Package {educabR}

Title:	Download and Process Brazilian Education Data from INEP
Version:	2.0.0
Description:	Download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira). Provides functions to access microdata from the School Census (Censo Escolar), ENEM (Exame Nacional do Ensino Médio), SAEB (Sistema de Avaliação da Educação Básica), Higher Education Census (Censo da Educação Superior), ENADE (Exame Nacional de Desempenho dos Estudantes), ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos), IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado), CPC (Conceito Preliminar de Curso), IGC (Índice Geral de Cursos), CAPES graduate education data, FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), IDEB (Índice de Desenvolvimento da Educação Básica), and other educational datasets. Returns data in tidy format ready for analysis. Data source: INEP Open Data Portal https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos.
License:	MIT + file LICENSE
Encoding:	UTF-8
URL:	https://github.com/SidneyBissoli/educabR, https://sidneybissoli.github.io/educabR/
BugReports:	https://github.com/SidneyBissoli/educabR/issues
Depends:	R (≥ 4.1.0)
Imports:	cli, dplyr, httr2 (≥ 1.0.0), lifecycle, purrr, readr, rlang, stringi, stringr, tidyr, tools
Suggests:	ggplot2, jsonlite, kableExtra, knitr, readxl, rmarkdown, systemfonts, testthat (≥ 3.0.0), textshaping, tibble, withr
Config/testthat/edition:	3
VignetteBuilder:	knitr
Config/roxygen2/version:	8.1.1
NeedsCompilation:	no
Packaged:	2026-11-03 22:23:01 UTC; SIDNEY
Author:	Sidney da Silva Pereira Bissoli [aut, cre, cph]
Maintainer:	Sidney da Silva Pereira Bissoli <sbissoli76@gmail.com>
Repository:	CRAN
Date/Publication:	2026-11-06 15:10:11 UTC

educabR: Download and Process Brazilian Education Data from INEP

Description

Download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira). Provides functions to access microdata from the School Census (Censo Escolar), ENEM (Exame Nacional do Ensino Médio), SAEB (Sistema de Avaliação da Educação Básica), Higher Education Census (Censo da Educação Superior), ENADE (Exame Nacional de Desempenho dos Estudantes), ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos), IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado), CPC (Conceito Preliminar de Curso), IGC (Índice Geral de Cursos), CAPES graduate education data, FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), IDEB (Índice de Desenvolvimento da Educação Básica), and other educational datasets. Returns data in tidy format ready for analysis. Data source: INEP Open Data Portal https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos.

The educabR package provides functions to download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira). It offers easy access to microdata from:

• School Census (Censo Escolar): Annual data on schools, enrollment, teachers, and classes in basic education

• ENEM: Data from the National High School Exam

• SAEB: Basic Education Assessment System (student performance)

• Higher Education Census (Censo da Educação Superior): Annual data on institutions, courses, students, and faculty in higher education

• ENADE: National Student Performance Exam (higher education)

• IDD: Value-added indicator for undergraduate courses

• CPC: Preliminary Course Concept (undergraduate quality indicator)

• IGC: General Index of Courses (institutional quality indicator)

• CAPES: Graduate education data (programs, students, faculty)

• ENCCEJA: Youth and Adult Education Certification Exam

• IDEB: Basic Education Development Index

All functions return data in tidy format, ready for analysis with tidyverse tools.

Main functions

School Census:

• get_censo_escolar(): Download School Census microdata

ENEM:

• get_enem(): Download ENEM microdata

SAEB:

• get_saeb(): Download SAEB microdata

Higher Education Census:

• get_censo_superior(): Download Higher Education Census microdata

ENEM por Escola:

• get_enem_escola(): Download ENEM results aggregated by school

ENADE:

• get_enade(): Download ENADE microdata

IDD:

• get_idd(): Download IDD data

CPC:

• get_cpc(): Download CPC data

IGC:

• get_igc(): Download IGC data

CAPES:

• get_capes(): Download CAPES graduate education data

ENCCEJA:

• get_encceja(): Download ENCCEJA microdata

IDEB:

• get_ideb(): Download IDEB data

Cache system

The package implements a local cache system to avoid repeated downloads. Use set_cache_dir() to configure a persistent cache directory. See get_cache_dir() to check the current cache location.

Package options

• educabR.cache_dir: path to the cache directory. Set via set_cache_dir() or directly with options(). Defaults to file.path(tempdir(), "educabR_cache").

• educabR.download_timeout: HTTP timeout in seconds for download_inep_file(). Defaults to 600 (10 minutes). Raise it for large microdata (e.g. ENEM participantes is ~1.6 GB) on slow connections: options(educabR.download_timeout = 1800).

Data source

Most data is downloaded from INEP's official portal: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados

CAPES data is downloaded from the CAPES Open Data Portal: https://dadosabertos.capes.gov.br

Author(s)

Maintainer: Sidney da Silva Pereira Bissoli sbissoli76@gmail.com (ORCID)

Authors:

• Sidney da Silva Pereira Bissoli sbissoli76@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/SidneyBissoli/educabR

• https://sidneybissoli.github.io/educabR/

• Report bugs at https://github.com/SidneyBissoli/educabR/issues

Identify an archive format from its first bytes

Description

Internal function. Returns "zip", "7z" or "rar" from the file signature, or NA when none matches. Used instead of the extension, which INEP does not always get right (IDD 2022 is a RAR named .zip).

Usage

archive_format(bytes)

Arguments

Value

A string or NA_character_.

Fold text to ASCII the same way on every platform

Description

Internal function used to build column names and to compare sheet names. iconv(to = "ASCII//TRANSLIT") is platform-dependent: macOS turns "i-acute" into "'i", so names such as "Municipio" (with accent) came out as munic_ipio there. This uses ICU's Latin-ASCII transform and then reproduces the names educabR already produced on Windows: ordinal indicators become letters ("N.o" -> "no"), superscript digits become digits, and Greek letters and sharp s are dropped.

Usage

ascii_fold(x)

Arguments

Value

A character vector.

Check available years for a dataset

Description

Returns the years available for a given dataset. On the first call in a session, queries the data source to discover which years are actually available (requires internet). Results are cached for the session. Falls back to a known list if discovery fails.

Usage

available_years(dataset)

Arguments

Value

An integer vector of available years.

Examples

## Not run: 
available_years("enem")
available_years("enade")
available_years("fundeb_enrollment")

## End(Not run)

Stack the parts of an IGC table

Description

Internal function. Stacks the per-institution-type parts of an IGC table. Parts with the same number of columns take the column names of the first part, because INEP sometimes words the same header differently across sheets (IGC 2010: "Porcentagem de" in one sheet, "%" in the others). Otherwise parts are stacked by name. Columns whose type differs across parts are converted to character.

Usage

bind_igc_parts(parts)

Arguments

Value

A tibble.

Build FUNDEB distribution URL from lookup table

Description

Internal function to construct the download URL for FUNDEB distribution data from STN. Uses a hardcoded lookup table of publication IDs.

