
library(testthat)

source("user_code.R")
test_that("missing_flag column flags all rows with missing values", {
    flagged <- flag_data_issues(customer_data)
    expect_true("missing_flag" %in% colnames(flagged),
                info = "missing_flag column is missing from the output data frame")
    expect_equal(flagged$missing_flag,
                 c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE),
                 info = "Rows with missing values are not correctly flagged in missing_flag column")
})

test_that("duplicate_flag column flags all duplicated rows", {
    flagged <- flag_data_issues(customer_data)
    expect_true("duplicate_flag" %in% colnames(flagged),
                info = "duplicate_flag column is missing from the output data frame")
    expect_equal(flagged$duplicate_flag,
                 c(FALSE, FALSE, TRUE, FALSE, FALSE, TRUE),
                 info = "Duplicated rows are not correctly flagged in duplicate_flag column")
})

test_that("inconsistent_flag column flags all rows with email missing '@'", {
    flagged <- flag_data_issues(customer_data)
    expect_true("inconsistent_flag" %in% colnames(flagged),
                info = "inconsistent_flag column is missing from the output data frame")
    expect_equal(flagged$inconsistent_flag,
                 c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE),
                 info = "Rows with inconsistent email are not correctly flagged in inconsistent_flag column")
})

test_main.R

Master the essential skills for ensuring data quality in R. Learn to detect and handle missing, duplicated, and inconsistent data, identify outliers, apply data validation rules, and build robust data cleaning pipelines. This course combines engaging theory with hands-on challenges to prepare you for real-world data cleaning tasks.

Explore the foundations of data cleaning and quality assurance in R. Each chapter introduces a core concept, followed by a hands-on challenge to reinforce your learning.

Challenge: Identify Data Issues in a Customer Dataset

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