Package 'healthbR'

Description

Shows information about cached ANS data files.

Usage

ans_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other ans: ans_clear_cache(), ans_data(), ans_info(), ans_operators(), ans_variables(), ans_years()

Examples

ans_cache_status()

Description

Deletes cached ANS data files.

Usage

ans_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other ans: ans_cache_status(), ans_data(), ans_info(), ans_operators(), ans_variables(), ans_years()

Examples

ans_clear_cache()

Description

Downloads and returns data from the ANS (Agencia Nacional de Saude Suplementar) open data portal. Supports three data types: beneficiary counts, consumer complaints (NIP), and financial statements.

Usage

ans_data(
  year,
  type = "beneficiaries",
  uf = NULL,
  month = NULL,
  quarter = NULL,
  vars = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

Data is downloaded from the ANS open data portal at ⁠https://dadosabertos.ans.gov.br/⁠.

Beneficiaries: Monthly per-state ZIP files containing CSV data with consolidated beneficiary counts by operator, plan type, sex, age group, and municipality. Available from April 2019.

Complaints: Annual national CSV files with consumer complaints filed through the NIP (Notificacao de Intermediacao Preliminar). Available from 2011.

Financial: Quarterly ZIP files with financial statements of health plan operators (balance sheets, income statements). Available from 2007.

Parallel downloads

When downloading multiple files (e.g., several months or quarters), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with ANS data. Includes partition columns: year (all types), month and uf_source (beneficiaries), quarter (financial).

See Also

ans_operators() for the operator registry, ans_variables() for variable descriptions.

Other ans: ans_cache_status(), ans_clear_cache(), ans_info(), ans_operators(), ans_variables(), ans_years()

Examples

# beneficiary counts for Acre, December 2023
ac <- ans_data(year = 2023, month = 12, uf = "AC")

# consumer complaints for 2022
nip <- ans_data(year = 2022, type = "complaints")

# financial statements Q1 2023
fin <- ans_data(year = 2023, type = "financial", quarter = 1)

Description

Displays information about the ANS (Agencia Nacional de Saude Suplementar) module, including data sources, available years, and usage guidance.

Usage

ans_info()

Value

A list with module information (invisibly).

See Also

Other ans: ans_cache_status(), ans_clear_cache(), ans_data(), ans_operators(), ans_variables(), ans_years()

Examples

ans_info()

Description

Downloads and returns the current registry of health plan operators from the ANS open data portal. This is a snapshot of the current operator status (not time-series data).

Usage

ans_operators(status = "active", vars = NULL, cache = TRUE, cache_dir = NULL)

Arguments

Value

A tibble with operator data. When status = "all", includes a status column indicating "active" or "cancelled".

See Also

Other ans: ans_cache_status(), ans_clear_cache(), ans_data(), ans_info(), ans_variables(), ans_years()

Examples

# active operators
ops <- ans_operators()

# all operators (active + cancelled)
all_ops <- ans_operators(status = "all")

Description

Returns a tibble with available variables in the ANS data, including descriptions and value types.

Usage

ans_variables(type = "beneficiaries", search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other ans: ans_cache_status(), ans_clear_cache(), ans_data(), ans_info(), ans_operators(), ans_years()

Examples

ans_variables()
ans_variables(type = "complaints")
ans_variables(search = "operadora")

Description

Returns an integer vector with years for which ANS data are available.

Usage

ans_years(type = "beneficiaries")

Arguments

Value

An integer vector of available years.

See Also

Other ans: ans_cache_status(), ans_clear_cache(), ans_data(), ans_info(), ans_operators(), ans_variables()

Examples

ans_years()
ans_years(type = "complaints")
ans_years(type = "financial")

Description

Shows information about cached ANVISA data files.

Usage

anvisa_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other anvisa: anvisa_clear_cache(), anvisa_data(), anvisa_info(), anvisa_types(), anvisa_variables()

Examples

anvisa_cache_status()

Description

Deletes cached ANVISA data files.

Usage

anvisa_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other anvisa: anvisa_cache_status(), anvisa_data(), anvisa_info(), anvisa_types(), anvisa_variables()

Examples

anvisa_clear_cache()

Description

Downloads and returns data from the ANVISA (Agencia Nacional de Vigilancia Sanitaria) open data portal. Supports 14 data types across 4 categories: product registrations, reference tables, post-market surveillance, and controlled substance sales (SNGPC).

Usage

anvisa_data(
  type = "medicines",
  year = NULL,
  month = NULL,
  vars = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

Data is downloaded from the ANVISA open data portal at ⁠https://dados.anvisa.gov.br/dados/⁠.

Snapshot types: Download a single CSV file representing the current state of the registry/database. No time dimension. Cached as flat files.

SNGPC types: Monthly CSV files with controlled substance sales data. Data available from January 2014 to October 2021, with new data from January 2026. Cached as Hive-style partitioned parquet datasets.

The three VigiMed types share the IDENTIFICACAO_NOTIFICACAO key for linking notifications, medicines, and reactions.

Parallel downloads

When downloading multiple SNGPC months, install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with ANVISA data. SNGPC types include year and month partition columns.

See Also

anvisa_types() for available types, anvisa_variables() for variable descriptions.

Other anvisa: anvisa_cache_status(), anvisa_clear_cache(), anvisa_info(), anvisa_types(), anvisa_variables()

Examples

# registered medicines
med <- anvisa_data(type = "medicines")

# hemovigilance notifications
hemo <- anvisa_data(type = "hemovigilance")

# SNGPC controlled substance sales, Jan 2020
sngpc <- anvisa_data(type = "sngpc", year = 2020, month = 1)

Description

Displays information about the ANVISA (Agencia Nacional de Vigilancia Sanitaria) module, including data sources, available types, and usage guidance.

Usage

anvisa_info()

Value

A list with module information (invisibly).

See Also

Other anvisa: anvisa_cache_status(), anvisa_clear_cache(), anvisa_data(), anvisa_types(), anvisa_variables()

Examples

anvisa_info()

Description

Returns a tibble with available ANVISA data types, their names, descriptions, and categories.

Usage

anvisa_types()

Value

A tibble with columns: code, name, description, category.

See Also

Other anvisa: anvisa_cache_status(), anvisa_clear_cache(), anvisa_data(), anvisa_info(), anvisa_variables()

Examples

anvisa_types()

Description

Returns a tibble with available variables for a given ANVISA data type, including descriptions.

Usage

anvisa_variables(type = "medicines", search = NULL)

Arguments

Value

A tibble with columns: variable, description.

See Also

Other anvisa: anvisa_cache_status(), anvisa_clear_cache(), anvisa_data(), anvisa_info(), anvisa_types()

Examples

anvisa_variables()
anvisa_variables(type = "hemovigilance")
anvisa_variables(search = "registro")

Description

Retrieves population estimates for intercensitary years (2001-2021) from SIDRA table 6579. These estimates provide population denominators for years between censuses.

Usage

censo_estimativa(
  year,
  territorial_level = "state",
  geo_code = "all",
  raw = FALSE
)

Arguments

Details

Table 6579 provides total population estimates (no sex/age breakdown). These estimates are published annually by IBGE and are widely used as denominators for health indicator calculations.

For census years with full demographic breakdowns, use censo_populacao instead.

Value

A tibble with population estimates.

Data source

Data is retrieved from IBGE SIDRA API, table 6579: ⁠https://sidra.ibge.gov.br/tabela/6579⁠

Examples

# estimates for 2020 by state
censo_estimativa(year = 2020)

# estimates for multiple years, Brazil level
censo_estimativa(year = 2015:2020, territorial_level = "brazil")

# estimates by municipality
censo_estimativa(year = 2021, territorial_level = "municipality")

Description

Displays information about the Brazilian Census and returns metadata.

Usage

censo_info(year = NULL)

Arguments

Value

Invisibly returns a list with Census metadata.

