tests/testthat/test-gd_clean_data.R

In PIP-Technical-Team/wbpip: What the Package Does (One Line, Title Case)

# library(data.table)
# library(tibble)

# Load example data
data("gd_GHI_2009_income")

# Re-create data from Datt 1998 and Group 5 in Povcalnet website.
# dt_datt <- tibble::tribble(
#   ~W,    ~X,      ~P,      ~L,
#   0.92,   24.84,  0.0092, 0.00208,
#   2.47,    35.8,  0.0339, 0.01013,
#   5.11,   45.36,   0.085, 0.03122,
#   7.9,    55.1,   0.164, 0.07083,
#   9.69,   64.92,  0.2609, 0.12808,
#   15.24,   77.08,  0.4133, 0.23498,
#   13.64,   91.75,  0.5497, 0.34887,
#   16.99,  110.64,  0.7196, 0.51994,
#   10,   134.9,  0.8196,  0.6427,
#   9.78,  167.76,  0.9174, 0.79201,
#   3.96,  215.48,   0.957, 0.86966,
#   1.81,  261.66,  0.9751, 0.91277,
#   2.49,  384.97,       1,       1
# )
# data.table::setDT(dt_datt)

dt_datt <- data.table::data.table(
  W = c(0.92, 2.47, 5.11, 7.9, 9.69, 15.24, 13.64, 16.99, 10, 9.78, 3.96, 1.81, 2.49),
  X = c(24.84, 35.8, 45.36, 55.1, 64.92, 77.08, 91.75, 110.64, 134.9, 167.76, 215.48, 261.66, 384.97),
  P = c(0.0092, 0.0339, 0.085, 0.164, 0.2609, 0.4133, 0.5497, 0.7196, 0.8196, 0.9174, 0.957, 0.9751, 1),
  L = c(0.00208, 0.01013, 0.03122, 0.07083, 0.12808, 0.23498, 0.34887, 0.51994, 0.6427, 0.79201, 0.86966, 0.91277, 1)
)

dt_datt[, R := (W * X) / sum(W * X)]

test_that("gd_clean_data() is working correctly", {

  # Test for correct output class
  res <- gd_clean_data(
    gd_GHI_2009_income,
    welfare = "welfare",
    population = "weight",
    gd_type = 5,
    quiet = TRUE
  )
  expect_identical(class(res), c("data.table", "data.frame"))

  # Test against example dataset (type5)
  res <- gd_clean_data(
    gd_GHI_2009_income,
    welfare = "welfare",
    population = "weight",
    gd_type = 5,
    quiet = TRUE
  )
  expect_identical(class(res), c("data.table", "data.frame"))
  expect_identical(names(res), names(gd_GHI_2009_income))
  expect_equal(res$weight, seq(0.1, 1, .1))
  expect_equal(
    res$welfare,
    c(
      0.02292242, 0.05774930, 0.10232770, 0.15716421, 0.22252476,
      0.29363491, 0.38883260, 0.50710468, 0.66958790, 1.00000000
    )
  )

  # Test for type 1 production data example
  welfare <- c(
    0.305, 0.732, 1.226, 1.77, 2.36, 2.993, 3.667, 4.379, 5.129,
    5.914, 10.369, 15.752, 22.148, 29.705, 38.748, 49.967, 64.962, 100
  )
  weight <- c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 30, 40, 50, 60, 70, 80, 90, 100)
  df <- data.frame(welfare = welfare, weight = weight)
  out <- gd_clean_data(df,
    welfare = "welfare",
    population = "weight",
    gd_type = 1,
    quiet = TRUE
  )
  expect_equal(out$weight, c(seq(.02, .18, .02), seq(.2, 1, .1)))
  expect_equal(
    out$welfare,
    c(
      0.00305, 0.00732, 0.01226, 0.01770, 0.02360,
      0.02993, 0.03667, 0.04379, 0.05129, 0.05914,
      0.10369, 0.15752, 0.22148, 0.29705, 0.38748,
      0.49967, 0.64962, 1.00000
    )
  )

  # Test for type 2 production data example
  welfare <- c(1.2, 2, 2.7, 3.6, 5, 6, 8.6, 11.4, 15.9, 43.7)
  weight <- rep(10, 10)
  df <- data.frame(welfare = welfare, weight = weight)
  out <- gd_clean_data(df,
    welfare = "welfare",
    population = "weight",
    gd_type = 2,
    quiet = TRUE
  )
  expect_equal(out$weight, seq(.1, 1, .1))
  expect_equal(out$welfare,
    c(
      0.01198801, 0.03196803, 0.05894106, 0.09490509, 0.14485514,
      0.20479520, 0.29070929, 0.40459540, 0.56343656, 1.00000000
    ),
    tolerance = .5e-7
  )

