
library(testthat)

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

source("user_code.R")
test_that("returns one row per unique group", {
  df <- tibble(
    group = c("X", "Y", "X", "Z", "Y", "Z", "X"),
    val = c(1, 2, 3, 4, 5, 6, 7)
  )
  result <- group_and_mean(df, "group", "val")
  expect_s3_class(result, "tbl_df")
  expect_equal(nrow(result), 3, info = "Should return one row per unique group value.")
})

test_that("output has mean_value column with correct means", {
  df <- tibble(
    group = c("A", "A", "B", "B", "C"),
    val = c(10, 20, 30, 40, 50)
  )
  result <- group_and_mean(df, "group", "val")
  expect_true("mean_value" %in% colnames(result), info = "Result should have a mean_value column.")
  expected <- c(
    mean(c(10, 20)),
    mean(c(30, 40)),
    mean(50)
  )
  expect_equal(sort(result$mean_value), sort(expected), tolerance=1e-8, info = "mean_value column should have correct means for each group.")
})

test_that("handles more than two groups", {
  df <- tibble(
    group = c("A", "B", "C", "D", "E"),
    val = c(1, 2, 3, 4, 5)
  )
  result <- group_and_mean(df, "group", "val")
  expect_equal(nrow(result), 5, info = "Should handle tibbles with more than two groups.")
  expect_equal(sort(result$mean_value), 1:5, info = "Mean values should match input for single-row groups.")
})

test_that("ignores NA values when calculating mean", {
  df <- tibble(
    group = c("A", "A", "B", "B"),
    val = c(NA, 10, 20, NA)
  )
  result <- group_and_mean(df, "group", "val")
  a_mean <- result$mean_value[result[[1]] == "A"]
  b_mean <- result$mean_value[result[[1]] == "B"]
  expect_equal(a_mean, 10, info = "Mean for group A should ignore NA and equal 10.")
  expect_equal(b_mean, 20, info = "Mean for group B should ignore NA and equal 20.")
})

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: Grouped Data

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