library(testthat)
library(dmPack)

# Test for miss_count function
test_that("miss_count returns correct missing value counts", {
  test_data <- data.frame(a = c(1, NA, 3), b = c(4, 5, NA))
  result <- miss_count(test_data)
  expect_equal(result, c(a = 1, b = 1))  # 1 missing value in each column
})

# Test for show_miss function
test_that("show_miss identifies rows with missing data correctly", {
  test_data <- data.frame(id = 1:3, value = c(1, NA, 3))
  result <- show_miss(test_data, value)
  expect_equal(result, test_data[2, ])  # Only row 2 has a missing value in 'value'
})

# Test for show_dup function
test_that("show_dup identifies duplicate values correctly", {
  test_data <- data.frame(id = c(1, 2, 2, 4), value = c(1, 2, 2, 4))
  result <- show_dup(test_data, value)
  expect_equal(result, test_data[2:3, ])  # Rows 2 and 3 are duplicates
})

# Test for show_out function (Basic method)
test_that("show_out identifies outliers based on lower and upper limits", {
  test_data <- data.frame(id = 1:5, value = c(1, 2, 3, 100, -50))
  result <- show_out(test_data, value, lower = 0, upper = 10)
  expect_equal(result, test_data[c(4, 5), ])  # Values 100 and -50 are outliers
})

# Test for show_out_iqr function (IQR method)
test_that("show_out_iqr identifies outliers using IQR method", {
  test_data <- data.frame(id = 1:5, value = c(1, 2, 3, 100, -50))
  result <- show_out_iqr(test_data, value)
  expect_equal(result, test_data[c(4, 5), ])  # 100 and -50 are outliers by IQR
})