# Mapping education indicators with geobr

This vignette shows how to combine educabR with geobr to create choropleth maps of education indicators across Brazilian municipalities and states.

    library(educabR)
    library(geobr)
    library(dplyr)
    library(ggplot2)

## IDEB by state

The simplest map uses state-level data. We download IDEB scores and join them with state geometries from geobr.

    ideb_uf <-
      get_ideb(level = "estado", stage = "anos_iniciais", metric = "indicador", year = 2023) |>
      filter(rede == "Total", indicador == "IDEB")

    states <- read_state(year = 2020, showProgress = FALSE)

    states |>
      left_join(ideb_uf, by = c("abbrev_state" = "uf_sigla")) |>
      ggplot() +
      geom_sf(aes(fill = valor), color = "white", linewidth = .2) +
      scale_fill_distiller(palette = "YlGn", direction = 1, name = "IDEB") +
      labs(title = "IDEB 2023 — Early elementary by state") +
      theme_void()

## IDEB by municipality

Municipality-level maps reveal within-state inequality that state averages hide. The `municipio_codigo` column in educabR uses the 7-digit IBGE code, which matches `code_muni` in geobr.

    ideb_muni <- get_ideb(
      level  = "municipio",
      stage  = "anos_iniciais",
      metric = "indicador",
      year   = 2023
    )

    # Keep only public schools and the IDEB indicator
    ideb_muni <-
      ideb_muni |>
      filter(rede == "P\u00fablica", indicador == "IDEB")

    municipalities <- read_municipality(year = 2020, showProgress = FALSE)

    municipalities |>
      mutate(code_muni = as.character(code_muni)) |>
      left_join(ideb_muni, by = c("code_muni" = "municipio_codigo")) |>
      ggplot() +
      geom_sf(aes(fill = valor), color = NA) +
      scale_fill_distiller(palette = "YlGn", direction = 1, name = "IDEB") +
      labs(title = "IDEB 2023 — Early elementary by municipality (public schools)") +
      theme_void()

## Zooming into a single state

For a closer look, filter both datasets to a single state. Here we map IDEB across municipalities in Minas Gerais.

    ideb_mg <-
      ideb_muni |>
      filter(uf_sigla == "MG")

    munis_mg <- read_municipality(code_muni = "MG", year = 2020, showProgress = FALSE)

    munis_mg |>
      mutate(code_muni = as.character(code_muni)) |>
      left_join(ideb_mg, by = c("code_muni" = "municipio_codigo")) |>
      ggplot() +
      geom_sf(aes(fill = valor), color = "grey90", linewidth = .1) +
      scale_fill_distiller(palette = "YlGn", direction = 1, name = "IDEB") +
      labs(title = "IDEB 2023 — Early elementary in Minas Gerais") +
      theme_void()

## Comparing IDEB editions over time

Side-by-side maps make it easy to visualize regional progress. We download two editions and use facets.

    ideb_time <-
      get_ideb(
        level  = "estado",
        stage  = "anos_iniciais",
        metric = "indicador",
        year   = c(2017, 2023)
      ) |>
      filter(rede == "Total", indicador == "IDEB")

    states |>
      left_join(ideb_time, by = c("abbrev_state" = "uf_sigla")) |>
      ggplot() +
      geom_sf(aes(fill = valor), color = "white", linewidth = .2) +
      scale_fill_distiller(palette = "YlGn", direction = 1, name = "IDEB") +
      facet_wrap(~ano, strip.position = "bottom") +
      labs(title = "IDEB evolution — Early elementary (2017 vs 2023)") +
      theme_void() +
      theme(
        legend.position = "bottom",
        plot.title = element_text(hjust = 0.5),
        strip.text = element_text(size = 11, margin = margin(t = 5))
      )

## Next steps

- Swap `"anos_iniciais"` for `"anos_finais"` or `"ensino_medio"` to map other stages.
- Use `metric = "nota"` to map SAEB proficiency scores instead of the composite IDEB.
- Combine with other educabR datasets (ENEM, School Census) using the same municipality codes.
- See the geobr documentation for additional geographic layers (regions, micro/mesoregions, etc.).