# healthbR or microdatasus? The two R packages for DATASUS, side by side on the same download Sidney Bissoli 2026-09-05 - [Coverage](#coverage) - [Same reader, two philosophies](#same-reader-two-philosophies) - [One download in both: SIH, Roraima, competence 2023-01](#one-download-in-both-sih-roraima-competence-2023-01) - [Timing](#timing) - [What comes with the data: provenance](#what-comes-with-the-data-provenance) - [Lazy: a year without downloading it](#lazy-a-year-without-downloading-it) - [SIM: deaths, Acre 2022](#sim-deaths-acre-2022) - [SINASC: births, Acre 2022](#sinasc-births-acre-2022) - [Where the bytes come from](#where-the-bytes-come-from) - [Other routes](#other-routes) - [Which one](#which-one) - [Reproducing](#reproducing) *Published 5 September 2026. Every number here was measured that day, in one R session, with healthbR 0.3.1 (development version), microdatasus 3.0.0, arrow 25.0.0, dplyr 1.2.1 and R 4.6.1 on Windows, over a residential connection in Brazil. The code of each measurement is pasted here and in the package’s [vignette](https://sidneybissoli.github.io/healthbR/articles/healthbr-vs-microdatasus.html); nothing is illustrative.* If you read Brazilian public-health microdata in R — deaths from SIM, live births from SINASC, hospital admissions from SIH, all published by DATASUS, the Ministry of Health’s informatics department — you have two packages to choose from. [microdatasus](https://CRAN.R-project.org/package=microdatasus), by Raphael Saldanha, described in Saldanha et al. (2019), is the usual answer to “how do I get DATASUS into R”. [healthbR](https://github.com/SidneyBissoli/healthbR) is mine. They overlap on six DATASUS systems and differ in almost everything they do with the bytes after the download. This post puts the two side by side **on the same download**, three times: admissions in Roraima for billing competence 2023-01, deaths in Acre in 2022, births in Acre in 2022. I show what each returns, how long it takes, and where the choice changes what a script computes. The point is not to win a comparison; it is that whoever reaches DATASUS through R knows what they are choosing. ## Coverage | | healthbR | microdatasus 3.0.0 | |:---|:---|:---| | SIM (mortality) | `sim_data()`: DO, 1996–2024 | DO, DOFET, DOEXT, DOINF, DOMAT | | SINASC (live births) | `sinasc_data()`: 1996–2024 | SINASC | | SIH (admissions) | `sih_data()`: RD (reduced AIH), 2008–2026 | RD, RJ, SP, ER | | SIA (outpatient) | 13 file types, 2008–2026 | 12 file types | | CNES (facilities) | 13 file types, 2005–2026 | 13 file types | | SINAN (notifiable diseases) | 31 diseases, 2007–2026 | 8 diseases | | SI-PNI (vaccination) | 1994–2026 | — | | SISAB (primary care) | yes | — | | Surveys | VIGITEL, PNS, PNAD Contínua, POF, Censo denominators | — | | Agencies | ANS, ANVISA | — | | Auxiliary tables | `*_dictionary()`, `*_variables()` per module | SIGTAP, CADGER, `tabMun`, `tabCBO`, `tabOcupacao`, `tabNaturalidade` | microdatasus goes deeper into SIM and SIH (the fetal, external-cause, infant and maternal SIM extracts; the RJ, SP and ER SIH files) and ships the SIGTAP procedure table. healthbR goes wider: more SINAN diseases, SI-PNI, SISAB, the household surveys and the agencies, under one `*_years()` / `*_data()` / `*_dictionary()` API. ## Same reader, two philosophies Both decompress the Ministry’s `.dbc` files (DBF compressed with PKWare DCL) with the **same C code**: microdatasus 3.0.0 dropped its `read.dbc` dependency and adopted healthbR’s vendored implementation — it is in its NEWS, with thanks. The bytes are identical; the difference starts right after. **healthbR keeps the codes and types the columns.** `SEXO` stays `1`/`3`, `MORTE` stays `0`/`1`, `DT_INTER` becomes a `Date`, `VAL_TOT` a double, `IDADE` an integer. Labels live in `sih_dictionary()`, `sim_dictionary()`, `sinasc_dictionary()`, joined when you want them. **microdatasus separates fetching from processing.