Skip to contents

Changelog

Source: NEWS.md

educabr 0.1.0

First public release. Initial set of harmonised long-run series on Brazilian education plus a bundled Shiny dashboard.

Datasets

  • enrollment_kang_fgv — 6,238 rows. Brazilian school enrollment counts and gross rates by stage (EF1, EF2, EF, EM, ES), 1933-2010 at national level and 1955-2010 at UF level, with breakdown by colour/race 1960-2010. Per-paper source attribution (kang_paese_felix_2021, kang_menetrier_2024, kang_menetrier_comim_2024).

  • enrollment_tertiary — 1,341 rows. Brazilian tertiary enrollment 1907-2024 compiled across seven primary sources: IBGE Estatísticas do Século XX, Durham (2005), Maduro Junior (2007), Kang/Paese/Felix (2021), INEP CENSUP Synopsis (1995-2008), INEP CENSUP Microdata (2009-2024), and the INEP CENSUP Power BI panel. Multiple sources per year-network are kept on purpose to support cross-source comparison. Includes 25 reconstructed total rows (is_derived = TRUE) that fill the 2000-2008 transition period where INEP published in-person and EAD enrollment separately.

  • schooling_kang_fgv — 2,287 rows. Mean years of schooling for the adult population, 1925-2015 (BR), 1950-2015 (region, UF), with sex and race breakdowns at BR level (Walter & Kang 2024).

API

  • get_enrollment() — long-format access to enrollment series with filters for level, network, institution_type, modality, year, geo_level/geo, dimension, indicator, source, include_derived. Returns the canonical schema with English labels (lang = "en") or PT-BR labels (lang = "pt").

  • get_schooling() — long-format access to the mean-years-of-schooling series with filters for year, geo_level/geo, dimension, source, lang.

  • run_dashboard() — launches the bundled Shiny dashboard locally.

  • educabr_cite() — builds bibentry objects (or APA-style prose, or BibTeX) for any of the harmonised data sources, driven by the controlled vocabulary in inst/dict/vocabularies/sources.yaml.

Dashboard

  • Three-theme navbar (English UI): Enrollment, Tertiary Education, Educational Attainment.
  • Tertiary Education tab features multi-source comparison with interaction-based colour palette (each source × modality combination gets a unique colour shade), shape-by-source, and linetype-by-modality encoding.
  • “View R code” button on every tab generates a self-contained, copy-pasteable R snippet (educabr + ggplot2) that reproduces the current chart locally.

Schema

  • Canonical tidy-long schema documented in inst/dict/schema.yaml with primary-key constraints, year domain, controlled vocabularies for factor levels, and conventions for missing values.
  • 13 primary source entries documented in inst/dict/vocabularies/sources.yaml with DOIs, URLs, and coverage metadata.
  • PT-BR labels for every factor level in inst/dict/i18n.yaml.

Build pipeline

  • data-raw/01_build_enrollment_kang_fgv.R — Kang/FGV-IBRE 2023 compilation (4 xlsx files → enrollment_kang_fgv.rda).
  • data-raw/02_build_schooling_kang_fgv.R — Walter & Kang 2024 series (1 xlsx file → schooling_kang_fgv.rda).
  • data-raw/03_build_enrollment_tertiary.R — multi-source tertiary compilation, with canonicalisation of 69 raw source strings into 7 canonical keys and 4 composite derived-row keys, plus exact-duplicate deduplication.

Tests

  • 9 tests for get_enrollment() (core filters and pivots).
  • 9 tests for get_schooling().
  • 7 tests for the tertiary-specific arguments (institution_type, modality, include_derived, composite source keys, loader normalisation of legacy datasets).