geobr is an R package that allows users to easily access shapefiles of the Brazilian Institute of Geography and Statistics (IBGE) and other official spatial data sets of Brazil. The package includes a wide range of geographic datasets as simple features, available at various geographic scales and for various years (see detailed list below):
# From CRAN
install.packages("geobr")
library(geobr)
# or use the development version with latest features
utils::remove.packages('geobr')
devtools::install_github("ipeaGIT/geobr")
library(geobr)obs. If you use Linux, you need to install a couple dependencies before installing the libraries sf and geobr. More info here.
# Read specific municipality at a given year
mun <- read_municipality(code_muni=1200179, year=2017)
# Read all municipalities of a state at a given year
mun <- read_municipality(code_muni=33, year=2010)
# alternatively
mun <- read_municipality(code_muni="RJ", year=2010)
# Read all municipalities in the country at a given year
mun <- read_municipality(code_muni="all", year=2018)More examples here and in the intro Vignette
| Function | Geographies available | Years available | Source |
|---|---|---|---|
read_country |
Country | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018 | IBGE |
read_region |
Region | 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018 | IBGE |
read_state |
States | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018 | IBGE |
read_meso_region |
Meso region | 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018 | IBGE |
read_micro_region |
Micro region | 2000, 2001, 2010, 2013, 2014, 2015, 2016, 2017, 2018 | IBGE |
read_municipality |
Municipality | 1872, 1900, 1911, 1920, 1933, 1940, 1950, 1960, 1970, 1980, 1991, 2000, 2001, 2005, 2007, 2010, 2013, 2014, 2015, 2016, 2017, 2018 | IBGE |
read_weighting_area |
Census weighting area (área de ponderação) | 2010 | IBGE |
read_census_tract |
Census tract (setor censitário) | 2000, 2010 | IBGE |
read_statistical_grid |
Statistical Grid of 200 x 200 meters | 2010 | IBGE |
read_health_facilities |
Health facilities | 2015 | CNES, DataSUS |
read_indigenous_land (dev.) |
Indigenous lands | 201907 | FUNAI |
read_biomes (dev.) |
Biomes | 2004 | IBGE |
read_disaster_risk_area (dev.) |
Disaster risk areas | 2010 | CEMADEN and IBGE |
read_amazon (dev.) |
Brazil's Legal Amazon | 2012 | MMA |
read_conservation_units (dev.) |
Environmental Conservation Units | 201909 | MMA |
read_urban_area (dev.) |
Urban footprints | 2005, 2015 | IBGE |
Note 1. Functions marked with "dev." are only available in the development version of geobr.
Note 2. All datasets use geodetic reference system "SIRGAS2000", CRS(4674). Most data sets are available at scale 1:250,000 (see documentation for details).
| Geography | Years available | Source |
|---|---|---|
read_census_tract |
2007 | IBGE |
| Metropolitan areas | ... | IBGE and state legislations |
| Longitudinal Database* of municipalities | ... | IBGE |
| Longitudinal Database* of micro regions | ... | IBGE |
| Longitudinal Database* of Census tracts | ... | IBGE |
| Schools | 2019 | School Census (Inep) |
| ... | ... | ... |
| ... | ... | ... |
'*' Longitudinal Database refers to áreas mínimas comparáveis (AMCs)
- Quadro geográfico de referência para produção, análise e disseminação de estatísticas
- Regiões Metropolitanas, Aglomerações Urbanas e Regiões Integradas de Desenvolvimento
- Outros arquivos e recortes estão disponiveis em ftp://geoftp.ibge.gov.br/.
The shapefiles are created by IBGE. The geobr package is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil. If you want to cite this package, you can cite it as:
- Pereira, R.H.M.; Gonçalves, C.N.; Araujo, P.H.F. de; Carvalho, G.D.; Nascimento, I.; Arruda, R.A. de. (2019) geobr: an R package to easily access shapefiles of the Brazilian Institute of Geography and Statistics. GitHub repository - https://github.com/ipeaGIT/geobr.
As of today, there are two other R packges with similar functionalities. These are the packages simplefeaturesbr and brazilmaps. The geobr package follows an intuitive syntax and it has a few advantages when compared to other packages, including for example:
- Access to a wider range of official spatial data sets, such as states and municipalities, but also macro-, meso- and micro-regions, weighting areas, census tracts, urbanized areas, etc
- Access to shapefiles with updated geometries for various years
- Harmonized attributes and geographic projections across geographies and years