  # Data type must be of 1, 2 or 5
  expect_error(gd_clean_data(
    gd_GHI_2009_income,
    welfare = "welfare",
    population = "weight",
    gd_type = 3,
    quiet = TRUE
  ))

  # Data can't contain NA's
  welfare <- c(gd_GHI_2009_income$welfare[1:9], NA)
  df <- data.frame(
    welfare = welfare,
    population = rep(10, 10)
  )
  expect_error(gd_clean_data(
    df,
    welfare = "welfare",
    population = "population",
    gd_type = 5,
    quiet = TRUE
  ))

  # Test that output messages are
  # suppressed with silent = TRUE
  expect_silent(gd_clean_data(
    gd_GHI_2009_income,
    welfare = "welfare",
    population = "weight",
    gd_type = 5,
    quiet = TRUE
  ))
})

test_that("gd_clean_data() standardizes correctly for Datt 1998 example", {
  cols <- c("population", "welfare")

  dt1 <- gd_clean_data(dt_datt,
    welfare = "L",
    population = "P",
    gd_type = 1,
    quiet = TRUE
  )
  dt1 <- dt1[, c("P", "L")]
  names(dt1) <- cols

  dt2 <- gd_clean_data(dt_datt,
    welfare = "R",
    population = "W",
    gd_type = 2,
    quiet = TRUE
  )
  dt2 <- dt2[, c("W", "R")]
  names(dt2) <- cols

  dt5 <- gd_clean_data(dt_datt,
    welfare = "X",
    population = "W",
    gd_type = 5,
    quiet = TRUE
  )
  dt5 <- dt5[, c("W", "X")]
  names(dt5) <- cols

  dt1a <- dt1[, lapply(.SD, round, digits = 4),
    .SDcols = cols
  ]
  dt2a <- dt2[, lapply(.SD, round, digits = 4),
    .SDcols = cols
  ]
  dt5a <- dt5[, lapply(.SD, round, digits = 4),
    .SDcols = cols
  ]

  expect_equal(dt2, dt5)
  expect_equal(dt1a, dt2a)
  expect_equal(dt1a, dt5a)
})


test_that("gd_standardize_type1() is working correctly", {
  welfare <- c(
    0.305, 0.732, 1.226, 1.77, 2.36, 2.993, 3.667, 4.379, 5.129,
    5.914, 10.369, 15.752, 22.148, 29.705, 38.748, 49.967, 64.962, 100
  )
  weight <- c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 30, 40, 50, 60, 70, 80, 90, 100)
  res <- gd_standardize_type1(
    welfare = welfare, population = weight
  )
  expect_equal(res$population, c(seq(.02, .18, .02), seq(.2, 1, .1)))
  expect_equal(res$welfare,
    c(
      0.00305, 0.00732, 0.01226, 0.01770, 0.02360,
      0.02993, 0.03667, 0.04379, 0.05129, 0.05914,
      0.10369, 0.15752, 0.22148, 0.29705, 0.38748,
      0.49967, 0.64962, 1.00000
    ),
    tolerance = .5e-7
  )
})

test_that("gd_standardize_type2() is working correctly", {
  welfare <- c(1.2, 2, 2.7, 3.6, 5, 6, 8.6, 11.4, 15.9, 43.7)
  weight <- rep(10, 10)
  res <- gd_standardize_type2(
    welfare = welfare, population = weight
  )
  expect_equal(res$population, seq(.1, 1, .1))
  expect_equal(res$welfare,
    c(
      0.01198801, 0.03196803, 0.05894106, 0.09490509, 0.14485514,
      0.20479520, 0.29070929, 0.40459540, 0.56343656, 1.00000000
    ),
    tolerance = .5e-7
  )
})

test_that("standardize_type5() is working correctly", {
  welfare <- c(337, 518, 630, 736, 845, 980, 1142, 1344, 1645, 2387)
  weight <- rep(10, 10)
  res <- gd_standardize_type5(
    welfare = welfare, population = weight
  )
  expect_equal(res$population, seq(.1, 1, .1))
  expect_equal(res$welfare,
    c(
      0.03190080, 0.08093525, 0.14057175, 0.21024233, 0.29023097,
      0.38299886, 0.49110186, 0.61832639, 0.77404392, 1.00000000
    ),
    tolerance = .5e-7
  )
})