---
title: "Getting Started with educabR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with educabR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  eval     = FALSE,
  message  = FALSE,
  warning  = FALSE
)
suppressPackageStartupMessages(library(systemfonts))
suppressPackageStartupMessages(library(textshaping))
```

The **educabR** package provides easy access to Brazilian public education data
from INEP, FNDE, and CAPES. With simple functions, you can download and process
data from 14 datasets:

**Basic Education:**  
- **IDEB** - Basic Education Development Index  
- **ENEM** - National High School Exam  
- **School Census**  
- **SAEB** - Basic Education Assessment System  
- **ENCCEJA** - Youth and Adult Education Certification Exam  
- **ENEM by School** - ENEM results aggregated by school (2005-2015)  

**Higher Education:**  
- **Higher Education Census**  
- **ENADE** - National Student Performance Exam  
- **IDD** - Value-Added Indicator  
- **CPC** - Preliminary Course Concept  
- **IGC** - General Courses Index  

**Graduate Education:**  
- **CAPES** - Graduate programs, students, faculty, courses, and theses  

**Education Funding:**  
- **FUNDEB** - Resource distribution and enrollment data  

This vignette covers IDEB, ENEM, and the School Census. For other datasets, see:  
- `vignette("basic-education-assessments")` — SAEB, ENCCEJA, ENEM by School  
- `vignette("higher-education")` — Higher Education Census, ENADE, IDD, CPC, IGC, CAPES  
- `vignette("education-funding")` — FUNDEB  

## Installation
```{r install}
# Install from GitHub (development version)
# install.packages("remotes")
remotes::install_github("SidneyBissoli/educabR")
```

```{r setup}
library(educabR)
library(dplyr)
library(ggplot2)
library(kableExtra)
```

## Configuring Cache

educabR caches downloaded files to avoid repeated downloads.
By default, it uses a temporary directory. To persist data across sessions:

```{r cache}
# Check current cache directory
get_cache_dir()

# Set a permanent directory
set_cache_dir("~/educabR_data")

# List cached files
list_cache()

# Clear cache (when needed)
clear_cache()
```

## IDEB - Basic Education Development Index

IDEB is the main indicator of basic education quality in Brazil,
combining test performance (SAEB) with grade promotion rates.

### Available years and levels

```{r ideb-available}
# See available combinations
list_ideb_available()
```

```{r ideb-available-table, echo = FALSE, eval = TRUE}
suppressPackageStartupMessages(library(educabR))
suppressPackageStartupMessages(library(kableExtra))
df <- list_ideb_available()
df[1:10, ] |>
  kbl(
    col.names = c("Level", "Stage", "Metric"),
    align = "lll"
  ) |>
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE
  ) |>
  column_spec(1:3, width = "12em") |>
  footnote(
    general = paste0("Showing 10 of ", nrow(df), " available combinations"),
    general_title = "",
    footnote_as_chunk = TRUE
  )
```

### Downloading IDEB data

```{r ideb-download}
# IDEB by school - Early elementary (1st-5th grade)
ideb_schools <- get_ideb(
  level  = "escola",
  stage  = "anos_iniciais",
  metric = "indicador",
  year   = 2021
)

# IDEB by municipality - High school (1st-4th grade)
ideb_municipalities <- get_ideb(
  level  = "municipio",
  stage  = "ensino_medio",
  metric = "indicador",
  year   = 2023
)

# IDEB by state - High school including EPT integrada students
# (cut first published with IDEB 2025; brasil/estado levels only, no "meta")
ideb_emi <- get_ideb(
  level  = "estado",
  stage  = "ensino_medio_integrado",
  metric = "indicador"
)

# Filter by state after downloading
ideb_sp <-
  ideb_schools |>
  dplyr::filter(uf_sigla == "SP")
```

### Data structure

