Download spatial data of Brazilian municipalities

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

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)
#> Using year/date 2017

# Read all municipalities of a state at a given year
mun <- read_municipality(code_muni = 33, year = 2010)
#> Using year/date 2010
mun <- read_municipality(code_muni = "RJ", year = 2010)
#> Using year/date 2010

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