R/read_municipality.R
read_municipality.RdData at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).
read_municipality(
code_muni = "all",
year = 2010,
simplified = TRUE,
showProgress = TRUE,
cache = TRUE,
keep_areas_operacionais = FALSE
)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.
Numeric. Year of the data in YYYY format. Defaults to 2010.
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.
Logical. Defaults to TRUE display progress bar.
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.
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.
An "sf" "data.frame" object
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()
# 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)