```{r ideb-structure}
# View structure
glimpse(ideb_schools)
#> Rows: 194,715
#> Columns: 9
#> $ uf_sigla         <chr> "RO", "RO", "RO", ...
#> $ municipio_codigo <chr> "1100015", "1100015", ...
#> $ municipio_nome   <chr> "Alta Floresta D'Oeste", ...
#> $ escola_id        <chr> "11024372", "11024666", ...
#> $ escola_nome      <chr> "EMEIEF ANA NERY", "EMEIEF BOA ESPERANCA", ...
#> $ rede             <chr> "Municipal", "Municipal", ...
#> $ ano              <int> 2021, 2021, ...
#> $ indicador        <chr> "IDEB", "IDEB", ...
#> $ valor            <dbl> NA, NA, 5.9, 4.1, ...
```

### Example analysis: Average IDEB by state

```{r ideb-analysis}
# Calculate average IDEB by state (observed indicator only)
ideb_by_state <-
  ideb_schools |>
  filter(indicador == "IDEB", !is.na(valor)) |>
  group_by(uf_sigla) |>
  summarise(
    n_schools   = n(),
    mean_ideb   = mean(valor, na.rm = TRUE),
    median_ideb = median(valor, na.rm = TRUE)
  ) |>
  arrange(desc(mean_ideb))

# Plot
ggplot(ideb_by_state, aes(x = reorder(uf_sigla, mean_ideb), y = mean_ideb)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Average IDEB by State - Early Elementary (2021)",
    x     = "State",
    y     = "Average IDEB"
  ) +
  theme_minimal()
```

![](../man/figures/vignette-gs-ideb-by-state.png)

### IDEB historical series

```{r ideb-series}
# Download historical series (get_ideb() already returns long format)
ideb_history <- get_ideb(
  level  = "municipio",
  stage  = "anos_iniciais",
  metric = "indicador",
  year   = c(2017, 2019, 2021, 2023)
)

# National trend
trend <-
  ideb_history |>
  filter(indicador == "IDEB") |>
  group_by(ano) |>
  summarise(mean_ideb = mean(valor, na.rm = TRUE))

ggplot(trend, aes(x = factor(ano), y = mean_ideb)) +
  geom_col(fill = "darkgreen", width = 0.6) +
  geom_text(
    aes(label = sprintf("%.2f", mean_ideb)),
    vjust = -0.5, size = 3.5
  ) +
  labs(
    title = "IDEB Trend - Early Elementary",
    x     = "Year",
    y     = "National Average IDEB"
  ) +
  theme_minimal() +
  scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
```

![](../man/figures/vignette-gs-ideb-trend.png)

## ENEM - National High School Exam

ENEM is Brazil's largest exam, with millions of participants annually.
The microdata includes demographics, socioeconomic data, and scores.

### Downloading ENEM data

```{r enem-download}
# WARNING: Large files (1-3 GB)!
# Use n_max for initial exploration

# Sample for exploration
enem_sample <- get_enem(2023, n_max = 10000)

# Full data (takes a while!)
# enem_full <- get_enem(2023)
```

### Data structure

```{r enem-structure}
glimpse(enem_sample)
#> Rows: 10,000
#> Columns: 76
#> $ nu_inscricao           <dbl> 210059085136, 210059527735, ...
#> $ nu_ano                 <dbl> 2023, 2023, ...
#> $ tp_faixa_etaria        <dbl> 14, 12, 6, ...
#> $ tp_sexo                <chr> "M", "M", "F", ...
#> $ tp_estado_civil        <dbl> 2, 2, 1, ...
#> $ tp_cor_raca            <dbl> 1, 1, 1, ...
#> $ sg_uf_prova            <chr> "DF", "DF", "RS", ...
#> $ nu_nota_cn             <dbl> NA, NA, 502.0, ...
#> $ nu_nota_ch             <dbl> NA, NA, 498.9, ...
#> $ nu_nota_lc             <dbl> NA, NA, 475.6, ...
#> $ nu_nota_mt             <dbl> NA, NA, 363.2, ...
#> $ nu_nota_redacao        <dbl> NA, NA, 700, ...
#> # ... 64 more variables
```

### Performance analysis

