healthbR

Overview

healthbR provides easy access to Brazilian public health data directly from R. The package downloads, caches, and processes data from official sources, returning clean, analysis-ready tibbles following tidyverse conventions.

Surveys (IBGE / Ministry of Health)

DATASUS (Ministry of Health FTP)

DATASUS modules download .dbc files (compressed DBF) and decompress them internally using vendored C code -- no external dependencies required.

Installation

You can install the development version of healthbR from GitHub:

# install.packages("pak")
pak::pak("SidneyBissoli/healthbR")

Quick start

library(healthbR)

# see all available data sources
list_sources()

DATASUS modules

All DATASUS modules follow a consistent API: *_years(), *_info(), *_variables(), *_dictionary(), *_data(), *_cache_status(), *_clear_cache().

# mortality data -- deaths in Acre, 2022
obitos <- sim_data(year = 2022, uf = "AC")

# filter by cause of death (CID-10 prefix)
obitos_cardio <- sim_data(year = 2022, uf = "AC", cause = "I")

# live births in Acre, 2022
nascimentos <- sinasc_data(year = 2022, uf = "AC")

# hospital admissions in Acre, January 2022
internacoes <- sih_data(year = 2022, month = 1, uf = "AC")

# filter by diagnosis (CID-10 prefix)
intern_resp <- sih_data(year = 2022, month = 1, uf = "AC", diagnosis = "J")

# outpatient production in Acre, January 2022
ambulatorial <- sia_data(year = 2022, month = 1, uf = "AC")

# different file type (e.g., high-cost medications)
medicamentos <- sia_data(year = 2022, month = 1, uf = "AC", type = "AM")

Survey modules

# VIGITEL telephone survey
vigitel <- vigitel_data(year = 2024)

# PNS national health survey
pns <- pns_data(year = 2019)

# PNAD Continua
pnadc <- pnadc_data(year = 2023, quarter = 1)

# POF household budget survey
pof <- pof_data(year = 2018, register = "morador")

# Census population
pop <- censo_populacao(year = 2022, territorial_level = "state")

Explore variables and dictionaries

# list variables for any module
sim_variables()
sia_variables(search = "sexo")

# data dictionary with category labels
sim_dictionary("SEXO")
sia_dictionary("PA_RACACOR")

Caching

All modules cache downloaded data automatically. Install arrow for optimized Parquet caching:

install.packages("arrow")

Each module provides cache management functions:

# check what is cached
sim_cache_status()
sih_cache_status()
sia_cache_status()

# clear cache for a module
sim_clear_cache()

Data sources

All data is downloaded from official Brazilian government repositories:

• VIGITEL: https://svs.aids.gov.br/daent/cgdnt/vigitel/
• PNS / PNAD Continua / POF: https://www.ibge.gov.br/
• Censo: SIDRA API (https://apisidra.ibge.gov.br/)
• SIM / SINASC / SIH / SIA: DATASUS FTP (ftp://ftp.datasus.gov.br/dissemin/publicos/)

Citation

If you use healthbR in your research, please cite it:

citation("healthbR")

Contributing

Contributions are welcome! Please open an issue to discuss proposed changes or submit a pull request.

Code of Conduct

Please note that the healthbR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

MIT © Sidney da Silva Pereira Bissoli