
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 essentials of handling text, dates, and files in R. This course guides you through string manipulation, regular expressions, date-time operations, and file I/O, with engaging explanations and practical challenges for beginners.

Learn to work with text data in R, from basic string operations to cleaning messy data using regular expressions.

Master handling dates and times in R, from parsing and formatting to performing calculations and managing time zones.

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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Challenge: Identify Data Issues in a Customer Dataset

Oplossing