Usage

build_fundeb_url(year)

Arguments

Value

A character string with the download URL.

Build IDEB download URL

Description

Internal function to construct IDEB download URLs based on level and stage.

Usage

build_ideb_url(level, stage, year)

Arguments

Value

A character string with the URL.

Build INEP microdata URL

Description

Internal function to construct URLs for INEP microdata.

Usage

build_inep_url(dataset, year, ...)

Arguments

Value

A character string with the URL.

Build SAEB ZIP filename

Description

Internal function to build the correct ZIP filename for SAEB data. Handles the special case of 2021 (split into two files).

Usage

build_saeb_zip_filename(year, level = "fundamental_medio")

Arguments

Value

The ZIP filename.

Reference year of a CAPES resource

Description

Internal function. CAPES file names carry the reference year followed by the publication date (⁠br-capes-colsucup-prog-2017-2021-11-10.csv⁠) or, in the 2013-2016 packages, a range and a ⁠_YYYY⁠ suffix (⁠br-capes-btd-2013a2016-2017-12-01_2014.csv⁠). Matching on "contains the year" picked the wrong file there (every 2013-2016 name contains "2016"), so the year is read from that structure.

Usage

capes_resource_year(x)

Arguments

Value

An integer vector (NA when no year is found).

Clean dash placeholder values

Description

Internal function to replace dash placeholders ("-", "\u2013") with NA in all character columns. Common in INEP datasets where missing values are encoded as dashes.

Usage

clean_dash_values(df)

Arguments

Value

The data frame with dashes replaced by NA.

Clean IDEB numeric columns

Description

Internal function to convert ⁠vl_*⁠ columns from character to numeric. Handles "-" and "ND" as NA, and replaces comma decimal separators with dots.

Usage

clean_ideb_values(df)

Arguments

Value

The data frame with ⁠vl_*⁠ columns as numeric.

Clear the educabR cache

Description

Removes all cached files from the educabR cache directory.

Usage

clear_cache(dataset = NULL)

Arguments

Value

Invisibly returns TRUE if successful.

See Also

Other cache functions: get_cache_dir(), list_cache(), set_cache_dir()

Examples

## Not run: 
# clear all cached data
clear_cache()

# clear only ENEM cache
clear_cache("enem")

## End(Not run)

Resolve the source file of a Conceito Enade edition

Description

Internal function. Returns the URL, the sheet and the resolved type for a year: the Conceito Enade spreadsheet, the combined Enade/CPC table for 2007-2011, or one of the two 2025 files.

Usage

conceito_enade_source(year, type = NULL)

Arguments

Value

A list with url, sheet and type.

Convert faixa and continuo columns to numeric

Description

Internal function to convert score columns (names ending in ⁠_faixa⁠, ⁠_faixas⁠ or ⁠_continuo⁠) from character to numeric. Non-numeric markers such as "SC" (Sem Conceito) and "sub judice" are converted to NA. Stacking the sheets of the IGC 2007-2016 can turn these columns into text when only one sheet carries such a marker.

Usage

convert_faixa_columns(df)

Arguments

Value

The data frame with score columns as numeric.

Detect file delimiter

Description

Internal function to detect the delimiter used in a CSV file by reading the first line and counting occurrences of common delimiters.

Usage

detect_delim(file)

Arguments

Value

The detected delimiter character.

Detect file encoding

Description

Internal function to detect the encoding of a text file. INEP files typically use Latin-1 or UTF-8.

Usage

detect_encoding(file, chunk_size = 4 * 1024^2)

Arguments

Value

A character string with the encoding name.

Discover CAPES download URL via CKAN API

Description

Internal function to discover the download URL for a specific CAPES dataset and year using the CKAN API. CAPES URLs contain UUIDs and cannot be predicted, so they must be discovered dynamically.

Usage

discover_capes_url(year, type)

Arguments

Value

A character string with the download URL.

Download a file from INEP

Description

Internal function to download files from INEP's servers with progress indication and error handling.

Usage

download_inep_file(url, destfile, quiet = FALSE)

Arguments

Value

The path to the downloaded file.

Drop blank, footnote and total rows from a sheet read with readxl

Description

Internal function. INEP spreadsheets end with blank rows, footnotes written in the first column, or a lone total in one column (CPC 2022). A data row always fills several columns, so rows with at most one filled cell are dropped.

Usage

drop_footer_rows(df)

Arguments

Value

The data frame without those rows.

Drop an incomplete UTF-8 sequence at the end of a raw vector

Description

Internal function. A fixed-size chunk read from a UTF-8 file can cut a multibyte character. Only a trailing lead byte whose sequence is incomplete (with its continuation bytes) is dropped, so a latin1 byte at the end is kept and still makes the chunk invalid.

Usage

drop_incomplete_utf8_tail(bytes)

Arguments

Value

The raw vector, possibly shorter.

Number of an ENADE data file

Description

Internal function. microdados2023_arq12.txt is file 12; a file without an arq number counts as file 1.

Usage

enade_file_number(files)

Arguments

Value

An integer vector.

Download and extract an ENADE edition

Description

Internal function shared by get_enade() and list_enade_files().

Usage

enade_prepare(year, keep_zip = TRUE, quiet = FALSE)

Arguments

Value

The extraction directory.

Summary statistics for ENEM scores

Description

Calculates summary statistics for ENEM scores, optionally grouped by demographic variables.

Usage

enem_summary(data, by = NULL)

Arguments

Value

A tibble with summary statistics for each score area.

See Also

Other ENEM functions: get_enem(), get_enem_escola(), get_enem_itens()

Examples

## Not run: 
enem <- get_enem(2023, n_max = 10000)

# overall summary
enem_summary(enem)

# summary by sex
enem_summary(enem, by = "tp_sexo")

## End(Not run)

Find how many title rows precede the header of an Excel sheet

Description

Internal function. Returns the number of rows to skip so that the header is the first row where at least half of the columns are filled. A sheet whose first row is already the header returns 0.

Usage

excel_header_skip(file, sheet = 1)

Arguments

Value

An integer.

Extract an archive file (ZIP, 7z, or RAR)

Description

Internal function to extract archive files. Supports ZIP, 7z, and RAR formats. For ZIP files, delegates to extract_zip(). For 7z and RAR files, uses the system ⁠7z⁠ command.

Usage

extract_archive(archive, exdir, quiet = FALSE)

Arguments

Value

A character vector of extracted file paths.

Extract a ZIP file

Description

Internal function to extract ZIP files with progress indication.

utils::unzip() is tried first. Its zlib backend cannot decode some entries INEP ships (Deflate64 compression, used for RESULTADOS_2025.csv in the ENEM 2025 archive) and skips them with only a "zip file is corrupt" warning, so the number of extracted files is compared with the archive's listing. When anything is missing, the archive is extracted again with an external tool (7-Zip, then PowerShell on Windows or unzip elsewhere), and an error is raised if the archive is still incomplete.

