Comparison with healthbR and microdatasus

datasusr is one of several R packages that pull Brazilian public health data into the tidyverse. This article compares it with the two most relevant alternatives so that you can pick the right tool — and, in many cases, that tool will not be datasusr.

• healthbR (Sidney Bissoli): modern, broad-scope toolkit covering DATASUS and IBGE surveys, primary-care indicators, ANS, and ANVISA, with module-specific helpers, variable dictionaries, parallel downloads, and Parquet caching.
• microdatasus (Saldanha, Bastos & Barcellos, 2019): the original tidyverse-friendly DATASUS reader, with rich process_*() functions for variable labelling. Currently archived on CRAN.

TL;DR — when to use what

Reach for healthbR first when you want analysis-ready data and your question spans more than the DATASUS FTP. It covers VIGITEL, PNS, PNAD Contínua, POF, Censo, SI-PNI (microdata 2020+), SISAB, ANS, and ANVISA in addition to SIM/SINASC/SIH/SIA/SINAN/CNES, exposes a uniform <module>_data() / <module>_variables() / <module>_dictionary() API, filters by ICD-10 prefix at the call site, parallelises downloads via future/furrr, and caches as Parquet via arrow. For most applied public-health analyses this is the most productive starting point.

Use datasusr when you specifically need a small, fast, dependency-light reader for raw DBC files from the DATASUS FTP, a persistent on-disk cache with age/size pruning, or low-level access to the FTP catalog (custom paths, custom file types, custom column types). It does not try to replace healthbR’s breadth or microdatasus’s variable-labelling features — it stays focused on fast I/O and the FTP.

Use microdatasus when you want its process_sim() / process_sinasc() / etc. labelling pipelines for SIM/SINASC/SIH/SIA/CNES and a select set of SINAN diseases, in a workflow you already know.

Feature comparison

Feature	datasusr	healthbR	microdatasus
Data sources	DATASUS FTP only (18 systems catalogued)	DATASUS FTP + IBGE surveys + SISAB + ANS + ANVISA	DATASUS FTP only (~15 systems)
Survey microdata (VIGITEL, PNS, PNAD-C, POF, Censo)	No	Yes	No
Regulatory data (ANS, ANVISA)	No	Yes	No
Primary-care indicators (SISAB)	No	Yes	No
DBC reading	In-memory C parser (bundled blast)	Vendored C decompressor	External read.dbc
Module-specific API	No (one generic datasus_fetch())	Yes (sim_data(), sih_data(), …)	Yes (fetch_datasus(information_system = "SIH-RD"))
Variable dictionaries / labels	No (raw fields)	Yes (*_variables(), *_dictionary())	Yes (process_*() family)
Filter by ICD-10 prefix	No (post-hoc with dplyr)	Yes (cause = "I", diagnosis = "J")	No
Parallel downloads	curl::multi_download (sequential per call)	future / furrr plans	Sequential
Caching	Persistent on disk, configurable, with prune by age/size	Per-module Parquet cache (via arrow) with *_cache_status()	Session-level temp files
Cache opt-in	tempdir() default, persistent only on opt-in	Per-module helpers	None
Column selection at read	Yes (select)	Module-dependent	Yes (vars)
Type control at read	Yes (col_types, guess_types)	No	No
Date parsing at read	Yes (parse_dates)	Module-dependent	Via process_*()
Return type	Tibble, snake_case by default	Tibble, snake_case	Data frame → tibble after process_*()
CRAN status	This submission	On CRAN	Archived
Dependencies	cli, curl, dplyr, purrr, rlang, stringr, tibble, tidyr	+ readr, jsonlite, foreign (Imports); arrow, furrr, future, survey, srvyr (Suggests)	foreign, read.dbc (archived)
License	MIT	MIT	MIT

Reading DBC files

All three packages ultimately rely on Mark Adler’s blast decompressor for the PKWare DCL format used by DATASUS. The differences are in how the decompressed data is materialised in R:

• microdatasus delegates to the read.dbc package, which writes a temporary .dbf file and parses it with foreign::read.dbf(). All numeric fields become double.
• healthbR vendors its own C decompressor and parses fields into tidy tibbles per module. Each <module>_data() call applies the module’s specific cleaning/labelling rules.
• datasusr decompresses and parses entirely in memory in a single C call. The compressed payload goes straight to a memory buffer and is parsed field-by-field into pre-allocated R vectors, with optional column selection, integer-vs-double inference, explicit col_types, and date parsing — all at the C level. There are no temporary files on disk.

