library(testthat)

source("user_code.R")

test_that("returns correct outlier indices using IQR method", {
  daily_steps <- c(
    4000, 5000, 6000, 7000, 8000, 15000,
    4000, 4200, 4300, 12000, 4500, 4700,
    6500, 70000
  )

  result <- detect_outliers(daily_steps)

  # Compute expected indices using same logic
  q1 <- as.numeric(quantile(daily_steps, 0.25))
  q3 <- as.numeric(quantile(daily_steps, 0.75))
  iqr <- q3 - q1
  lower_bound <- q1 - 1.5 * iqr
  upper_bound <- q3 + 1.5 * iqr

  expected_indices <- which(daily_steps < lower_bound | daily_steps > upper_bound)

  expect_equal(
    sort(result),
    sort(expected_indices),
    info = "Returned outlier indices are incorrect."
  )
})

# test_that("returned value is a numeric vector of indices", {
#  steps <- c(1, 2, 3, 4, 100)
#  result <- detect_outliers(steps)

#  expect_true(is.numeric(result),
 #             info = "Function should return numeric indices.")
#  expect_true(all(result %% 1 == 0),
#              info = "Returned indices must be integers.")
#})

test_that("handles case with no outliers", {
  steps <- c(4000, 4200, 4500, 4700, 4800)
  result <- detect_outliers(steps)

  expect_equal(
    length(result),
    0,
    info = "Function should return empty vector when no outliers exist."
  )
})

test_that("function prints output without error", {
  steps <- c(10, 20, 30, 1000)
  expect_silent(capture.output(detect_outliers(steps)))
})


test_main.R

Master the essentials of data visualization and reporting in R. Learn to create compelling graphics, perform exploratory data analysis, and communicate insights effectively using R's powerful tools.

Learn the basics of data visualization in R, focusing on the ggplot2 package and its core plotting capabilities.

Dive into EDA techniques in R, including summary statistics, visual inspection, and outlier detection.

Learn how to turn your visualizations and analyses into clear, actionable reports.

Challenge: Detect and Interpret Outliers

Ratkaisu

Challenge: Detect and Interpret Outliers

Ratkaisu