1 Introducción
direccion <- switch(2,"C:/Users/enamo/Desktop/Shiny-R/Casen_en_pandemia_2020/casen/","C:/Users/chris/OneDrive/Documentos/archivos_grandes/")
dataset_06 <<- readRDS(paste0(direccion,"casen_2006_c.rds"))
dataset_06 <- mutate_if(dataset_06, is.factor, as.character)
dataset_09 <<- readRDS(paste0(direccion,"casen_2009_c.rds"))
dataset_09 <- mutate_if(dataset_09, is.factor, as.character)
dataset_11 <<- readRDS(paste0(direccion,"casen_2011_c.rds"))
dataset_11 <- mutate_if(dataset_11, is.factor, as.character)
dataset_13 <<- readRDS(paste0(direccion,"casen_2013_c.rds"))
dataset_13 <- mutate_if(dataset_13, is.factor, as.character)
dataset_15 <<- readRDS(paste0(direccion,"casen_2015_c.rds"))
dataset_15 <- mutate_if(dataset_15, is.factor, as.character)
dataset_17 <<- readRDS(paste0(direccion,"casen_2017_c.rds"))
dataset_17 <- mutate_if(dataset_17, is.factor, as.character)
dataset_20 <<- readRDS(paste0(direccion,"casen_2020_e1.rds"))
dataset_20 <- mutate_if(dataset_20, is.factor, as.character)casen_2006 <- dataset_06
casen_2009 <- dataset_09
casen_2011 <- dataset_11
casen_2013 <- dataset_13
casen_2015 <- dataset_15
casen_2017 <- dataset_17
casen_2020 <- dataset_202 Categorías de respuesta
Obtenemos las frecuencias de respuestas ya expandidas a la población, por categoría. , include = FALSE
## # A tibble: 13 x 2
## Variable Homologacion_038
## <chr> <chr>
## 1 No No
## 2 No tuvo ninguna enfermedad o accidente No
## 3 No sabe / No recuerda No sabe o no responde
## 4 No sabe/No recuerda No sabe o no responde
## 5 Ns/Nr No sabe o no responde
## 6 Sí Sí
## 7 Sí, por enfermedad Sí
## 8 Sí, por accidente laboral o escolar Sí
## 9 Sí, por accidente no laboral ni escolar Sí
## 10 Sí, enfermedad no provocada por el trabajo Sí
## 11 Sí, enfermedad provocada por el trabajo Sí
## 12 Sí, accidente no laboral ni escolar Sí
## 13 Sí, accidente laboral o escolar Sí
La base de datos necesaria
diccionario <- function(df){
#entre 2006 y 2020: 7
variable <- switch(i,"S5","S9","s20","s17","s15","s15","s16")
names(carrera)[1] <- variable
df <- merge(df, carrera, by = variable)
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)
}df_tablas <- data.frame()
funcion1 <- function(n){
xx<-switch(n,"2006","2009","2011","2013" ,"2015","2017","2020")
tanio <<- xx
v1 <- switch(n, "Homologacion_038","Homologacion_038", "Homologacion_038","Homologacion_038", "Homologacion_038","Homologacion_038", "Homologacion_038")
if(xx==2006) {
eliminated <- casen_2006
c <- eliminated[,c(v1)]
anio <- 2006
}
if(xx==2009) {
eliminated <- casen_2009
c <- eliminated[,c(v1)]
anio <- 2009
}
if(xx==2011) {
eliminated <- casen_2011
c <- eliminated[,c(v1)]
anio <- 2011
}
if(xx==2013) {
eliminated <- casen_2013
c <- eliminated[,c(v1)]
anio <- 2013
}
if(xx==2015) {
eliminated <- casen_2015
c <- eliminated[,c(v1)]
anio <- 2015
}
if(xx==2017) {
eliminated <- casen_2017
c <- eliminated[,c(v1)]
anio <- 2017
}
if(xx==2020) {
eliminated <- casen_2020
c <- eliminated[,c(v1)]
anio <- 2020
}
################ -- frecuencia
expan<-switch(n, "EXPC","EXPC", "expc_full","expc", "expc_todas","expc", "expc")
tabla_matp <-xtabs(eliminated[,(expan)]~c, data = eliminated)
tabla_matp <- as.data.frame(tabla_matp)
names(tabla_matp)[1] <- "Homologación"
data_df1 <<- tabla_matp
################
}
for (n in 1:7){
funcion1(n)
assign(paste0("tabla_",tanio),data_df1)
}
tabla_f <- 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## Homologación Freq.x Freq.y Freq.x Freq.y Freq.x Freq.y
## 1 No 13575588 14104629 14559585 13599275 13272719 14019394
## 2 No sabe o no responde 39116 107648 102350 393266 383098 180636
## 3 Sí 2538037 2394774 2261813 3242756 3859757 3569136
## Freq
## 1 16350891
## 2 43463
## 3 3106108
colnames(tabla_f) <- c("variable", "2006","2009","2011","2013","2015","2017","2020")
tabla_f <- mutate_all(tabla_f, ~replace(., is.na(.), 0))
tabla_t <- 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")]
receptaculo <- data.frame()
for (n in 1:nrow(tabla_t)) {
calculado <- na.approx(c(tabla_t[n,c(2:ncol(tabla_t))]))
receptaculo <- rbind(receptaculo,calculado)
}
receptaculo <- cbind(tabla_t$variable,receptaculo)
colnames(receptaculo) <- c("Homologación",paste0(seq(2006,2020,1)))
# receptaculo$categorias<- as.character(receptaculo$categorias)
################is.num <- sapply(receptaculo, is.numeric)
receptaculo [is.num] <- lapply(receptaculo [is.num], round, 2)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(2015,2017,1))) ,mark = "", digits=0)3 Análisis