Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
Usage
read_micro_region(
code_micro = "all",
year = 2010,
simplified = TRUE,
showProgress = TRUE,
cache = TRUE
)Arguments
- code_micro
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.- year
Numeric. Year of the data in YYYY format. Defaults to
2010.- simplified
Logic
FALSEorTRUE, indicating whether the function should return the data set with 'original' spatial resolution or a data set with 'simplified' geometry. Defaults toTRUE. For spatial analysis and statistics users should setsimplified = FALSE. Borders have been simplified by removing vertices of borders usingst_simplify{sf}preserving topology with adToleranceof 100.- showProgress
Logical. Defaults to
TRUEdisplay progress bar.- cache
Logical. Whether the function should read the data cached locally, which is faster. Defaults to
cache = TRUE. By default,geobrstores data files in a temporary directory that exists only within each R session. Ifcache = FALSE, the function will download the data again and overwrite the local file.
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_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()
Examples
# Read an specific micro region a given year
micro <- read_micro_region(code_micro=11008, year=2018)
#> Using year/date 2018
# Read micro regions of a state at a given year
micro <- read_micro_region(code_micro=12, year=2017)
#> Using year/date 2017
micro <- read_micro_region(code_micro="AM", year=2000)
#> Using year/date 2000
# Read all micro regions at a given year
micro <- read_micro_region(code_micro="all", year=2010)
#> Using year/date 2010