Examples

censo_info()
censo_info(2022)

Description

Retrieves population data from the Brazilian Demographic Census via SIDRA API. Automatically selects the correct SIDRA table based on year and requested variables.

Usage

censo_populacao(
  year,
  variables = "total",
  territorial_level = "state",
  geo_code = "all",
  raw = FALSE
)

Arguments

Details

This function provides an easy interface for the most common Census queries. It automatically resolves the correct SIDRA table:

• Table 200: Historical population 1970-2010 (by sex, age, situation)

• Table 9514: Census 2022 population by sex and age

• Table 136: Population by race 2000-2010

• Table 9605: Population by race 2022

• Table 9515: Population by urban/rural 2022

For more flexibility, use censo_sidra_data to query any table with custom parameters.

Value

A tibble with population data.

Data source

Data is retrieved from IBGE SIDRA API: ⁠https://sidra.ibge.gov.br/⁠

Examples

# total population by state, 2022
censo_populacao(year = 2022)

# population by sex, Brazil level
censo_populacao(year = 2022, variables = "sex", territorial_level = "brazil")

# population by age and sex, 2010
censo_populacao(year = 2010, variables = "age_sex")

# population by race, 2022
censo_populacao(year = 2022, variables = "race")

Description

Queries the IBGE SIDRA API to retrieve any Census table. This is the most flexible function, allowing full control over SIDRA query parameters.

Usage

censo_sidra_data(
  table,
  territorial_level = "brazil",
  geo_code = "all",
  year = NULL,
  variable = NULL,
  classifications = NULL,
  raw = FALSE
)

Arguments

Value

A tibble with queried data.

Examples

# population by state from 2022 Census
censo_sidra_data(
  table = 9514,
  territorial_level = "state",
  year = 2022,
  variable = 93
)

# population by race, Brazil level
censo_sidra_data(
  table = 9605,
  territorial_level = "brazil",
  year = 2022,
  variable = 93,
  classifications = list("86" = "allxt")
)

Description

Searches Census SIDRA tables by keyword in the table name. Supports partial matching, case-insensitive, and accent-insensitive search.

Usage

censo_sidra_search(keyword, year = NULL)

Arguments

Value

A tibble with matching tables (same structure as censo_sidra_tables).

Examples

censo_sidra_search("deficiencia")
censo_sidra_search("raca")
censo_sidra_search("indigena")

Description

Returns a catalog of available SIDRA tables for the Census, organized by theme.

Usage

censo_sidra_tables(theme = NULL, year = NULL)

Arguments

Value

A tibble with columns: table_code, table_name, theme, years, territorial_levels.

Examples

# list all Census tables
censo_sidra_tables()

# filter by theme
censo_sidra_tables(theme = "population")

# tables with 2022 data
censo_sidra_tables(year = 2022)

Description

Returns a character vector with available Census years.

Usage

censo_years()

Value

A character vector of available years.

Examples

censo_years()

Description

Shows information about cached CNES data files.

Usage

cnes_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other cnes: cnes_clear_cache(), cnes_data(), cnes_dictionary(), cnes_info(), cnes_variables(), cnes_years()

Examples

cnes_cache_status()

Description

Deletes cached CNES data files.

Usage

cnes_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other cnes: cnes_cache_status(), cnes_data(), cnes_dictionary(), cnes_info(), cnes_variables(), cnes_years()

Examples

cnes_clear_cache()

Description

Downloads and returns health facility registry data from DATASUS FTP. Each row represents one health facility record (for the ST type). Data is organized monthly – one .dbc file per type, state (UF), and month.

Usage

cnes_data(
  year,
  type = "ST",
  month = NULL,
  vars = NULL,
  uf = NULL,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

Data is downloaded from DATASUS FTP as .dbc files (one per type/state/month). The .dbc format is decompressed internally using vendored C code from the blast library. No external dependencies are required.

CNES data is monthly, so downloading an entire year for all states requires 324 files (27 UFs x 12 months) per type. Use uf and month to limit downloads.

The CNES has 13 file types. The default "ST" (establishments) is the most commonly used. Use cnes_info() to see all types.

Parallel downloads

When downloading multiple files (e.g., several months or states), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with health facility data. Includes columns year, month, and uf_source to identify the source when multiple years/months/states are combined.

See Also

cnes_info() for file type descriptions, censo_populacao() for population denominators.

Other cnes: cnes_cache_status(), cnes_clear_cache(), cnes_dictionary(), cnes_info(), cnes_variables(), cnes_years()

Examples

# all establishments in Acre, January 2023
ac_jan <- cnes_data(year = 2023, month = 1, uf = "AC")

# only key variables
cnes_data(year = 2023, month = 1, uf = "AC",
          vars = c("CNES", "CODUFMUN", "TP_UNID", "VINC_SUS"))

# hospital beds
leitos <- cnes_data(year = 2023, month = 1, uf = "AC", type = "LT")

# health professionals
prof <- cnes_data(year = 2023, month = 1, uf = "AC", type = "PF")

Description

Returns a tibble with the complete data dictionary for the CNES, including variable descriptions and category labels.

Usage

cnes_dictionary(variable = NULL)

Arguments

Value

A tibble with columns: variable, description, code, label.

See Also

Other cnes: cnes_cache_status(), cnes_clear_cache(), cnes_data(), cnes_info(), cnes_variables(), cnes_years()

Examples

cnes_dictionary()
cnes_dictionary("TP_UNID")
cnes_dictionary("ESFERA_A")

Description

Displays information about the National Health Facility Registry (CNES), including data sources, available years, file types, and usage guidance.

Usage

cnes_info()

Value

A list with module information (invisibly).

See Also

Other cnes: cnes_cache_status(), cnes_clear_cache(), cnes_data(), cnes_dictionary(), cnes_variables(), cnes_years()

Examples

cnes_info()

Description

Returns a tibble with available variables in the CNES data (ST type), including descriptions and value types.

Usage

cnes_variables(type = "ST", search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other cnes: cnes_cache_status(), cnes_clear_cache(), cnes_data(), cnes_dictionary(), cnes_info(), cnes_years()

Examples

cnes_variables()
cnes_variables(search = "tipo")
cnes_variables(search = "gestao")

Description

Returns an integer vector with years for which health facility registry data are available from DATASUS FTP.

Usage

cnes_years(status = "final")

Arguments

Value

An integer vector of available years.

See Also

Other cnes: cnes_cache_status(), cnes_clear_cache(), cnes_data(), cnes_dictionary(), cnes_info(), cnes_variables()

Examples

cnes_years()
cnes_years(status = "all")

Description

Returns information about all data sources available in healthbR.

Usage

list_sources()

Value

A tibble with columns:

• source: Source code (e.g., "vigitel", "sim")

• name: Full name of the data source

• description: Brief description

• years: Range of available years

• status: Implementation status ("available", "planned")

Examples

list_sources()

Description

Shows cache status including downloaded files and their sizes.

Usage

pnadc_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache information

Examples

pnadc_cache_status()

Description

Removes all cached PNADC data files.

Usage

pnadc_clear_cache(module = NULL, cache_dir = NULL)

Arguments

Value

NULL (invisibly)

Examples

pnadc_clear_cache()

Description

Downloads and returns PNADC microdata for the specified module and year(s) from the IBGE FTP. Data is cached locally to avoid repeated downloads. When the arrow package is installed, data is cached in parquet format for faster subsequent reads.

Usage

pnadc_data(
  module,
  year = NULL,
  vars = NULL,
  as_survey = FALSE,
  cache_dir = NULL,
  refresh = FALSE,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

PNAD Continua (Pesquisa Nacional por Amostra de Domicilios Continua) is a quarterly household survey conducted by IBGE. This function provides access to supplementary modules with health-related content.

