## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----download, eval = FALSE---------------------------------------------------
# url <- "https://redatam.org/cdr/descargas/censos/poblacion/CP2017CHL.zip"
# zip <- "CP2017CHL.zip"
# 
# if (!file.exists(zip)) {
#   download.file(url, zip, method = "wget")
# }

## ----extract, eval = FALSE----------------------------------------------------
# # install.packages("archive")
# dout <- basename(zip)
# dout <- sub("\\.zip$", "", dout)
# archive::archive_extract(zip, dir = dout)

## ----read_dic, eval = FALSE---------------------------------------------------
# library(redatam)
# 
# fout <- "chile2017.rds"
# 
# if (!file.exists(fout)) {
#   chile2017 <- read_redatam("CP2017CHL/BaseOrg16/CPV2017-16.dicx")
#   saveRDS(chile2017, fout)
# } else {
#   chile2017 <- readRDS(fout)
# }

## ----eval = FALSE-------------------------------------------------------------
# library(dplyr)
# 
# overcrowding <- chile2017$comuna %>%
#   select(ncomuna, comuna_ref_id) %>%
#   inner_join(
#     chile2017$distrito %>%
#       select(distrito_ref_id, comuna_ref_id)
#   ) %>%
#   inner_join(
#     chile2017$area %>%
#       select(area_ref_id, distrito_ref_id)
#   ) %>%
#   inner_join(
#     chile2017$zonaloc %>%
#       select(zonaloc_ref_id, area_ref_id)
#   ) %>%
#   inner_join(
#     chile2017$vivienda %>%
#       select(zonaloc_ref_id, vivienda_ref_id, cant_per, p04) %>%
#       mutate(
#         p04 = case_when(
#           p04 == 98 ~ NA_integer_,
#           p04 == 99 ~ NA_integer_,
#           TRUE ~ p04
#         )
#       ) %>%
#       filter(!is.na(p04))
#   ) %>%
#   mutate(
#     overcrowding = case_when(
#       p04 >= 1 ~ cant_per / p04,
#       p04 == 0 ~ cant_per / (p04 + 1)
#     )
#   ) %>%
#   mutate(
#     overcrowding_discrete = case_when(
#       overcrowding < 2.5 ~ "No Overcrowding",
#       overcrowding >= 2.5 & overcrowding < 3.5 ~ "Mean",
#       overcrowding >= 3.5 & overcrowding < 5 ~ "High",
#       overcrowding >= 5 ~ "Critical"
#     )
#   ) %>%
#   group_by(comuna = ncomuna, overcrowding_discrete) %>%
#   count()

## ----metropolitana, eval = FALSE----------------------------------------------
# overcrowding %>%
#   filter(comuna == "VITACURA")
# 
# overcrowding %>%
#   filter(comuna == "LA PINTANA")