Higher and Graduate Education: Census, ENADE, IDD, CPC, IGC, and CAPES

This vignette covers higher education and graduate education datasets available in educabR. These datasets allow you to analyze institutions, courses, student performance, and quality indicators across Brazilian higher education.

Higher Education Census

The Higher Education Census is an annual survey covering all Brazilian higher education institutions (IES), including data on institutions, courses, student enrollment, and faculty.

Available data types

Type	Description
`"ies"`	Institutions (location, administrative type, accreditation)
`"cursos"`	Undergraduate courses (area, modality, enrollment)
`"alunos"`	Student enrollment (demographics, enrollment status)
`"docentes"`	Faculty (qualifications, employment type)

Downloading data

# Institution data
ies_2023 <- get_censo_superior(year = 2023, type = "ies")

# Course data filtered by state
cursos_sp <- get_censo_superior(year = 2023, type = "cursos", uf = "SP")

# Faculty data with limited rows
docentes_sample <- get_censo_superior(
  year  = 2023,
  type  = "docentes",
  n_max = 10000
)

Available years

Data is available from 2009 to 2024.

Exploring ZIP contents

# See what files are inside the downloaded ZIP
list_censo_superior_files(2023)

Example analysis: Institutions by administrative type

ies <- get_censo_superior(2023, type = "ies")

ies_summary <- 
  ies |>
  mutate(
    admin_type = case_when(
      tp_categoria_administrativa == 1 ~ "Public Federal",
      tp_categoria_administrativa == 2 ~ "Public State",
      tp_categoria_administrativa == 3 ~ "Public Municipal",
      tp_categoria_administrativa == 4 ~ "Private For-Profit",
      tp_categoria_administrativa == 5 ~ "Private Non-Profit",
      TRUE ~ "Other"
    )
  ) |>
  count(admin_type, sort = TRUE)

ggplot(ies_summary, aes(x = reorder(admin_type, n), y = n)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Higher Education Institutions by Type (2023)",
    x     = NULL,
    y     = "Number of Institutions"
  ) +
  theme_minimal() +
  scale_y_continuous(label = scales::number_format(big.mark = ".", decimal.mark = ","))

ENADE - National Student Performance Exam

ENADE (Exame Nacional de Desempenho dos Estudantes) is an annual exam assessing undergraduate student performance. It follows a rotating cycle where different course areas are evaluated each year.

One file per call

INEP publishes each ENADE edition as several files (arq1, arq2, …), each with one group of variables. To comply with the LGPD, each file is sorted by different variables, so the files cannot be joined student by student: the first row of one file is not the same student as the first row of another. get_enade() reads one file per call, and list_enade_files() shows which variables each file holds.

# which file holds which variables?
files <- list_enade_files(2023)
files[grepl("nt_ger", files$variaveis), ]

# scores are in file 3 in 2023
scores <- get_enade(2023, arquivo = 3)

# sample for exploration
scores_sample <- get_enade(2023, arquivo = 3, n_max = 5000)

Files can be combined at the course level (co_curso), for example by averaging scores per course, as INEP’s user manual recommends.

Available years

Data is available for 2004-2019 and 2021-2023 (there was no ENADE in 2020).

Example analysis: Score distribution

enade <- get_enade(2023, arquivo = 3)

enade |>
  filter(!is.na(nt_ger)) |>
  ggplot(aes(x = nt_ger)) +
  geom_histogram(binwidth = 5, fill = "darkorange", color = "white") +
  labs(
    title = "ENADE 2023 - General Score Distribution",
    x     = "General Score (nt_ger)",
    y     = "Number of Students"
  ) +
  theme_minimal() +
  scale_y_continuous(label = scales::number_format(big.mark = ".", decimal.mark = ","))

IDD - Value-Added Indicator

IDD (Indicador de Diferenca entre os Desempenhos Observado e Esperado) measures the value added by an undergraduate course. It compares ENADE scores with the expected performance based on students’ ENEM admission scores.

Downloading IDD data

# Download IDD data
idd_2023 <- get_idd(year = 2023)

# Sample for exploration
idd_sample <- get_idd(year = 2023, n_max = 5000)

Available years

Data is available for 2014-2019 and 2021-2023 (no 2020 edition due to COVID).

Example analysis: Value-added by institution type

idd <- get_idd(2023)

glimpse(idd)

Conceito Enade

The Conceito Enade is the course-level indicator derived from ENADE: the performance of graduating students, on a continuous scale and in five levels (faixas 1 to 5). get_enade() returns the student microdata; get_conceito_enade() returns the official indicator, one row per course.

# Conceito Enade 2023
ce_2023 <- get_conceito_enade(2023)

# 2025 is published as two files with their own methodology
ce_lic <- get_conceito_enade(2025, type = "licenciaturas")
ce_med <- get_conceito_enade(2025, type = "medicina")

Up to 2014 a row is a course unit (all courses of the same institution, municipality and area); from 2015 on, a row is a course with its e-MEC code (codigo_do_curso). For 2007-2011, INEP publishes the Conceito Enade inside the Enade/CPC table, which get_conceito_enade() returns as is. Column names change across editions, so map them to a common set before stacking years.

