
library(testthat)

source("user_code.R")
test_that("simulated_data is a data frame with at least three columns", {
  expect_true(exists("simulated_data"), info = "simulated_data should exist.")
  expect_true(is.data.frame(simulated_data), info = "simulated_data should be a data frame.")
  expect_true(ncol(simulated_data) >= 3, info = "simulated_data should have at least three columns.")
})

test_that("structure_info is assigned from str()", {
  expect_true(exists("structure_info"), info = "structure_info should exist.")
})

test_that("summary_info is assigned from summary()", {
  expect_true(exists("summary_info"), info = "summary_info should exist.")
})

test_that("issue_1 and issue_2 are descriptive strings about data quality", {
  expect_true(exists("issue_1"), info = "issue_1 should exist.")
  expect_true(is.character(issue_1), info = "issue_1 should be a string.")
  expect_true(nchar(issue_1) > 10, info = "issue_1 should be descriptive.")

  expect_true(exists("issue_2"), info = "issue_2 should exist.")
  expect_true(is.character(issue_2), info = "issue_2 should be a string.")
  expect_true(nchar(issue_2) > 10, info = "issue_2 should be descriptive.")
  
  # Check for mention of missing values or NA in at least one issue
  expect_true(any(grepl("NA|missing", c(issue_1, issue_2), ignore.case = TRUE)),
    info = "At least one issue should mention missing values or NA.")
  # Check for mention of unexpected value or 'Unknown' in at least one issue
  expect_true(any(grepl("Unknown|unexpected", c(issue_1, issue_2), ignore.case = TRUE)),
    info = "At least one issue should mention 'Unknown' or unexpected value.")
})

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: Introduction to Data Cleaning and Wrangling

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