
library(testthat)

source("user_code.R")
test_that("returns a data frame with at least two index columns and one aggregated numeric column", {
  result <- create_hierarchical_df()
  expect_true(is.data.frame(result), "Returned object should be a data frame")
  expect_true(all(c("Region", "City", "Sales") %in% colnames(result)), "Data frame should have columns 'Region', 'City', and 'Sales'")
})

test_that("aggregates the numeric column by both index levels", {
  result <- create_hierarchical_df()
  expect_true(nrow(result) >= 4, "Data frame should have at least 4 rows for grouped combinations")
  expect_equal(length(unique(result$Region)) >= 2, TRUE, "There should be at least two unique regions")
  expect_equal(length(unique(result$City)) >= 2, TRUE, "There should be at least two unique cities")
})

test_that("returns the grouped data frame as output", {
  result <- create_hierarchical_df()
  expect_true(is.data.frame(result), "Function should return a data frame")
})

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: Hierarchical Indexing

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