
library(testthat)

source("user_code.R")
test_that("returns character vector of same length as input", {
  input <- c("A", "B ", " C")
  output <- clean_product_names(input)
  expect_equal(length(output), length(input),
    info = "Output should have same length as input.")
  expect_type(output, "character")
})

test_that("removes leading and trailing whitespace", {
  input <- c("  Widget  ", "Gadget ", "  Tool")
  output <- clean_product_names(input)
  expect_false(any(grepl("^ | $", output)),
    info = "No product name should have leading or trailing whitespace.")
})

test_that("converts all letters to lower case", {
  input <- c("WiDgEt", "GADGET", "tool")
  output <- clean_product_names(input)
  expect_true(all(output == tolower(output)),
    info = "All product names should be in lower case.")
})

test_that("removes unwanted characters, keeps only letters, numbers, and spaces", {
  input <- c("Super-Widget!", "eco_friendly*Gadget", "CLEANER#2023", "basic-widget")
  output <- clean_product_names(input)
  expect_equal(output, c("superwidget", "ecofriendlygadget", "cleaner2023", "basicwidget"),
    info = "Unwanted characters should be removed, only letters, numbers, and spaces kept.")
})

test_that("preserves spaces within product names", {
  input <- c("Super Widget Deluxe", "eco Gadget", "ULTRA cleaner 2023")
  output <- clean_product_names(input)
  expect_equal(output, c("super widget deluxe", "eco gadget", "ultra cleaner 2023"),
    info = "Spaces within product names should be preserved.")
})

test_main.R

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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: Prepare Product Names for Analysis

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