```{r enem-analysis}
# Summary statistics for scores
enem_summary(enem_sample)
```

```{r enem-summary-table, echo = FALSE, eval = TRUE}
suppressPackageStartupMessages(library(kableExtra))
data.frame(
  Variable        = c("nu_nota_cn", "nu_nota_ch", "nu_nota_lc", "nu_nota_mt",
                       "nu_nota_comp1", "nu_nota_comp2", "nu_nota_comp3",
                       "nu_nota_comp4", "nu_nota_comp5", "nu_nota_redacao"),
  n               = 10000L,
  n_valid         = c(7281L, 7562L, 7562L, 7281L, 7562L, 7562L, 7562L, 7562L, 7562L, 7562L),
  Mean            = c(492, 529, 520, 520, 126, 147, 124, 136, 117, 649),
  SD              = c(79.8, 81.7, 70.4, 121, 32.1, 48.4, 41.0, 41.2, 60.0, 201),
  Min             = 0,
  Q25             = c(440, 480, 476, 426, 120, 120, 100, 120, 80, 520),
  Median          = c(489, 535, 524, 507, 120, 160, 120, 120, 120, 640),
  Q75             = c(541, 584, 568, 608, 160, 200, 160, 160, 160, 820),
  Max             = c(817, 823, 731, 945, 200, 200, 200, 200, 200, 980)
) |>
  kbl(align = "lrrrrrrrrr") |>
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE
  ) |>
  column_spec(1, width = "10em") |>
  footnote(
    general = "Based on a random sample of 10,000 observations",
    general_title = "",
    footnote_as_chunk = TRUE
  )
```

```{r enem-analysis-2}
# Summary by sex
enem_summary(enem_sample, by = "tp_sexo")
```

```{r enem-summary-by-sex-table, echo = FALSE, eval = TRUE}
suppressPackageStartupMessages(library(kableExtra))
data.frame(
  Sex      = c("F", "M", "F", "M", "F", "M", "F", "M", "F", "M"),
  Variable = c("nu_nota_cn", "nu_nota_cn", "nu_nota_ch", "nu_nota_ch",
                "nu_nota_lc", "nu_nota_lc", "nu_nota_mt", "nu_nota_mt",
                "nu_nota_comp1", "nu_nota_comp1"),
  n        = c(7042L, 2958L, 7042L, 2958L, 7042L, 2958L, 7042L, 2958L, 7042L, 2958L),
  n_valid  = c(5130L, 2151L, 5328L, 2234L, 5328L, 2234L, 5130L, 2151L, 5328L, 2234L),
  Mean     = c(485, 507, 527, 535, 520, 522, 509, 547, 128, 121),
  SD       = c(76.5, 85.1, 78.6, 88.2, 68.2, 75.5, 116, 129, 30.8, 34.6),
  Min      = 0,
  Q25      = c(435, 455, 480, 483, 476, 478, 420, 447, 120, 100),
  Median   = c(481, 508, 532, 542, 523, 527, 494, 540, 120, 120),
  Q75      = c(532, 560, 579, 596, 566, 574, 589, 643, 160, 140),
  Max      = c(817, 804, 784, 823, 731, 729, 944, 945, 200, 200)
) |>
  kbl(align = "clrrrrrrrrr") |>
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE
  ) |>
  column_spec(1, width = "3em") |>
  column_spec(2, width = "10em") |>
  footnote(
    general = "Showing 10 of 20 rows",
    general_title = "",
    footnote_as_chunk = TRUE
  )
```

