## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true"),
  out.width = "100%"
)



## ----eval=FALSE, message=FALSE, warning=FALSE---------------------------------
# # From CRAN
# install.packages("geobr")
# 
# # Development version
# utils::remove.packages('geobr')
# devtools::install_github("ipeaGIT/geobr", subdir = "r-package")
# 

## ----message=FALSE, warning=FALSE, results='hide'-----------------------------
# library(geobr)
# library(sf)
# library(dplyr)
# library(ggplot2)

## ----message=FALSE, warning=FALSE---------------------------------------------
# # Available data sets
# datasets <- list_geobr(wide = TRUE)
# 
# head(datasets)
# 

## ----message=FALSE, warning=FALSE---------------------------------------------
# # State of Sergipe
# state <- read_state(
#   year = 2022,
#   code_state = "SE",
#   showProgress = FALSE
#   )
# 
# # Municipality of Sao Paulo
# muni <- read_municipality(
#   year = 2022,
#   code_muni = 3550308,
#   showProgress = FALSE
#   )
# 
# ggplot() +
#   geom_sf(data = muni, color=NA, fill = '#1ba185') +
#   theme_void()

## ----message=FALSE, warning=FALSE, results='hide'-----------------------------
# # All municipalities in the state of Minas Gerais
# muni <- read_municipality(
#   year = 2022,
#   code_muni = "MG",
#   showProgress = FALSE
#   )
# 
# head(muni)

## ----message=FALSE, warning=FALSE---------------------------------------------
# # read all schools
# inter <- read_schools(
#   year = 2022,
#   showProgress = FALSE
#   )
# 
# # read all states
# states <- read_state(
#   year = 2025,
#   showProgress = FALSE
#   )
# 
# head(states)

## ----message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"----
# # Remove plot axis
# no_axis <- theme(axis.title=element_blank(),
#                  axis.text=element_blank(),
#                  axis.ticks=element_blank())
# 
# # Plot all Brazilian states
# ggplot() +
#   geom_sf(data=states, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
#   labs(subtitle="States", size=8) +
#   theme_minimal() +
#   no_axis
# 

## ----message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"----
# 
# # Download all municipalities of Rio
# all_muni <- read_municipality(
#   year= 2022,
#   code_muni = "RJ",
#   showProgress = FALSE
#   )
# 
# # plot
# ggplot() +
#   geom_sf(data=all_muni, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
#   labs(subtitle="Municipalities of Rio de Janeiro, 2000", size=8) +
#   theme_minimal() +
#   no_axis
# 

## ----message=FALSE, warning=FALSE---------------------------------------------
# # return duckdb duckspatial_df
# muni_duck <- geobr::read_municipality(
#   year = 2022,
#   output = "duckdb"
#   )
# 
# # return arrow table
# muni_arrow <- geobr::read_municipality(
#   year = 2022,
#   output = "arrow"
#   )
# 

## ----message=FALSE, warning=FALSE, results='hide'-----------------------------
# # Read data.frame with life expectancy data
# df <- data.table::fread(
#   system.file("extdata/br_states_lifexpect2017.csv", package = "geobr")
#   )
# 
# # join the databases
# states <- dplyr::left_join(
#   x = states,
#   y = df,
#   by = c("name_state" = "uf")
#   )
# 

## ----message=FALSE, warning=FALSE, fig.height = 8, fig.width = 8, fig.align = "center"----
# ggplot() +
#   geom_sf(data=states, aes(fill=ESPVIDA2017), color= NA, size=.15) +
#     labs(subtitle="Life Expectancy at birth, Brazilian States, 2014", size=8) +
#     scale_fill_distiller(palette = "Blues", name="Life Expectancy", limits = c(65,80)) +
#     theme_minimal() +
#     no_axis
# 

## -----------------------------------------------------------------------------
# library(censobr)
# library(arrow)
# 
# hs <- read_households(
#   year = 2010,
#   showProgress = FALSE
#   )
# 

## ----warning = FALSE----------------------------------------------------------
# esg <- hs |>
#         collect() |>
#         group_by(code_muni) |>                                             # (a)
#         summarize(rede = sum(V0010[which(V0207=='1')]),                    # (b)
#                   total = sum(V0010)) |>                                   # (b)
#         mutate(cobertura = rede / total) |>                                # (c)
#         collect()                                                          # (d)
# 
# head(esg)

## ----warning = FALSE----------------------------------------------------------
# # download municipality geometries
# muni_sf <- geobr::read_municipality(
#   year = 2010,
#   showProgress = FALSE
#   )
# 
# # merge data
# esg_sf <- left_join(muni_sf, esg, by = 'code_muni')
# 
# # plot map
# ggplot() +
#   geom_sf(data = esg_sf, aes(fill = cobertura), color=NA) +
#   labs(title = "Share of households connected to a sewage network") +
#   scale_fill_distiller(palette = "Greens", direction = 1,
#                        name='Share of\nhouseholds',
#                        labels = scales::percent) +
#   theme_void()
# 

