
library(testthat)

source("user_code.R")
test_that("returns tibble with only Name and Score columns", {
  names_vec <- c("Alice", "Bob", "Charlie", "Dana")
  ages_vec <- c(25, 30, 22, 28)
  scores_vec <- c(90, 75, 85, 60)
  out <- create_and_filter_tibble(names_vec, ages_vec, scores_vec)
  expect_true(is_tibble(out) || (inherits(out, "tbl_df") && inherits(out, "tbl")), "Result is not a tibble.")
  expect_equal(colnames(out), c("Name", "Score"), info = "Columns are not exactly 'Name' and 'Score'.")
})

test_that("filters out rows with Score <= 80", {
  names_vec <- c("Alice", "Bob", "Charlie", "Dana")
  ages_vec <- c(25, 30, 22, 28)
  scores_vec <- c(90, 75, 85, 60)
  out <- create_and_filter_tibble(names_vec, ages_vec, scores_vec)
  expect_true(all(out$Score > 80), "Returned tibble contains rows with Score <= 80.")
})

test_that("preserves order of rows from original input", {
  names_vec <- c("Alice", "Bob", "Charlie", "Dana")
  ages_vec <- c(25, 30, 22, 28)
  scores_vec <- c(90, 75, 85, 60)
  out <- create_and_filter_tibble(names_vec, ages_vec, scores_vec)
  expect_equal(as.character(out$Name), c("Alice", "Charlie"), info = "Order of names is not preserved.")
  expect_equal(out$Score, c(90, 85), info = "Order of scores is not preserved.")
})

test_that("returns empty tibble with correct columns when no scores above 80", {
  names_vec <- c("Eve", "Frank")
  ages_vec <- c(40, 45)
  scores_vec <- c(50, 80)
  out <- create_and_filter_tibble(names_vec, ages_vec, scores_vec)
  expect_true(is_tibble(out), "Result is not a tibble.")
  expect_equal(colnames(out), c("Name", "Score"), info = "Columns are not exactly 'Name' and 'Score'.")
  expect_equal(nrow(out), 0, info = "Tibble should be empty when no scores are above 80.")
})

test_main.R

Master the essential R data structures used in exploratory data analysis (EDA), focusing on practical applications and hands-on challenges. This course is designed for learners already familiar with R syntax, core data structures, and data cleaning/wrangling techniques.

Explore the essential R data structures that power effective exploratory data analysis, with a focus on practical usage and hands-on challenges.

Challenge: Tibbles

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