Exame Nacional de Desempenho de Estudantes (ENADE)
The nationwide mandatory examination of college graduates.
One table with one row per individual undergraduate student in Brazil.
An enumeration of undergraduate students in Brazil.
Released annually since 2004.
Compiled by the Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP).
Please skim before you begin:
Cálculo da nota final do Exame Nacional de Desempenho dos Estudiantes
A haiku regarding this microdata:
# undergraduates
# sit for standardized testing
# exit interviewDownload, 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'
)