** `fetch_datasus()` returns every column as character, as the DBF has it. `process_sih()`, `process_sim()`, `process_sinasc()` replace codes with labels (`SEXO` becomes `"Masculino"`/`"Feminino"`, `MORTE` becomes `"Não"`/`"Sim"`), convert dates, split the SIM age field into one column per unit and, with `municipality_data = TRUE`, join municipality names and coordinates. The result is a tibble of character columns. Neither is wrong. Labels are what you want in a report table; codes are what you want to join, compare across years, or model. The trap is mixing them: after `process_sih()`, `MORTE == 1` is `FALSE` on every row. ## One download in both: SIH, Roraima, competence 2023-01 Roraima is the smallest SIH file; January 2023 has 4,734 admissions. Both reads start from an empty cache. ``` r library(healthbR) library(microdatasus) library(dplyr) rr_h <- sih_data(year = 2023, month = 1, uf = "RR") # healthbR #> ℹ Reading SIH data from R2: 2023/01 (1 UF(s))... dim(rr_h) #> [1] 4734 116 table(vapply(rr_h, function(x) class(x)[1], "")) #> character Date integer numeric #> 101 3 7 5 rr_m <- fetch_datasus(year_start = 2023, month_start = 1, # microdatasus year_end = 2023, month_end = 1, uf = "RR", information_system = "SIH-RD") dim(rr_m) #> [1] 4734 113 rr_mp <- process_sih(rr_m) dim(rr_mp) #> [1] 4734 121 ``` Same 4,734 rows, identical sets of `N_AIH`. healthbR adds `year`, `month` and `uf_source` (the competence and state of the file, which the AIH record does not carry); `process_sih()` adds eight municipality columns. The same five records, three ways: ``` r cols <- c("N_AIH", "SEXO", "COD_IDADE", "IDADE", "MORTE", "DT_INTER", "DIAG_PRINC", "MUNIC_RES", "VAL_TOT") as.data.frame(head(rr_h[cols], 5)) # healthbR #> N_AIH SEXO COD_IDADE IDADE MORTE DT_INTER DIAG_PRINC MUNIC_RES VAL_TOT #> 1 1423100411392 3 4 18 0 2022-12-13 O809 140017 568.80 #> 2 1423100911661 1 4 83 0 2022-12-26 J189 140010 994.59 #> 3 1423100911672 3 4 65 0 2022-12-03 I219 140010 620.12 #> 4 1423100911683 1 4 58 0 2022-12-26 I248 140020 333.08 #> 5 1423100911694 1 4 35 0 2022-11-01 A499 140010 11202.59 as.data.frame(head(rr_m[cols], 5)) # microdatasus, raw #> N_AIH SEXO COD_IDADE IDADE MORTE DT_INTER DIAG_PRINC MUNIC_RES VAL_TOT #> 1 1423100411392 3 4 18 0 20221213 O809 140017 568.8 #> 2 1423100911661 1 4 83 0 20221226 J189 140010 994.59 #> 3 1423100911672 3 4 65 0 20221203 I219 140010 620.12 #> 4 1423100911683 1 4 58 0 20221226 I248 140020 333.08 #> 5 1423100911694 1 4 35 0 20221101 A499 140010 11202.59 as.data.frame(head(rr_mp[c(cols, "munResNome")], 5)) # microdatasus, processed #> N_AIH SEXO COD_IDADE IDADE MORTE DT_INTER DIAG_PRINC MUNIC_RES VAL_TOT munResNome #> 1 1423100411392 Feminino Anos 18 Não 2022-12-13 O809 140017 568.8 Cantá #> 2 1423100911661 Masculino Anos 83 Não 2022-12-26 J189 140010 994.59 Boa Vista #> 3 1423100911672 Feminino Anos 65 Não 2022-12-03 I219 140010 620.12 Boa Vista #> 4 1423100911683 Masculino Anos 58 Não 2022-12-26 I248 140020 333.08 Caracaraí #> 5 1423100911694 Masculino Anos 35 Não 2022-11-01 A499 140010 11202.59 Boa Vista ``` Note `DT_INTER`: admissions billed in competence 2023-01 happened in November and December 2022. In both packages the year and month select the **billing competence**, not the admission date; the admissions of a calendar year keep arriving for a few competences after it (measured on the whole mirror: the four following competences close 99.7–99.9% of a year). The trap, one line each: ``` r sum(rr_h$MORTE == 1) # healthbR: integer code #> [1] 113 sum(rr_m$MORTE == "1") # microdatasus raw: character code #> [1] 113 sum(rr_mp$MORTE == 1) # microdatasus processed: the code is gone #> [1] 0 table(rr_mp$MORTE) #> Não Sim #> 4621 113 ``` Same 113 deaths; the third line silently returns zero in a script written against codes. I learned it the hard way: an aggregate cube behind my own SIH MCP server sat for months with sex “unknown” on every row and zero deaths, because