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.
You can install geobr from CRAN or the development version to use the latest features.
# From CRAN install.packages("geobr") # Development version ::remove.packages('geobr') utils::install_github("ipeaGIT/geobr", subdir = "r-package") devtools
Now let’s load the libraries we’ll use in this vignette.
library(geobr) library(ggplot2) library(sf) library(dplyr)
The geobr package covers 21 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
# Available data sets <- list_geobr() datasets print(datasets, n=21)
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 <- read_state(code_state="SE", year=2018) state # Municipality of Sao Paulo <- read_municipality( code_muni = 3550308, year=2010 ) muni
Download all geographic areas within a state at a given year
# All municipalities in the state of Alagoas <- read_municipality(code_muni= "AL", year=2007) muni # All census tracts in the state of Rio de Janeiro <- read_census_tract(code_tract = "RJ", year = 2010) cntr
If the parameter
code_ is not passed to the function, geobr returns the data for the whole country by default.
<- read_intermediate_region(year=2017) meso <- read_state(year=2019)states
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.
Once you’ve downloaded the data, it is really simple to plot maps using
# Remove plot axis <- theme(axis.title=element_blank(), no_axis 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:
library(ggplot2) # Download all municipalities of Rio <- read_municipality( code_muni = "RJ", year= 2010) all_muni # 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
The next step is to combine data from geobr package with other data sets to create thematic maps. In this example, we will be using data from the (Atlas of Human Development (a project of our colleagues at Ipea))[https://atlasbrasil.org.br/] to create a choropleth map showing the spatial variation of Life Expectancy at birth across Brazilian states.
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 <- utils::read.csv(system.file("extdata/br_states_lifexpect2017.csv", package = "geobr"), encoding = "UTF-8") df $name_state <- tolower(states$name_state) states$uf <- tolower(df$uf) df # join the databases <- dplyr::left_join(states, df, by = c("name_state" = "uf"))states
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