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educabr2

Harmonised historical series on Brazilian education — enrollment and educational attainment, compiled and reconciled across decades of heterogeneous official and academic sources.

License: GPL (>= 3) R-CMD-check pkgdown Lifecycle: experimental

educabr2 provides curated long-run series on Brazilian formal education — enrollment by stage and network, years of schooling, literacy and attainment — compiled and reconciled from heterogeneous official and academic sources (Censo Escolar, PNAD, Censo Demográfico, Anuário Estatístico do IBGE, INEP CENSUP, Kang/FGV-IBRE, Walter & Kang, among others) into a single tidy schema with explicit per-row provenance.

🇧🇷 educabr2 reúne séries históricas tratadas sobre educação formal no Brasil — matrículas, anos de estudo, atingimento educacional — em um único schema tidy, com proveniência explícita. Veja a vinheta em português.

Installation

educabr2 is not yet on CRAN. Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("mancano-tales/educabr2")

Usage

The package exposes six top-level data-access functions, two utility functions, and the bundled dashboard launcher.

get_enrollment() — school enrollment

Who is enrolled at a given stage in a given year. Annual flow, school perspective.

library(educabr2)

# Gross enrollment rate for ensino fundamental, Brazil, all years
get_enrollment(
  level     = "fundamental",
  indicator = "rate",
  geo_level = "BR"
)

# Tertiary enrollment by network, all sources kept side by side
get_enrollment(
  level    = "superior",
  network  = c("publica", "privada"),
  modality = "total",
  year     = c(1990, 2024)
)

# Enrollment rates by race/colour, 1960 onward
get_enrollment(
  level     = "fundamental",
  indicator = "rate",
  dimension = "race",
  year      = c(1960, 2010)
)

get_schooling() — educational attainment

How much the population has already studied. Accumulated stock, person perspective.

# Mean years of schooling for adults, Brazil
get_schooling(geo_level = "BR")

# Same series broken down by race and by sex
get_schooling(dimension = "race")
get_schooling(dimension = "sex")

get_expenditure() — public expenditure on education

How much the State spends on education. Annual flow, public-purse perspective.

# Total public expenditure on education as share of GDP
get_expenditure(indicator = "share_gdp", level = "total")

# Per-student spending in tertiary education, all years
get_expenditure(indicator = "per_student", level = "superior")

# Kang & Menetrier's "double ratio" of fiscal regressivity (ES / EF1)
get_expenditure(indicator = "double_ratio_es_ef1")

get_progression() — grade-progression indicators

How students flow through the early primary grades. Internal-flow ratio.

# National GDR6 series (1955-2010)
get_progression()

# GDR6 across Northeast states from 1980
get_progression(geo_level = "UF",
                geo = c("BA", "PE", "CE", "PB", "MA", "PI", "RN", "AL", "SE"),
                year = c(1980, 2010))

get_attainment() — comparative international attainment

Share of the population aged 15–64 who completed at least a given education level. Accumulated stock, cross-country perspective. Source: Lee & Lee (2016), ~111 countries, 1870–2010.

# Tertiary completion in Brazil over time
get_attainment(level = "tertiary", geo = "BRA")

# Primary completion across Latin America, post-1950
get_attainment(level = "primary",
               geo   = c("BRA", "ARG", "CHL", "MEX", "URY"),
               year  = c(1950, 2010))

# Compare male vs female secondary completion in Brazil
get_attainment(level = "secondary", geo = "BRA", dimension = "sex")

list_sources() — list bundled data sources

Returns a tibble describing every source in the controlled vocabulary, with short name, type, temporal/geographic coverage, DOI and URL.

src <- list_sources()

# Filter to academic papers only
src[src$type == "academic", c("key", "short_name", "doi")]

# Sources that include UF-level coverage
src[grepl("UF", src$geo), c("key", "year_start", "year_end")]

run_dashboard() — interactive Shiny app 

educabr2::run_dashboard()

A curated Overview tab — four fixed story charts for broad audiences (secular expansion, public × private with the 1997/2005 regulatory landmarks, the rise of EAD, the gender reversal in schooling) — plus six thematic tabs:

