
library(testthat)

source("user_code.R")
test_that("data frame has correct columns", {
  expect_true(exists("updated_students"), info = "'updated_students' does not exist.")
  expect_true(is.data.frame(updated_students), info = "'updated_students' is not a data frame.")
  expected_cols <- c("name", "grade", "attendance")
  expect_equal(colnames(updated_students), expected_cols, info = "Column names are not correct.")
})

test_that("age column has been removed", {
  expect_false("age" %in% colnames(updated_students), info = "'age' column should not be present.")
})

test_that("attendance column has correct values", {
  expect_true("attendance" %in% colnames(updated_students), info = "'attendance' column missing.")
  expect_equal(updated_students$attendance, c(90, 95, 88, 98), info = "'attendance' values are incorrect.")
})

test_that("Diana row is added correctly", {
  expect_true("Diana" %in% updated_students$name, info = "Diana is not present in the data frame.")
  diana_row <- updated_students[updated_students$name == "Diana", ]
  expect_equal(diana_row$grade, 87, info = "Diana's grade is incorrect.")
  expect_equal(diana_row$attendance, 98, info = "Diana's attendance is incorrect.")
})

test_that("original students' data remains correct", {
  expect_equal(as.character(updated_students$name[1:3]), c("Alice", "Bob", "Charlie"), info = "Original names are incorrect.")
  expect_equal(updated_students$grade[1:3], c(85, 92, 78), info = "Original grades are incorrect.")
  expect_equal(updated_students$attendance[1:3], c(90, 95, 88), info = "Original attendance values are incorrect for first three students.")
})

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: Modify Data Frames

Solution

Challenge: Modify Data Frames

Solution