Exame Nacional de Desempenho de Estudantes (ENADE)

License: GPL v3

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The nationwide mandatory examination of college graduates.


Please skim before you begin:

  1. Cálculo da nota final do Exame Nacional de Desempenho dos Estudiantes

  2. Wikipedia Entry

  3. A haiku regarding this microdata:

# undergraduates
# sit for standardized testing
# exit interview

Download, Import, Preparation

Download, import, and merge two of the 2021 files:

library(httr)
library(archive)

tf <- tempfile()

this_url <- "https://download.inep.gov.br/microdados/microdados_enade_2021.zip"

GET( this_url , write_disk( tf ) , progress() )

archive_extract( tf , dir = tempdir() )

read_enade_archive <-
    function( this_regular_expression , this_directory ){
        this_filename <- 
            grep( 
                this_regular_expression , 
                list.files( 
                    this_directory ,
                    recursive = TRUE ,
                    full.names = TRUE 
                ) , 
                value = TRUE 
            )

        this_df <-
            read.table(
                this_filename ,
                header = TRUE ,
                sep = ";" ,
                na.strings = ""
            )
            
        names( this_df ) <- tolower( names( this_df ) )
        
        this_df
    }

arq1_df <- read_enade_archive( 'arq1\\.txt$' , tempdir() )

arq1_df <- unique( arq1_df[ c( 'co_curso' , 'co_uf_curso' , 'co_categad' , 'co_grupo' ) ] )

arq3_df <- read_enade_archive( 'arq3\\.txt$' , tempdir() )

enade_df <- merge( arq3_df , arq1_df )

stopifnot( nrow( enade_df ) == nrow( arq3_df ) )

Save locally  

Save the object at any point:

# enade_fn <- file.path( path.expand( "~" ) , "ENADE" , "this_file.rds" )
# saveRDS( enade_df , file = enade_fn , compress = FALSE )

Load the same object:

# enade_df <- readRDS( enade_fn )

Variable Recoding

Add new columns to the data set:

enade_df <- 
    transform( 
        enade_df , 
        
        # qual foi o tempo gasto por voce para concluir a prova?
        less_than_two_hours = as.numeric( co_rs_i9 %in% c( 'A' , 'B' ) ) ,
        
        administrative_category =
            factor(
                co_categad ,
                levels = c( 1:5 , 7 ) ,
                labels = c( '1. Pública Federal' , '2. Pública Estadual' , 
                '3. Pública Municipal' , '4. Privada com fins lucrativos' , 
                '5. Privada sem fins lucrativos' , '7. Especial' )
            ) ,

        state_name = 
            factor( 
                co_uf_curso , 
                levels = c( 11:17 , 21:29 , 31:33 , 35 , 41:43 , 50:53 ) ,
                labels = c( "Rondonia" , "Acre" , "Amazonas" , 
                "Roraima" , "Para" , "Amapa" , "Tocantins" , 
                "Maranhao" , "Piaui" , "Ceara" , "Rio Grande do Norte" , 
                "Paraiba" , "Pernambuco" , "Alagoas" , "Sergipe" , 
                "Bahia" , "Minas Gerais" , "Espirito Santo" , 
                "Rio de Janeiro" , "Sao Paulo" , "Parana" , 
                "Santa Catarina" , "Rio Grande do Sul" , 
                "Mato Grosso do Sul" , "Mato Grosso" , "Goias" , 
                "Distrito Federal" )
            )

    )

Analysis Examples with base R  

Unweighted Counts

Count the unweighted number of records in the table, overall and by groups:

nrow( enade_df )

table( enade_df[ , "administrative_category" ] , useNA = "always" )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

mean( enade_df[ , "nt_obj_fg" ] , na.rm = TRUE )

tapply(
    enade_df[ , "nt_obj_fg" ] ,
    enade_df[ , "administrative_category" ] ,
    mean ,
    na.rm = TRUE 
)

Calculate the distribution of a categorical variable, overall and by groups:

prop.table( table( enade_df[ , "state_name" ] ) )

prop.table(
    table( enade_df[ , c( "state_name" , "administrative_category" ) ] ) ,
    margin = 2
)