Available modules

• deficiencia: Persons with disabilities (2019, 2022, 2024)

• habitacao: Housing characteristics (2012-2019, 2022-2024)

• moradores: General characteristics of residents (2012-2019, 2022-2024)

• aps: Primary health care (2022)

Survey design variables

For proper statistical analysis with complex survey design, the following variables are always included:

• UPA: Primary sampling unit

• Estrato: Stratum

• V1028: Survey weight

Use as_survey = TRUE to get a properly weighted survey design object for analysis with the srvyr package.

Parallel downloads

When downloading multiple years, install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with PNADC microdata, or a srvyr survey design object if as_survey = TRUE.

Data source

Data is downloaded from the IBGE FTP server: ⁠https://ftp.ibge.gov.br/Trabalho_e_Rendimento/Pesquisa_Nacional_por_Amostra_de_Domicilios_continua/⁠

Examples

# download deficiencia module for 2022
df <- pnadc_data(module = "deficiencia", year = 2022, cache_dir = tempdir())

# download with survey design
svy <- pnadc_data(
  module = "deficiencia",
  year = 2022,
  as_survey = TRUE,
  cache_dir = tempdir()
)

# select specific variables
df_subset <- pnadc_data(
  module = "deficiencia",
  year = 2022,
  vars = c("S11001", "S11002"),
  cache_dir = tempdir()
)

Description

Downloads and returns the variable dictionary for PNADC microdata. The dictionary is cached locally to avoid repeated downloads.

Usage

pnadc_dictionaries(module, year = NULL, cache_dir = NULL, refresh = FALSE)

Arguments

Details

The dictionary includes variable names, positions, and widths from the IBGE input specification file. This is useful for understanding the structure of the data returned by pnadc_data.

Value

A tibble with variable definitions.

Data source

Dictionaries are downloaded from the IBGE FTP server.

Examples

# get dictionary for deficiencia module
dict <- pnadc_dictionaries(module = "deficiencia", cache_dir = tempdir())

Description

Displays information about PNAD Continua and returns metadata.

Usage

pnadc_info()

Value

Invisibly returns a list with survey metadata.

Examples

pnadc_info()

Description

Returns information about the available supplementary modules in PNAD Continua that are supported by this package.

Usage

pnadc_modules()

Value

A tibble with module information including name, available years, and descriptions.

Examples

pnadc_modules()

Description

Returns a list of available variables in the PNADC microdata for a given module. This is a convenience wrapper around pnadc_dictionaries.

Usage

pnadc_variables(module, year = NULL, cache_dir = NULL, refresh = FALSE)

Arguments

Value

A character vector of variable names.

Examples

# list variables for deficiencia module
pnadc_variables(module = "deficiencia", cache_dir = tempdir())

Description

Returns a vector of years for which data is available for the specified module.

Usage

pnadc_years(module)

Arguments

Value

An integer vector of available years.

Examples

pnadc_years("deficiencia")
pnadc_years("habitacao")

Description

Shows cache status including downloaded files and their sizes.

Usage

pns_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache information

Examples

pns_cache_status()

Description

Removes all cached PNS data files.

Usage

pns_clear_cache(cache_dir = NULL)

Arguments

Value

NULL (invisibly)

Examples

pns_clear_cache()

Description

Downloads and returns PNS microdata for specified years from the IBGE FTP. Data is cached locally to avoid repeated downloads. When the arrow package is installed, data is cached in parquet format for faster subsequent reads.

Usage

pns_data(
  year = NULL,
  vars = NULL,
  cache_dir = NULL,
  refresh = FALSE,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

The PNS (Pesquisa Nacional de Saude) is a household survey conducted by IBGE in partnership with the Ministry of Health. It provides comprehensive data on health conditions, lifestyle, and healthcare access of the Brazilian population.

Survey design variables

For proper statistical analysis with complex survey design, use the following weight variables with the srvyr or survey packages:

• V0028: household weight

• V0029: selected person weight

• V0030: person weight with non-response adjustment

• UPA_PNS: primary sampling unit

• V0024: stratum

Parallel downloads

When downloading multiple years, install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with PNS microdata.

Data source

Data is downloaded from the IBGE FTP server: ⁠https://ftp.ibge.gov.br/PNS/⁠

Examples

# download PNS 2019 data
df <- pns_data(year = 2019, cache_dir = tempdir())

# download all years
df_all <- pns_data(cache_dir = tempdir())

# select specific variables
df_subset <- pns_data(
  year = 2019,
  vars = c("V0001", "C006", "C008", "V0028"),
  cache_dir = tempdir()
)

Description

Downloads and returns the variable dictionary for PNS microdata. The dictionary is cached locally to avoid repeated downloads.

Usage

pns_dictionary(year = 2019, cache_dir = NULL, refresh = FALSE)

Arguments

Details

The dictionary includes variable names, labels, and response categories for the PNS microdata. This is useful for understanding the structure of the data returned by pns_data.

Value

A tibble with variable definitions.

Data source

Dictionaries are downloaded from the IBGE FTP server: ⁠https://ftp.ibge.gov.br/PNS/⁠

Examples

# get dictionary for 2019
dict <- pns_dictionary(year = 2019, cache_dir = tempdir())

# get dictionary for 2013
dict_2013 <- pns_dictionary(year = 2013, cache_dir = tempdir())

Description

Displays information about the PNS survey and returns metadata.

Usage

pns_info(year = NULL)

Arguments

Value

Invisibly returns a list with survey metadata.

Examples

pns_info()
pns_info(2019)

Description

Returns information about the questionnaire modules available in the PNS.

Usage

pns_modules(year = NULL)

Arguments

Value

A tibble with module codes, names, and descriptions.

Examples

pns_modules()
pns_modules(year = 2019)

Description

Queries the IBGE SIDRA API to retrieve tabulated PNS indicators. Returns pre-aggregated data (prevalences, means, proportions) with confidence intervals and coefficients of variation.

Usage

pns_sidra_data(
  table,
  territorial_level = "brazil",
  geo_code = "all",
  year = NULL,
  variable = NULL,
  classifications = NULL,
  raw = FALSE
)

Arguments

Value

A tibble with queried indicators.

Examples

# self-rated health by state, 2019
pns_sidra_data(
  table = 4751,
  territorial_level = "state",
  year = 2019
)

# same table, Brazil-level, both years
pns_sidra_data(
  table = 4751,
  territorial_level = "brazil",
  year = c(2013, 2019)
)

# hypertension data
pns_sidra_data(
  table = 4416,
  territorial_level = "brazil"
)

Description

Searches PNS SIDRA tables by keyword in the table name/description. Supports partial matching, case-insensitive, and accent-insensitive search.

Usage

pns_sidra_search(keyword, year = NULL)

Arguments

Value

A tibble with matching tables (same structure as pns_sidra_tables()).

Examples

pns_sidra_search("diabetes")
pns_sidra_search("hipertensao")
pns_sidra_search("fumante")

Description

Returns a catalog of available SIDRA tables for the PNS, organized by health theme.

Usage

pns_sidra_tables(theme = NULL, year = NULL)

Arguments

Value

A tibble with columns: table_code, table_name, theme, theme_label, years, territorial_levels.

Examples

# list all tables
pns_sidra_tables()

# filter by theme
pns_sidra_tables(theme = "chronic_diseases")

# tables with 2013 data
pns_sidra_tables(year = 2013)

Description

Returns a list of available variables in the PNS microdata with their labels. This is a convenience wrapper around pns_dictionary that returns only unique variable names and labels.

Usage

pns_variables(year = 2019, module = NULL, cache_dir = NULL, refresh = FALSE)

Arguments

Value

A tibble with variable names and labels.

Examples

# list all variables for 2019
pns_variables(year = 2019, cache_dir = tempdir())

# list variables for a specific module
pns_variables(year = 2019, module = "J", cache_dir = tempdir())

Description

Returns a character vector with available PNS survey years.

Usage

pns_years()

Value

A character vector of available years.