Usage

extract_zip(zipfile, exdir, quiet = FALSE)

Arguments

Value

A character vector of extracted file paths.

Fetch FUNDEB enrollment data from FNDE OData API

Description

Internal function to fetch FUNDEB enrollment data from FNDE's OData API with pagination support.

Usage

fetch_fundeb_enrollment(year, uf = NULL, n_max = Inf, quiet = FALSE)

Arguments

Value

A tibble with enrollment data.

Fetch the IDEB xlsx into cache

Description

Internal function to download the IDEB spreadsheet to xlsx_path. For 2023 and earlier, the URL points straight at the xlsx. From 2025 on, INEP packages the xlsx inside a zip (alongside an ods copy and an md5 file); the zip is downloaded, the xlsx extracted into the cache, and the zip and extraction leftovers removed.

Usage

fetch_ideb_file(url, xlsx_path, quiet = FALSE)

Arguments

Value

xlsx_path, invisibly.

Find the Censo Escolar main data file

Description

Internal function to locate the main data file within the extracted census directory.

Usage

find_censo_file(exdir, year)

Arguments

Value

The path to the data file.

Find a Censo Escolar file by user-supplied name

Description

Internal function to locate a specific CSV file within the extracted census directory by partial name match.

Usage

find_censo_file_by_name(exdir, file, year)

Arguments

Value

The full path to the matched file.

Find the Higher Education Census data file

Description

Internal function to locate a Higher Education Census data file within the extracted directory based on the requested type.

Usage

find_censo_superior_file(exdir, year, type = "ies")

Arguments

Value

The path to the data file.

Find data files in extracted directory

Description

Internal function to locate the main data files after extraction.

Usage

find_data_files(exdir, pattern = "\\.(csv|CSV|txt|TXT)$")

Arguments

Value

A character vector of file paths.

Find ENADE data files

Description

Internal function to locate all ENADE data files within the extracted directory. INEP distributes ENADE microdata split across multiple files (arq1.txt, arq2.txt, ...) that share the columns NU_ANO and CO_CURSO but are sorted differently (they cannot be joined by row).

Usage

find_enade_files(exdir, year)

Arguments

Value

A character vector of file paths, sorted by split number.

Find the ENCCEJA data file

Description

Internal function to locate the ENCCEJA data file within the extracted directory.

Usage

find_encceja_file(exdir, year, type = "regular")

Arguments

Details

Every published edition ships a regular file (REG_NAC or REGULAR) and a PPL file (PPL_NAC or PPL), plus PPL questionnaire and item files. The participant file is chosen by type, so the small PPL file is never returned in place of the regular one. Layouts without either marker fall back to the generic name patterns.

Value

The path to the data file.

Find the ENEM por Escola data file

Description

Internal function to locate the ENEM por Escola data file within the extracted directory.

Usage

find_enem_escola_file(exdir)

Arguments

Value

The path to the data file.

Find the ENEM data file

Description

Internal function to locate the main ENEM data file within the extracted directory.

Usage

find_enem_file(exdir, year, type = "participantes")

Arguments

Value

The path to the data file.

Find the IDD data file

Description

Internal function to locate the IDD data file within the extracted directory.

Usage

find_idd_file(exdir, year)

Arguments

Value

The path to the data file.

Find the per-grade SAEB student file

Description

Internal function that matches ⁠TS_ALUNO_<serie>.csv⁠ by exact name.

Usage

find_saeb_aluno_by_serie(exdir, year, serie = NULL)

Arguments

Value

The path to the file, or NULL when the edition has no per-grade student files.

Find the SAEB data file

Description

Internal function to locate a SAEB data file within the extracted directory based on the requested type.

Usage

find_saeb_file(exdir, year, type = "aluno", serie = NULL)

Arguments

Details

Since 2013 INEP ships one student file per grade (TS_ALUNO_2EF.csv, TS_ALUNO_5EF.csv, ...). These are matched by exact name: with more than one grade available and no serie, the function aborts listing the grades instead of picking one silently.

Value

The path to the data file.

Fix brasil unnamed columns

Description

Internal function to rename columns in brasil data.

Usage

fix_brasil_cols(df)

Arguments

Value

Data frame with renamed columns.

Fix regiao/estado unnamed columns and filter rows

Description

Internal function to rename unnamed columns in regiao_uf data and filter to keep only regions or states depending on the requested level.

Usage

fix_regiao_uf_cols(df, level)

Arguments

Value

Data frame with renamed columns, filtered to the requested level.

Source name of a FUNDEB sheet

Description

Internal function. The sheet name is the destination prefix (E_ for states, M_ for municipalities) followed by the source. The VAAF, VAAT and VAAR shares of the Complementacao da Uniao (E_COUN_VAAF) keep the short name ("VAAF"); every other sheet keeps its full suffix (E_COUN -> "COUN", E_AFE_EC123 -> "AFE_EC123").

Usage

fundeb_sheet_source(sheet)

Arguments

Value

A string.

Get the current cache directory

Description

Returns the current cache directory used by educabR.

Usage

get_cache_dir()

Value

A character string with the path to the cache directory.

See Also

Other cache functions: clear_cache(), list_cache(), set_cache_dir()

Examples

get_cache_dir()

Get CAPES graduate education data

Description

Downloads and processes data from CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior) on Brazilian graduate programs (stricto sensu). Data is retrieved from the CAPES Open Data Portal via the CKAN API.

Usage

get_capes(
  year,
  type = c("programas", "discentes", "docentes", "cursos", "catalogo"),
  n_max = Inf,
  keep_file = TRUE,
  quiet = FALSE
)

Arguments

Details

CAPES is the federal agency responsible for evaluating and regulating graduate programs in Brazil. The data covers stricto sensu programs (master's and doctoral).

The data types include:

• programas: Program identifiers, area, evaluation scores

• discentes: Student enrollment, demographics, scholarship status

• docentes: Faculty information, qualifications, employment

• cursos: Course details, modality, start dates

• catalogo: Catalog of theses and dissertations

Important notes:

• Data is sourced from the CAPES Open Data Portal (CKAN), not INEP.

• Files are large CSV files. Downloading may take several minutes.

• Column names are standardized to lowercase with underscores.

• Internet connection is required to discover download URLs via the CKAN API before downloading.

Value

A tibble with CAPES data in tidy format.

Data source

https://dadosabertos.capes.gov.br

Examples

## Not run: 
# get graduate programs for 2023
programas <- get_capes(2023, type = "programas")

# get student data for 2022 with limited rows
discentes <- get_capes(2022, type = "discentes", n_max = 1000)

## End(Not run)

Get School Census (Censo Escolar) data

Description

Downloads and processes microdata from the Brazilian School Census (Censo Escolar), conducted annually by INEP. Returns school-level data with information about infrastructure, location, and administrative details.