Calculate the sum of a linear variable, overall and by groups:

sum( enade_df[ , "nt_obj_fg" ] , na.rm = TRUE )

tapply(
    enade_df[ , "nt_obj_fg" ] ,
    enade_df[ , "administrative_category" ] ,
    sum ,
    na.rm = TRUE 
)

Calculate the median (50th percentile) of a linear variable, overall and by groups:

quantile( enade_df[ , "nt_obj_fg" ] , 0.5 , na.rm = TRUE )

tapply(
    enade_df[ , "nt_obj_fg" ] ,
    enade_df[ , "administrative_category" ] ,
    quantile ,
    0.5 ,
    na.rm = TRUE 
)

Subsetting

Limit your data.frame to students reporting that the general training section was easy or very easy:

sub_enade_df <- subset( enade_df , co_rs_i1 %in% c( "A" , "B" ) )

Calculate the mean (average) of this subset:

mean( sub_enade_df[ , "nt_obj_fg" ] , na.rm = TRUE )

Measures of Uncertainty

Calculate the variance, overall and by groups:

var( enade_df[ , "nt_obj_fg" ] , na.rm = TRUE )

tapply(
    enade_df[ , "nt_obj_fg" ] ,
    enade_df[ , "administrative_category" ] ,
    var ,
    na.rm = TRUE 
)

Regression Models and Tests of Association

Perform a t-test:

t.test( nt_obj_fg ~ less_than_two_hours , enade_df )

Perform a chi-squared test of association:

this_table <- table( enade_df[ , c( "less_than_two_hours" , "state_name" ) ] )

chisq.test( this_table )

Perform a generalized linear model:

glm_result <- 
    glm( 
        nt_obj_fg ~ less_than_two_hours + state_name , 
        data = enade_df
    )

summary( glm_result )

Replication Example

This example matches the tecnologia em gestão da tecnologia da informação test scores on PDF page 48 of the 2021 final results document:

it_students <- subset( enade_df , co_grupo %in% 6409 )

results <- sapply( it_students[ c( 'nt_fg' , 'nt_ce' , 'nt_ger' ) ] , mean , na.rm = TRUE )

stopifnot( round( results[ 'nt_fg' ] , 1 ) == 30.4 )
stopifnot( round( results[ 'nt_ce' ] , 1 ) == 38.2 )
stopifnot( round( results[ 'nt_ger' ] , 1 ) == 36.3 )

Analysis Examples with dplyr  

The R dplyr library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, and the tidyverse style of non-standard evaluation. This vignette details the available features. As a starting point for ENADE users, this code replicates previously-presented examples:

library(dplyr)
enade_tbl <- as_tibble( enade_df )

Calculate the mean (average) of a linear variable, overall and by groups:

enade_tbl %>%
    summarize( mean = mean( nt_obj_fg , na.rm = TRUE ) )

enade_tbl %>%
    group_by( administrative_category ) %>%
    summarize( mean = mean( nt_obj_fg , na.rm = TRUE ) )

Analysis Examples with data.table  

The R data.table library provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. data.table offers concise syntax: fast to type, fast to read, fast speed, memory efficiency, a careful API lifecycle management, an active community, and a rich set of features. This vignette details the available features. As a starting point for ENADE users, this code replicates previously-presented examples:

library(data.table)
enade_dt <- data.table( enade_df )

Calculate the mean (average) of a linear variable, overall and by groups:

enade_dt[ , mean( nt_obj_fg , na.rm = TRUE ) ]

enade_dt[ , mean( nt_obj_fg , na.rm = TRUE ) , by = administrative_category ]

Analysis Examples with duckdb  

The R duckdb library provides an embedded analytical data management system with support for the Structured Query Language (SQL). duckdb offers a simple, feature-rich, fast, and free SQL OLAP management system. This vignette details the available features. As a starting point for ENADE users, this code replicates previously-presented examples:

library(duckdb)
con <- dbConnect( duckdb::duckdb() , dbdir = 'my-db.duckdb' )
dbWriteTable( con , 'enade' , enade_df )

Calculate the mean (average) of a linear variable, overall and by groups:

dbGetQuery( con , 'SELECT AVG( nt_obj_fg ) FROM enade' )

dbGetQuery(
    con ,
    'SELECT
        administrative_category ,
        AVG( nt_obj_fg )
    FROM
        enade
    GROUP BY
        administrative_category'
)