read_country: Download spatial data of Brazil's national borders

In geobr: Download Official Spatial Data Sets of Brazil

Download spatial data of Brazil's national borders

Description

Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674).

Usage

read_country(year = 2010, simplified = TRUE, showProgress = TRUE, cache = TRUE)

Arguments

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.

Value

An ⁠"sf" "data.frame"⁠ object

See Also

Other area functions: read_amazon(), read_biomes(), read_capitals(), read_comparable_areas(), 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_region(), read_schools(), read_semiarid(), read_state(), read_statistical_grid(), read_urban_area(), read_urban_concentrations(), read_weighting_area()

Examples

# Read specific year
br <- read_country(year = 2018)

geobr documentation built on Sept. 11, 2024, 6:58 p.m.