Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
read_region(year = 2010, simplified = TRUE, showProgress = TRUE, cache = TRUE)
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_micro_region()
,
read_municipal_seat()
,
read_municipality()
,
read_neighborhood()
,
read_pop_arrangements()
,
read_schools()
,
read_semiarid()
,
read_state()
,
read_statistical_grid()
,
read_urban_area()
,
read_urban_concentrations()
,
read_weighting_area()
# Read specific year
reg <- read_region(year=2018)