For a single large DBC file with column selection, datasusr is the fastest option. For a multi-source analysis touching surveys and DATASUS together, healthbR’s integrated workflow saves more wall time than the per-file speedup is worth.

API ergonomics

healthbR provides a uniform per-module API:

library(healthbR)

obitos    <- sim_data(year = 2022, uf = "AC")
obitos_cv <- sim_data(year = 2022, uf = "AC", cause = "I")  # ICD-10 prefix
nascidos  <- sinasc_data(year = 2022, uf = "AC")
intern    <- sih_data(year = 2022, month = 1:6, uf = c("SP", "RJ"))
ambul     <- sia_data(year = 2022, month = 1, uf = "AC", type = "AM")

# Variable metadata and value labels
sim_variables()
sim_dictionary("SEXO")

datasusr is intentionally one level lower. There is a single datasus_fetch() entry point, plus a layered API that exposes the catalog and the FTP:

library(datasusr)

# Browse the catalog
datasus_sources()
datasus_file_types(source = "SINAN")

# Validate availability against the FTP
files <- datasus_list_files(
  source = "SINAN", file_type = "DENG",
  year   = 2020:2024
)

# Download with caching
downloads <- datasus_download(files, cache_dir = tempdir())

# Read with column selection and explicit types
df <- read_datasus_dbc(
  downloads$local_file[[1]],
  select      = c("dt_notific", "id_municip", "classi_fin"),
  col_types   = c(dt_notific = "date"),
  parse_dates = TRUE
)

If you do not need that level of control, datasus_fetch() collapses the four steps above into one call, similar to healthbR’s <module>_data().

Caching and parallelism

healthbR writes per-module Parquet files (when arrow is installed) and exposes per-module cache helpers (sim_cache_status(), sih_clear_cache(), …). Parallel downloads are opt-in via a future plan, so a single call can pull many UFs/months/years at once.

datasusr keeps the cache as raw .dbc files in a single configurable directory (default: a session-scoped subdirectory of tempdir()). Pruning is global rather than per-module:

datasus_cache_info()
datasus_cache_prune(older_than_days = 90)
datasus_cache_prune(max_size_bytes  = 5 * 1024^3)
datasus_cache_clear()

If you want a persistent cross-session cache, point cache_dir (or the DATASUSR_CACHE_DIR env var, or the datasusr.cache_dir option) at a directory of your choice, e.g. tools::R_user_dir("datasusr", "cache").

datasusr does not bundle a parallel-download abstraction, but datasus_list_files() returns a tibble of URLs that you can hand to curl::multi_download() or your own future_map() if you need parallel I/O.

Variable labelling

Neither datasusr nor base R itself attaches semantic labels to DATASUS variables. healthbR and microdatasus both do, with different styles:

• healthbR: per-module *_variables() and *_dictionary() return tibbles describing each field and its categorical levels.
• microdatasus: process_sim(), process_sinasc(), … rewrite the data frame in place with decoded values.

If you start in datasusr, you can join against the territorial tables to recover human-readable municipality / region names, but ICD codes, sex codes, race codes, etc. remain raw:

library(dplyr)
mun <- datasus_get_territory("tb_municip", cache_dir = tempdir())

df <- datasus_fetch(
  source = "SIM", file_type = "DO",
  year = 2022, uf = "PE",
  cache_dir = tempdir()
) |>
  left_join(
    select(mun, co_municip, ds_nome),
    by = c("codmunocor" = "co_municip")
  )

For analyses where labelled variables matter, prefer healthbR (or microdatasus’s process_*() family).

Composing the packages

The packages are not mutually exclusive. A common pattern:

• Use healthbR as the default entry point for surveys and labelled DATASUS data.
• Drop down to datasusr when you need fast custom reads of arbitrary DBC files, or when you want to navigate the FTP catalog directly (datasus_ftp_ls(), datasus_list_files(), datasus_docs_url()).
• Reach for microdatasus::process_*() if you already rely on its labelling pipelines.

Summary

healthbR is the most complete and currently the most actively maintained option for Brazilian public health data in R, and is the recommended starting point for most users. datasusr is a focused DATASUS-FTP reader and catalog tool: it shines when you need fast, flexible, dependency-light I/O against raw DBC files, but it does not try to match healthbR’s scope.