NA NA 162 173 1697
## 6 De 6 a 10 119549 72154 92993 83103 89267 70185
## 7 Más de 50 168 NA NA NA NA NA
## 8 Ninguna 13460188 14342589 13964837 14310178 14340595 15070502
## 9 No sabe o no responde NA 4186 116 4067 11045 60322
colnames(tabla_f) <- c("variable", "2006","2009","2011","2013","2015","2017")
<- 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# tabla_t$a2018 <- NA
# tabla_t$a2019 <- NA
<- tabla_t[,c("variable","2006","a2007","a2008","2009","a2010","2011","a2012","2013","a2014","2015","a2016","2017")]
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$variable,receptaculo)
receptaculo colnames(receptaculo) <- c("Homologación",paste0(seq(2006,2017,1)))
# receptaculo$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 = 'recibio_atencion'),
list(extend='pdf',
filename= 'recibio_atencion')),
text = 'Download')), scrollX = TRUE))%>%
formatRound(columns=c(paste0(seq(2006,2017,1))) ,mark = "", digits=2)
3 Análisis