Usage

get_censo_escolar(
  year,
  file = NULL,
  uf = NULL,
  n_max = Inf,
  keep_zip = TRUE,
  quiet = FALSE
)

Arguments

Details

The School Census is the main statistical survey on basic education in Brazil. It collects data from all public and private schools offering basic education (early childhood, elementary, and high school).

Important notes:

• The microdata contains one row per school (~217,000 schools in 2023).

• Column names are standardized to lowercase with underscores.

• Use the uf parameter to filter by state for faster processing.

• Older years (1995-2006) contain multiple CSV files with different data. Use list_censo_files() to discover available files, then pass the desired file name to the file parameter.

Value

A tibble with school data in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-escolar

See Also

Other School Census functions: list_censo_files()

Examples

## Not run: 
# get schools data for 2023
escolas <- get_censo_escolar(2023)

# get schools from Sao Paulo state only
escolas_sp <- get_censo_escolar(2023, uf = "SP")

# read only first 1000 rows for exploration
escolas_sample <- get_censo_escolar(2023, n_max = 1000)

# list available files for an older year
list_censo_files(1995)
# [1] "CENSOESC_1995.CSV" "DADOS_DESP_1995.CSV" "DADOSCURSO_1995.CSV"

# load a specific file from an older year
cursos <- get_censo_escolar(1995, file = "DADOSCURSO")

# 2025: data is split into separate tables
list_censo_files(2025)
escolas_2025 <- get_censo_escolar(2025)
matriculas_2025 <- get_censo_escolar(2025, file = "Matricula")
docentes_2025 <- get_censo_escolar(2025, file = "Docente")
turmas_2025 <- get_censo_escolar(2025, file = "Turma")

## End(Not run)

Get Higher Education Census (Censo da Educação Superior) data

Description

Downloads and processes microdata from the Brazilian Higher Education Census (Censo da Educação Superior), conducted annually by INEP. Returns data on institutions, courses, students, or faculty.

Usage

get_censo_superior(
  year,
  type = c("ies", "cursos", "alunos", "docentes"),
  uf = NULL,
  n_max = Inf,
  keep_zip = TRUE,
  quiet = FALSE
)

Arguments

Details

The Higher Education Census is the most comprehensive statistical survey on higher education institutions (HEIs) in Brazil. It collects data from all HEIs offering undergraduate and graduate programs.

Data types:

• "ies": One row per institution — administrative data, location, academic organization, funding type.

• "cursos": One row per undergraduate course — area of study, modality (in-person/distance), enrollment counts.

• "alunos": One row per student enrollment — demographics, program, admission type, enrollment status.

• "docentes": One row per faculty member — education level, employment type, teaching regime.

Important notes:

• Student files ("alunos") can be very large (several GB). Use n_max to read a sample first.

• Column names are standardized to lowercase with underscores.

• Use the uf parameter to filter by state for faster processing.

Value

A tibble with Higher Education Census microdata in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-da-educacao-superior

See Also

Other Higher Education Census functions: list_censo_superior_files()

Examples

## Not run: 
# get institution data for 2023
ies <- get_censo_superior(2023)

# get course data for Sao Paulo
cursos_sp <- get_censo_superior(2023, type = "cursos", uf = "SP")

# get a sample of student data
alunos <- get_censo_superior(2023, type = "alunos", n_max = 10000)

# get faculty data
docentes <- get_censo_superior(2023, type = "docentes")

## End(Not run)

Get Conceito Enade data

Description

Downloads and processes the Conceito Enade spreadsheets published by INEP. The Conceito Enade is the course-level quality indicator derived from the performance of graduating students in ENADE, on a continuous scale and in five levels (faixas 1 to 5).

Unlike get_enade(), which returns student-level microdata, this function returns one row per course (or per course unit, before 2015) with the official indicator.

Usage

get_conceito_enade(
  year,
  type = NULL,
  n_max = Inf,
  keep_file = TRUE,
  quiet = FALSE
)

Arguments

Details

Unit of observation. Up to 2014 a row is a "course unit": all courses of the same institution, municipality and evaluation area, identified by the institution, municipality and area codes (the number of courses grouped is in a column such as numero_de_cursos_da_unidade). From 2015 on, a row is a course, identified by codigo_do_curso (the e-MEC course code).

Sources by edition.

• 2004-2006 and 2012-2023: INEP's Conceito Enade spreadsheet.

• 2007-2011: INEP publishes the Conceito Enade inside the combined Enade/CPC table, so this function returns that table (the same one as get_cpc()). The Conceito Enade is in conceito_enade (2007, 2008) or conceito_enade_faixa (2009-2011).

• 2025: see type. These files report the share of graduates at or above a proficiency standard and the faixa, with no continuous score.

Important notes:

• Column names change across editions and are standardized to lowercase with underscores, not harmonized: map them to a common set before stacking years.

• Faixa and continuous columns are numeric; "SC" (sem conceito) and similar markers become NA.

• Files are in Excel format (xls/xlsx).

Value

A tibble with Conceito Enade data in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/indicadores-de-qualidade-da-educacao-superior

See Also

Other CPC/IGC functions: get_cpc(), get_igc()

Examples

## Not run: 
# Conceito Enade 2023
ce <- get_conceito_enade(2023)

# 2025 comes in two files
ce_lic <- get_conceito_enade(2025, type = "licenciaturas")

# a series: one call per year, then harmonize column names
series <- purrr::map(c(2017, 2018, 2019), get_conceito_enade)

## End(Not run)

Get CPC (Conceito Preliminar de Curso) data

Description

Downloads and processes CPC data from INEP. The CPC is a quality indicator for undergraduate courses in Brazil, composed of ENADE scores, IDD, faculty qualifications, pedagogical resources, and other institutional factors.

Usage

get_cpc(year, n_max = Inf, keep_file = TRUE, quiet = FALSE)

Arguments

Details

CPC is calculated by INEP as part of the higher education quality assessment system (SINAES). It serves as a preliminary indicator used to determine which courses require on-site evaluation.

The data includes:

• Course and institution identifiers

• CPC scores (continuous and categorical/faixa)

• Component scores (ENADE, IDD, faculty, infrastructure, etc.)

• Number of students evaluated

Important notes:

• CPC follows ENADE's rotating cycle of course areas, so each year covers a specific set of fields.

• There is no 2020 edition (COVID-19 suspension).

• Column names are standardized to lowercase with underscores.

• Files are in Excel format (xls/xlsx), not CSV.

Value

A tibble with CPC data in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/indicadores-de-qualidade-da-educacao-superior

See Also

Other CPC/IGC functions: get_conceito_enade(), get_igc()

Examples

## Not run: 
# get CPC data for 2023
cpc <- get_cpc(2023)

# get CPC data for 2021 with limited rows
cpc_2021 <- get_cpc(2021, n_max = 1000)

## End(Not run)

Get ENADE (Exame Nacional de Desempenho dos Estudantes) data

Description

Downloads and processes microdata from ENADE, the Brazilian National Student Performance Exam. ENADE evaluates the performance of undergraduate students in higher education.

