Help for package geobr

Type:

Package

Title:

Download Official Spatial Data Sets of Brazil

Version:

1.9.1

URL:

https://ipeagit.github.io/geobr/, https://github.com/ipeaGIT/geobr

BugReports:

https://github.com/ipeaGIT/geobr/issues

Description:

Easy access to official spatial data sets of Brazil as 'sf' objects in R. The package includes a wide range of geospatial data available at various geographic scales and for various years with harmonized attributes, projection and fixed topology.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

TRUE

Depends:

R (≥ 3.5.0)

Imports:

curl (≥ 5.0.0), dplyr (≥ 0.8-3), data.table, fs, methods, sf (≥ 0.9-3), utils

Suggests:

arrow (≥ 15.0.1), censobr (≥ 0.3.2), covr, ggplot2 (≥ 3.3.1), knitr, rmarkdown, scales, testthat

RoxygenNote:

7.3.2

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2024-09-06 10:28:48 UTC; user

Author:

Rafael H. M. Pereira

[aut, cre], Caio Nogueira Goncalves [aut], Paulo Henrique Fernandes de Araujo [ctb], Guilherme Duarte Carvalho [ctb], Rodrigo Almeida de Arruda [ctb], Igor Nascimento [ctb], Barbara Santiago Pedreira da Costa [ctb], Welligtton Silva Cavedo [ctb], Pedro R. Andrade [ctb], Alan da Silva [ctb], Carlos Kauê Vieira Braga [ctb], Carl Schmertmann [ctb], Alessandro Samuel-Rosa [ctb], Daniel Ferreira [ctb], Marcus Saraiva [ctb], Beatriz Milz

[ctb], Ipea - Institue for Applied Economic Research [cph, fnd]

Maintainer:

Rafael H. M. Pereira <rafa.pereira.br@gmail.com>

Repository:

CRAN

Date/Publication:

2024-09-06 16:10:02 UTC

geobr: Download Official Spatial Data Sets of Brazil

Description

Usage

Please check the vignettes for more on the package usage:

Introduction to geobr (R) on the website.

Author(s)

Maintainer: Rafael H. M. Pereira rafa.pereira.br@gmail.com (ORCID)

Authors:

Caio Nogueira Goncalves

Other contributors:

Paulo Henrique Fernandes de Araujo [contributor]
Guilherme Duarte Carvalho [contributor]
Rodrigo Almeida de Arruda [contributor]
Igor Nascimento [contributor]
Barbara Santiago Pedreira da Costa [contributor]
Welligtton Silva Cavedo [contributor]
Pedro R. Andrade [contributor]
Alan da Silva [contributor]
Carlos Kauê Vieira Braga [contributor]
Carl Schmertmann [contributor]
Alessandro Samuel-Rosa [contributor]
Daniel Ferreira [contributor]
Marcus Saraiva [contributor]
Beatriz Milz (ORCID) [contributor]
Ipea - Institue for Applied Economic Research [copyright holder, funder]

Determine the state of a given CEP postal code

Description

Zips codes in Brazil are known as CEP, the abbreviation for postal code address. CEPs in Brazil are 8 digits long, with the format 'xxxxx-xxx'.

Usage

cep_to_state(cep)

Arguments

cep

A character string with 8 digits in the format "xxxxxxxx", or with the format 'xxxxx-xxx'.

Value

A character string with a state abbreviation.

Examples


uf <- cep_to_state(cep = '69900-000')

# Or:
uf <- cep_to_state(cep = '69900000')

Check internet connection with Ipea server

Description

Checks if there is an internet connection with Ipea server.

Usage

check_connection(
  url = "https://www.ipea.gov.br/geobr/metadata/metadata_gpkg.csv",
  silent = FALSE
)

Arguments

url

A string with the url address of an aop dataset

silent

Logical. Throw a message when silent is FALSE (default)

Value

Logical. TRUE if url is working, FALSE if not.

