Accessing DATASUS tables"

In datasus: An Interface to Brazilian Public Health Data

knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, comment = "#>")
library(datasus)

Overview

datasus provides catalog-driven interfaces to the tables published through TABNET. The main workflow is:

1. discover the system and dataset with datasus_catalogo();
2. inspect the dimensions and filters with datasus_opcoes();
3. submit a focused query through the corresponding function;
4. retain the provenance attached to the result.

Network examples are not evaluated when this vignette is built because the availability and response time of DATASUS are outside the package's control.

Discover datasets

The local catalog can be searched without contacting DATASUS:

datasus_catalogo()
datasus_catalogo("mortalidade")
datasus_catalogo("cnes")
datasus_catalogo("sinan")

The result identifies the system, dataset and public function to use. Once a dataset has been selected, inspect the current TABNET form:

options <- datasus_opcoes(
  sistema = "sim",
  conjunto = "obitos",
  abrangencia = "uf"
)

options$linha
options$coluna
options$conteudo
names(options$filtros)

Using the labels returned by datasus_opcoes() avoids embedding assumptions about a form that the portal may change later.

Vital statistics

sim() retrieves mortality data and sinasc() retrieves live-birth data. Both accept a year, "last" for the latest available period, and geographic or demographic filters.

deaths <- sim(
  conjunto = "obitos",
  abrangencia = "uf",
  periodo = 2024,
  coluna = "Ano do óbito"
)

male_deaths <- sim(
  conjunto = "obitos",
  uf = "MS",
  periodo = 2024,
  filtros = list(sexo = "Masculino")
)

births <- sinasc(
  uf = "MS",
  periodo = 2024,
  coluna = "Ano do nascimento"
)

The older sim_*() and sinasc_*() functions remain as compatibility wrappers, but new code should use the unified functions above.

Health services and population

Hospital, ambulatory and establishment tables follow the same conventions:

admissions <- sih_producao(
  uf = "MS",
  conteudo = "Internações",
  periodo = 2025,
  filtros = list(carater_atendimento = "Urgência")
)

procedures <- sia_producao(
  uf = "MS",
  conteudo = "Qtd.aprovada",
  periodo = 2025
)

beds <- cnes(
  conjunto = "leitos_internacao",
  uf = "MS",
  periodo = "last"
)

population <- populacao_residente(
  uf = "MS",
  periodo = 2021
)

Use sih_morbidade() when the analysis is diagnosis-oriented rather than production-oriented:

morbidity <- sih_morbidade(
  uf = "MS",
  linha = "Capítulo CID-10",
  conteudo = "Internações",
  periodo = 2025
)

Surveillance, screening and financing

The catalog also covers disease-specific SINAN tables, immunization, nutritional surveillance, cancer screening and SUS financing:

dengue <- sinan("dengue", uf = "MS", periodo = 2025)

coverage <- pni_imunizacoes(
  conjunto = "cobertura",
  uf = "MS"
)

mammograms <- siscan(
  conjunto = "mamografia_residencia",
  uf = "MS",
  periodo = 2025
)

nutrition <- sisvan(uf = "MS")
financing <- financiamento_sus(uf = "MS")

Provenance and reproducibility

Results carry a provenance record with the requested system, filters, source URL and retrieval time:

source <- datasus_proveniencia(deaths)
str(source)

For a reproducible analysis, save the query arguments together with the result, request explicit years instead of "last", and record the package version:

analysis_metadata <- list(
  package_version = as.character(packageVersion("datasus")),
  query = list(
    sistema = "sim",
    conjunto = "obitos",
    abrangencia = "uf",
    periodo = 2024
  ),
  provenance = datasus_proveniencia(deaths)
)

datasus documentation built on Oct. 7, 2026, 1:07 a.m.