Examples

pns_years()

Description

Shows cache status including downloaded files and their sizes.

Usage

pof_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache information

See Also

Other pof: pof_clear_cache(), pof_data(), pof_dictionary(), pof_info(), pof_registers(), pof_variables(), pof_years()

Examples

pof_cache_status()

Description

Removes all cached POF data files.

Usage

pof_clear_cache(cache_dir = NULL)

Arguments

Value

NULL (invisibly)

See Also

Other pof: pof_cache_status(), pof_data(), pof_dictionary(), pof_info(), pof_registers(), pof_variables(), pof_years()

Examples

pof_clear_cache()

Description

Downloads POF microdata from IBGE FTP and returns as a tibble. Data is cached locally to avoid repeated downloads.

Usage

pof_data(
  year = "2017-2018",
  register = "morador",
  vars = NULL,
  cache_dir = NULL,
  as_survey = FALSE,
  refresh = FALSE,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

The POF (Pesquisa de Orcamentos Familiares) is a household survey conducted by IBGE that investigates household budgets, living conditions, and nutritional profiles of the Brazilian population.

Health-related data

The POF contains several health-related modules:

• EBIA (Food Security Scale): Available in 2017-2018, variable V6199 in the domicilio register

• Food Consumption: Detailed food consumption data in the consumo_alimentar register (2008-2009, 2017-2018)

• Health Expenses: Expenses with medications, health insurance, consultations in the despesa_individual register

• Anthropometry: Weight, height, BMI in morador register (2008-2009 only)

Survey design

For proper statistical analysis with complex survey design, use as_survey = TRUE which creates a survey design object with:

• Weight variable: PESO_FINAL

• Stratum variable: ESTRATO_POF

• PSU variable: COD_UPA

Value

A tibble with microdata, or tbl_svy if as_survey = TRUE.

Data source

Data is downloaded from the IBGE FTP server: ⁠https://ftp.ibge.gov.br/Orcamentos_Familiares/⁠

See Also

pof_years, pof_info, pof_registers, pof_variables

Other pof: pof_cache_status(), pof_clear_cache(), pof_dictionary(), pof_info(), pof_registers(), pof_variables(), pof_years()

Examples

# basic usage - download morador register
morador <- pof_data("2017-2018", "morador", cache_dir = tempdir())

# download domicilio register (includes EBIA)
domicilio <- pof_data("2017-2018", "domicilio", cache_dir = tempdir())

# select specific variables
df <- pof_data(
  "2017-2018", "morador",
  vars = c("COD_UPA", "ESTRATO_POF", "PESO_FINAL", "V0403"),
  cache_dir = tempdir()
)

# with survey design (requires srvyr package)
morador_svy <- pof_data("2017-2018", "morador", as_survey = TRUE,
                         cache_dir = tempdir())

Description

Downloads and returns the variable dictionary for POF microdata. The dictionary is cached locally to avoid repeated downloads.

Usage

pof_dictionary(
  year = "2017-2018",
  register = NULL,
  cache_dir = NULL,
  refresh = FALSE
)

Arguments

Value

A tibble with variable definitions including: variable, description, position, length, decimals, register.

See Also

pof_variables, pof_data

Other pof: pof_cache_status(), pof_clear_cache(), pof_data(), pof_info(), pof_registers(), pof_variables(), pof_years()

Examples

pof_dictionary("2017-2018", "morador", cache_dir = tempdir())

Description

Returns metadata about the POF survey edition including available health modules and sampling design information.

Usage

pof_info(year = "2017-2018")

Arguments

Value

A list with survey metadata (invisibly).

See Also

pof_years, pof_data

Other pof: pof_cache_status(), pof_clear_cache(), pof_data(), pof_dictionary(), pof_registers(), pof_variables(), pof_years()

Examples

pof_info()
pof_info("2017-2018")
pof_info("2008-2009")

Description

Returns information about the data registers available in the POF.

Usage

pof_registers(year = "2017-2018", health_only = FALSE)

Arguments

Value

A tibble with register names and descriptions.

See Also

Other pof: pof_cache_status(), pof_clear_cache(), pof_data(), pof_dictionary(), pof_info(), pof_variables(), pof_years()

Examples

pof_registers()
pof_registers("2017-2018", health_only = TRUE)

Description

Returns a list of available variables in the POF microdata with their labels. This is a convenience wrapper around pof_dictionary that returns a simplified view.

Usage

pof_variables(
  year = "2017-2018",
  register = NULL,
  search = NULL,
  cache_dir = NULL,
  refresh = FALSE
)

Arguments

Value

A tibble with columns: variable, description, position, length, register.

See Also

pof_dictionary, pof_data

Other pof: pof_cache_status(), pof_clear_cache(), pof_data(), pof_dictionary(), pof_info(), pof_registers(), pof_years()

Examples

pof_variables("2017-2018", "morador", cache_dir = tempdir())
pof_variables("2017-2018", "domicilio", search = "ebia", cache_dir = tempdir())

Description

Returns a character vector with available POF survey years.

Usage

pof_years()

Value

A character vector of available years in "YYYY-YYYY" format.

See Also

Other pof: pof_cache_status(), pof_clear_cache(), pof_data(), pof_dictionary(), pof_info(), pof_registers(), pof_variables()

Examples

pof_years()

Description

Shows information about cached SIA data files.

Usage

sia_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sia: sia_clear_cache(), sia_data(), sia_dictionary(), sia_info(), sia_variables(), sia_years()

Examples

sia_cache_status()

Description

Deletes cached SIA data files.

Usage

sia_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sia: sia_cache_status(), sia_data(), sia_dictionary(), sia_info(), sia_variables(), sia_years()

Examples

sia_clear_cache()

Description

Downloads and returns outpatient production microdata from DATASUS FTP. Each row represents one outpatient production record. Data is organized monthly – one .dbc file per type, state (UF), and month.

Usage

sia_data(
  year,
  type = "PA",
  month = NULL,
  vars = NULL,
  uf = NULL,
  procedure = NULL,
  diagnosis = NULL,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

Data is downloaded from DATASUS FTP as .dbc files (one per type/state/month). The .dbc format is decompressed internally using vendored C code from the blast library. No external dependencies are required.

SIA data is monthly, so downloading an entire year for all states requires 324 files (27 UFs x 12 months) per type. Use uf and month to limit downloads.

The SIA has 13 file types. The default "PA" (outpatient production) is the most commonly used. Use sia_info() to see all types.

Parallel downloads

When downloading multiple files (e.g., several months or states), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with outpatient production microdata. Includes columns year, month, and uf_source to identify the source when multiple years/months/states are combined.

See Also

sia_info() for file type descriptions, censo_populacao() for population denominators.

Other sia: sia_cache_status(), sia_clear_cache(), sia_dictionary(), sia_info(), sia_variables(), sia_years()

Examples

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

# filter by procedure code
consult <- sia_data(year = 2022, month = 1, uf = "AC",
                    procedure = "0301")

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

# only key variables
sia_data(year = 2022, month = 1, uf = "AC",
         vars = c("PA_PROC_ID", "PA_CIDPRI", "PA_SEXO",
                  "PA_IDADE", "PA_VALAPR"))

# different file type (APAC Medicamentos)
med <- sia_data(year = 2022, month = 1, uf = "AC", type = "AM")

Description

Returns a tibble with the complete data dictionary for the SIA, including variable descriptions and category labels.

Usage

sia_dictionary(variable = NULL)

Arguments

Value

A tibble with columns: variable, description, code, label.

See Also

Other sia: sia_cache_status(), sia_clear_cache(), sia_data(), sia_info(), sia_variables(), sia_years()

Examples

sia_dictionary()
sia_dictionary("PA_SEXO")
sia_dictionary("PA_RACACOR")

Description

Displays information about the Outpatient Information System (SIA), including data sources, available years, file types, and usage guidance.

Usage

sia_info()

Value

A list with module information (invisibly).