Download geopackage to tempdir

Description

Download geopackage to tempdir

Usage

download_gpkg(
  file_url = parent.frame()$file_url,
  showProgress = parent.frame()$showProgress,
  cache = parent.frame()$cache
)

Arguments

file_url

A string with the file_url address of a geobr dataset

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Logical. Whether the function should read the data cached locally, which is faster. Defaults to cache = TRUE. By default, geobr stores data files in a temporary directory that exists only within each R session. If cache = FALSE, the function will download the data again and overwrite the local file.

Support function to download metadata internally used in geobr

Description

Support function to download metadata internally used in geobr

Usage

download_metadata()

Examples

## Not run:  if (interactive()) {
df <- download_metadata()
}
## End(Not run)

Filter data set to return specific states

Description

Filter data set to return specific states

Usage

filter_state(
  temp_sf = parent.frame()$temp_sf,
  code = parent.frame()$code_state
)

Arguments

temp_sf

An internal simple feature or data.frame

code

The two-digit code of a state or a two-letter uppercase abbreviation (e.g. 33 or "RJ"). If code_state="all" (the default), the function downloads all states.

Value

A simple feature sf or data.frame.

A correspondence table indicating what quadrants of IBGE's statistical grid intersect with each Brazilian state

Description

Built-in dataset

name_state: Title-case name of state (character)
abbrev_state: Two-letter uppercase abbreviation of a state
code_grid: Unique code of each quadrant of IBGE's statistical grid

Usage

data(grid_state_correspondence_table)

Format

A data frame sf with 139 rows and 3 columns

Details

correspondence table indicating what quadrants of IBGE's statistical grid intersect with each Brazilian state

Note

Last updated 2021-o3-21

List all data sets available in the geobr package

Description

Returns a data frame with all datasets available in the geobr package

Usage

list_geobr()

Value

A data.frame

Examples


df <- list_geobr()

Load geopackage from tempdir to global environment

Description

Load geopackage from tempdir to global environment

Usage

load_gpkg(temps = NULL)

Arguments

temps

The address of a gpkg file stored in tempdir. Defaults to NULL

Look up municipality codes and names

Description

Input a municipality name or code and get the names and codes of the municipality's corresponding state, meso, micro, intermediate, and immediate regions

Usage

lookup_muni(name_muni = NULL, code_muni = NULL)

Arguments

name_muni

The municipality name to be looked up.

code_muni

The municipality code to be looked up.

Details

Only available from 2010 Census data so far

Value

A data.frame with 13 columns identifying the geographies information of that municipality.

A data.frame

Examples


# Get lookup table for municipality Rio de Janeiro
mun <- lookup_muni(name_muni = "Rio de Janeiro")

# Or you can get a lookup table for the same municipality searching for its code
mun <- lookup_muni(code_muni = 3304557)

# Get lookup table for all municipalities
mun_all <- lookup_muni(name_muni = "all")

# Or:
mun_all <- lookup_muni(code_muni = "all")

Check if vector only has numeric characters

Description

Checks if vector only has numeric characters

Usage

numbers_only(x)

Arguments

x

A vector.

Value

Logical. TRUE if vector only has numeric characters.

Download spatial data of Brazil's Legal Amazon

Description

This data set covers the whole of Brazil's Legal Amazon as defined in the federal law n. 12.651/2012). The original data comes from the Brazilian Ministry of Environment (MMA) and can be found at "http://mapas.mma.gov.br/i3geo/datadownload.htm".

Usage

read_amazon(year = 2012, simplified = TRUE, showProgress = TRUE, cache = TRUE)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2012.

simplified

Logic FALSE or TRUE, indicating whether the function should return the data set with 'original' spatial resolution or a data set with 'simplified' geometry. Defaults to TRUE. For spatial analysis and statistics users should set simplified = FALSE. Borders have been simplified by removing vertices of borders using ⁠st_simplify{sf}⁠ preserving topology with a dTolerance of 100.