Every edition is published as several files (arq1, arq2, ...), each with one group of variables. The files cannot be joined student by student: to comply with the LGPD, INEP sorts each file by different variables, so the first row of one file is not the same student as the first row of another. get_enade() therefore reads one file per call; use list_enade_files() to see which variables each file holds.

Usage

get_enade(year, arquivo = NULL, n_max = Inf, keep_zip = TRUE, quiet = FALSE)

Arguments

Details

ENADE is conducted annually by INEP and evaluates undergraduate students nearing the end of their programs. Each year, a different set of course areas is assessed on a rotating cycle (typically every 3 years per area).

Every file carries nu_ano and co_curso, so files can be combined at the course level (for example, averages by co_curso), as INEP's user manual recommends. Combining them row by row produces records that mix different students; up to educabR 1.2.0, get_enade() did exactly that. Student-level linkage is only available through INEP's protected data service (Sedap).

Important notes:

• Use n_max to read a sample first for exploration.

• Column names are standardized to lowercase with underscores.

• Not all course areas are assessed every year due to the rotating cycle.

• The numbering of files can skip numbers (2013 has no arq3).

Value

A tibble with the microdata of one ENADE file.

Data dictionary

Each edition ships a dictionary of files and variables (⁠Dicionario_arquivos_variaveis_microdados_Enade_<year>.xlsx⁠) in the downloaded archive. See also INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enade

See Also

Other ENADE functions: list_enade_files()

Examples

## Not run: 
# which variables are in which file?
list_enade_files(2023)

# read one file
arq3 <- get_enade(2023, arquivo = 3)

# combine files at the course level (co_curso), never row by row
scores <- get_enade(2023, arquivo = 3)
by_course <- aggregate(nt_ger ~ co_curso, data = scores, FUN = mean)

## End(Not run)

Get ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos) data

Description

Downloads and processes microdata from ENCCEJA, the Brazilian National Exam for Youth and Adult Education Certification. ENCCEJA assesses competencies of young people and adults who did not complete basic education at the regular age.

Usage

get_encceja(
  year,
  type = c("regular", "ppl"),
  n_max = Inf,
  keep_zip = TRUE,
  quiet = FALSE
)

Arguments

Details

ENCCEJA is conducted by INEP and provides certification for elementary and high school equivalency for youth and adults (EJA). The exam covers four knowledge areas:

• Natural Sciences (Ciências Naturais)

• Mathematics (Matemática)

• Portuguese Language (Língua Portuguesa)

• Social Sciences (Ciências Humanas)

Important notes:

• ENCCEJA files can be large (several hundred MB).

• Use n_max to read a sample first for exploration.

• Column names are standardized to lowercase with underscores.

Value

A tibble with ENCCEJA microdata in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/encceja

Examples

## Not run: 
# get ENCCEJA data for 2023
encceja <- get_encceja(2023, n_max = 10000)

# get full dataset for 2022
encceja_2022 <- get_encceja(2022)

# participants deprived of liberty (PPL)
encceja_ppl <- get_encceja(2023, type = "ppl")

## End(Not run)

Get ENEM (Exame Nacional do Ensino Médio) data

Description

Downloads and processes microdata from ENEM, the Brazilian National High School Exam. ENEM is used for university admissions and as a high school equivalency exam.

Usage

get_enem(
  year,
  type = "participantes",
  n_max = Inf,
  keep_zip = TRUE,
  quiet = FALSE
)

Arguments

Details

ENEM is conducted annually by INEP and is the largest exam in Brazil, with millions of participants. The microdata includes:

• Participant demographics (age, sex, race, etc.)

• Socioeconomic questionnaire responses

• Scores for each test area

• Essay scores

• School information (when applicable)

Important notes:

• ENEM files are very large (several GB when extracted).

• Use n_max to read a sample first for exploration.

• Column names are standardized to lowercase with underscores.

• Score variables start with nu_nota_ prefix.

• From 2024 onwards, INEP split the microdata into separate files. Use the type parameter to choose which file to load.

Value

A tibble with the ENEM microdata in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem

See Also

Other ENEM functions: enem_summary(), get_enem_escola(), get_enem_itens()

Examples

## Not run: 
# get a sample of 10000 rows for exploration
enem_sample <- get_enem(2023, n_max = 10000)

# get full data (warning: large file)
enem_2023 <- get_enem(2023)

# ENEM 2024+: choose data type
participantes <- get_enem(2024, type = "participantes", n_max = 1000)
resultados <- get_enem(2024, type = "resultados", n_max = 1000)

## End(Not run)

Get ENEM por Escola (ENEM by School) data

Description

Downloads and processes ENEM results aggregated by school. This dataset contains average ENEM scores, participation rates, and other indicators for each school in Brazil.

Usage

get_enem_escola(n_max = Inf, keep_zip = TRUE, quiet = FALSE)

Arguments

Details

ENEM por Escola is a single bundled dataset covering years 2005 to 2015. It was discontinued by INEP after 2015 and no per-year files exist.

The data includes:

• School identification (code, name, municipality, state)

• Average ENEM scores by knowledge area

• Number of participants and participation rates

• School-level indicators

Important notes:

• This is a single file covering all years (2005-2015), not per-year.

• Column names are standardized to lowercase with underscores.

• Data was discontinued after 2015.

Value

A tibble with ENEM by School data in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem-por-escola

See Also

Other ENEM functions: enem_summary(), get_enem(), get_enem_itens()

Examples

## Not run: 
# get all ENEM by School data (2005-2015)
enem_escola <- get_enem_escola()

# read only first 1000 rows for exploration
enem_escola_sample <- get_enem_escola(n_max = 1000)

## End(Not run)

Get ENEM item response data

Description

Downloads and processes ENEM item response (gabarito) data, which contains detailed information about each question.

Usage

get_enem_itens(year, n_max = Inf, keep_zip = TRUE, quiet = FALSE)

Arguments

Value

A tibble with item response data.

See Also

Other ENEM functions: enem_summary(), get_enem(), get_enem_escola()

Examples

## Not run: 
# get item data for 2023
itens <- get_enem_itens(2023)

## End(Not run)

Get FUNDEB distribution data

Description

Downloads and processes FUNDEB resource distribution data from STN (Secretaria do Tesouro Nacional). Each year's Excel file contains multiple sheets with monthly transfer data by state/municipality, broken down by funding source.

Usage

get_fundeb_distribution(
  year,
  uf = NULL,
  source = NULL,
  destination = NULL,
  n_max = Inf,
  keep_file = TRUE,
  quiet = FALSE
)

Arguments

Details

FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica e de Valorizacao dos Profissionais da Educacao) is the main funding mechanism for basic education in Brazil.

Each Excel file from STN contains ~20 data sheets named with a prefix indicating the destination (E_ for states, M_ for municipalities) and a suffix indicating the funding source (e.g., E_FPE, M_ICMS). Each sheet contains two tables: the main FUNDEB transfers and a FUNDEB adjustment table.

