
library(testthat)

source("user_code.R")
test_that("function returns a factor", {
  categories <- c("Electronics", "Toys", "Clothing", "Toys", "Clothing", "Electronics")
  result <- convert_to_factor(categories)
  expect_true(is.factor(result), info = "Result should be of class 'factor'.")
})

test_that("factor contains same values as input", {
  categories <- c("Books", "Books", "Clothing", "Electronics")
  result <- convert_to_factor(categories)
  expect_equal(as.character(result), categories, info = "Factor values should match input vector values.")
})

test_that("factor levels match unique categories", {
  categories <- c("Electronics", "Toys", "Clothing", "Toys", "Clothing", "Electronics")
  result <- convert_to_factor(categories)
  expected_levels <- sort(unique(categories))
  expect_equal(sort(levels(result)), expected_levels, info = "Factor levels should match unique categories in input.")
})

test_that("factor preserves input order", {
  categories <- c("Books", "Books", "Clothing", "Electronics")
  result <- convert_to_factor(categories)
  expect_equal(as.character(result), categories, info = "Factor should preserve the order of the input vector.")
})

test_main.R

Master the essential data structures in R, including lists, data frames, and factors. Learn how to create, manipulate, and analyze data using R's powerful tools, with engaging explanations and real-world challenges designed for beginners.

Explore the fundamentals of lists in R, including how to create, access, and manipulate both simple and nested lists. Learn the differences between lists and data frames, and discover practical uses for each.

Master the creation and manipulation of data frames in R, including adding, removing, and modifying columns and rows, as well as filtering and sorting data for analysis.

Discover how to use factors in R to handle categorical data, including setting levels, ordering, and avoiding common mistakes.

Challenge: Work with Factors and Characters

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