
library(testthat)

source("user_code.R")
test_that("Only valid emails are returned", {
  emails <- c("john.doe@gmail.com", "invalid.email@", "user123@outlook.com", "noatsign.com", "bob.smith@company.org")
  result <- clean_emails(emails)
  expect_true(all(grepl("^[A-Za-z0-9._%+-]+@company.com$", result)), "All returned emails should be valid and use @company.com domain.")
  expect_false(any(result %in% c("invalid.email@", "noatsign.com")), "Invalid emails should not be returned.")
})

test_that("All returned emails have domain @company.com", {
  emails <- c("john.doe@gmail.com", "user123@outlook.com", "bob.smith@company.org")
  result <- clean_emails(emails)
  expect_true(all(endsWith(result, "@company.com")), "All cleaned emails must end with @company.com.")
})

test_that("Function works with mixed case emails", {
  emails <- c("JANE_DOE@YAHOO.COM", "MiXeD.Case@Gmail.com")
  result <- clean_emails(emails)
  expect_true(all(grepl("@company.com$", result)), "All valid emails should be standardized to @company.com, regardless of case.")
})

test_that("Emails without @ or with incomplete domains are excluded", {
  emails <- c("noatsign.com", "missingdomain@", "user@domain", "another@.com", "valid.user@valid.com")
  result <- clean_emails(emails)
  expect_true("valid.user@company.com" %in% result, "Valid emails should be included.")
  expect_false(any(result %in% c("noatsign.com", "missingdomain@", "user@domain", "another@.com")), "Invalid emails should not be included.")
})

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: Clean Messy Text Data

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