the build script compared codes after a `process_sih()`. No test complained — the label is exactly what prevents suspicion. ### Timing | Step | healthbR | microdatasus | |:---|---:|---:| | Empty cache, mirror (manifest of 11,157 partitions + one Parquet) | 11.4 s | — | | Empty cache, DATASUS FTP `.dbc` (`source = "datasus"`) | 1.1 s | 2.7 s | | Same call again in the session | 0.5 s (local Parquet cache) | downloads again (no cache) | | Labels (`process_sih()`) | — | 0.8 s | | In-memory size | 4.8 MB | 5.4 MB raw, 5.7 MB processed | On a 349 KB file the FTP wins the first read: the mirror’s price of entry is its 10 MB manifest, downloaded once and revalidated by ETag afterwards (`sih_status()` reads it in 0.4 s from then on). The mirror pays back on repeated reads, on lazy queries over many competences, and in what comes with the data. A measurement confession: in the first round, healthbR’s “warm” read took 5.5 s — slower than the cold FTP. The cause was the package’s: building the manifest summary (11k partitions) cost 4.6 s and ran on *every* call. It was fixed before publishing (the summary is now memoised per manifest content) and the numbers above are from the fixed code. Measuring against the neighbour is a good way to find your own slow paths. ## What comes with the data: provenance From the mirror, the tibble carries the source it was served from and one row per DATASUS file behind it — URL, MD5 and size of the `.dbc`, record count, processing timestamp and pipeline version: ``` r attr(rr_h, "healthbr_source") #> [1] "r2" attr(rr_h, "healthbr_provenance") |> select(year, month, uf, records, source_hash_md5, source_size_bytes) #> year month uf records source_hash_md5 source_size_bytes #> 1 2023 1 RR 4734 12e74d4b059589ceb47e4136e3b2ce5f 349244 st <- sih_status() nrow(st); attr(st, "last_updated"); range(st$year) #> [1] 11157 #> [1] "2026-08-18T12:33:31" #> [1] 1992 2026 ``` microdatasus 3.0.0 has `track_source = TRUE`, which appends the name of the `.dbc` each row came from; it records no hash or timestamp, and the FTP offers none. If a derived product must say exactly which files it was built from, this is the difference that matters. ## Lazy: a year without downloading it ``` r sih_data(year = 2023, uf = "RR", lazy = TRUE) |> filter(MORTE == 1) |> count(month) |> collect() |> arrange(month) #> month n #> 1 1 113 #> 2 2 120 #> 3 3 175 #> 4 4 166 #> 5 5 118 #> 6 6 78 #> 7 7 97 #> 8 8 144 #> 9 9 105 #> 10 10 104 #> 11 11 128 #> 12 12 102 ``` 19.4 s for twelve competences over the mirror, no local copy. With microdatasus the equivalent downloads twelve `.dbc` files into memory and filters afterwards. For one small state it makes no difference; for the whole country (a year of SIH is about 2 GB of `.dbc`) it decides whether the query fits on a laptop. ## SIM: deaths, Acre 2022 ``` r ac_h <- sim_data(year = 2022, uf = "AC") # 1.5 s ac_m <- fetch_datasus(2022, year_end = 2022, uf = "AC", information_system = "SIM-DO") # 2.1 s ac_mp <- process_sim(ac_m) # 0.7 s dim(ac_h); dim(ac_m); dim(ac_mp) #> [1] 4159 90 #> [1] 4159 87 #> [1] 4159 100 ``` Same 4,159 deaths. The interesting column is age: SIM stores it in three characters, unit first (0 minutes, 1 hours, 2 days, 3 months, 4 years, 5 years over 100), then the value. The packages decode it differently: ``` r # six infant deaths, the same rows in both #> IDADE healthbR_age_years md_IDADEanos md_IDADEmeses md_IDADEdias md_IDADEhoras #> 1 021 3.992699e-05 #> 2 101 1.140771e-04 1 #> 3 201 2.737851e-03 1 #> 4 302 1.666667e-01 2 #> 5 102 2.281542e-04 2 #> 6 108 9.126169e-04 8 ``` healthbR adds one continuous `age_years` (21 minutes is 0.00004 years, 2 months is 0.167), so age bands and rates come from one column. microdatasus splits the field into `IDADEminutos`, `IDADEhoras`, `IDADEdias`, `IDADEmeses`, `IDADEanos`, each `NA` outside its unit, closer to how the death certificate reads. For adult mortality they agree to