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

## ----setup--------------------------------------------------------------------
# library(educabR)
# library(dplyr)
# library(tidyr)
# library(ggplot2)

## ----download-----------------------------------------------------------------
# # WARNING: each year downloads ~600 MB from INEP. This may take several minutes.
# years <- c(2019, 2021, 2022, 2023)
# 
# enem <-
#   years |>
#   purrr::map(\(y) get_enem(year = y, n_max = 50000) |> mutate(year = y)) |>
#   bind_rows()

## ----math-by-state------------------------------------------------------------
# scores_by_state <-
#   enem |>
#   filter(!is.na(nu_nota_mt), !is.na(sg_uf_prova)) |>
#   summarise(
#     mean_math = mean(nu_nota_mt, na.rm = TRUE),
#     n = n(),
#     .by = c(sg_uf_prova, year)
#   )
# 
# # Top 5 states in the most recent year
# top_states <-
#   scores_by_state |>
#   filter(year == max(year)) |>
#   slice_max(mean_math, n = 5) |>
#   pull(sg_uf_prova)
# 
# scores_by_state |>
#   filter(sg_uf_prova %in% top_states) |>
#   ggplot(aes(x = factor(year), y = mean_math, color = sg_uf_prova, group = sg_uf_prova)) +
#   geom_line(linewidth = 1) +
#   geom_point(size = 2) +
#   labs(
#     title = "ENEM Math Score Evolution - Top 5 States",
#     x     = "Year",
#     y     = "Average Math Score",
#     color = "State"
#   ) +
#   theme_minimal()

## ----region-gap---------------------------------------------------------------
# region_map <- c(
#   AC = "North",     AP = "North",     AM = "North",     PA = "North",
#   RO = "North",     RR = "North",     TO = "North",
#   AL = "Northeast", BA = "Northeast", CE = "Northeast",
#   MA = "Northeast", PB = "Northeast", PE = "Northeast",
#   PI = "Northeast", RN = "Northeast", SE = "Northeast",
#   DF = "Midwest",   GO = "Midwest",   MT = "Midwest",   MS = "Midwest",
#   ES = "Southeast", MG = "Southeast", RJ = "Southeast", SP = "Southeast",
#   PR = "South",     RS = "South",     SC = "South"
# )
# 
# enem |>
#   filter(!is.na(nu_nota_mt), !is.na(sg_uf_prova)) |>
#   mutate(region = region_map[sg_uf_prova]) |>
#   summarise(
#     mean_math = mean(nu_nota_mt, na.rm = TRUE),
#     .by = c(region, year)
#   ) |>
#   ggplot(aes(x = factor(year), y = mean_math, color = region, group = region)) +
#   geom_line(linewidth = 1) +
#   geom_point(size = 2) +
#   labs(
#     title = "ENEM Math Score by Region",
#     x     = "Year",
#     y     = "Average Math Score",
#     color = "Region"
#   ) +
#   theme_minimal()

## ----all-scores---------------------------------------------------------------
# enem |>
#   filter(!is.na(sg_uf_prova)) |>
#   summarise(
#     math       = mean(nu_nota_mt, na.rm = TRUE),
#     languages  = mean(nu_nota_lc, na.rm = TRUE),
#     humanities = mean(nu_nota_ch, na.rm = TRUE),
#     sciences   = mean(nu_nota_cn, na.rm = TRUE),
#     essay      = mean(nu_nota_redacao, na.rm = TRUE),
#     .by = year
#   ) |>
#   pivot_longer(-year, names_to = "subject", values_to = "mean_score") |>
#   ggplot(aes(x = factor(year), y = mean_score, color = subject, group = subject)) +
#   geom_line(linewidth = 1) +
#   geom_point(size = 2) +
#   labs(
#     title = "National Average ENEM Scores by Subject",
#     x     = "Year",
#     y     = "Average Score",
#     color = "Subject"
#   ) +
#   theme_minimal()