educabR

# install package "pacman" if it is not installed
if (!require("pacman")) install.packages("pacman")

# install and/or load packages
p_load(
  educabR,
  geobr,
  tidyverse
)

# read ideb data
ideb <- get_ideb(
  level  = "estado", 
  stage  = "anos_iniciais", 
  metric = "indicador", 
  year   = 2023
  )

# read spatial data
states <- read_state(year = 2020, showProgress = FALSE)

# plot data
states |>
  left_join(ideb, by = c("abbrev_state" = "uf_sigla")) |>
  drop_na() |>
  filter(rede == "Pública" & indicador == "IDEB") |>
  ggplot() +
  geom_sf(aes(fill = valor), color = "white", size = .2) +
  scale_fill_distiller(palette = "YlGn", direction = 1, name = "IDEB") +
  labs(
    title    = "IDEB - public school system - 2023",
    subtitle = "Early elementary (1º - 4º) by state"
  ) +
  theme_void()

install.packages("educabR")

# install.packages("remotes")
remotes::install_github("SidneyBissoli/educabR")

library(educabR)

# Download IDEB 2021 - Early elementary - Schools
ideb <- get_ideb(
  year  = 2021,
  stage = "anos_iniciais",
  level = "escola"
)

# Historical series
ideb_series <- get_ideb_series(
  years = c(2017, 2019, 2021, 2023),
  level = "municipio",
  stage = "anos_iniciais"
)

# Download a sample for exploration
enem <- get_enem(year = 2023, n_max = 10000)

# Statistical summary
enem_summary(enem)
#> # A tibble: 10 × 10
#>    variable            n n_valid  mean    sd   min   q25 median   q75   max
#>    <chr>           <int>   <int> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
#>  1 nu_nota_cn      10000    7281  492.  79.8     0  440.   489.  541.  817.
#>  2 nu_nota_ch      10000    7562  529.  81.7     0  480.   535.  584.  823
#>  3 nu_nota_lc      10000    7562  520.  70.4     0  476.   524.  568.  731.
#>  4 nu_nota_mt      10000    7281  520. 121.      0  426.   507.  608   945.
#>  5 nu_nota_comp1   10000    7562  126.  32.1     0  120    120   160   200
#>  6 nu_nota_comp2   10000    7562  147.  48.4     0  120    160   200   200
#>  7 nu_nota_comp3   10000    7562  124.  41.0     0  100    120   160   200
#>  8 nu_nota_comp4   10000    7562  136.  41.2     0  120    120   160   200
#>  9 nu_nota_comp5   10000    7562  117.  60.0     0   80    120   160   200
#> 10 nu_nota_redacao 10000    7562  649. 201.      0  520    640   820   980

# Summary by sex
enem_summary(enem, by = "tp_sexo")
#> # A tibble: 20 × 11
#>    tp_sexo variable            n n_valid  mean    sd   min   q25 median   q75   max
#>    <chr>   <chr>           <int>   <int> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
#>  1 F       nu_nota_cn       7042    5130  485.  76.5     0  435    481.  532   817.
#>  2 M       nu_nota_cn       2958    2151  507.  85.1     0  455.   508.  560.  804.
#>  3 F       nu_nota_ch       7042    5328  527.  78.6     0  480.   532.  579.  784.
#>  4 M       nu_nota_ch       2958    2234  535.  88.2     0  483.   542.  596.  823
#>  5 F       nu_nota_lc       7042    5328  520.  68.2     0  476.   523.  566.  731.
#>  6 M       nu_nota_lc       2958    2234  522.  75.5     0  478.   527.  574.  729.
#>  7 F       nu_nota_mt       7042    5130  509. 116.      0  420.   494.  589   944.
#>  8 M       nu_nota_mt       2958    2151  547. 129.      0  447.   540.  643.  945.
#>  9 F       nu_nota_comp1    7042    5328  128.  30.8     0  120    120   160   200
#> 10 M       nu_nota_comp1    2958    2234  121.  34.6     0  100    120   140   200
#> # ℹ 10 more rows

# Download School Census 2023 - filter by state
censo_sp <- get_censo_escolar(year = 2023, uf = "SP")

# Higher Education Census - institutions
ies <- get_censo_superior(2023, type = "ies")

# ENADE microdata
enade <- get_enade(2023, n_max = 10000)

# CAPES graduate programs
programas <- get_capes(2023, type = "programas")

# Resource distribution by state
dist <- get_fundeb_distribution(2023, uf = "SP")

# Enrollment counts
mat <- get_fundeb_enrollment(2023, uf = "SP")

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

# List cached files
list_cache()

# Clear cache
clear_cache()