Important notes:

• Data is sourced from STN (Tesouro Nacional), not INEP.

• Files are in Excel format (XLS) — requires the readxl package.

• Column names are standardized to lowercase with underscores.

• Summary sheets (Resumo, Total, etc.) are automatically excluded; every other sheet is read, so the sum of valor over all sources equals STN's TOTAL sheet.

• From 2021 on, the Complementacao da Uniao comes in VAAF, VAAT and VAAR shares (sources "VAAF", "VAAT", "VAAR"); up to 2020 it is a single source, "COUN".

Value

A tibble in tidy (long) format with columns:

estados

State name

uf

State code (UF)

mes_ano

Date (last day of the month)

origem

Funding source (FPE, FPM, ICMS, etc.)

destino

Transfer destination ("UF" or "Municipio")

tabela

Table type ("Fundeb" or "Ajuste Fundeb")

valor

Transfer amount in BRL (numeric)

Data source

https://www.tesourotransparente.gov.br

See Also

Other FUNDEB functions: get_fundeb_enrollment()

Examples

## Not run: 
# get all FUNDEB distribution data for 2023
dist_2023 <- get_fundeb_distribution(2023)

# get only FPE transfers to states
fpe_estados <- get_fundeb_distribution(2023, source = "FPE",
                                        destination = "uf")

# get data for Sao Paulo only
sp <- get_fundeb_distribution(2023, uf = "SP")

## End(Not run)

Get FUNDEB enrollment data

Description

Downloads and processes FUNDEB enrollment data from FNDE's OData API. These are the enrollment counts considered for FUNDEB funding calculation.

Usage

get_fundeb_enrollment(
  year,
  uf = NULL,
  n_max = Inf,
  keep_file = TRUE,
  quiet = FALSE
)

Arguments

Details

Enrollment data comes from FNDE (Fundo Nacional de Desenvolvimento da Educacao) via its OData API. It includes the number of enrollments considered for FUNDEB funding, broken down by state, municipality, education type, school network, class type, and location.

Important notes:

• Data is sourced from FNDE, not INEP.

• Requires the jsonlite package.

• Results are cached locally as CSV after first download.

• Column names are standardized to lowercase with underscores.

• When uf is used with a cached file, filtering is done locally.

Value

A tibble with columns:

ano_censo

Census year

uf

State code (UF)

municipio

Municipality name

tipo_rede_educacao

Education network type

descricao_tipo_educacao

Education type description

descricao_tipo_ensino

Teaching type description

descricao_tipo_turma

Class type description

descricao_tipo_carga_horaria

Class hours type description

descricao_tipo_localizacao

Location type description

qtd_matricula

Number of enrollments

Data source

FNDE: https://www.fnde.gov.br

See Also

Other FUNDEB functions: get_fundeb_distribution()

Examples

## Not run: 
# get FUNDEB enrollment data for 2023
mat_2023 <- get_fundeb_enrollment(2023)

# get enrollment data for Sao Paulo only
mat_sp <- get_fundeb_enrollment(2023, uf = "SP")

# get enrollment data with limited rows
mat_sample <- get_fundeb_enrollment(2023, n_max = 1000)

## End(Not run)

Get IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado) data

Description

Downloads and processes microdata from IDD, an indicator that measures the value added by an undergraduate course to student performance. It compares ENADE scores with expected performance based on students' prior achievement (ENEM scores at admission).

Usage

get_idd(year, n_max = Inf, keep_zip = TRUE, quiet = FALSE)

Arguments

Details

IDD is calculated by INEP as part of the higher education quality assessment system. It complements ENADE by isolating the contribution of the course itself to student learning, controlling for student input quality.

The data includes:

• Course and institution identifiers

• IDD scores (continuous and categorical)

• Number of students considered in the calculation

• Related ENADE and ENEM metrics

Important notes:

• IDD is published alongside ENADE results, following the same rotating cycle of course areas.

• Column names are standardized to lowercase with underscores.

• Not all courses have IDD values (minimum sample requirements apply).

Value

A tibble with IDD data in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/idd

Examples

## Not run: 
# get IDD data for 2023
idd <- get_idd(2023)

# get IDD data for 2021 with limited rows
idd_2021 <- get_idd(2021, n_max = 1000)

## End(Not run)

Get IDEB (Índice de Desenvolvimento da Educação Básica) data

Description

Downloads and processes IDEB data from INEP in tidy (long) format. IDEB is the main indicator of education quality in Brazil, combining student performance (from SAEB) with grade promotion rates.

Usage

get_ideb(level, stage, metric, year = NULL, quiet = FALSE)

Arguments

Details

IDEB is calculated every two years since 2005 based on:

• Learning: Average scores in Portuguese and Mathematics from SAEB

• Flow: Grade promotion rate (inverse of repetition/dropout)

The index ranges from 0 to 10. Brazil's national goal is to reach 6.0 by 2022 (the level of developed countries in PISA).

The function always downloads the most recent IDEB file available from INEP, which contains the full historical series (2005-2025).

stage = "ensino_medio_integrado" reads the "Ensino médio mais educação profissional técnica integrada" spreadsheets first published with IDEB 2025. Unlike the traditional high school IDEB — which excludes students of integrated technical-professional education (EPT integrada) from the SAEB performance component to preserve historical comparability — this cut includes them. INEP publishes it only at the national and state levels (state networks only), covering editions 2017-2025 for states and 2019-2025 for Brazil, with no official targets.

Value

A tibble in tidy (long) format. Columns vary by level and metric:

ID columns (vary by level):

• escola: uf_sigla, municipio_codigo, municipio_nome, escola_id, escola_nome, rede

• municipio: uf_sigla, municipio_codigo, municipio_nome, rede

• brasil: rede

• estado: uf_nome, uf_sigla, rede

• regiao: regiao, rede

Value columns (vary by metric):

• indicador: ano, indicador, valor

• aprovacao: ano, ano_escolar or serie (ensino_medio), taxa_aprovacao

• nota: ano, disciplina, nota

• meta: ano, meta

Data source

Official IDEB portal: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/ideb

See Also

Other IDEB functions: get_ideb_series(), list_ideb_available()

Examples

## Not run: 
# school-level IDEB indicators for early elementary
ideb <- get_ideb("escola", "anos_iniciais", "indicador")

# municipality-level approval rates, only 2023 and 2025
aprov <- get_ideb("municipio", "anos_finais", "aprovacao", year = c(2023, 2025))

# national IDEB targets
metas <- get_ideb("brasil", "ensino_medio", "meta")

# state-level SAEB scores
notas <- get_ideb("estado", "anos_iniciais", "nota")

# region-level IDEB indicators
regioes <- get_ideb("regiao", "anos_finais", "indicador")

# state-level IDEB including EPT integrada students (2025+ cut)
emi <- get_ideb("estado", "ensino_medio_integrado", "indicador")

## End(Not run)

Get IDEB historical series

Description

get_ideb_series() is deprecated because the new get_ideb() already returns data in long format with all historical editions. Use get_ideb(level, stage, metric) instead.