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read Brazilian Legal Amazon
a <- read_amazon(year = 2012)

Download spatial data of Brazilian biomes

Description

This data set includes polygons of all biomes present in Brazilian territory and coastal area. The latest data set dates to 2019 and it is available at scale 1:250.000. The 2004 data set is at the scale 1:5.000.000. The original data comes from IBGE. More information at https://www.ibge.gov.br/apps/biomas/

Usage

read_biomes(year = 2019, simplified = TRUE, showProgress = TRUE, cache = TRUE)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2019.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read biomes
b <- read_biomes(year = 2019)

Download data of state capitals

Description

This function downloads either a spatial sf object with the location of the municipal seats (sede dos municipios) of state capitals, or a data.frame with the names and codes of state capitals. Data downloaded for the latest available year.

Usage

read_capitals(as_sf = TRUE, showProgress = TRUE)

Arguments

as_sf

Logic FALSE or TRUE, indicating whether the function should return a spatial data in sf format (Defaults to TRUE) or in a data.frame format without spatial information (FALSE).

showProgress

Logical. Defaults to TRUE display progress bar.

Value

An ⁠"sf" "data.frame"⁠ object or a "data.frame"

Examples


# Read spatial data with the  municipal seats of state capitals
capitals_sf <- read_capitals(as_sf = TRUE)

# Read simple data.frame of state capitals
capitals_df <- read_capitals(as_sf = FALSE)

Download spatial data of census tracts of the Brazilian Population Census

Description

Download spatial data of census tracts of the Brazilian Population Census

Usage

read_census_tract(
  code_tract,
  year = 2010,
  zone = "urban",
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_tract

The 7-digit code of a Municipality. If the two-digit code or a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or "RJ") the function will load all census tracts of that state. If code_tract="all", the function downloads all census tracts of the country.

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

zone

For census tracts before 2010, 'urban' and 'rural' census tracts are separate data sets.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read rural census tracts for years before 2007
  c <- read_census_tract(code_tract=5201108, year=2000, zone="rural")

# Read all census tracts of a state at a given year
  c <- read_census_tract(code_tract=53, year=2010) # or
  c <- read_census_tract(code_tract="DF", year=2010)
  plot(c)

# Read all census tracts of a municipality at a given year
  c <- read_census_tract(code_tract=5201108, year=2010)
  plot(c)

# Read all census tracts of the country at a given year
  c <- read_census_tract(code_tract="all", year=2010)

Download spatial data of historically comparable municipalities

Description

This function downloads the shape file of minimum comparable area of municipalities, known in Portuguese as 'Areas minimas comparaveis (AMCs)'. The data is available for any combination of census years between 1872-2010. These data sets are generated based on the Stata code originally developed by Ehrl (2017) doi:10.1590/0101-416147182phe, and translated into R by the geobr team.

Usage

read_comparable_areas(
  start_year = 1970,
  end_year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

start_year

Numeric. Start year to the period in the YYYY format. Defaults TO 1970.

end_year

Numeric. End year to the period in the YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Details

These data sets are generated based on the original Stata code developed by Philipp Ehrl. If you use these data, please cite:

Ehrl, P. (2017). Minimum comparable areas for the period 1872-2010: an aggregation of Brazilian municipalities. Estudos Econômicos (São Paulo), 47(1), 215-229. https://doi.org/10.1590/0101-416147182phe

Value

An ⁠"sf" "data.frame"⁠ object

Examples


  amc <- read_comparable_areas(start_year=1970, end_year=2010)

Download spatial data of Brazilian environmental conservation units

Description

This data set covers the whole of Brazil and it includes the polygons of all conservation units present in Brazilian territory. The last update of the data was 09-2019. The original data comes from MMA and can be found at "http://mapas.mma.gov.br/i3geo/datadownload.htm".

Usage

read_conservation_units(
  date = 201909,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

date

Numeric. Date of the data in YYYYMM format. Defaults to 201909.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read conservation_units
b <- read_conservation_units(date = 201909)

Download spatial data of Brazil's national borders

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).

