What does school infrastructure look like across Brazil?

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This vignette shows how to use educabR to explore school infrastructure across Brazil using the School Census. We look at internet access, libraries, science labs, sports courts, and accessibility features – and how they vary by region, administrative type, and location.

Downloading School Census data

The School Census contains one row per school (~217,000 schools in 2023) with over 400 variables covering infrastructure, staffing, and programs.

# Download all schools for 2023
escolas <- get_censo_escolar(year = 2023)

# Or filter by state for faster exploration
escolas_sp <- get_censo_escolar(year = 2023, uf = "SP")

Note: the full national file is about 30 MB compressed. When filtering by state, all rows are read before filtering, so the first call may take a moment.

Overview: key infrastructure indicators

The School Census uses binary columns (1 = yes, 0 = no) for each infrastructure item. Let us compute the percentage of schools that have each resource nationwide.

indicators <- c(
  "in_internet",
  "in_banda_larga",
  "in_biblioteca",
  "in_laboratorio_informatica",
  "in_laboratorio_ciencias",
  "in_quadra_esportes",
  "in_agua_potavel",
  "in_esgoto_rede_publica"
)

infra_summary <-
  escolas |>
  summarise(across(all_of(indicators), ~ mean(. == 1, na.rm = TRUE) * 100)) |>
  pivot_longer(everything(), names_to = "indicator", values_to = "pct") |>
  mutate(
    label = c(
      "Internet", "Broadband", "Library", "Computer lab",
      "Science lab", "Sports court", "Drinking water", "Public sewage"
    )
  )

ggplot(infra_summary, aes(x = reorder(label, pct), y = pct)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Percentage of Schools with Key Infrastructure (2023)",
    x     = NULL,
    y     = "% of schools"
  ) +
  theme_minimal()

Infrastructure by administrative type

Federal, state, municipal, and private schools have very different resource levels. The tp_dependencia column encodes the administrative type.

admin_labels <- c(
  "1" = "Federal",
  "2" = "State",
  "3" = "Municipal",
  "4" = "Private"
)

infra_admin <-
  escolas |>
  mutate(admin = admin_labels[as.character(tp_dependencia)]) |>
  group_by(admin) |>
  summarise(
    Internet       = mean(in_internet == 1, na.rm = TRUE) * 100,
    Library        = mean(in_biblioteca == 1, na.rm = TRUE) * 100,
    `Computer lab` = mean(in_laboratorio_informatica == 1, na.rm = TRUE) * 100,
    `Science lab`  = mean(in_laboratorio_ciencias == 1, na.rm = TRUE) * 100,
    .groups = "drop"
  ) |>
  pivot_longer(-admin, names_to = "resource", values_to = "pct")

ggplot(infra_admin, aes(x = resource, y = pct, fill = admin)) +
  geom_col(position = "dodge") +
  labs(
    title = "School Infrastructure by Administrative Type (2023)",
    x     = NULL,
    y     = "% of schools",
    fill  = "Type"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 20, hjust = 1))

Regional inequality

Northern and Northeastern states typically have fewer resources than the South and Southeast. Grouping by region reveals the gap.

region_labels <- c(
  "Norte"        = "North",
  "Nordeste"     = "Northeast",
  "Sudeste"      = "Southeast",
  "Sul"          = "South",
  "Centro-Oeste" = "Central-West"
)

infra_region <-
  escolas |>
  mutate(region = region_labels[no_regiao]) |>
  group_by(region) |>
  summarise(
    Internet       = mean(in_internet == 1, na.rm = TRUE) * 100,
    Library        = mean(in_biblioteca == 1, na.rm = TRUE) * 100,
    `Science lab`  = mean(in_laboratorio_ciencias == 1, na.rm = TRUE) * 100,
    `Sports court` = mean(in_quadra_esportes == 1, na.rm = TRUE) * 100,
    .groups = "drop"
  ) |>
  pivot_longer(-region, names_to = "resource", values_to = "pct")

ggplot(infra_region, aes(x = region, y = pct, fill = resource)) +
  geom_col(position = "dodge") +
  labs(
    title = "School Infrastructure by Region (2023)",
    x     = NULL,
    y     = "% of schools",
    fill  = NULL
  ) +
  theme_minimal()

Urban vs rural schools

The tp_localizacao column distinguishes urban (1) from rural (2) schools. The infrastructure gap between them is one of the starkest in Brazilian education.

infra_location <-
  escolas |>
  mutate(
    location = ifelse(tp_localizacao == 1, "Urban", "Rural")
  ) |>
  group_by(location) |>
  summarise(
    Internet       = mean(in_internet == 1, na.rm = TRUE) * 100,
    Broadband      = mean(in_banda_larga == 1, na.rm = TRUE) * 100,
    Library        = mean(in_biblioteca == 1, na.rm = TRUE) * 100,
    `Computer lab` = mean(in_laboratorio_informatica == 1, na.rm = TRUE) * 100,
    `Science lab`  = mean(in_laboratorio_ciencias == 1, na.rm = TRUE) * 100,
    `Sports court` = mean(in_quadra_esportes == 1, na.rm = TRUE) * 100,
    .groups = "drop"
  ) |>
  pivot_longer(-location, names_to = "resource", values_to = "pct")

ggplot(infra_location, aes(x = resource, y = pct, fill = location)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("Urban" = "steelblue", "Rural" = "coral")) +
  labs(
    title = "School Infrastructure: Urban vs Rural (2023)",
    x     = NULL,
    y     = "% of schools",
    fill  = NULL
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 20, hjust = 1))

Accessibility features

The School Census tracks specific accessibility features. The in_acessibilidade_inexistente flag marks schools with no accessibility at all.

access_cols <- c(
  "in_acessibilidade_rampas",
  "in_acessibilidade_corrimao",
  "in_acessibilidade_elevador",
  "in_acessibilidade_pisos_tateis",
  "in_acessibilidade_sinal_sonoro",
  "in_acessibilidade_sinal_tatil",
  "in_acessibilidade_sinal_visual",
  "in_acessibilidade_inexistente"
)

access_labels <- c(
  "Ramps", "Handrails", "Elevator", "Tactile floors",
  "Sound signals", "Tactile signals", "Visual signals", "None"
)

access_summary <-
  escolas |>
  summarise(across(all_of(access_cols), ~ mean(. == 1, na.rm = TRUE) * 100)) |>
  pivot_longer(everything(), names_to = "feature", values_to = "pct") |>
  mutate(label = access_labels)

ggplot(access_summary, aes(x = reorder(label, pct), y = pct)) +
  geom_col(fill = "#2a9d8f") +
  coord_flip() +
  labs(
    title = "School Accessibility Features (2023)",
    x     = NULL,
    y     = "% of schools"
  ) +
  theme_minimal()

Internet access by state

A per-state view highlights which states are lagging behind in digital connectivity.

internet_uf <-
  escolas |>
  group_by(sg_uf) |>
  summarise(
    pct_internet = mean(in_internet == 1, na.rm = TRUE) * 100,
    .groups = "drop"
  ) |>
  arrange(pct_internet)

ggplot(internet_uf, aes(x = reorder(sg_uf, pct_internet), y = pct_internet)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Schools with Internet Access by State (2023)",
    x     = NULL,
    y     = "% of schools"
  ) +
  theme_minimal()

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