
library(testthat)

source("user_code.R")
test_that("Function returns numeric vector with correct values", {
  ratings <- factor(c("Good", "Poor", "Excellent", "Fair", "Good", "Excellent"))
  result <- convert_and_reorder_ratings(ratings)
  expect_true(is.numeric(result), info = "Returned value should be numeric.")
  expect_equal(result, c(3,1,4,2,3,4),
    info = "Numeric values should correspond to ordered levels: Poor=1, Fair=2, Good=3, Excellent=4.")
})

test_that("Levels are set so that Poor=1, Fair=2, Good=3, Excellent=4", {
  ratings <- factor(c("Poor", "Fair", "Good", "Excellent"))
  result <- convert_and_reorder_ratings(ratings)
  expect_equal(result, 1:4,
    info = "Returned values should be 1, 2, 3, 4 for Poor, Fair, Good, Excellent.")
})

test_that("Handles input where levels are not initially in order", {
  ratings <- factor(c("Excellent", "Good", "Poor", "Fair"), levels = c("Excellent", "Good", "Poor", "Fair"))
  result <- convert_and_reorder_ratings(ratings)
  expect_equal(result, c(4,3,1,2),
    info = "Function should correctly reorder and assign numeric values regardless of initial levels.")
})

test_that("Handles repeated values and all possible rating levels", {
  ratings <- factor(c("Poor", "Poor", "Fair", "Good", "Good", "Excellent", "Fair", "Excellent"))
  result <- convert_and_reorder_ratings(ratings)
  expect_equal(result, c(1,1,2,3,3,4,2,4),
    info = "Function should correctly handle repeated values and all possible levels.")
})

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: Avoid Factor Pitfalls

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Challenge: Avoid Factor Pitfalls

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