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Download spatial data of Brazil's Immediate Geographic Areas

Source: R/read_immediate_region.R
read_immediate_region.Rd

The Immediate Geographic Areas are part of the geographic division of Brazil created in 2017 by IBGE. These regions were created to replace the "Micro Regions" division. Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)

Usage

read_immediate_region(
  code_immediate = "all",
  year = 2019,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

code_immediate

6-digit code of an immediate 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 immediate regions of that state. If code_immediate="all" (Default), the function downloads all immediate regions of the country.

year

Numeric. Year of the data in YYYY format. Defaults to 2019.

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_country(), read_disaster_risk_area(), read_health_facilities(), read_health_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 an specific immediate region
  im <- read_immediate_region(code_immediate=110006)
#> Using year/date 2019

# Read immediate regions of a state
  im <- read_immediate_region(code_immediate=12)
#> Using year/date 2019
  im <- read_immediate_region(code_immediate="AM")
#> Using year/date 2019

# Read all immediate regions of the country
  im <- read_immediate_region()
#> Using year/date 2019
  im <- read_immediate_region(code_immediate="all")
#> Using year/date 2019