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
)
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 |
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 |
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.
|
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
See Also
Other area functions:
read_amazon(),
read_biomes(),
read_capitals(),
read_comparable_areas(),
read_country(),
read_disaster_risk_area(),
read_health_facilities(),
read_health_region(),
read_immediate_region(),
read_indigenous_land(),
read_intermediate_region(),
read_meso_region(),
read_metro_area(),
read_micro_region(),
read_municipal_seat(),
read_neighborhood(),
read_pop_arrangements(),
read_region(),
read_schools(),
read_semiarid(),
read_state(),
read_statistical_grid(),
read_urban_area(),
read_urban_concentrations(),
read_weighting_area()
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)
mun <- read_municipality(code_muni = 1200179, year = 2017)
mun <- read_municipality(code_muni = 33, year = 2010)
mun <- read_municipality(code_muni = "RJ", year = 2010)
mun <- read_municipality(code_muni = "all", year = 2018)
,
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]