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# ColombiAPI - Access Colombian Data via APIs and Curated Datasets
# Version 0.3.1
# Copyright (C) 2025 Renzo Caceres Rossi
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# admitted_students_df
library(testthat)
# Test dataset structure and class
test_that("admitted_students_df loads correctly and has the expected structure", {
expect_s3_class(admitted_students_df, "data.frame") # Check if it's a data frame
expect_equal(nrow(admitted_students_df), 445) # Check number of rows
expect_equal(ncol(admitted_students_df), 15) # Check number of columns
expect_equal(names(admitted_students_df)[1], "carr") # Check first column name
expect_equal(names(admitted_students_df)[15], "age") # Check last column name
})
# Test data types of columns
test_that("admitted_students_df has correct column types", {
expect_true(is.factor(admitted_students_df$carr))
expect_true(is.numeric(admitted_students_df$mate))
expect_true(is.numeric(admitted_students_df$cien))
expect_true(is.numeric(admitted_students_df$soci))
expect_true(is.numeric(admitted_students_df$text))
expect_true(is.numeric(admitted_students_df$imag))
expect_true(is.numeric(admitted_students_df$exam))
expect_true(is.factor(admitted_students_df$gene))
expect_true(is.factor(admitted_students_df$estr))
expect_true(is.factor(admitted_students_df$orig))
expect_true(is.factor(admitted_students_df$edad))
expect_true(is.factor(admitted_students_df$niLE))
expect_true(is.factor(admitted_students_df$niMa))
expect_true(is.factor(admitted_students_df$stra))
expect_true(is.numeric(admitted_students_df$age))
})
# Test for missing values (NA) in mandatory columns
mandatory_columns <- names(admitted_students_df)
test_that("admitted_students_df mandatory columns have no NA values", {
for (column in mandatory_columns) {
expect_false(any(is.na(admitted_students_df[[column]])), info = paste("Found NA values in", column))
}
})
# Test to verify dataset immutability
test_that("admitted_students_df remains unchanged after tests", {
original_dataset <- admitted_students_df # Create a copy of the original dataset
# Example checks
sum(is.na(admitted_students_df$mate))
sum(is.na(admitted_students_df$cien))
sum(is.na(admitted_students_df$carr))
# Verify the dataset hasn't changed
expect_identical(original_dataset, admitted_students_df)
expect_equal(nrow(original_dataset), nrow(admitted_students_df))
expect_equal(ncol(original_dataset), ncol(admitted_students_df))
expect_equal(names(original_dataset), names(admitted_students_df))
})
# Optional logical checks for numeric columns (e.g., scores should be non-negative)
numeric_columns <- c("mate", "cien", "soci", "text", "imag", "exam", "age")
test_that("admitted_students_df numeric columns have valid values", {
for (col in numeric_columns) {
expect_true(all(admitted_students_df[[col]] >= 0, na.rm = TRUE), info = paste("Negative values in", col))
}
})
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