  • Enrollment — Kang/FGV series by stage, year, race
  • Tertiary Education — multi-source comparison 1907–2024 (Kang, Maduro Jr., IBGE Século XX, INEP CENSUP), with optional reconstructed totals for the 2000–2008 in-person + EAD split
  • Educational Attainment — Walter & Kang mean years of schooling by BR / region / UF, with race and sex breakdowns
  • Public Expenditure — Kang & Menetrier (2024) series on public spending in education: share of GDP by stage, per-student spending as share of GDP per capita, and the “double ratio” indicators of fiscal regressivity (Brazil, 1933–2010)
  • Grade Progression — Kang/Paese/Felix (2021) GDR6 progression ratio (enrollment in grades 4-6 / grades 1-3 of the old eight-year primary system), BR and 20 UFs, 1955–2010
  • International Comparison — Lee & Lee (2016) attainment shares (primary/secondary/tertiary completed, population 15–64), 111 countries, 1870–2010, with sex breakdowns — Brazil against the world record in the same interface

Every chart has a “View R code” button that prints a self-contained snippet (educabr2 + ggplot2 + plotly, styled with the package’s own theme_educabr() / Okabe-Ito scales / scale_x_year_educabr()) you can paste into RStudio to reproduce the interactive chart locally.

Datasets

Six internal datasets back the public functions:

Dataset Rows Coverage Source(s)
enrollment_kang_fgv 6,238 BR + UF, EF1/EF2/EF/EM/ES, 1933–2010 (+ race breakdown 1960–2010) Kang, Paese & Felix (2021); Kang & Menetrier (2024); Kang, Menetrier & Comim (2024)
enrollment_tertiary 1,341 BR, ensino superior, 1907–2024, by network/institution/modality IBGE Século XX; Maduro Jr. (2007); Kang/Paese/Felix (2021); INEP CENSUP Synopsis / Microdata / Power BI
schooling_kang_fgv 2,287 BR + region + UF, mean years of schooling, 1925–2015, with race and sex breakdowns Walter & Kang (2024)
expenditure_kang_fgv 1,170 BR, public expenditure on education (% GDP, per-student, “double ratios”), 1933–2010 Kang & Menetrier (2024)
progression_kang_fgv 1,090 BR + 20 UFs, GDR6 grade-progression ratio, 1955–2010 Kang, Paese & Felix (2021)
lee_lee_2016 ~29,000 111 countries, primary/secondary/tertiary attainment, 1870–2010 (5-year steps), by sex Lee & Lee (2016)

End users should call the get_*() functions. The datasets are exposed for inspection but the public API normalises schema differences, applies per-source filters and translates labels.

Deduplication hierarchy. When the same year and sector are covered by multiple sources, the recommended order of precedence is: INEP CENSUP microdata (2009–2024) → INEP statistical synopses (1995–2008) → Kang, Paese & Felix (1990–1994) → Maduro Junior → Durham → IBGE Estatísticas do Século XX. The most disaggregated and official source available always wins; all competing estimates remain in the panel for auditing.

Validation. Overlapping windows are preserved deliberately so the sources can be checked against each other: in 1995, for example, the INEP synopsis, Durham (2005) and Kang et al. (2021) all report exactly 1,759,703 tertiary enrollments — a perfect triangulation of the academic reconstructions against the official record.

Data Visualization

The educabr2 package includes a built-in visualization system based on Kieran Healy’s Data Visualization (2018/2026) design principles. It provides a consistent aesthetic theme and colorblind-safe palettes matching the ongoing MA Thesis The Politics of Reforming Tertiary Education (Tales Mançano, 2026).

To style your plots, use the theme_educabr() and scale_fill_educabr() / scale_colour_educabr() functions:

library(ggplot2)
library(educabr2)

# Query some data
df <- get_enrollment(level = "superior", dimension = "race")

# Build a plot
ggplot(df, aes(x = year, y = value, fill = dim_race)) +
  geom_area() +
  theme_educabr() +
  scale_fill_educabr() +
  scale_x_year_educabr(df$year)

Year Axis for Historical Series (scale_x_year_educabr())

Most series in educabr2 span many decades (some more than a century), and default ggplot2 breaks become unreadable at that scale. The exported scale_x_year_educabr(years) scale picks the break spacing from the span of the data you actually plot — every 20 years beyond six decades, every 10 years for intermediate spans, pretty() breaks below that — and always labels the first and last year present in the series, dropping any grid break that would collide with those extreme labels. Pass it the vector of years being plotted (typically df$year).

