# R packages


Brazilian public data rarely arrives ready to use. It comes as a
fixed-width file, a spreadsheet with a three-row header, something
behind a form, or an API returning nested JSON with uppercase field
names. The cleanup work is always the same, and always redone by whoever
arrives next.

I wrote these packages to do that work once, in R, and hand back a tidy
data frame.

## On CRAN

CRAN is not hosting: it is a gate with a human reviewer, checks across
several platforms, and a requirement that documentation and examples
actually run. I put two packages through it.

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### educabR

**Brazilian education data from INEP** — school census, IDEB, ENEM,
SAEB, higher education census, ENCCEJA, IDD and ENADE. Downloads,
unpacks and tidies.

`install.packages("educabR")`

Version 1.1.0 on CRAN since 2026-08-23 ·
[documentation](https://sidneybissoli.github.io/educabR/) ·
[GitHub](https://github.com/SidneyBissoli/educabR)

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### healthbR

**Brazilian public health data** from several DATASUS and Ministry of
Health sources — hospital admissions, mortality, births, facilities, and
surveys such as VIGITEL.

`install.packages("healthbR")`

Version 0.2.0 on CRAN since 2026-02-15 ·
[documentation](https://sidneybissoli.github.io/healthbR/) ·
[GitHub](https://github.com/SidneyBissoli/healthbR)

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## In development, on GitHub

Installable with `remotes::install_github()`. I have not submitted these
to CRAN yet.

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### cvmdata

**Open data from the CVM**, Brazil’s securities regulator — listed
companies and funds, in tidy form.

`0.2.0.9000` · [documentation](https://sidneybissoli.github.io/cvmdata/)
· [GitHub](https://github.com/SidneyBissoli/cvmdata)

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### welfarebR

**Brazilian social assistance data** — programmes, benefits and
coverage.

`0.1.0` · [GitHub](https://github.com/SidneyBissoli/welfarebR)

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### senado

**The Brazilian Federal Senate open data API**, with a tidy interface.
The R sibling of the Senate MCP server.

`0.0.0.9000` · [GitHub](https://github.com/SidneyBissoli/senado)

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## Why R, and why MCP as well

The [MCP servers](..\tools/) and these packages are how I attack the
same problem from opposite sides. A package serves someone already
inside an analysis, writing code, who wants the data in the environment
where the modelling will happen. An MCP server serves someone talking to
an assistant who wants the answer with its source, without writing any
code.

Neither replaces the other, and both depend on the same thing: somebody
having read the source’s documentation, found where it lies, and written
that down in code someone else can reuse. That is the work I do.