Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
read_micro_region(
code_micro = "all",
year = 2010,
simplified = TRUE,
showProgress = TRUE,
cache = TRUE
)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.
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.
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_municipal_seat(),
read_municipality(),
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 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)