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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(grepl("blica", rede), 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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