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Download spatial data of Brazilian health regions and health macro regions

Source: R/read_health_region.R
read_health_region.Rd

Health regions are used to guide the the regional and state planning of health services. Macro health regions, in particular, are used to guide the planning of high complexity health services. These services involve larger economics of scale and are concentrated in few municipalities because they are generally more technology intensive, costly and face shortages of specialized professionals. A macro region comprises one or more health regions.

Usage

read_health_region(
  year = 2013,
  macro = FALSE,
  simplified = TRUE,
  showProgress = TRUE,
  cache = TRUE
)

Arguments

year

Numeric. Year of the data in YYYY format. Defaults to 2013, the latest available.

macro

Logic. If FALSE (default), the function downloads health regions data. If TRUE, the function downloads macro regions data.

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_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 all health regions for a given year
hr <- read_health_region( year=2013 )
#> Using year/date 2013

# Read all macro health regions
mhr <- read_health_region( year=2013, macro =TRUE)
#> Using year/date 2013