See Also

Other sia: sia_cache_status(), sia_clear_cache(), sia_data(), sia_dictionary(), sia_variables(), sia_years()

Examples

sia_info()

Description

Returns a tibble with available variables in the SIA microdata (PA type), including descriptions and value types.

Usage

sia_variables(type = "PA", search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other sia: sia_cache_status(), sia_clear_cache(), sia_data(), sia_dictionary(), sia_info(), sia_years()

Examples

sia_variables()
sia_variables(search = "sexo")
sia_variables(search = "procedimento")

Description

Returns an integer vector with years for which outpatient production microdata are available from DATASUS FTP.

Usage

sia_years(status = "final")

Arguments

Value

An integer vector of available years.

See Also

Other sia: sia_cache_status(), sia_clear_cache(), sia_data(), sia_dictionary(), sia_info(), sia_variables()

Examples

sia_years()
sia_years(status = "all")

Description

Shows information about cached SIH data files.

Usage

sih_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sih: sih_clear_cache(), sih_data(), sih_dictionary(), sih_info(), sih_status(), sih_variables(), sih_years()

Examples

sih_cache_status()

Description

Deletes cached SIH data files.

Usage

sih_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sih: sih_cache_status(), sih_data(), sih_dictionary(), sih_info(), sih_status(), sih_variables(), sih_years()

Examples

sih_clear_cache()

Description

Returns hospital admission microdata (SIH-RD, "AIH reduzida"). Each row represents one hospital admission record (AIH). Data is organized monthly – one file per state (UF) per billing competence (month).

Usage

sih_data(
  year,
  month = NULL,
  vars = NULL,
  uf = NULL,
  diagnosis = NULL,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb"),
  source = c("r2", "datasus"),
  r2_credentials = NULL
)

Arguments

Details

By default data are read from the healthbr-data R2 mirror (Parquet on Cloudflare R2, values byte-identical to the Ministry's .dbc files, with provenance metadata for every file) and the package falls back to the DATASUS FTP if the mirror is unreachable – see source. Both transports share the same local cache, because the content is the same by construction.

Sources

The mirror stores each DATASUS file ⁠RD{UF}{yy}{mm}.dbc⁠ as one Parquet partition ⁠sih/rd/ano=YYYY/mes=MM/uf=XX/⁠, all columns as character, and publishes a manifest with the MD5 and size of every source file. Reading from it needs no decompression and transfers only the columns actually used. The FTP path downloads the .dbc (decompressed internally with vendored C code from the blast library; no external dependencies).

Competence versus admission date

A file of competence 2023-01 holds the admissions billed in January 2023, including admissions that started months earlier; conversely the admissions of December 2023 are spread over the competences of December 2023 to April 2024. Measured on the whole mirror (2016-2026), the four competences following a year close 99.7-99.9% of that year's admissions. To count admissions by the date they started, filter on DT_INTER after reading a window of competences.

Lazy evaluation with R2

With lazy = TRUE and the R2 source first, the returned object is a remote dataset over the mirror: nothing is downloaded until dplyr::collect(), and filters/column selections are pushed down to the Parquet files. Listing the mirror takes a few seconds. With source = "datasus", lazy evaluation works over the local cache as before.

SIH data is monthly, so an entire year for all states means 324 files (27 UFs x 12 months). Use uf and month to limit reads.

Parallel downloads

When downloading multiple files (e.g., several months or states), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with hospital admission microdata. Includes columns year, month, and uf_source to identify the source file when multiple years/months/states are combined: year/month are the BILLING COMPETENCE of the AIH (the month the account was processed), not the admission date, which is DT_INTER; uf_source is the state of the hospital, not of the patient's residence (MUNIC_RES). Two attributes record where the data came from: attr(x, "healthbr_source") ("r2" or "datasus") and, for the mirror, attr(x, "healthbr_provenance"), a tibble with one row per source file read (competence, UF, DATASUS URL, MD5, size, record count and processing timestamp) – see sih_status().

See Also

censo_populacao() for population denominators to calculate hospitalization rates.

Other sih: sih_cache_status(), sih_clear_cache(), sih_dictionary(), sih_info(), sih_status(), sih_variables(), sih_years()

Examples

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

# heart attacks in Sao Paulo, first semester 2022
infarct_sp <- sih_data(year = 2022, month = 1:6, uf = "SP",
                        diagnosis = "I21")

# only key variables, Rio de Janeiro, March 2022
sih_data(year = 2022, month = 3, uf = "RJ",
         vars = c("DIAG_PRINC", "DT_INTER", "SEXO",
                  "IDADE", "MORTE", "VAL_TOT"))

Description

Returns a tibble with the complete data dictionary for the SIH, including variable descriptions and category labels.

Usage

sih_dictionary(variable = NULL)

Arguments

Value

A tibble with columns: variable, description, code, label.

See Also

Other sih: sih_cache_status(), sih_clear_cache(), sih_data(), sih_info(), sih_status(), sih_variables(), sih_years()

Examples

sih_dictionary()
sih_dictionary("SEXO")
sih_dictionary("CAR_INT")

Description

Displays information about the Hospital Information System (SIH), including data sources, available years, and usage guidance.

Usage

sih_info()

Value

A list with module information (invisibly).

See Also

Other sih: sih_cache_status(), sih_clear_cache(), sih_data(), sih_dictionary(), sih_status(), sih_variables(), sih_years()

Examples

sih_info()

Description

Reads the mirror's manifest.json (revalidated by ETag, cached locally) and returns one row per published partition (billing competence x state of the hospital): the DATASUS source file it came from (URL, MD5, size), the Parquet it became (path in the bucket, SHA-256, size, record count), when it was processed and by which version of the healthbr-data pipeline. This is what sih_data() reads by default; use it to see which competences are published, to detect a re-issued file (the MD5 changes) and to record the exact files behind a derived product.

Usage

sih_status(cache_dir = NULL)

Arguments

Value

A tibble with one row per partition and columns dataset, year, month, uf, records, processing_timestamp (UTC), source_url, source_hash_md5, source_size_bytes (the DATASUS .dbc), parquet_path, parquet_sha256, parquet_size_bytes (the mirror's Parquet), pipeline_version and git_commit (the healthbr-data pipeline that wrote it). The manifest's last_updated timestamp and manifest_version come along as attributes of the same names (attr(st, "last_updated")). Empty (with a warning) if the manifest cannot be read and no local copy exists.

See Also

Other sih: sih_cache_status(), sih_clear_cache(), sih_data(), sih_dictionary(), sih_info(), sih_variables(), sih_years()

Examples

st <- sih_status()
# competences published for Roraima in 2024
st[st$uf == "RR" & st$year == 2024, ]
# when the mirror's manifest was last updated
attr(st, "last_updated")

Description

Returns a tibble with available variables in the SIH microdata, including descriptions and value types.

Usage

sih_variables(year = NULL, search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other sih: sih_cache_status(), sih_clear_cache(), sih_data(), sih_dictionary(), sih_info(), sih_status(), sih_years()

Examples

sih_variables()
sih_variables(search = "diag")
sih_variables(search = "valor")

Description

Returns an integer vector with years for which hospital admission microdata are available. Since 0.4.0.9000 the range starts in 1992: years before 2008 exist only on the healthbr-data R2 mirror (the DATASUS FTP fallback starts in 2008) and have no RACA_COR column (race/colour was added to the AIH layout in 2008).

Usage

sih_years(status = "final")

Arguments

Value

An integer vector of available years.

See Also

Other sih: sih_cache_status(), sih_clear_cache(), sih_data(), sih_dictionary(), sih_info(), sih_status(), sih_variables()

Examples

sih_years()
sih_years(status = "all")

Description

Shows information about cached SIM data files.

Usage

sim_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sim: sim_clear_cache(), sim_data(), sim_dictionary(), sim_info(), sim_variables(), sim_years()

Examples

sim_cache_status()

Description

Deletes cached SIM data files.