CPC - Preliminary Course Concept

CPC (Conceito Preliminar de Curso) is a quality indicator for undergraduate courses. It combines ENADE scores, IDD, faculty qualifications, pedagogical resources, and student perceptions.

CPC scores range from 1 to 5, where courses scoring 1 or 2 are considered unsatisfactory.

Downloading CPC data

# Download CPC data (Excel format, requires readxl)
cpc_2023 <- get_cpc(year = 2023)

# Sample for exploration
cpc_sample <- get_cpc(year = 2023, n_max = 5000)

Available years

Data is available for 2007-2019 and 2021-2023 (no 2020 edition).

Example analysis: Course quality distribution

cpc <- get_cpc(2023)

# Distribution of CPC scores
cpc |>
  filter(!is.na(cpc_faixa)) |>
  count(cpc_faixa) |>
  ggplot(aes(x = factor(cpc_faixa), y = n)) +
  geom_col(fill = "coral") +
  labs(
    title = "CPC 2023 - Course Quality Distribution",
    x     = "CPC Score (1-5)",
    y     = "Number of Courses"
  ) +
  theme_minimal() +
  scale_y_continuous(label = scales::number_format(big.mark = ".", decimal.mark = ","))

IGC - General Courses Index

IGC (Indice Geral de Cursos) is a quality indicator for higher education institutions. It is calculated as a weighted average of CPC scores for undergraduate courses plus CAPES scores for graduate programs.

IGC scores range from 1 to 5, providing an overall quality measure for each institution.

Downloading IGC data

# Download IGC data (Excel format, requires readxl)
igc_2023 <- get_igc(year = 2023)

# Sample for exploration
igc_sample <- get_igc(year = 2023, n_max = 1000)

Available years

Data is available for 2007-2019 and 2021-2023 (no 2020 edition). Note: IGC 2007 comes as a 7z archive containing an Excel file.

Example analysis: Institution quality

igc <- get_igc(2023)

# Top institutions by continuous IGC
igc |>
  filter(!is.na(igc_continuo)) |>
  filter(!is.na(sigla_da_ies)) |>
  arrange(desc(igc_continuo)) |>
  head(20) |>
  ggplot(aes(x = reorder(sigla_da_ies, igc_continuo), y = igc_continuo)) +
  geom_col(fill = "darkblue") +
  coord_flip() +
  labs(
    title = "Top 20 Institutions by IGC (2023)",
    x     = NULL,
    y     = "IGC (Continuous)"
  ) +
  theme_minimal()

CAPES - Graduate Education Data

CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior) provides open data on Brazilian graduate programs (stricto sensu: masters and doctoral programs).

Available data types

Type	Description
`"programas"`	Graduate programs (area, institution, CAPES score)
`"discentes"`	Students (enrollment, demographics, funding)
`"docentes"`	Faculty (qualifications, research output)
`"cursos"`	Graduate courses within programs
`"catalogo"`	Theses and dissertations catalog

Downloading CAPES data

# Graduate programs
programas_2023 <- get_capes(year = 2023, type = "programas")

# Students (large dataset!)
discentes_sample <- get_capes(year = 2023, type = "discentes", n_max = 10000)

# Theses and dissertations catalog
catalogo_2023 <- get_capes(year = 2023, type = "catalogo")

Available years

Data is available from 2013 to 2024. Data is retrieved from the CAPES Open Data Portal via CKAN API.

Example analysis: Graduate programs by knowledge area

programas <- get_capes(2023, type = "programas")

# Count programs by broad knowledge area
programas |>
  count(nm_grande_area_conhecimento, sort = TRUE) |>
  head(10) |>
  ggplot(aes(
    x = reorder(nm_grande_area_conhecimento, n),
    y = n
  )) +
  geom_col(fill = "purple4") +
  coord_flip() +
  labs(
    title = "Graduate Programs by Knowledge Area (2023)",
    x     = NULL,
    y     = "Number of Programs"
  ) +
  theme_minimal()

Combining quality indicators

CPC, IGC, IDD, and ENADE are closely related. Here is an example of how to combine them for a comprehensive view.

# Load CPC and IGC for the same year
cpc <- get_cpc(2023)
igc <- get_igc(2023)

# Compare institution-level quality
# IGC gives the overall institution score
# CPC gives individual course scores within each institution
igc_summary <- 
  igc |>
  filter(!is.na(igc_faixa)) |>
  select(codigo_da_ies, sigla_da_ies, igc_continuo, igc_faixa)

cpc_summary <- 
  cpc |>
  filter(!is.na(cpc_continuo)) |>
  group_by(codigo_da_ies) |>
  summarise(
    n_courses = n(),
    mean_cpc  = mean(cpc_continuo, na.rm = TRUE),
    .groups   = "drop"
  )

combined <- inner_join(igc_summary, cpc_summary, by = "codigo_da_ies")

ggplot(combined, aes(x = mean_cpc, y = igc_continuo, size = n_courses)) +
  geom_point(alpha = 0.4, color = "steelblue") +
  labs(
    title = "IGC vs Average CPC by Institution (2023)",
    x     = "Average CPC (Continuous)",
    y     = "IGC (Continuous)",
    size  = "Courses Evaluated"
  ) +
  theme_minimal()