Usage

get_ideb_series(
  years = NULL,
  level = c("escola", "municipio"),
  stage = c("anos_iniciais", "anos_finais", "ensino_medio"),
  uf = NULL,
  quiet = FALSE
)

Arguments

Value

A tibble with IDEB data.

See Also

Other IDEB functions: get_ideb(), list_ideb_available()

Examples

## Not run: 
# deprecated: use get_ideb() instead
ideb <- get_ideb("municipio", "anos_iniciais", "indicador")

## End(Not run)

Get IDEB Excel sheet name

Description

Internal function to determine which sheet to read for brasil/regiao_uf levels.

Usage

get_ideb_sheet(level, stage)

Arguments

Value

Sheet name (character) or NULL for escola/municipio.

Get IGC (Indice Geral de Cursos) data

Description

Downloads and processes IGC data from INEP. The IGC is a quality indicator for higher education institutions in Brazil, calculated as a weighted average of CPC scores across all evaluated courses plus CAPES scores for graduate programs.

Usage

get_igc(year, n_max = Inf, keep_file = TRUE, quiet = FALSE)

Arguments

Details

IGC is calculated by INEP as part of the higher education quality assessment system (SINAES). It provides an overall quality measure for institutions, considering both undergraduate and graduate programs.

The data includes:

• Institution identifiers (code, name, organization type)

• IGC scores (continuous and categorical/faixa)

• Number of courses and students considered

• Component breakdown (undergraduate CPC average, graduate CAPES scores)

Important notes:

• IGC is published annually based on the last three ENADE cycles.

• There is no 2020 edition (COVID-19 suspension).

• Column names are standardized to lowercase with underscores.

• Files are in Excel format (xls/xlsx), except 2007, a 7z archive of CSV files.

• From 2007 to 2016, INEP splits the table by type of institution (one sheet or CSV each for universities, university centers and faculties); all parts are read and stacked into one tibble.

Value

A tibble with IGC data in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/indicadores-de-qualidade-da-educacao-superior

See Also

Other CPC/IGC functions: get_conceito_enade(), get_cpc()

Examples

## Not run: 
# get IGC data for 2023
igc <- get_igc(2023)

# get IGC data for 2021 with limited rows
igc_2021 <- get_igc(2021, n_max = 1000)

## End(Not run)

Get SAEB (Sistema de Avaliação da Educação Básica) data

Description

Downloads and processes microdata from SAEB, the Brazilian Basic Education Assessment System. SAEB evaluates educational quality through student performance assessments in Portuguese and Mathematics.

Usage

get_saeb(
  year,
  type = c("aluno", "escola", "diretor", "professor", "secretario"),
  level = c("fundamental_medio", "educacao_infantil"),
  serie = NULL,
  n_max = Inf,
  keep_zip = TRUE,
  quiet = FALSE
)

Arguments

Details

SAEB is conducted biennially by INEP and assesses students in grades 5 and 9 of elementary school, and grade 3 of high school. The data includes:

• Student performance scores in Portuguese and Mathematics

• School infrastructure and management questionnaires

• Teacher and principal profiles

Important notes:

• SAEB files can be large (several hundred MB).

• Use n_max to read a sample first for exploration.

• Column names are standardized to lowercase with underscores.

• In 2021, INEP split SAEB into two separate downloads (elementary/high school and early childhood). Use the level parameter to choose.

• Student files are split by grade from 2013 on; choose one with serie. The 5th and 9th grade files are about 1 GB of CSV each. To combine grades, call once per grade, e.g. purrr::map(c("5ef", "9ef"), function(s) get_saeb(2023, serie = s)).

• For 2011, type = "aluno" reads TS_RESULTADO_ALUNO (proficiency results), which covers all grades in one file.

Value

A tibble with SAEB microdata in tidy format.

Data dictionary

For detailed information about variables, see INEP's documentation: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/saeb

Examples

## Not run: 
# get 5th grade student results for 2023
saeb_5ef <- get_saeb(2023, serie = "5ef", n_max = 10000)

# 5th and 9th grades, one call per grade
saeb_ef <- purrr::map(c("5ef", "9ef"), function(s) get_saeb(2023, serie = s))

# get school questionnaire data
saeb_escola <- get_saeb(2023, type = "escola")

# SAEB 2021: early childhood education
saeb_infantil <- get_saeb(2021, level = "educacao_infantil", n_max = 1000)

## End(Not run)

Decide which IDEB columns to keep for a given metric/year

Description

Internal helper for read_ideb_excel(). Keeps all non-⁠vl_*⁠ columns (id columns) and the subset of ⁠vl_*⁠ columns that match the requested metric (and year, if given). Matching is done against a standardized lowercase/ASCII form of the names so it works against the raw INEP headers regardless of accents or casing.

Usage

ideb_keep_cols(col_names, metric, year = NULL)

Arguments

Value

Logical vector of the same length as col_names.

Fix IDEB column codes that INEP repeated with the wrong year

Description

Internal helper for read_ideb_excel(). Row 10 of an IDEB sheet holds the column codes (VL_INDICADOR_REND_2023); rows above it hold the block headers ("Taxa de Aprovacao - 2025"), merged over the columns of each edition. When a ⁠VL_*⁠ code appears more than once, the year of each copy is taken from its block header. Codes that are not repeated are returned unchanged.

Usage

ideb_resolve_codes(file, sheet, col_names)

Arguments

Value

A character vector of column names.

Find the institution-level sheets of an IGC spreadsheet

Description

Internal function. From 2008 to 2016, INEP splits the IGC table into one sheet per type of institution ("Universidades", "Centros Universitarios", "Faculdades" or "Outros"), followed by sheets with course- or program-level inputs. Returns the institution sheets, or the first sheet when the file is not split (2017 onwards).

Usage

igc_institution_sheets(file)

Arguments

Value

A character vector of sheet names.

List cached files

Description

Lists all files currently in the educabR cache.

Usage

list_cache(dataset = NULL)

Arguments

Value

A tibble with information about cached files.

See Also

Other cache functions: clear_cache(), get_cache_dir(), set_cache_dir()

Examples

## Not run: 
list_cache()

## End(Not run)

List available Censo Escolar files

Description

Lists the data files available in a downloaded School Census. Use this to discover which files are available for a given year, then pass the desired file name to get_censo_escolar()'s file parameter.

Usage

list_censo_files(year)

Arguments

Value

A character vector of file names found.

See Also

Other School Census functions: get_censo_escolar()

Examples

## Not run: 
# first download the data
get_censo_escolar(1995)

# then see what files are available
list_censo_files(1995)
# [1] "CENSOESC_1995.CSV" "DADOS_DESP_1995.CSV" "DADOSCURSO_1995.CSV"

# load a specific file
cursos <- get_censo_escolar(1995, file = "DADOSCURSO")

## End(Not run)

List available Higher Education Census files

Description

Lists the data files available in a downloaded Higher Education Census. Useful for exploring the contents of the ZIP file.

