context("na_ma")

test_that("All NA vector throws error", {
  expect_error(na_ma(c(NA, NA, NA, NA, NA)))
})

test_that("Correct results for all options with a modifed tsAirgap dataset (additionalNAs at end)", {
  skip_on_cran()

  # Using mean over resulting vector to check correctness
  # In order to avoid writing down the complete resulting vector
  # Using rounded version in order to avoid writing down all decimals
  x <- tsAirgap
  x[135:144] <- NA
  expect_equal(round(mean(na_ma(x, weighting = "simple", k = 4)), digits = 1), 275.2)
  expect_equal(round(mean(na_ma(x, weighting = "simple", k = 20)), digits = 1), 276.1)
  expect_equal(round(mean(na_ma(x, weighting = "linear", k = 4)), digits = 1), 275.0)
  expect_equal(round(mean(na_ma(x, weighting = "linear", k = 20)), digits = 1), 276.1)
  expect_equal(round(mean(na_ma(x, weighting = "exponential", k = 4)), digits = 1), 274.6)
  expect_equal(round(mean(na_ma(x, weighting = "exponential", k = 20)), digits = 1), 274.7)
})

test_that("Correct results for all options with a modifed tsAirgap dataset (additionalNAs at start)", {
  skip_on_cran()

  # Using mean over resulting vector to check correctness
  # In order to avoid writing down the complete resulting vector
  # Using rounded version in order to avoid writing down all decimals
  x <- tsAirgap
  x[1:5] <- NA
  expect_equal(round(mean(na_ma(x, weighting = "simple", k = 4)), digits = 1), 282.1)
  expect_equal(round(mean(na_ma(x, weighting = "simple", k = 20)), digits = 1), 281.1)
  expect_equal(round(mean(na_ma(x, weighting = "linear", k = 4)), digits = 1), 281.9)
  expect_equal(round(mean(na_ma(x, weighting = "linear", k = 20)), digits = 1), 281.3)
  expect_equal(round(mean(na_ma(x, weighting = "exponential", k = 4)), digits = 1), 281.7)
  expect_equal(round(mean(na_ma(x, weighting = "exponential", k = 20)), digits = 1), 281.6)
})


test_that("Correct results for all options with the tsAirgap dataset", {
  skip_on_cran()

  # Using mean over resulting vector to check correctness
  # In order to avoid writing down the complete resulting vector
  # Using rounded version in order to avoid writing down all decimals
  x <- tsAirgap
  expect_equal(round(mean(na_ma(x, weighting = "simple", k = 4)), digits = 1), 281.5)
  expect_equal(round(mean(na_ma(x, weighting = "simple", k = 20)), digits = 1), 280.7)
  expect_equal(round(mean(na_ma(x, weighting = "linear", k = 4)), digits = 1), 281.3)
  expect_equal(round(mean(na_ma(x, weighting = "linear", k = 20)), digits = 1), 280.9)
  expect_equal(round(mean(na_ma(x, weighting = "exponential", k = 4)), digits = 1), 281.2)
  expect_equal(round(mean(na_ma(x, weighting = "exponential", k = 20)), digits = 1), 281.1)
})

test_that("Imputation works for data.frame", {
  # Checking if NAs remain in data.frame
  x <- data.frame(tsAirgap, tsAirgap, tsAirgapComplete)
  expect_false(anyNA(na_ma(x)))
  expect_false(anyNA(na_ma(x, weighting = "simple", k = 4)))
  expect_false(anyNA(na_ma(x, weighting = "simple", k = 20)))
  expect_false(anyNA(na_ma(x, weighting = "linear", k = 4)))
  expect_false(anyNA(na_ma(x, weighting = "linear", k = 20)))
  expect_false(anyNA(na_ma(x, weighting = "exponential", k = 4)))
  expect_false(anyNA(na_ma(x, weighting = "exponential", k = 20)))
})


test_that("Warning for wrong input for k parameter", {
  expect_error(na_ma(tsAirgap, k = -1))
})

test_that("Warning for wrong input for weighting parameter", {
  expect_error(na_ma(tsAirgap, weighting = "Wrong"))
})


test_that("Test NA at beginning", {
  x <- tsAirgap
  x[1:2] <- NA
  expect_false(anyNA(na_ma(x, k = 4, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "exponential")))
  expect_false(anyNA(na_ma(x)))
})

test_that("Test NA at end", {
  x <- tsAirgap
  x[143:144] <- NA
  expect_false(anyNA(na_ma(x, k = 4, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "exponential")))
  expect_false(anyNA(na_ma(x)))
})

test_that("Multiple NAs in a row", {
  x <- tsAirgap
  x[40:80] <- NA
  expect_false(anyNA(na_ma(x, k = 4, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "exponential")))
  expect_false(anyNA(na_ma(x)))
})

test_that("Over 90% NAs", {
  x <- tsAirgap
  x[10:140] <- NA
  expect_false(anyNA(na_ma(x, k = 4, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "simple")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "linear")))
  expect_false(anyNA(na_ma(x, k = 4, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 1, weighting = "exponential")))
  expect_false(anyNA(na_ma(x, k = 20, weighting = "exponential")))
  expect_false(anyNA(na_ma(x)))
})