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