Usage

read_country(year = 2010, simplified = TRUE, showProgress = TRUE, cache = TRUE)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read specific year
br <- read_country(year = 2018)

Download spatial data of disaster risk areas

Description

This function reads the the official data of disaster risk areas in Brazil (currently only available for 2010). It specifically focuses on geodynamic and hydro-meteorological disasters capable of triggering landslides and floods. The data set covers the whole country. Each risk area polygon (known as 'BATER') has unique code id (column 'geo_bater'). The data set brings information on the extent to which the risk area polygons overlap with census tracts and block faces (column "acuracia") and number of ris areas within each risk area (column 'num'). Original data were generated by IBGE and CEMADEN. For more information about the methodology, see deails at https://www.ibge.gov.br/geociencias/organizacao-do-territorio/tipologias-do-territorio/21538-populacao-em-areas-de-risco-no-brasil.html

Usage

read_disaster_risk_area(
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read all disaster risk areas in an specific year
d <- read_disaster_risk_area(year=2010)

Download geolocated data of health facilities

Description

Data comes from the National Registry of Healthcare facilities (Cadastro Nacional de Estabelecimentos de Saude - CNES), originally collected by the Brazilian Ministry of Health. According to the Ministry of Health: "The coordinates of each facility were obtained by CNES and validated by means of space operations. These operations verify if the point is in the municipality, considering a radius of 5,000 meters. When the coordinate is not correct, further searches are done in other systems of the Ministry of Health and in web services like Google Maps. Finally, if the coordinates have been correctly obtained in this process, the coordinates of the municipal head office are used. The geocode source used is registered in the database in a specific column data_source. Periodically the coordinates are revised with the objective of improving the quality of the data." The date of the last data update is registered in the database in the columns date_update and year_update. More information in the CNES data set available at https://dados.gov.br/. These data use Geodetic reference system "SIRGAS2000" and CRS(4674).

Usage

read_health_facilities(date = 202303, showProgress = TRUE, cache = TRUE)

Arguments

date

Numeric. Date of the data in YYYYMM format. Defaults to 202303, which was the latest data available by the time of this update.

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read all health facilities of the whole country
h <- read_health_facilities( date = 202303)

Download spatial data of Brazilian health regions and health macro regions

Description

Health regions are used to guide the the regional and state planning of health services. Macro health regions, in particular, are used to guide the planning of high complexity health services. These services involve larger economics of scale and are concentrated in few municipalities because they are generally more technology intensive, costly and face shortages of specialized professionals. A macro region comprises one or more health regions.

Usage

read_health_region(
  year = 2013,
  macro = FALSE,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2013, the latest available.

macro

Logic. If FALSE (default), the function downloads health regions data. If TRUE, the function downloads macro regions data.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read all health regions for a given year
hr <- read_health_region( year=2013 )

# Read all macro health regions
mhr <- read_health_region( year=2013, macro =TRUE)

Download spatial data of Brazil's Immediate Geographic Areas

Description

The Immediate Geographic Areas are part of the geographic division of Brazil created in 2017 by IBGE. These regions were created to replace the "Micro Regions" division. Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_immediate_region(
  code_immediate = "all",
  year = 2019,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_immediate

6-digit code of an immediate region. If the two-digit code or a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or "RJ") the function will load all immediate regions of that state. If code_immediate="all" (Default), the function downloads all immediate regions of the country.

year

Numeric. Year of the data in YYYY format. Defaults to 2019.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read an specific immediate region
  im <- read_immediate_region(code_immediate=110006)

# Read immediate regions of a state
  im <- read_immediate_region(code_immediate=12)
  im <- read_immediate_region(code_immediate="AM")

# Read all immediate regions of the country
  im <- read_immediate_region()
  im <- read_immediate_region(code_immediate="all")

Download spatial data of indigenous lands in Brazil

Description

The data set covers the whole of Brazil and it includes indigenous lands from all ethnicities and in different stages of demarcation. The original data comes from the National Indian Foundation (FUNAI) and can be found at https://www.gov.br/funai/pt-br/atuacao/terras-indigenas/geoprocessamento-e-mapas. Although original data is updated monthly, the geobr package will only keep the data for a few months per year.

