
library(testthat)

source("user_code.R")
test_that("returns only passed students", {
  students_df <- data.frame(
    name = c("Alice", "Bob", "Charlie", "David", "Eva"),
    score = c(85, 58, 92, 73, 66),
    passed = c(TRUE, FALSE, TRUE, TRUE, FALSE)
  )
  result <- filter_and_sort_passed_students(students_df)
  expect_true(all(result$passed == TRUE),
    info = "Result should only contain students who passed.")
  expect_equal(nrow(result), 3,
    info = "Result should have 3 rows for passed students.")
})

test_that("sorts by score descending", {
  students_df <- data.frame(
    name = c("Alice", "Bob", "Charlie", "David", "Eva"),
    score = c(85, 58, 92, 73, 66),
    passed = c(TRUE, FALSE, TRUE, TRUE, FALSE)
  )
  result <- filter_and_sort_passed_students(students_df)
  expect_equal(result$score, sort(result$score, decreasing = TRUE),
    info = "Scores should be sorted in descending order.")
})

test_that("preserves all columns", {
  students_df <- data.frame(
    name = c("Alice", "Bob", "Charlie", "David", "Eva"),
    score = c(85, 58, 92, 73, 66),
    passed = c(TRUE, FALSE, TRUE, TRUE, FALSE)
  )
  result <- filter_and_sort_passed_students(students_df)
  expect_equal(names(result), names(students_df),
    info = "All columns should be preserved.")
})

test_that("returns empty data frame if no students passed", {
  students_df <- data.frame(
    name = c("Alice", "Bob"),
    score = c(10, 20),
    passed = c(FALSE, FALSE)
  )
  result <- filter_and_sort_passed_students(students_df)
  expect_equal(nrow(result), 0,
    info = "Should return 0 rows if no students passed.")
  expect_equal(names(result), names(students_df),
    info = "Column names should match input data frame.")
})

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: Filter and Sort Data

Ratkaisu

Challenge: Filter and Sort Data

Ratkaisu