Usage

sim_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sim: sim_cache_status(), sim_data(), sim_dictionary(), sim_info(), sim_variables(), sim_years()

Examples

sim_clear_cache()

Description

Downloads and returns mortality microdata from DATASUS FTP. Each row represents one death record (Declaracao de Obito). Data is downloaded per state (UF) as compressed .dbc files, decompressed internally, and returned as a tibble.

Usage

sim_data(
  year,
  vars = NULL,
  uf = NULL,
  cause = NULL,
  decode_age = TRUE,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

Data is downloaded from DATASUS FTP as .dbc files (one per state per year). The .dbc format is decompressed internally using vendored C code from the blast library. No external dependencies are required.

When uf is specified, only the requested state(s) are downloaded, making the operation much faster than downloading the entire country.

Parallel downloads

When downloading multiple files (e.g., several years or states), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with mortality microdata. Includes columns year and uf_source to identify the source when multiple years/states are combined.

See Also

censo_populacao() for population denominators to calculate mortality rates.

Other sim: sim_cache_status(), sim_clear_cache(), sim_dictionary(), sim_info(), sim_variables(), sim_years()

Examples

# all deaths in Acre, 2022
ac_2022 <- sim_data(year = 2022, uf = "AC")

# deaths by infarct in Sao Paulo, 2020-2022
infarct_sp <- sim_data(year = 2020:2022, uf = "SP", cause = "I21")

# only key variables, Rio de Janeiro, 2022
sim_data(year = 2022, uf = "RJ",
         vars = c("DTOBITO", "SEXO", "IDADE",
                  "RACACOR", "CODMUNRES", "CAUSABAS"))

Description

Returns a tibble with the complete data dictionary for the SIM, including variable descriptions and category labels.

Usage

sim_dictionary(variable = NULL)

Arguments

Value

A tibble with columns: variable, description, code, label.

See Also

Other sim: sim_cache_status(), sim_clear_cache(), sim_data(), sim_info(), sim_variables(), sim_years()

Examples

sim_dictionary()
sim_dictionary("SEXO")
sim_dictionary("RACACOR")

Description

Displays information about the Mortality Information System (SIM), including data sources, available years, and usage guidance.

Usage

sim_info()

Value

A list with module information (invisibly).

See Also

Other sim: sim_cache_status(), sim_clear_cache(), sim_data(), sim_dictionary(), sim_variables(), sim_years()

Examples

sim_info()

Description

Returns a tibble with available variables in the SIM microdata, including descriptions and value types.

Usage

sim_variables(year = NULL, search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other sim: sim_cache_status(), sim_clear_cache(), sim_data(), sim_dictionary(), sim_info(), sim_years()

Examples

sim_variables()
sim_variables(search = "causa")
sim_variables(search = "mae")

Description

Returns an integer vector with years for which mortality microdata are available from DATASUS FTP.

Usage

sim_years(status = "final")

Arguments

Value

An integer vector of available years.

See Also

Other sim: sim_cache_status(), sim_clear_cache(), sim_data(), sim_dictionary(), sim_info(), sim_variables()

Examples

sim_years()
sim_years(status = "all")

Description

Shows information about cached SINAN data files.

Usage

sinan_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sinan: sinan_clear_cache(), sinan_data(), sinan_dictionary(), sinan_diseases(), sinan_info(), sinan_variables(), sinan_years()

Examples

sinan_cache_status()

Description

Deletes cached SINAN data files.

Usage

sinan_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sinan: sinan_cache_status(), sinan_data(), sinan_dictionary(), sinan_diseases(), sinan_info(), sinan_variables(), sinan_years()

Examples

sinan_clear_cache()

Description

Downloads and returns notifiable disease microdata from DATASUS FTP. Each row represents one notification record (Ficha de Notificacao). Data is downloaded as national .dbc files (one file per disease per year), decompressed internally, and returned as a tibble.

Usage

sinan_data(
  year,
  disease = "DENG",
  vars = NULL,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

SINAN files are national (not per-state). Each file contains all notifications for a given disease in a given year across all of Brazil. To filter by state, use the SG_UF_NOT (UF of notification) or ID_MUNICIP (municipality code) columns after download.

Data is downloaded from DATASUS FTP as .dbc files. The .dbc format is decompressed internally using vendored C code from the blast library. No external dependencies are required.

Parallel downloads

When downloading multiple files (e.g., several years or diseases), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with notifiable disease microdata. Includes columns year and disease to identify the source when multiple years are combined.

See Also

Other sinan: sinan_cache_status(), sinan_clear_cache(), sinan_dictionary(), sinan_diseases(), sinan_info(), sinan_variables(), sinan_years()

Examples

# dengue notifications, 2022
dengue_2022 <- sinan_data(year = 2022)

# tuberculosis, 2020-2022
tb <- sinan_data(year = 2020:2022, disease = "TUBE")

# only key variables
sinan_data(year = 2022, disease = "DENG",
           vars = c("DT_NOTIFIC", "CS_SEXO", "NU_IDADE_N",
                    "CS_RACA", "ID_MUNICIP", "CLASSI_FIN"))

Description

Returns a tibble with the complete data dictionary for the SINAN, including variable descriptions and category labels.

Usage

sinan_dictionary(variable = NULL)

Arguments

Value

A tibble with columns: variable, description, code, label.

See Also

Other sinan: sinan_cache_status(), sinan_clear_cache(), sinan_data(), sinan_diseases(), sinan_info(), sinan_variables(), sinan_years()

Examples

sinan_dictionary()
sinan_dictionary("CS_SEXO")
sinan_dictionary("EVOLUCAO")

Description

Returns a tibble with all notifiable diseases (agravos) available in SINAN, including codes, names, and descriptions.

Usage

sinan_diseases(search = NULL)

Arguments

Value

A tibble with columns: code, name, description.

See Also

Other sinan: sinan_cache_status(), sinan_clear_cache(), sinan_data(), sinan_dictionary(), sinan_info(), sinan_variables(), sinan_years()

Examples

sinan_diseases()
sinan_diseases(search = "dengue")
sinan_diseases(search = "sifilis")

Description

Displays information about the Notifiable Diseases Information System (SINAN), including data sources, available years, diseases, and usage guidance.

Usage

sinan_info()

Value

A list with module information (invisibly).

See Also

Other sinan: sinan_cache_status(), sinan_clear_cache(), sinan_data(), sinan_dictionary(), sinan_diseases(), sinan_variables(), sinan_years()

Examples

sinan_info()

Description

Returns a tibble with available variables in the SINAN microdata, including descriptions and value types.

Usage

sinan_variables(disease = "DENG", search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other sinan: sinan_cache_status(), sinan_clear_cache(), sinan_data(), sinan_dictionary(), sinan_diseases(), sinan_info(), sinan_years()

Examples

sinan_variables()
sinan_variables(search = "sexo")
sinan_variables(search = "municipio")

Description

Returns an integer vector with years for which notifiable diseases microdata are available from DATASUS FTP.

Usage

sinan_years(status = "final")

Arguments

Value

An integer vector of available years.

See Also

Other sinan: sinan_cache_status(), sinan_clear_cache(), sinan_data(), sinan_dictionary(), sinan_diseases(), sinan_info(), sinan_variables()

Examples

sinan_years()
sinan_years(status = "all")

Description

Shows information about cached SINASC data files.

Usage

sinasc_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sinasc: sinasc_clear_cache(), sinasc_data(), sinasc_dictionary(), sinasc_info(), sinasc_variables(), sinasc_years()

Examples

sinasc_cache_status()

Description

Deletes cached SINASC data files.

