Configuração

Instalação

# Load packages for bivariate analysis workflow
if (!require("pacman")) install.packages("pacman")

pacman::p_load(
    sf,            # Spatial vector data handling
    terra,         # Modern raster data processing
    elevatr,       # Elevation data access
    cowplot,       # Plot composition tools
    geobr,
    censobr,
    dplyr,
    ggplot2,
    arrow
)

Datasets disponíveis

# Available data sets
datasets <- list_geobr()
knitr::kable(head(datasets), row.names = FALSE)
function geography years source
read_country Country 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_region Region 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_state States 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_meso_region Meso region 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_micro_region Micro region 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 IBGE
read_intermediate_region Intermediate region 2017, 2019, 2020 IBGE

Baixar uma área geográfica específica em um determinado ano

# State of Sergipe
state <- read_state(
  code_state="SE",
  year=2018,
  showProgress = FALSE
  )
mp1 <- ggplot() + 
  geom_sf(data = state, color=NA, fill = 'lightblue') +
  theme_void()

# Municipality of Sao Paulo
muni <- read_municipality(
  code_muni = 3550308, 
  year=2010, 
  showProgress = FALSE
  )
mp2 <- ggplot() + 
  geom_sf(data = muni, color=NA, fill = '#1ba185') +
  theme_void()

plot_grid(mp1, mp2, labels = c('Estado de Sergipe', 'Município de São Paulo'), label_size = 10)

Baixar todas as áreas geográficas de um estado em um determinado ano

# All municipalities in the state of Minas Gerais
muni <- read_municipality(code_muni = "MG", 
                          year = 2007,
                          showProgress = FALSE)
ggplot() +
  geom_sf(data=muni, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
  labs(subtitle="Municípios de Minas Gerais, 2007", size=8) +
  theme_minimal()

# All census tracts in the state of Rio de Janeiro
cntr <- read_census_tract(
  code_tract = "RJ", 
  year = 2010,
  showProgress = FALSE
  )
head(cntr)
Simple feature collection with 6 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -42.1792 ymin: -22.9694 xmax: -42.0189 ymax: -22.9409
Geodetic CRS:  SIRGAS 2000
       code_tract   zone code_muni       name_muni name_neighborhood
1 330025805000046 URBANO   3300258 Arraial Do Cabo              <NA>
2 330025805000047 URBANO   3300258 Arraial Do Cabo              <NA>
3 330025805000048 URBANO   3300258 Arraial Do Cabo              <NA>
4 330025805000049 URBANO   3300258 Arraial Do Cabo              <NA>
5 330025805000050 URBANO   3300258 Arraial Do Cabo              <NA>
6 330025805000051 URBANO   3300258 Arraial Do Cabo              <NA>
  code_neighborhood code_subdistrict name_subdistrict code_district
1              <NA>      33002580500             <NA>     330025805
2              <NA>      33002580500             <NA>     330025805
3              <NA>      33002580500             <NA>     330025805
4              <NA>      33002580500             <NA>     330025805
5              <NA>      33002580500             <NA>     330025805
6              <NA>      33002580500             <NA>     330025805
    name_district code_state                           geom
1 Arraial Do Cabo         33 MULTIPOLYGON (((-42.0195 -2...
2 Arraial Do Cabo         33 MULTIPOLYGON (((-42.0228 -2...
3 Arraial Do Cabo         33 MULTIPOLYGON (((-42.1602 -2...
4 Arraial Do Cabo         33 MULTIPOLYGON (((-42.174 -22...
5 Arraial Do Cabo         33 MULTIPOLYGON (((-42.1759 -2...
6 Arraial Do Cabo         33 MULTIPOLYGON (((-42.1785 -2...

Se o parâmetro code_ não for passado para a função, o geobr retornará os dados de todo o país por padrão.

# read all intermediate regions
inter <- read_intermediate_region(
  year = 2017,
  showProgress = FALSE
  )

# read all states
states <- read_state(
  year = 2019, 
  showProgress = FALSE
  )

plot(states)

Depois de baixar os dados, é muito simples plotar mapas usando o ggplot2.

# 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

Trace todos os municípios de um determinado estado, como São Paulo:

# Download all municipalities
all_muni <- read_municipality(
  code_muni = "SP", 
  year= 2010,
  showProgress = FALSE
  )

# plot
ggplot() +
  geom_sf(data=all_muni, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
  labs(subtitle="Municípios de São Paulo, 2010", size=8) +
  theme_minimal() +
  no_axis

Mapas Temáticos

Unindo dados externos

# Read data.frame with life expectancy data
df <- utils::read.csv(system.file("extdata/br_states_lifexpect2017.csv", package = "geobr"), encoding = "UTF-8")

states$name_state <- tolower(states$name_state)
df$uf <- tolower(df$uf)

# join the databases
states <- dplyr::left_join(states, df, by = c("name_state" = "uf"))

ggplot() +
  geom_sf(data=states, aes(fill=ESPVIDA2017), color= NA, size=.15) +
    labs(subtitle="Atlas do Desenvolvimento Humano no Brasil. Pnud Brasil, Ipea e FJP, 2022", size=8) +
    scale_fill_distiller(palette = "Blues", name="Expectativa de vida ao nascer\n2017", limits = c(65,80)) +
    theme_minimal() +
    no_axis

Usando o geobr junto com o censobr

# First, we need to download households data from the Brazilian census using the read_households() function.
hs <- read_households(year = 2010, 
                      showProgress = FALSE)
# Now we’re going to (a) group observations by municipality, (b) get the number of households connected to a sewage network, (c) calculate the proportion of households connected, and (d) collect the results.
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)
knitr::kable(head(esg), row.names = FALSE)

Agora só precisamos baixar as geometrias dos municípios brasileiros do geobr, unir os dados espaciais com nossas estimativas e mapear os resultados.

# download municipality geometries
muni_sf <- geobr::read_municipality(year = 2010,
                                    showProgress = FALSE)
#> Using year/date 2010

# 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 = "Proporção de domicílios conectados a uma rede de esgoto (Censo 2010)") +
  scale_fill_distiller(palette = "Greens", direction = 1, 
                       name='Proporção de\ndomicílios', 
                       labels = scales::percent) +
  theme_void()

Referências

  1. geobr: Download Official Spatial Data Sets of Brazil https://ipeagit.github.io/geobr
  2. Introduction to geobr (R) https://cran.r-project.org/web/packages/geobr/vignettes/intro_to_geobr.html
  3. Atlas do Desenvolvimento Humano no Brasil. Pnud Brasil, Ipea e FJP, 2022 http://www.atlasbrasil.org.br/consulta/planilha