
library(testthat)

source("user_code.R")
library(testthat)

source("user_code.R")
test_that("data frame has correct column names", {
  student_names <- c("Alice", "Bob", "Charlie", "Diana")
  student_ages <- c(20, 21, 19, 22)
  student_scores <- c(88, 92, 85, 90)
  roster <- build_class_roster(student_names, student_ages, student_scores)
  expect_true(all(c("Name", "Age", "Score") %in% colnames(roster)),
              info = "Data frame must have columns 'Name', 'Age', and 'Score'")
})

test_that("data frame contains correct values", {
  student_names <- c("Alice", "Bob", "Charlie", "Diana")
  student_ages <- c(20, 21, 19, 22)
  student_scores <- c(88, 92, 85, 90)
  roster <- build_class_roster(student_names, student_ages, student_scores)
  expect_equal(as.character(roster$Name), student_names,
               info = "Student names should match input vector")
  expect_equal(as.integer(roster$Age), student_ages,
               info = "Student ages should match input vector")
  expect_equal(as.integer(roster$Score), student_scores,
               info = "Student scores should match input vector")
})

test_that("function returns the created data frame", {
  student_names <- c("Alice", "Bob", "Charlie", "Diana")
  student_ages <- c(20, 21, 19, 22)
  student_scores <- c(88, 92, 85, 90)
  roster <- build_class_roster(student_names, student_ages, student_scores)
  expect_true(is.data.frame(roster), info = "Function must return a 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: Build a Data Frame

Oplossing

Challenge: Build a Data Frame

Oplossing