Font Auto-Registration (TinyTeX & Latin Modern)

To match formal academic publications, the theme tries to use the standard LaTeX font family Latin Modern Roman. * Automatic Registration: If you have TinyTeX installed on your system (e.g. via tinytex::install_tinytex()) and have the showtext and sysfonts R packages installed, theme_educabr() will automatically locate the .otf font files in your TinyTeX directories and load them dynamically using sysfonts::font_add(). * Graceful Fallback: If the font files are not found on your system or the required packages are missing, the theme will automatically and silently fall back to your system’s default "serif" font family.

Omit/Include Titles Inside Plot (plot_titles)

Depending on where you are presenting your charts, you may want to include titles within the image or handle them in the document text: * For General Presentations (plot_titles = TRUE): This is the default. The plot titles, subtitle, and caption are styled and rendered inside the image canvas itself. * For LaTeX/Quarto Manuscripts (plot_titles = FALSE): This hides the title, subtitle, and caption from the ggplot canvas, allowing you to specify them as metadata captions (e.g. fig-cap in Quarto or \caption{} in LaTeX) in the main document, which prevents duplicate titles and enables proper page layout.

# Hide title elements within the plot canvas for manuscripts
ggplot(df, aes(x = year, y = value, fill = dim_race)) +
  geom_area() +
  theme_educabr(plot_titles = FALSE) +
  scale_fill_educabr()

Schema

All get_*() functions return a tibble in the canonical tidy-long schema documented in inst/dict/schema.yaml: one row per observation, alternative sources for the same indicator as separate rows (column source), aggregations as an explicit factor level ("total") rather than NA. Controlled vocabularies live in inst/dict/vocabularies/, and PT-BR labels for every factor level in inst/dict/i18n.yaml.

educabr2 is complementary to educabR (Sidney Bissoli), which organises access by official source (get_ideb(), get_enem(), get_censo_escolar(), …). Here the axis is by theme and historical series: single indicators compiled across multiple sources over long time spans.

Design inspirations: geobr (coherent function family), PNADCperiods (embedded dashboard + methodological delivery), and the brverse ecosystem.

Citation

Cite both the package (for the harmonisation work) and the originating sources for any data you actually use. The educabr_cite() helper builds APA / BibTeX entries for every bundled source:

citation("educabr2")                              # cite the package

educabr_cite("kang_paese_felix_2021")            # cite one source
educabr_cite(c("walter_kang_2023",               # cite many
              "inep_microdados_censup"))
educabr_cite()                                   # all bundled sources

# Typical workflow — query first, then cite only what you used
d   <- get_enrollment(level = "fundamental", indicator = "rate")
educabr_cite(unique(d$source), style = "text")

Contributing

Issues and PRs welcome. To contribute new data, open an issue first describing the source file, author, geographic/temporal coverage and licence — before submitting a PR.

License

Code under GPL (>= 3). Data redistributed under CC BY 4.0 (except where the original source imposes restrictions; see inst/dict/vocabularies/sources.yaml). See LICENSE.md.

Como citar / How to cite

Se você usar este pacote em uma publicação, cite tanto o pacote quanto as fontes originais dos dados que utilizou. Para obter a entrada BibTeX ou a referência APA do pacote, use:

If you use this package in a publication, cite both the package and the original data sources you drew from. To get the BibTeX entry or APA reference for the package, run:

citation("educabr2")

Referência sugerida / Suggested citation:

Mançano, T., Alcantara, V., & Damião, A. (2026). educabr2: Harmonized Historical Series on Brazilian Education (R package version 0.1.0.9000). GitHub. https://github.com/mancano-tales/educabr2

BibTeX:

@Manual{educabr2,
  title  = {educabr2: Harmonized Historical Series on Brazilian Education},
  author = {Tales Mançano and Victor Alcantara and Artur Damião},
  year   = {2026},
  note   = {R package version 0.1.0.9000},
  url    = {https://github.com/mancano-tales/educabr2},
}

Cite also the originating sources for any data you actually use. The educabr_cite() helper builds APA / BibTeX entries for every bundled source (see Citation above).

Um DOI Zenodo acompanhará o primeiro lançamento marcado com uma tag de versão. / A Zenodo DOI will accompany the first tagged release.