```{r enem-analysis-3}
# Average scores by race/ethnicity
scores_by_race <- 
  enem_sample |>
  filter(!is.na(nu_nota_mt)) |>
  mutate(
    race = case_when(
      tp_cor_raca == 0 ~ "Not declared",
      tp_cor_raca == 1 ~ "White",
      tp_cor_raca == 2 ~ "Black",
      tp_cor_raca == 3 ~ "Mixed race",
      tp_cor_raca == 4 ~ "Asian",
      tp_cor_raca == 5 ~ "Indigenous"
    )
  ) |>
  group_by(race) |>
  summarise(
    n          = n(),
    mean_math  = mean(nu_nota_mt, na.rm = TRUE),
    mean_essay = mean(nu_nota_redacao, na.rm = TRUE)
  )
```

```{r enem-race-table, echo = FALSE, eval = TRUE}
suppressPackageStartupMessages(library(kableExtra))
data.frame(
  Race       = c("Asian", "Black", "Indigenous", "Mixed race", "Not declared", "White"),
  n          = c(116L, 1066L, 30L, 3383L, 74L, 2612L),
  Mean_Math  = c(511, 491, 479, 507, 533, 550),
  Mean_Essay = c(660, 619, 550, 643, 643, 690)
) |>
  kbl(
    col.names = c("Race", "n", "Mean Math", "Mean Essay"),
    align = "lrrr"
  ) |>
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE
  ) |>
  column_spec(1, width = "10em") |>
  footnote(
    general = "Based on a random sample of 10,000 observations",
    general_title = "",
    footnote_as_chunk = TRUE
  )
```

### Item response data

```{r enem-itens}
# Data about exam questions
items <- get_enem_itens(2023)

glimpse(items)
#> Rows: 5,550
#> Columns: 14
#> $ co_posicao       <chr> "21", "20", "11", ...
#> $ sg_area          <chr> "LC", "LC", "LC", ...
#> $ co_item          <chr> "141283", "118144", ...
#> $ tx_gabarito      <chr> "B", "C", "D", ...
#> $ co_habilidade    <chr> "14", "23", "26", ...
#> $ in_item_aban     <dbl> 0, 0, 0, ...
#> $ nu_param_a       <dbl> 2.20, 2.43, 1.95, ...
#> $ nu_param_b       <dbl> 0.83, 0.90, -0.13, ...
#> $ nu_param_c       <dbl> 0.21, 0.13, 0.20, ...
#> $ tx_cor           <chr> "AMARELA", "AMARELA", ...
#> $ co_prova         <chr> "1286", "1286", ...
#> # ... 3 more variables
```

## School Census

The School Census is Brazil's main statistical survey on basic education,
covering all public and private schools.

### Downloading Census data

```{r censo-download}
# School data
schools_2023 <- get_censo_escolar(2023)

# Filter by state (faster)
schools_sp <- get_censo_escolar(2023, uf = "SP")

# Sample for exploration
schools_sample <- get_censo_escolar(2023, n_max = 1000)
```

### Data structure

```{r censo-structure}
# The census contains over 400 variables per school!
glimpse(schools_2023)
#> Rows: 217,625
#> Columns: 408
#> $ nu_ano_censo              <dbl> 2023, 2023, ...
#> $ no_regiao                 <chr> "Norte", "Norte", ...
#> $ co_regiao                 <chr> "1", "1", ...
#> $ no_uf                     <chr> "Rondônia", "Rondônia", ...
#> $ sg_uf                     <chr> "RO", "RO", ...
#> $ co_uf                     <chr> "11", "11", ...
#> $ no_municipio              <chr> "Porto Velho", "Porto Velho", ...
#> $ co_municipio              <chr> "1100205", "1100205", ...
#> $ no_regiao_geog_interm     <chr> "Porto Velho", "Porto Velho", ...
#> $ co_regiao_geog_interm     <chr> "1101", "1101", ...
#> # ... 398 more variables
```

### Analysis: Schools by administrative type