Usage

sinasc_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sinasc: sinasc_cache_status(), sinasc_data(), sinasc_dictionary(), sinasc_info(), sinasc_variables(), sinasc_years()

Examples

sinasc_clear_cache()

Description

Downloads and returns live birth microdata from DATASUS FTP. Each row represents one live birth record (Declaracao de Nascido Vivo). Data is downloaded per state (UF) as compressed .dbc files, decompressed internally, and returned as a tibble.

Usage

sinasc_data(
  year,
  vars = NULL,
  uf = NULL,
  anomaly = NULL,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb")
)

Arguments

Details

Data is downloaded from DATASUS FTP as .dbc files (one per state per year). The .dbc format is decompressed internally using vendored C code from the blast library. No external dependencies are required.

When uf is specified, only the requested state(s) are downloaded, making the operation much faster than downloading the entire country.

Parallel downloads

When downloading multiple files (e.g., several years or states), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with live birth microdata. Includes columns year and uf_source to identify the source when multiple years/states are combined.

See Also

censo_populacao() for population denominators to calculate birth rates.

Other sinasc: sinasc_cache_status(), sinasc_clear_cache(), sinasc_dictionary(), sinasc_info(), sinasc_variables(), sinasc_years()

Examples

# all births in Acre, 2022
ac_2022 <- sinasc_data(year = 2022, uf = "AC")

# births with anomalies in Sao Paulo, 2020-2022
anomalies_sp <- sinasc_data(year = 2020:2022, uf = "SP", anomaly = "Q")

# only key variables, Rio de Janeiro, 2022
sinasc_data(year = 2022, uf = "RJ",
            vars = c("DTNASC", "SEXO", "PESO",
                     "IDADEMAE", "PARTO", "CONSULTAS"))

Description

Returns a tibble with the complete data dictionary for the SINASC, including variable descriptions and category labels.

Usage

sinasc_dictionary(variable = NULL)

Arguments

Value

A tibble with columns: variable, description, code, label.

See Also

Other sinasc: sinasc_cache_status(), sinasc_clear_cache(), sinasc_data(), sinasc_info(), sinasc_variables(), sinasc_years()

Examples

sinasc_dictionary()
sinasc_dictionary("SEXO")
sinasc_dictionary("PARTO")

Description

Displays information about the Live Birth Information System (SINASC), including data sources, available years, and usage guidance.

Usage

sinasc_info()

Value

A list with module information (invisibly).

See Also

Other sinasc: sinasc_cache_status(), sinasc_clear_cache(), sinasc_data(), sinasc_dictionary(), sinasc_variables(), sinasc_years()

Examples

sinasc_info()

Description

Returns a tibble with available variables in the SINASC microdata, including descriptions and value types.

Usage

sinasc_variables(year = NULL, search = NULL)

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other sinasc: sinasc_cache_status(), sinasc_clear_cache(), sinasc_data(), sinasc_dictionary(), sinasc_info(), sinasc_years()

Examples

sinasc_variables()
sinasc_variables(search = "mae")
sinasc_variables(search = "parto")

Description

Returns an integer vector with years for which live birth microdata are available from DATASUS FTP.

Usage

sinasc_years(status = "final")

Arguments

Value

An integer vector of available years.

See Also

Other sinasc: sinasc_cache_status(), sinasc_clear_cache(), sinasc_data(), sinasc_dictionary(), sinasc_info(), sinasc_variables()

Examples

sinasc_years()
sinasc_years(status = "all")

Description

Shows information about cached SI-PNI data files.

Usage

sipni_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sipni: sipni_clear_cache(), sipni_data(), sipni_dictionary(), sipni_info(), sipni_status(), sipni_variables(), sipni_years()

Examples

sipni_cache_status()

Description

Deletes cached SI-PNI data files.

Usage

sipni_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sipni: sipni_cache_status(), sipni_data(), sipni_dictionary(), sipni_info(), sipni_status(), sipni_variables(), sipni_years()

Examples

sipni_clear_cache()

Description

Downloads and returns vaccination data from SI-PNI. For years 1994–2019 the data are aggregated (doses applied / coverage); for years 2020+ they are individual-level microdata (one row per vaccination dose).

Usage

sipni_data(
  year,
  type = "DPNI",
  uf = NULL,
  month = NULL,
  vars = NULL,
  parse = TRUE,
  col_types = NULL,
  cache = TRUE,
  cache_dir = NULL,
  lazy = FALSE,
  backend = c("arrow", "duckdb"),
  source = c("r2", "datasus"),
  r2_credentials = NULL
)

Arguments

Details

By default data are read from the healthbr-data R2 mirror (Parquet on Cloudflare R2, values byte-identical to the Ministry's files, complete 2020+ series), falling back automatically to the official DATASUS/ OpenDataSUS sources if the mirror is unreachable – see source. The result carries a healthbr_source attribute recording which source actually served each era, and (for R2 reads) a healthbr_provenance attribute with the processing timestamp and Ministry source URL of each partition, taken from the mirror's manifests.

Aggregated data (1994–2019): SI-PNI aggregated data (dose counts and coverage rates per municipality, vaccine, and age group). Two file types: DPNI (doses) and CPNI (coverage). Served from the R2 mirror as Parquet, or from DATASUS FTP as plain .DBF files.

Microdata (2020+): Individual-level microdata (one row per vaccination dose). The type parameter is ignored for these years. Via R2 the data come from the Ministry's JSON exports (no CSV serialization artifacts) and only the requested UF/month partitions are transferred. Via DATASUS the national monthly CSV ZIP (~1.4 GB) is downloaded and, with arrow installed, streamed straight into the local cache with one partition per state (so other states of that month cost no further download); without arrow only the requested state is kept. Either way the file is never held whole in memory.

Availability note (2026): the Ministry decommissioned the old OpenDataSUS host and removed the 2020–2025 files from the new one. The R2 mirror holds the complete series; use sipni_status() to see exactly which months are published and when they were processed.

Lazy evaluation with R2: with lazy = TRUE and the default source, the function returns the remote arrow dataset itself – dplyr verbs are pushed down and only the touched partitions are transferred. In this mode the partition columns keep the bucket layout names (ano, mes, uf, as strings) instead of year/month/uf_source.

Parallel downloads

When downloading multiple files (e.g., several years or states), install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with vaccination data. Includes columns year, uf_source (and month for 2020+) to identify the source partition when multiple years/states are combined. Attributes: healthbr_source (which source served each era) and, for R2 reads, healthbr_provenance (per-partition processing timestamp and Ministry source URL from the mirror manifests).

Output differs by year range – and, for 2020+, by source:

• 1994–2019 (aggregated): DPNI (12 vars) or CPNI (7 vars) columns. Identical for both sources.

• 2020+ via R2 (default): 56 fields from the Ministry's JSON exports (dt_vacina, ds_vacina, sg_uf_paciente, ...). Use sipni_variables(type = "API", source = "r2") to see the list.

• 2020+ via DATASUS CSV: ~47 fields with different names (data_vacina, descricao_vacina, ...). Use sipni_variables(type = "API", source = "datasus").

Each source returns its columns exactly as published by the Ministry; healthbR does not rename or remap them.

See Also

sipni_info() for type descriptions, censo_populacao() for population denominators.

Other sipni: sipni_cache_status(), sipni_clear_cache(), sipni_dictionary(), sipni_info(), sipni_status(), sipni_variables(), sipni_years()

Examples

# FTP: doses applied in Acre, 2019
ac_doses <- sipni_data(year = 2019, uf = "AC")

# FTP: vaccination coverage in Acre, 2019
ac_cob <- sipni_data(year = 2019, type = "CPNI", uf = "AC")

# API: microdata for Acre, January 2024
ac_api <- sipni_data(year = 2024, uf = "AC", month = 1)

# API: select specific variables
sipni_data(year = 2024, uf = "AC", month = 1,
           vars = c("descricao_vacina", "tipo_sexo_paciente",
                    "data_vacina"))

Description

Returns a tibble with the data dictionary for the SI-PNI aggregated data (1994–2019), including variable descriptions and category labels.

