
library(testthat)

source("user_code.R")
test_that("returns a list with indices and values elements", {
  result <- find_sales_outliers(c(1,2,3,100))
  expect_true(is.list(result), info = "Result should be a list.")
  expect_true("indices" %in% names(result), info = "List should contain 'indices' element.")
  expect_true("values" %in% names(result), info = "List should contain 'values' element.")
})

test_that("correctly identifies outliers above and below bounds", {
  sales <- c(10, 12, 13, 14, 15, 16, 17, 18, 100, 2)
  result <- find_sales_outliers(sales)
  expect_equal(sort(result$indices), sort(c(9,10)), info = "Indices of outliers should be 9 and 10.")
  expect_equal(sort(result$values), sort(c(100, 2)), info = "Values of outliers should be 100 and 2.")
})

test_that("handles vectors with no outliers", {
  sales <- c(10, 12, 14, 16, 18)
  result <- find_sales_outliers(sales)
  expect_equal(length(result$indices), 0, info = "Should return empty indices when no outliers.")
  expect_equal(length(result$values), 0, info = "Should return empty values when no outliers.")
})

test_that("works with outliers at the beginning and end of the vector", {
  sales <- c(100, 11, 12, 13, 14, 15, 16, 17, 18, 200)
  result <- find_sales_outliers(sales)
  expect_true(1 %in% result$indices && 10 %in% result$indices, info = "Should detect outliers at position 1 and 10.")
  expect_true(100 %in% result$values && 200 %in% result$values, info = "Should detect outlier values 100 and 200.")
})

# test_that("accurately detects multiple outliers in the same vector", {
#   sales <- c(5, 7, 8, 9, 10, 100, 101, 102)
#   result <- find_sales_outliers(sales)
#   expect_true(all(c(6,7,8) %in% result$indices), info = "Should detect all upper outliers.")
#   expect_true(100 %in% result$values && 101 %in% result$values && 102 %in% result$values, info = "Should detect all upper outlier values.")
# })

test_main.R

Master the essentials of handling text, dates, and files in R. This course guides you through string manipulation, regular expressions, date-time operations, and file I/O, with engaging explanations and practical challenges for beginners.

Learn to work with text data in R, from basic string operations to cleaning messy data using regular expressions.

Master handling dates and times in R, from parsing and formatting to performing calculations and managing time zones.

Explore the foundations of data cleaning and quality assurance in R. Each chapter introduces a core concept, followed by a hands-on challenge to reinforce your learning.

Challenge: Find Outliers in Sales Data

Solution

Challenge: Find Outliers in Sales Data

Solution