
library(testthat)

source("user_code.R")
test_that("Ages less than 18 are set to 18", {
  test_df <- data.frame(employee_id = c("A1", "A2"), name = c("X", "Y"), age = c(15, 18), email = c("x@a.com", "y@a.com"), stringsAsFactors = FALSE)
  res <- validate_and_correct_employees(test_df)
  expect_equal(res$age[1], 18, info = "Age below 18 should be set to 18")
  expect_equal(res$age[2], 18, info = "Age at lower bound should remain 18")
})

test_that("Ages greater than 65 are set to 65", {
  test_df <- data.frame(employee_id = c("A1", "A2"), name = c("X", "Y"), age = c(70, 65), email = c("x@a.com", "y@a.com"), stringsAsFactors = FALSE)
  res <- validate_and_correct_employees(test_df)
  expect_equal(res$age[1], 65, info = "Age above 65 should be set to 65")
  expect_equal(res$age[2], 65, info = "Age at upper bound should remain 65")
})

test_that("All employee_id values are unique after correction", {
  test_df <- data.frame(employee_id = c("A1", "A2", "A2", "A3"), name = c("X", "Y", "Z", "W"), age = c(25, 30, 40, 50), email = c("x@a.com", "y@a.com", "z@a.com", "w@a.com"), stringsAsFactors = FALSE)
  res <- validate_and_correct_employees(test_df)
  expect_equal(length(unique(res$employee_id)), nrow(res), info = "All employee_id values should be unique after correction")
})

test_that("Duplicate employee_id values are modified by appending a suffix", {
  test_df <- data.frame(employee_id = c("A1", "A2", "A2", "A3"), name = c("X", "Y", "Z", "W"), age = c(25, 30, 40, 50), email = c("x@a.com", "y@a.com", "z@a.com", "w@a.com"), stringsAsFactors = FALSE)
  res <- validate_and_correct_employees(test_df)
  expect_true(any(grepl("A2_", res$employee_id)), info = "Duplicate employee_id should be modified with suffix")
})

test_that("Email values without both '@' and '.' are set to NA", {
  test_df <- data.frame(employee_id = c("A1", "A2", "A3"), name = c("X", "Y", "Z"), age = c(25, 30, 40), email = c("x@a.com", "yexample.com", "z@bcom"), stringsAsFactors = FALSE)
  res <- validate_and_correct_employees(test_df)
  expect_true(is.na(res$email[2]), info = "Email without '@' should be NA")
  expect_true(is.na(res$email[3]), info = "Email without '.' should be NA")
})

test_that("Valid email formats are preserved", {
  test_df <- data.frame(employee_id = c("A1", "A2"), name = c("X", "Y"), age = c(25, 30), email = c("x@a.com", "y.b@c.org"), stringsAsFactors = FALSE)
  res <- validate_and_correct_employees(test_df)
  expect_equal(res$email, test_df$email, info = "Valid emails should not be modified")
})

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: Apply Validation Rules to Employee Records

Ratkaisu

Challenge: Apply Validation Rules to Employee Records

Ratkaisu