Usage

sipni_dictionary(
  variable = NULL,
  source = c("r2", "datasus"),
  cache = TRUE,
  cache_dir = NULL,
  lookup = FALSE
)

Arguments

Details

The dictionary covers aggregated data variables (DPNI/CPNI, 1994–2019): IMUNO (separately for doses and coverage), DOSE, FX_ETARIA, ANO, MES. API microdata (2020+) has description fields embedded in the data itself (e.g., ds_vacina, no_raca_cor_paciente), so a separate dictionary is not needed.

code vs source_codes. In the Ministry's .cnv dictionary files, code is a sequential category code and source_codes holds the value(s) actually found in the data – possibly several per label (comma-separated or ranges), reflecting code changes over the years. Joining data against code silently decodes to the wrong labels. Use lookup = TRUE to get one row per data code. When more than one category claims the same data code, the more specific claim wins: explicitly listed codes take precedence over codes that only fall inside a range – ranges are residual catch-alls by construction (e.g. FX_ETARIA "Idade ignorada" spans 00-99 and must not override the explicit age groups). The built-in fallback dictionary (source = "datasus") is already stored as a data-code lookup, so lookup has no effect there.

Value

A tibble with columns: variable, description, code, label (and source_codes when served from R2 with lookup = FALSE – the original data codes each dictionary entry groups, for traceability to the .cnv files).

See Also

Other sipni: sipni_cache_status(), sipni_clear_cache(), sipni_data(), sipni_info(), sipni_status(), sipni_variables(), sipni_years()

Examples

# built-in fallback dictionary: no network, no cache
sipni_dictionary(source = "datasus")
sipni_dictionary("IMUNO", source = "datasus")

# join-ready lookup (data code -> label)
sipni_dictionary("IMUNO", source = "datasus", lookup = TRUE)

# official .cnv dictionaries from the healthbr-data mirror (downloads once)
sipni_dictionary()
sipni_dictionary("DOSE")

Description

Displays information about the National Immunization Program Information System (SI-PNI), including data sources, available years, file types, and usage guidance.

Usage

sipni_info()

Value

A list with module information (invisibly).

See Also

Other sipni: sipni_cache_status(), sipni_clear_cache(), sipni_data(), sipni_dictionary(), sipni_status(), sipni_variables(), sipni_years()

Examples

sipni_info()

Description

Reads the manifest.json provenance files of the healthbr-data R2 mirror and returns, for each published partition, when it was processed, from which Ministry source file, and how many records it holds. Use it to see which years/months are actually available before calling sipni_data(), and to audit the provenance of the mirror.

Usage

sipni_status(dataset = c("microdados", "doses", "cobertura"), cache_dir = NULL)

Arguments

Details

The manifests are the mirror's source of truth: they record the source file's URL, size and hash, the processing timestamp, and the SHA-256 of every published Parquet. Data on the mirror are byte-identical to the Ministry's files; see the healthbr-data reproducibility policy for the full audit recipe.

Value

A tibble with columns: dataset, year, month (NA for aggregates), uf (NA for microdata – partitions are monthly and national), records, processing_timestamp (as recorded by the pipeline, no timezone), source_url (the Ministry file the partition was derived from).

See Also

Other sipni: sipni_cache_status(), sipni_clear_cache(), sipni_data(), sipni_dictionary(), sipni_info(), sipni_variables(), sipni_years()

Examples

# everything the mirror currently holds
sipni_status()

# which 2026 microdata months are published?
dplyr::filter(sipni_status("microdados"), year == 2026)

Description

Returns a tibble with available variables in the SI-PNI data, including descriptions and value types.

Usage

sipni_variables(type = "DPNI", search = NULL, source = c("r2", "datasus"))

Arguments

Value

A tibble with columns: variable, description, type, section.

See Also

Other sipni: sipni_cache_status(), sipni_clear_cache(), sipni_data(), sipni_dictionary(), sipni_info(), sipni_status(), sipni_years()

Examples

sipni_variables()
sipni_variables(type = "CPNI")
sipni_variables(type = "API")
sipni_variables(type = "API", source = "datasus")
sipni_variables(search = "dose")

Description

Returns an integer vector with years for which vaccination data are available.

Usage

sipni_years()

Details

SI-PNI data come in two eras:

• 1994–2019: Aggregated data (doses applied and coverage).

• 2020+: Individual-level microdata (one row per vaccination dose), published monthly.

The returned vector is a static range extended with the microdata years actually published on the healthbr-data R2 mirror once its manifest has been read in the session (e.g. after calling sipni_status()); this function itself never touches the network. Use sipni_status() for month-level availability.

Value

An integer vector of available years (1994 onwards).

See Also

Other sipni: sipni_cache_status(), sipni_clear_cache(), sipni_data(), sipni_dictionary(), sipni_info(), sipni_status(), sipni_variables()

Examples

sipni_years()

Description

Shows information about cached SISAB data files.

Usage

sisab_cache_status(cache_dir = NULL)

Arguments

Value

A tibble with cache file information (invisibly).

See Also

Other sisab: sisab_clear_cache(), sisab_data(), sisab_info(), sisab_variables(), sisab_years()

Examples

sisab_cache_status()

Description

Deletes cached SISAB data files.

Usage

sisab_clear_cache(cache_dir = NULL)

Arguments

Value

Invisible NULL.

See Also

Other sisab: sisab_cache_status(), sisab_data(), sisab_info(), sisab_variables(), sisab_years()

Examples

sisab_clear_cache()

Description

Downloads and returns primary care coverage data from the SISAB relatorioaps API. Data is aggregated (coverage indicators per geographic unit and period), not individual-level microdata.

Usage

sisab_data(
  year,
  type = "aps",
  level = "uf",
  month = NULL,
  uf = NULL,
  vars = NULL,
  cache = TRUE,
  cache_dir = NULL
)

Arguments

Details

Data is fetched from the relatorioaps REST API (⁠https://relatorioaps.saude.gov.br⁠), the public reporting portal for primary care in Brazil.

Four report types are available:

• "aps" (default): APS coverage – number of primary care teams (eSF, eAP, eSFR, eCR, eAPP) and estimated coverage percentage. Available from 2019.

• "sb": Oral health coverage – dental care teams and coverage. Available from 2024.

• "acs": Community health agents – number of active ACS and population coverage. Available from 2007.

• "pns": PNS coverage – coverage estimates from the National Health Survey. Available 2020–2023.

For municipality-level data, it is recommended to filter by UF using the uf parameter to avoid large downloads.

Parallel downloads

When downloading multiple months, install furrr and future and set a parallel plan to speed up downloads: future::plan(future::multisession, workers = 4). See vignette("healthbR") for details.

Value

A tibble with coverage data. Includes columns year and type to identify the source when multiple years/types are combined. Column names are preserved from the API (camelCase).

See Also

sisab_info() for report type descriptions, censo_populacao() for population denominators.

Other sisab: sisab_cache_status(), sisab_clear_cache(), sisab_info(), sisab_variables(), sisab_years()

Examples

# APS coverage by state, January 2024
sisab_data(year = 2024, month = 1)

# National total, full year 2023
sisab_data(year = 2023, level = "brazil")

# Oral health coverage by UF
sisab_data(year = 2024, type = "sb", month = 6)

# Municipality level for Sao Paulo
sisab_data(year = 2024, level = "municipality", uf = "SP", month = 1)

Description

Displays information about the Primary Care Health Information System (SISAB), including data sources, available report types, and usage guidance.

Usage

sisab_info()

Value

A list with module information (invisibly).

See Also

Other sisab: sisab_cache_status(), sisab_clear_cache(), sisab_data(), sisab_variables(), sisab_years()

Examples

sisab_info()

Description

Returns a tibble with available variables in the SISAB coverage data, including descriptions and value types.

Usage

sisab_variables(type = "aps", search = NULL)

Arguments