Usage

list_censo_superior_files(year)

Arguments

Value

A character vector of file names found.

See Also

Other Higher Education Census functions: get_censo_superior()

Examples

## Not run: 
list_censo_superior_files(2023)

## End(Not run)

List the files of an ENADE edition

Description

Downloads (if needed) the microdata of an ENADE edition and lists its data files with the variables each one holds. Use the arquivo number with get_enade().

Usage

list_enade_files(year, quiet = FALSE)

Arguments

Value

A tibble with one row per file: arquivo (number to pass to get_enade()), nome (file name), n_variaveis and variaveis (the variables other than nu_ano and co_curso, comma-separated).

See Also

Other ENADE functions: get_enade()

Examples

## Not run: 
files <- list_enade_files(2023)

# which file has the overall score?
files[grepl("nt_ger", files$variaveis), ]

## End(Not run)

List available IDEB data

Description

Lists the IDEB data combinations available for download.

Usage

list_ideb_available()

Value

A tibble with available IDEB datasets (level, stage, metric).

See Also

Other IDEB functions: get_ideb(), get_ideb_series()

Examples

list_ideb_available()

Merge a sub-header row into the column names

Description

Internal function. Some INEP sheets have a second header row: IGC 2008 has "IGC" over two columns, with "Continuo" and "Faixas" below it, and repeats "Cod. IES". That row is detected as a first row that fills less than half of the columns and holds only text. Each of its cells completes the name of its column (a cell under an empty header completes the nearest named header to its left; a cell that repeats the header is ignored). The row is then dropped and the affected columns are re-typed.

Usage

merge_subheader_row(df)

Arguments

Value

The data frame.

Normalize the SAEB grade argument

Description

Internal function that turns the user's serie into INEP's lowercase grade code: 5 becomes "5ef", "5EF" becomes "5ef".

Usage

normalize_saeb_serie(serie)

Arguments

Value

A lowercase string, or NULL.

Normalize character columns to UTF-8 NFC

Description

Ensures every character column in a data frame is valid UTF-8 in canonical NFC form. Without this step, equality comparisons against string literals fail silently on Windows when the source file's encoding produces decomposed (NFD) or otherwise non-canonical strings (e.g. filter(rede == "Pública") returns 0 rows even though "Pública" is in the data). Applied at every read entrypoint — read_inep_file(), read_ideb_excel(), read_excel_safe(), and the FUNDEB OData fetcher — so downstream user code can compare with literals safely on any platform.

Usage

normalize_utf8_nfc(df)

Arguments

Value

The data frame with character columns normalized; non-character columns are returned unchanged.

Parse date columns (dt_, dh_) to Date

Description

Internal function to convert text date columns to Date objects. INEP and CAPES use several formats: SAS datetime ("12FEB2024:00:00:00"), "12/12/2013 00:00:00", "12/12/2013" and ISO "2013-12-12". For each column the format that reads every non-empty value is used. If no format reads them all, the column is kept as text: converting with a single fixed format turned every value of the CAPES catalogo 2013-2016 dates into NA.

Usage

parse_sas_dates(df)

Arguments

Value

The data frame with date columns converted.

Read Excel file safely

Description

Internal function to read Excel files (xls/xlsx) with error handling. Reads the first sheet by default. Three INEP layout quirks are handled:

• a title row above the header (e.g. IGC 2009) is skipped, by taking as header the first row where at least half of the columns are filled;

• a sub-header row below the header (e.g. IGC 2008: "IGC" over "Continuo" and "Faixas") is merged into the column names;

• blank, footnote and total rows (e.g. CPC 2021 and 2022, IGC 2021), i.e. rows with at most one filled cell, are dropped.

Usage

read_excel_safe(file, n_max = Inf, sheet = 1)

Arguments

Value

A tibble with the data.

Read IDEB Excel file

Description

Internal function to read IDEB Excel files. When metric is provided, performs a two-pass read: the header is parsed first to identify which ⁠vl_*⁠ columns belong to the requested metric (and optionally the requested years), then the file is re-read with col_types = "skip" on the irrelevant columns. This drastically reduces peak memory for school-level files, which can otherwise balloon to several GB when read in full.

Usage

read_ideb_excel(file, sheet = NULL, metric = NULL, year = NULL)

Arguments

Value

A tibble with the data.

Read INEP data file

Description

Internal function to read INEP data files with appropriate settings.

Usage

read_inep_file(file, delim = ";", encoding = NULL, n_max = Inf, quiet = FALSE)

Arguments

Value

A tibble with the data.

Re-read columns that readr failed to parse

Description

Internal helper for read_inep_file(). readr guesses each column type from the first 1,000 rows; a column that is empty there is read as logical and every later value becomes NA (Censo Escolar 1995: 234 values of VEM2206, 1997: 567 of VPE626). Columns with parsing problems are read again as text and their type is guessed from all values. Columns without problems are untouched.

Usage

reread_problem_columns(df, file, delim, encoding, n_max = Inf)

Arguments

Value

The tibble with those columns replaced.

Reshape IDEB data from wide to long format

Description

Internal function to transform IDEB data from wide format (one column per year/metric) to tidy long format, filtered by the requested metric type.

Usage

reshape_ideb(df, level, metric, stage = NULL)

Arguments

Value

A tibble in long format.

Set the cache directory for educabR

Description

Sets the directory where downloaded files will be cached. This avoids repeated downloads of the same data.

Usage

set_cache_dir(path = NULL, persistent = FALSE)

Arguments

Value

Invisibly returns the cache directory path.

See Also

Other cache functions: clear_cache(), get_cache_dir(), list_cache()

Examples

## Not run: 
# set a custom cache directory (use tempdir() in examples)
set_cache_dir(file.path(tempdir(), "educabR_cache"))

## End(Not run)

Standardize column names

Description

Internal function to standardize column names to lowercase with underscores.

When two names collide after standardization (CPC 2007/2008 have both "Docentes Doutores" and "% Docentes Doutores"), a "%" in the colliding original name becomes "pct"; any collision left gets a numeric suffix (⁠_2⁠, ⁠_3⁠, ...). Names that do not collide are unchanged.

Usage

standardize_names(df)

Arguments

Value

The data frame with standardized names.

Convert UF abbreviation to code

Description

Internal function to convert state abbreviations to IBGE codes.

Usage

uf_to_code(uf)

Arguments

Value

The numeric UF code.

Validate downloaded data structure

Description

Internal function to validate that downloaded data has the expected structure. Issues warnings for potential problems and errors for critical issues.

Usage

validate_data(data, dataset, year)

Arguments

Value

The input data (invisibly), or aborts with an error.

Validate year parameter

Description

Internal function to validate that a year is available for a dataset.

Usage

validate_year(year, dataset)

Arguments

Value

The validated year (invisibly), or aborts with error.