Usage

read_indigenous_land(
  date = 201907,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

date

Numeric. Date of the data in YYYYMM format. Defaults to 201907.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read all indigenous land in an specific date
i <- read_indigenous_land(date=201907)

Download spatial data of Brazil's Intermediate Geographic Areas

Description

The intermediate Geographic Areas are part of the geographic division of Brazil created in 2017 by IBGE. These regions were created to replace the "Meso Regions" division. Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_intermediate_region(
  code_intermediate = "all",
  year = 2019,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_intermediate

4-digit code of an intermediate region. If the two-digit code or a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or "RJ") the function will load all intermediate regions of that state. If code_intermediate="all" (Default), the function downloads all intermediate regions of the country.

year

Numeric. Year of the data in YYYY format. Defaults to 2019.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read an specific intermediate region
  im <- read_intermediate_region(code_intermediate=1202)

# Read intermediate regions of a state
  im <- read_intermediate_region(code_intermediate=12)
  im <- read_intermediate_region(code_intermediate="AM")

# Read all intermediate regions of the country
  im <- read_intermediate_region()
  im <- read_intermediate_region(code_intermediate="all")

Download spatial data of meso regions

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_meso_region(
  code_meso = "all",
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_meso

The 4-digit code of a meso region. If the two-digit code or a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or "RJ") the function will load all meso regions of that state. If code_meso="all" (Default), the function downloads all meso regions of the country.

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read specific meso region at a given year
  meso <- read_meso_region(code_meso=3301, year=2018)

# Read all meso regions of a state at a given year
  meso <- read_meso_region(code_meso=12, year=2017)
  meso <- read_meso_region(code_meso="AM", year=2000)

# Read all meso regions of the country at a given year
  meso <- read_meso_region(code_meso="all", year=2010)

Download spatial data of official metropolitan areas in Brazil

Description

The function returns the shapes of municipalities grouped by their respective metro areas. Metropolitan areas are created by each state in Brazil. The data set includes the municipalities that belong to all metropolitan areas in the country according to state legislation in each year. Original data were generated by Institute of Geography. Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).

Usage

read_metro_area(
  year = 2018,
  code_state = "all",
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2018.

code_state

The two-digit code of a state or a two-letter uppercase abbreviation (e.g. 33 or "RJ"). If code_state="all" (the default), the function downloads all states.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read all official metropolitan areas for a given year
  m <- read_metro_area(2005)

  m <- read_metro_area(2018)

Download spatial data of micro regions

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_micro_region(
  code_micro = "all",
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_micro

5-digit code of a micro region. If the two-digit code or a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or "RJ") the function will load all micro regions of that state. If code_micro="all" (Default), the function downloads all micro regions of the country.

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read an specific micro region a given year
  micro <- read_micro_region(code_micro=11008, year=2018)

# Read micro regions of a state at a given year
  micro <- read_micro_region(code_micro=12, year=2017)
  micro <- read_micro_region(code_micro="AM", year=2000)

# Read all micro regions at a given year
  micro <- read_micro_region(code_micro="all", year=2010)

Download spatial data of municipal seats (sede dos municipios) in Brazil

Description

This function reads the official data on the municipal seats (sede dos municipios) of Brazil. The data brings the geographical coordinates (lat lon) of municipal seats for various years between 1872 and 2010. Original data were generated by Brazilian Institute of Geography and Statistics (IBGE).

Usage

read_municipal_seat(year = 2010, showProgress = TRUE, cache = TRUE)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read municipal seats in an specific year
m <- read_municipal_seat(year = 1991)

Download spatial data of Brazilian municipalities

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).

Usage

read_municipality(
  code_muni = "all",
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE,
  keep_areas_operacionais = FALSE
)

Arguments

code_muni

The 7-digit identification code of a municipality. If code_muni = "all" (Default), the function downloads all municipalities of the country. Alternatively, if a two-digit identification code or a two-letter uppercase abbreviation of a state is passed (e.g. 33 or "RJ"), all municipalities of that state will be downloaded. Municipality identification codes can be consulted with the geobr::lookup_muni() function.

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

keep_areas_operacionais

Logic. Whether the function should keep the polygons of Lagoas dos Patos and Lagoa Mirim in the State of Rio Grande do Sul (considered as areas estaduais operacionais). Defaults to FALSE.

