Introduction to geobr (R)

The geobr package provides quick and easy access to official spatial data sets of Brazil. The syntax of all geobr functions operate on a simple logic that allows users to easily download a wide variety of data sets with updated geometries and harmonized attributes and geographic projections across geographies and years. This vignette presents a quick intro to geobr.

Installation

You can install geobr from CRAN or the development version to use the latest features.

# From CRAN
install.packages("geobr")

# Development version
utils::remove.packages('geobr')
devtools::install_github("ipeaGIT/geobr", subdir = "r-package")

Now let’s load the libraries we’ll use in this vignette.

library(geobr)
library(sf)
library(dplyr)
library(ggplot2)

General usage

Available data sets

The geobr package covers 27 spatial data sets, including a variety of political-administrative and statistical areas used in Brazil. You can view what data sets are available using the list_geobr() function.

# Available data sets
datasets <- list_geobr()

head(datasets)

Download spatial data as `sf` objects

The syntax of all geobr functions operate one the same logic, so the code to download the data becomes intuitive for the user. Here are a few examples.

Download an specific geographic area at a given year

# State of Sergige
state <- read_state(
  code_state="SE",
  year=2018,
  showProgress = FALSE
  )

# Municipality of Sao Paulo
muni <- read_municipality(
  code_muni = 3550308, 
  year=2010, 
  showProgress = FALSE
  )

ggplot() + 
  geom_sf(data = muni, color=NA, fill = '#1ba185') +
  theme_void()

Download all geographic areas within a state at a given year

# All municipalities in the state of Minas Gerais
muni <- read_municipality(code_muni = "MG", 
                          year = 2007,
                          showProgress = FALSE)

# All census tracts in the state of Rio de Janeiro
cntr <- read_census_tract(
  code_tract = "RJ", 
  year = 2010,
  showProgress = FALSE
  )

head(muni)

If the parameter code_ is not passed to the function, geobr returns the data for the whole country by default.

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

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

head(states)

Important note about data resolution

All functions to download polygon data such as states, municipalities etc. have a simplified argument. When simplified = FALSE, geobr will return the original data set with high resolution at detailed geographic scale (see documentation). By default, however, simplified = TRUE and geobr returns data set geometries with simplified borders to improve speed of downloading and plotting the data.

Plot the data

Once you’ve downloaded the data, it is really simple to plot maps using 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

Plot all the municipalities of a particular state, such as Rio de Janeiro:


# Download all municipalities of Rio
all_muni <- read_municipality(
  code_muni = "RJ", 
  year= 2010,
  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

Thematic maps

The next step is to combine data from geobr package with other data sets to create thematic maps. In this first example, we will be using data from the (Atlas of Human Development (by Ipea/FJP and UNPD) to create a choropleth map showing the spatial variation of Life Expectancy at birth across Brazilian states.

Merge external data

First, we need a data.frame with estimates of Life Expectancy and merge it to our spatial database. The two-digit abbreviation of state name is our key column to join these two databases.

# 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"))

Plot thematic map

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

Using geobr together with censobr

Following the same steps as above, we can use together geobr with our sister package censobr to map the proportion of households connected to a sewage network in Brazilian municipalities

First, we need to download households data from the Brazilian census using the read_households() function.

library(censobr)
library(arrow)

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)

head(esg)

Now we only need to download the geometries of Brazilian municipalities from geobr, merge the spatial data with our estimates and map the results.

# 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()