load_datasus: DATASUS - Mortality, hospitalizations and hospital beds

View source: R/datasus.R

load_datasusR Documentation

DATASUS - Mortality, hospitalizations and hospital beds

Description

Loads DATASUS data on health establishments, mortality, access to health services and several health indicators.

Usage

load_datasus(
  dataset,
  time_period,
  states = "all",
  raw_data = FALSE,
  keep_all = FALSE,
  language = "eng"
)

Arguments

dataset

A dataset name, can be one of ("datasus_sim_do", "datasus_sih", "datasus_cnes_lt"), or more. For more details, try vignette("DATASUS").

time_period

A numeric indicating for which years the data will be loaded, in the format YYYY. Can be any vector of numbers, such as 2010:2012.

states

A string specifying for which states to download the data. It is "all" by default, but can be a single state such as "AC" or any vector such as c("AC", "AM").

raw_data

A boolean setting the return of raw (TRUE) or processed (FALSE) data.

keep_all

A boolean choosing whether to aggregate the data by municipality, in turn losing individual-level variables (FALSE) or to keep all the original variables. Only applies when raw_data is TRUE.

language

A string that indicates in which language the data will be returned. Portuguese ("pt") and English ("eng") are supported.

Value

A tibble.

Examples

## Not run: 
# download raw data for the year 2010 in the state of AM.
data <- load_datasus(
  dataset = "datasus_sim_do",
  time_period = 2010,
  states = "AM",
  raw_data = TRUE
)

# download treated data with the number of deaths by cause in AM and PA.
data <- load_datasus(
  dataset = "datasus_sim_do",
  time_period = 2010,
  states = c("AM", "PA"),
  raw_data = FALSE
)

# download treated data with the number of deaths by cause in AM and PA
# keeping all individual variables.
data <- load_datasus(
  dataset = "datasus_sim_do",
  time_period = 2010,
  states = c("AM", "PA"),
  raw_data = FALSE,
  keep_all = TRUE
)

## End(Not run)


datazoompuc/datazoom.amazonia documentation built on April 20, 2024, 8:50 a.m.