
library(testthat)

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

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

source("user_code.R")
test_that("student_scores has correct structure", {
  expect_true(exists("student_scores"))
  expect_s3_class(student_scores, "data.frame")
  expect_equal(names(student_scores), c("id", "name", "math_2021", "math_2022", "science_2021", "science_2022"))
  expect_equal(student_scores$id, c(1,2,3))
  expect_equal(student_scores$name, c("Alice", "Bob", "Charlie"))
  expect_equal(student_scores$math_2021, c(88,92,79))
  expect_equal(student_scores$math_2022, c(91,89,85))
  expect_equal(student_scores$science_2021, c(84,90,82))
  expect_equal(student_scores$science_2022, c(90,93,88))
})

test_that("scores_long exists and has correct columns", {
  expect_true(exists("scores_long"))
  expect_s3_class(scores_long, "data.frame")
  expect_true(all(c("id", "name", "subject_year", "score") %in% names(scores_long)))
  expect_equal(nrow(scores_long), 12)
  expect_equal(length(unique(scores_long$subject_year)), 4)
  expect_true(all(c("math_2021", "math_2022", "science_2021", "science_2022") %in% scores_long$subject_year))
  # Check that scores for Alice are correct
  alice_scores <- scores_long[scores_long$name == "Alice",]
  expect_equal(alice_scores$score[alice_scores$subject_year == "math_2021"], 88)
  expect_equal(alice_scores$score[alice_scores$subject_year == "math_2022"], 91)
  expect_equal(alice_scores$score[alice_scores$subject_year == "science_2021"], 84)
  expect_equal(alice_scores$score[alice_scores$subject_year == "science_2022"], 90)
})

test_that("tidy_scores exists and has correct columns and values", {
  expect_true(exists("tidy_scores"))
  expect_s3_class(tidy_scores, "data.frame")
  expect_true(all(c("id", "name", "subject", "year", "score") %in% names(tidy_scores)))
  expect_equal(nrow(tidy_scores), 12)
  expect_equal(sort(unique(tidy_scores$subject)), c("math", "science"))
  expect_equal(sort(unique(tidy_scores$year)), c("2021", "2022"))
  # Check that Alice's math 2021 score is correct
  alice_math2021 <- tidy_scores[tidy_scores$name == "Alice" & tidy_scores$subject == "math" & tidy_scores$year == "2021",]
  expect_equal(alice_math2021$score, 88)
  # Check that Charlie's science 2022 score is correct
  charlie_science2022 <- tidy_scores[tidy_scores$name == "Charlie" & tidy_scores$subject == "science" & tidy_scores$year == "2022",]
  expect_equal(charlie_science2022$score, 88)
})

test_main.R

This practical course introduces learners to essential data cleaning and wrangling techniques in R. Through hands-on exercises, students will learn how to handle missing data, transform and recode variables, filter and select relevant data, and prepare datasets for analysis. The course emphasizes using popular R packages such as dplyr and tidyr to streamline and automate data preparation workflows, equipping learners with the foundational skills needed for effective data analysis.

Master the core techniques for cleaning and wrangling data in R using dplyr and tidyr. Each concept is introduced in a theory chapter, followed by a practical challenge.

Challenge: Tidying Data with tidyr

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