# How to compare CPC indicators between public and private institutions?

This vignette shows how to use educabR to compare course quality (CPC) between public and private higher education institutions in Brazil.

    library(educabR)
    library(dplyr)
    library(tidyr)
    library(ggplot2)

## Downloading CPC data

CPC (Conceito Preliminar de Curso) is a quality indicator for undergraduate courses, ranging from 1 (lowest) to 5 (highest). Courses scoring 1 or 2 are flagged for on-site evaluation.

    cpc <- get_cpc(year = 2023)
    glimpse(cpc)

    #> Rows: 9,812
    #> Columns: 39
    #> $ ano                      <dbl> 2023, 2023, 2023, 2023, 2023, …
    #> $ codigo_da_ies            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, …
    #> $ nome_da_ies              <chr> "UNIVERSIDADE FEDERAL DE MATO G…
    #> $ sigla_da_ies             <chr> "UFMT", "UFMT", "UFMT", "UFMT"…
    #> $ categoria_administrativa <chr> "Pública Federal", "Pública Fed…
    #> $ codigo_do_curso          <dbl> 3, 9, 10, 12, 16, 17, 20, 37, …
    #> $ area_de_avaliacao        <chr> "ENGENHARIA CIVIL", "AGRONOMIA"…
    #> $ sigla_da_uf              <chr> "MT", "MT", "MT", "MT", "MT", "…
    #> $ cpc_continuo             <dbl> 3.429, 3.482, 3.064, 2.792, 4.…
    #> $ cpc_faixa                <dbl> 4, 4, 4, 3, 5, 4, 4, 4, 4, 5, …
    #> # ℹ 29 more variables

## Identifying public vs private institutions

The `categoria_administrativa` column classifies institutions. The exact column name and coding may vary by year – check `names(cpc)` after downloading.

    cpc_classified <-
      cpc |>
      mutate(
        sector = case_when(
          categoria_administrativa %in% c(
            "Publica Federal", "Publica Estadual", "Publica Municipal",
            "P\u00fablica Federal", "P\u00fablica Estadual", "P\u00fablica Municipal"
          ) ~ "Public",
          .default = "Private"
        )
      )

## CPC score distribution by sector

    cpc_classified |>
      filter(!is.na(cpc_faixa)) |>
      count(sector, cpc_faixa) |>
      mutate(pct = n / sum(n) * 100, .by = sector) |>
      ggplot(aes(x = factor(cpc_faixa), y = pct, fill = sector)) +
      geom_col(position = "dodge") +
      labs(
        title = "CPC Score Distribution: Public vs Private (2023)",
        x     = "CPC Score (1-5)",
        y     = "Percentage of Courses (%)",
        fill  = "Sector"
      ) +
      theme_minimal()

## Average CPC by sector and knowledge area

    cpc_classified |>
      filter(!is.na(cpc_continuo), !is.na(area_de_avaliacao)) |>
      summarise(
        mean_cpc = mean(cpc_continuo, na.rm = TRUE),
        n = n(),
        .by = c(sector, area_de_avaliacao)
      ) |>
      filter(n >= 10) |>
      pivot_wider(
        names_from  = sector,
        values_from = c(mean_cpc, n)
      ) |>
      mutate(gap = mean_cpc_Public - mean_cpc_Private) |>
      slice_max(abs(gap), n = 15) |>
      ggplot(aes(x = reorder(area_de_avaliacao, gap), y = gap)) +
      geom_col(aes(fill = gap > 0)) +
      coord_flip() +
      scale_fill_manual(
        values = c("TRUE" = "#2a9d8f", "FALSE" = "#e76f51"),
        labels = c("TRUE" = "Public higher", "FALSE" = "Private higher")
      ) +
      labs(
        title = "CPC Gap: Public minus Private, by Knowledge Area (2023)",
        x     = NULL,
        y     = "CPC difference (public - private)",
        fill  = NULL
      ) +
      theme_minimal() +
      theme(legend.position = "none")

## Combining with IGC for institutional view

IGC (Indice Geral de Cursos) provides an institution-level quality score. Combining CPC and IGC gives a course-level and institution-level perspective.

    igc <- get_igc(year = 2023)

    igc |>
      mutate(
        sector = case_when(
          categoria_administrativa %in% c(
            "Publica Federal", "Publica Estadual", "Publica Municipal",
            "P\u00fablica Federal", "P\u00fablica Estadual", "P\u00fablica Municipal"
          ) ~ "Public",
          .default = "Private"
        )
      ) |>
      filter(!is.na(igc_continuo)) |>
      ggplot(aes(x = sector, y = igc_continuo, fill = sector)) +
      geom_boxplot(alpha = 0.7) +
      scale_fill_manual(values = c("Public" = "#2a9d8f", "Private" = "#e76f51")) +
      labs(
        title = "IGC Distribution: Public vs Private (2023)",
        x     = NULL,
        y     = "IGC (Continuous)"
      ) +
      theme_minimal() +
      theme(legend.position = "none")