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# 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)
mun <- read_municipality(code_muni = "RJ", year = 2010)

# Read all municipalities of the country at a given year
mun <- read_municipality(code_muni = "all", year = 2018)

Download spatial data of neighborhood limits of Brazilian municipalities

Description

This data set includes the neighborhood limits of 720 Brazilian municipalities. It is based on aggregations of the census tracts from the Brazilian census. Only 2010 data is currently available.

Usage

read_neighborhood(
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read neighborhoods of Brazilian municipalities
n <- read_neighborhood(year=2010)

Download population arrangements in Brazil

Description

This function reads the official data on population arrangements (Arranjos Populacionais) of Brazil. Original data were generated by the Institute of Geography and Statistics (IBGE) For more information about the methodology, see details at https://www.ibge.gov.br/apps/arranjos_populacionais/2015/pdf/publicacao.pdf

Usage

read_pop_arrangements(
  year = 2015,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2015.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read urban footprint of Brazilian cities in an specific year
uc <- read_pop_arrangements(year=2015)

Download spatial data of Brazil Regions

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_region(year = 2010, simplified = TRUE, showProgress = TRUE, cache = TRUE)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read specific year
reg <- read_region(year=2018)

Download geolocated data of schools

Description

Data comes from the School Census collected by INEP, the National Institute for Educational Studies and Research Anisio Teixeira. The date of the last data update is registered in the database in the column 'date_update'. These data uses Geodetic reference system "SIRGAS2000" and CRS(4674). The coordinates of each school if collected by INEP. Periodically the coordinates are revised with the objective of improving the quality of the data. More information available at https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/inep-data/catalogo-de-escolas/

Usage

read_schools(year = 2020, showProgress = TRUE, cache = TRUE)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2020.

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read all schools in the country
s <- read_schools( year = 2020)

Download spatial data of the Brazilian Semiarid region

Description

This data set covers the whole of Brazilian Semiarid as defined in the resolution in 23/11/2017). The original data comes from the Brazilian Institute of Geography and Statistics (IBGE) and can be found at https://www.ibge.gov.br/geociencias/cartas-e-mapas/mapas-regionais/15974-semiarido-brasileiro.html?=&t=downloads

Usage

read_semiarid(
  year = 2017,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2017.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read Brazilian semiarid
a <- read_semiarid(year=2017)

Download spatial data of Brazilian states

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_state(
  code_state = "all",
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_state

The two-digit code of a state or a two-letter uppercase abbreviation (e.g. 33 or "RJ"). If code_state="all" (the default), the function downloads all states.

year

Numeric. Year of the data in YYYY format. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read specific state at a given year
  uf <- read_state(code_state=12, year=2017)

# Read specific state at a given year
  uf <- read_state(code_state="SC", year=2000)

# Read all states at a given year
  ufs <- read_state(code_state="all", year=2010)

Download spatial data of IBGE's statistical grid

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_statistical_grid(
  code_grid,
  year = 2010,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_grid

If two-letter abbreviation or two-digit code of a state is passed, the function will load all grid quadrants that intersect with that state. If code_grid="all", the grid of the whole country will be loaded. Users may also pass a grid quadrant id to load an specific quadrant. Quadrant ids can be consulted at geobr::grid_state_correspondence_table.

year

Numeric. Year of the data in YYYY format. Defaults to 2010. The only year available thus far is 2010.

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read a particular grid at a given year
grid <- read_statistical_grid(code_grid = 45, year=2010)

# Read the grid covering a given state at a given year
state_grid <- read_statistical_grid(code_grid = "RJ")

Download spatial data of urbanized areas in Brazil

Description

This function reads the official data on the urban footprint of Brazilian cities in the years 2005 and 2015. Original data were generated by the Institute of Geography and Statistics (IBGE) For more information about the methodology, see details at https://biblioteca.ibge.gov.br/visualizacao/livros/liv100639.pdf

Usage

read_urban_area(
  year = 2015,
  code_state = "all",
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2015.

code_state

The two-digit code of a state or a two-letter uppercase abbreviation (e.g. 33 or "RJ"). If code_state="all" (the default), the function downloads all states.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read urban footprint of Brazilian cities in an specific year
d <- read_urban_area(year=2005)