```{r censo-analysis}
# Count by administrative type
schools_by_type <- 
  schools_2023 |>
  mutate(
    admin_type = case_when(
      tp_dependencia == 1 ~ "Federal",
      tp_dependencia == 2 ~ "State",
      tp_dependencia == 3 ~ "Municipal",
      tp_dependencia == 4 ~ "Private"
    )
  ) |>
  count(admin_type) |>
  mutate(pct = n / sum(n) * 100)

ggplot(schools_by_type, aes(x = reorder(admin_type, n), y = n, fill = admin_type)) +
  geom_col() +
  geom_text(aes(label = sprintf("%.1f%%", pct)), hjust = -0.1) +
  coord_flip() +
  scale_fill_brewer(palette = "Set2") +
  labs(
    title = "Number of Schools by Administrative Type (2023)",
    x     = NULL,
    y     = "Number of Schools"
  ) +
  theme_minimal() +
  theme(legend.position = "none") +
  scale_y_continuous(
    labels = scales::label_number(big.mark = ".", decimal.mark = ","),
    expand = expansion(mult = c(0, 0.12))
  )
```

![](../man/figures/vignette-gs-schools-by-type.png)

### Analysis: School infrastructure

```{r censo-infrastructure}
# Check infrastructure availability in public schools
infra <- 
  schools_2023 |>
  filter(tp_dependencia %in% c(2, 3)) |>  # Public schools only
  summarise(
    pct_internet      = mean(in_internet == 1, na.rm = TRUE) * 100,
    pct_library       = mean(in_biblioteca == 1, na.rm = TRUE) * 100,
    pct_computer_lab  = mean(in_laboratorio_informatica == 1, na.rm = TRUE) * 100,
    pct_sports_court  = mean(in_quadra_esportes == 1, na.rm = TRUE) * 100,
    pct_ramps         = mean(in_acessibilidade_rampas == 1, na.rm = TRUE) * 100
  )

print(infra)
```

```{r censo-infra-table, echo = FALSE, eval = TRUE}
library(kableExtra)
data.frame(
  Internet     = 88.5,
  Library      = 31.2,
  Computer_Lab = 29.3,
  Sports_Court = 36.0,
  Ramps        = 53.5
) |>
  kbl(
    col.names = c("Internet", "Library", "Computer Lab", "Sports Court", "Ramps"),
    align = "ccccc",
    caption = "Infrastructure availability in public schools (%)"
  ) |>
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE
  )
```

## Best practices

### 1. Set a permanent cache
```{r best-practices-cache}
# At the start of each project
set_cache_dir("~/educabR_data")
```

### 2. Use samples for exploration
```{r best-practices-sample}
# Explore with a small sample first
test_data <- get_enem(2023, n_max = 1000)

# Then download the full data
# full_data <- get_enem(2023)
```

### 3. Filter by state after downloading
```{r best-practices-filter}
# Download once, then filter
ideb <- get_ideb(level = "escola", stage = "anos_iniciais", metric = "indicador", year = 2021)
ideb_sp <- ideb |> dplyr::filter(uf_sigla == "SP")
```

### 4. Monitor memory usage
```{r best-practices-memory}
# Large files can consume a lot of RAM
# Use n_max or filter data after loading
data <- get_censo_escolar(2023)
data_filtered <- 
  data |>
  select(co_entidade, no_entidade, sg_uf, tp_dependencia)
rm(data)  # Free memory
gc()
```

## Data dictionary

For detailed information about variables, see the official documentation:

- **IDEB**: https://www.gov.br/inep/pt-br/areas-de-atuacao/pesquisas-estatisticas-e-indicadores/ideb
- **ENEM**: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem
- **School Census**: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-escolar
- **SAEB**: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/saeb
- **ENADE**: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enade
- **CAPES**: https://dadosabertos.capes.gov.br
- **FUNDEB/FNDE**: `https://www.fnde.gov.br`

## Known issues

1. **Slow downloads**: INEP servers can be slow. The package retries automatically up to 3 times.
2. **Large files**: ENEM and School Census files can be several GB. Use `n_max` for testing.
3. **Encoding**: Some files have encoding issues. The package handles this automatically.

## Contributing

Found a bug or want to suggest improvements? Open an issue on GitHub:
https://github.com/SidneyBissoli/educabR/issues