the year; for infant mortality you reach for different columns. ## SINASC: births, Acre 2022 ``` r ac_n <- sinasc_data(year = 2022, uf = "AC") # 3.1 s class(ac_n$PESO); sum(is.na(ac_n$PESO)); median(ac_n$PESO, na.rm = TRUE) #> [1] "integer" #> [1] 94 #> [1] 3230 ac_nm <- fetch_datasus(2022, year_end = 2022, uf = "AC", information_system = "SINASC") # 3.3 s ac_nmp <- process_sinasc(ac_nm) # 3.5 s sum(is.na(ac_nm$PESO)); sum(is.na(ac_nmp$PESO)) #> [1] 94 #> [1] 14483 ``` Same 14,483 births. One thing to know before relying on `process_sinasc()` in the versions measured here: it returned `PESO` (birth weight) as `NA` on every row, while the raw column had 94 missing out of 14,483. It reproduces offline with the package’s own sample: ``` r sum(is.na(microdatasus::sinasc_sample$PESO)) #> [1] 2 sum(is.na(process_sinasc(microdatasus::sinasc_sample)$PESO)) #> [1] 100 ``` The sentinel recode (`"0"` and `"9999"` to `NA`) is followed by an `as.numeric()`, and with dplyr 1.2.1 the whole column comes back missing. It is the kind of thing a maintainer fixes in an afternoon once reported; check the package’s news and issues for your versions and, meanwhile, take `PESO` from the raw tibble. healthbR does not rewrite the column: `PESO` is parsed to integer and the sentinels are left for you to treat. ## Where the bytes come from | | healthbR | microdatasus | |:---|:---|:---| | SIM, SINASC, SIA, SINAN, CNES | DATASUS FTP, `.dbc`, decompressed locally | DATASUS FTP, `.dbc`, decompressed locally | | SIH, SI-PNI | [healthbr-data](https://github.com/SidneyBissoli/healthbr-data) mirror: partitioned Parquet on Cloudflare R2, one partition per DATASUS file, byte-identical values, provenance per file; FTP as fallback | DATASUS FTP | | Local cache | partitioned Parquet per module; a second call never touches the network | none | | Re-issued files | `sih_status()`: MD5 and size per partition, mirror `last_updated` | — | | Lazy queries | `lazy = TRUE`, arrow or duckdb, over the cache or the mirror | — | | Parallel downloads | `future::plan(multisession)` + furrr | sequential | ## Other routes Not measured here: [PySUS](https://github.com/AlertaDengue/PySUS), the Python package over the same FTP, and [Base dos Dados](https://basedosdados.org), treated SIM and SINASC tables on BigQuery with an R client. ## Which one - **Labels in the tibble, a quick table**: microdatasus. - **Joins, comparisons across years, models**: healthbR. Codes stay codes, types are parsed, dictionaries are functions, nothing is rewritten under you. - **SIH at scale, or repeatedly**: healthbR. Local Parquet cache, lazy queries over the mirror, provenance you can cite. - **SIM fetal/infant/maternal extracts, RJ/SP/ER SIH files, SIGTAP**: microdatasus, which has them and healthbR (yet) does not. - **SI-PNI, SISAB, surveys, ANS, ANVISA**: healthbR. They compose: `fetch_datasus()` and `sih_data(parse = FALSE)` return the same character columns for the same file, so you can read with one and label with the other: ``` r sih_data(year = 2023, month = 1, uf = "RR", parse = FALSE) |> process_sih() ``` ## Reproducing ``` r install.packages(c("microdatasus", "arrow")) # install.packages("pak"); pak::pak("SidneyBissoli/healthbR") # dev version sih_clear_cache(); sim_clear_cache(); sinasc_clear_cache() system.time(sih_data(year = 2023, month = 1, uf = "RR")) system.time(fetch_datasus(2023, 1, 2023, 1, uf = "RR", information_system = "SIH-RD")) ``` The same comparison, as a package vignette: [healthbR vs microdatasus](https://sidneybissoli.github.io/healthbR/articles/healthbr-vs-microdatasus.html). healthbR is on [CRAN](https://CRAN.R-project.org/package=healthbR) (0.2.0) and [GitHub](https://github.com/SidneyBissoli/healthbR) (development version, with the SIH mirror). Thanks to Raphael Saldanha: microdatasus opened the road healthbR travels, and its adoption of healthbR’s `.dbc` reader in 3.0.0 is the kind of exchange that makes both better.