Download urban concentration areas in Brazil

Description

This function reads the official data on the urban concentration areas (Areas de Concentracao de Populacao) of Brazil. Original data were generated by the Institute of Geography and Statistics (IBGE) For more information about the methodology, see details at https://www.ibge.gov.br/apps/arranjos_populacionais/2015/pdf/publicacao.pdf

Usage

read_urban_concentrations(
  year = 2015,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. A year number in YYYY format. Defaults to 2015.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read urban footprint of Brazilian cities in an specific year
uc <- read_urban_concentrations(year=2015)

Download spatial data of Census Weighting Areas (area de ponderacao) of the Brazilian Population Census

Description

Only 2010 data is currently available.

Usage

read_weighting_area(
  code_weighting = "all",
  year = 2010,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_weighting

The 7-digit code of a Municipality. If the two-digit code or a two-letter uppercase abbreviation of a state is passed, (e.g. 33 or "RJ") the function will load all weighting areas of that state. If code_weighting="all", all weighting areas of the country are loaded.

year

Numeric. Year of the data. Defaults to 2010.

simplified

showProgress

Logical. Defaults to TRUE display progress bar.

cache

Value

An ⁠"sf" "data.frame"⁠ object

Examples


# Read specific weighting area at a given year
w <- read_weighting_area(code_weighting=5201108005004, year=2010)

# Read all weighting areas of a state at a given year
w <- read_weighting_area(code_weighting=53, year=2010) # or
w <- read_weighting_area(code_weighting="DF", year=2010)
plot(w)

# Read all weighting areas of a municipality at a given year
w <- read_weighting_area(code_weighting=5201108, year=2010)
plot(w)

# Read all weighting areas of the country at a given year
w <- read_weighting_area(code_weighting="all", year=2010)

Select data type: 'original' or 'simplified' (default)

Description

Select data type: 'original' or 'simplified' (default)

Usage

select_data_type(temp_meta, simplified = NULL)

Arguments

temp_meta

A dataframe with the file_url addresses of geobr datasets

simplified

Logical TRUE or FALSE indicating whether the function returns the 'original' dataset with high resolution or a dataset with 'simplified' borders (Defaults to TRUE)

Select metadata

Description

Select metadata

Usage

select_metadata(geography, year = NULL, simplified = NULL)

Arguments

geography

Which geography will be downloaded.

year

Year of the dataset (passed by read_ function).

simplified

Logical TRUE or FALSE indicating whether the function returns the 'original' dataset with high resolution or a dataset with 'simplified' borders (Defaults to TRUE).

Examples

## Not run:  if (interactive()) {

library(geobr)

df <- download_metadata()

}
## End(Not run)

Select year input

Description

Select year input

Usage

select_year_input(temp_meta, y = year)

Arguments

temp_meta

A dataframe with the file_url addresses of geobr datasets

y

Year of the dataset (passed by red_ function)

geobr: Download Official Spatial Data Sets of Brazil

Description

Usage

Author(s)

See Also

Determine the state of a given CEP postal code

Description

Usage

Arguments

Value

Examples

Check internet connection with Ipea server

Description

Usage

Arguments

Value

Download geopackage to tempdir

Description

Usage

Arguments

Support function to download metadata internally used in geobr

Description

Usage

Examples

Filter data set to return specific states

Description

Usage

Arguments

Value

A correspondence table indicating what quadrants of IBGE's statistical grid intersect with each Brazilian state

Description

Usage

Format

Details

Note

List all data sets available in the geobr package

Description

Usage

Value

See Also

Examples

Load geopackage from tempdir to global environment

Description

Usage

Arguments

Look up municipality codes and names

Description

Usage

Arguments

Details

Value

See Also

Examples

Check if vector only has numeric characters

Description

Usage

Arguments

Value

Download spatial data of Brazil's Legal Amazon

Description

Usage

Arguments

Value

See Also

Examples

Download spatial data of Brazilian biomes

Description

Usage

Arguments

Value

See Also

Examples

Download data of state capitals

Description

Usage

Arguments

Value

See Also

Examples

Download spatial data of census tracts of the Brazilian Population Census