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OpenDataSUS publishes record-level surveillance files separately from
TABNET. datasus provides a generic catalog client and
convenience functions for frequently used datasets:
| Dataset | Function | Standardization key |
|---|---|---|
| Serious adverse events following immunization | esavi() |
"esavi" |
| Influenza-like illness notifications | esus_sindrome_gripal() |
"sindrome_gripal" |
| Individual PNI doses | pni_doses() |
"pni_doses" |
| COVID-19 hospital occupancy | ocupacao_hospitalar() |
"ocupacao_hospitalar" |
Search the catalog and inspect its resources before requesting a large file:
opendatasus_catalogo("ESAVI")
opendatasus_catalogo("doses aplicadas PNI")
resources <- opendatasus_recursos("esavi")
resources[, c("id", "nome", "formato", "ano", "tamanho")]"last" follows the latest partition found in the live
catalog. Use an explicit year and month when the analysis must remain
reproducible.
The convenience functions accept n_max for exploratory
reads and colunas to avoid parsing fields that are not
needed:
events <- esavi(
n_max = 1000,
colunas = c(
"nu_notificacao", "dt_notificacao", "nu_idade", "ds_sexo"
),
normalizar = TRUE
)
illness <- esus_sindrome_gripal(
uf = "MS",
ano = 2024,
n_max = 1000,
colunas = c(
"dataNotificacao", "municipioIBGE", "idade", "sexo"
),
normalizar = TRUE
)
doses <- pni_doses(
ano = 2026,
mes = 1,
n_max = 1000,
colunas = c(
"co_paciente", "dt_vacina", "co_vacina",
"co_municipio_paciente"
),
normalizar = TRUE
)
occupancy <- ocupacao_hospitalar(
ano = 2022,
n_max = 1000,
colunas = c(
"dataNotificacao", "cnes", "ocupacaoHospitalarUti"
),
normalizar = TRUE
)Column names supplied to colunas are the names in the
source file. With normalizar = TRUE, the returned names are
the stable analysis names defined by the package dictionary.
Standardization can also be applied after data have been imported elsewhere. This offline example uses fields from the ESAVI dictionary:
raw_events <- data.frame(
nu_notificacao = c("A-001", "A-002"),
dt_notificacao = c("2026-01-10", "2026-01-11"),
nu_idade = c("34", "67"),
ds_sexo = c("Feminino", "Masculino"),
stringsAsFactors = FALSE
)
events <- datasus_padronizar(raw_events, sistema = "esavi")
str(events)
#> 'data.frame': 2 obs. of 4 variables:
#> $ id_notificacao : chr "A-001" "A-002"
#> $ data_notificacao: Date, format: "2026-01-10" "2026-01-11"
#> $ idade : num 34 67
#> $ sexo : chr "Feminino" "Masculino"
#> - attr(*, "datasus_dicionario")='data.frame': 4 obs. of 7 variables:
#> ..$ sistema : chr [1:4] "esavi" "esavi" "esavi" "esavi"
#> ..$ tipo : chr [1:4] "*" "*" "*" "*"
#> ..$ campo : chr [1:4] "nu_notificacao" "ds_sexo" "nu_idade" "dt_notificacao"
#> ..$ campo_padronizado: chr [1:4] "id_notificacao" "sexo" "idade" "data_notificacao"
#> ..$ descricao : chr [1:4] "Identificador da notificacao" "Sexo" "Idade no evento" "Data da notificacao"
#> ..$ classe : chr [1:4] "character" "character" "numeric" "date"
#> ..$ formato : chr [1:4] "" "" "" "%d/%m/%Y|%Y-%m-%d"The dictionary documents source names, standardized names, semantic labels and expected classes:
head(datasus_dicionario("esavi"), 8)
#> sistema tipo campo campo_padronizado
#> 1 esavi * nu_notificacao id_notificacao
#> 2 esavi * ds_sexo sexo
#> 3 esavi * nu_idade idade
#> 4 esavi * st_comunidade_tradicional comunidade_tradicional
#> 5 esavi * ds_not_mae_filho exposicao_materna
#> 6 esavi * nu_mes_gestante mes_gestacao
#> 7 esavi * ds_versao_medra versao_meddra
#> 8 esavi * ds_mulher_amamentando amamentando
#> descricao classe formato
#> 1 Identificador da notificacao character
#> 2 Sexo character
#> 3 Idade no evento numeric
#> 4 Pertence a comunidade tradicional logical
#> 5 Exposicao pela gestacao ou aleitamento logical
#> 6 Mes de gestacao na vacinacao numeric
#> 7 Versao da terminologia MedDRA character
#> 8 Amamentando no momento da vacinacao logicaldatasus_validar_esquema() checks whether important
fields are present and whether their classes agree with the curated
schema:
validation <- datasus_validar_esquema(
events,
sistema = "esavi",
campos = c(
"id_notificacao", "data_notificacao", "idade", "sexo"
)
)
validation
#> campo campo_padronizado presente coluna_observada classe_observada
#> 1 nu_notificacao id_notificacao TRUE id_notificacao character
#> 2 ds_sexo sexo TRUE sexo character
#> 3 nu_idade idade TRUE idade numeric
#> 4 dt_notificacao data_notificacao TRUE data_notificacao Date
#> classe_esperada status
#> 1 character padronizado
#> 2 character padronizado
#> 3 numeric padronizado
#> 4 date padronizadoSet estrito = TRUE in automated pipelines to stop when a
required field is missing or has an incompatible class:
Some historical influenza-like illness resources publish their physical files as links in the resource description. Expand them before building a download plan:
resources <- opendatasus_recursos(
"notificacoes-de-sindrome-gripal-leve-2020"
)
ms_id <- resources$id[
resources$formato == "CSV" & grepl("^Dados MS", resources$nome)
][1]
files <- opendatasus_arquivos(
"notificacoes-de-sindrome-gripal-leve-2020",
recurso = ms_id,
formato = "CSV"
)
files[, c("recurso", "ano", "parte", "url")]esus_sindrome_gripal() reads all these parts
transparently and applies n_max across the combined result,
rather than independently to every file.
Downloaded data retain the resource identifier, official URLs, update and download times, local cache paths and checksums:
Use atualizar = TRUE to ignore a cached copy and obtain
the current portal version.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.