
library(testthat)

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

source("user_code.R")
test_that("returned list contains long_df and summary_df data frames", {
  result <- reshape_and_summarize(data)
  expect_true(is.list(result), "Result should be a list.")
  expect_true("long_df" %in% names(result), "List should have an element named 'long_df'.")
  expect_true("summary_df" %in% names(result), "List should have an element named 'summary_df'.")
  expect_true(is.data.frame(result$long_df), "long_df should be a data frame.")
  expect_true(is.data.frame(result$summary_df), "summary_df should be a data frame.")
})

test_that("long_df has a row for each subject-variable combination", {
  result <- reshape_and_summarize(data)
  long_df <- result$long_df
  expect_equal(nrow(long_df), 9, info = "long_df should have 9 rows (3 subjects x 3 variables).")
  expect_true(all(c("Subject", "variable", "value") %in% colnames(long_df)), "long_df must have columns 'Subject', 'variable', 'value'.")
})

test_that("summary_df has correct mean values per variable", {
  result <- reshape_and_summarize(data)
  summary_df <- result$summary_df
  expect_equal(nrow(summary_df), 3, info = "summary_df should have one row per variable (3 variables).")
  expect_true(all(c("variable", "value") %in% colnames(summary_df)), "summary_df must have columns 'variable' and 'value'.")
  means <- c(Math = mean(c(85,90,78)), English = mean(c(88,85,92)), Science = mean(c(90,80,85)))
  summary_means <- setNames(summary_df$value, summary_df$variable)
  expect_equal(summary_means["Math"], means["Math"], tolerance = 1e-8, info = "Mean for Math is incorrect.")
  expect_equal(summary_means["English"], means["English"], tolerance = 1e-8, info = "Mean for English is incorrect.")
  expect_equal(summary_means["Science"], means["Science"], tolerance = 1e-8, info = "Mean for Science is incorrect.")
})

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: Reshaping Data

解答