tests/testthat/test-calc_addVariable.R

In pik-piam/quitte: Bits and pieces of code to use with quitte-style data frames

context('calc_addVariable()')

library(tidyverse)

data <- inline.data.frame(c(
  "model;    scenario;   region;   variable;     unit;                 period;   value",
  "REMIND;   Baseline;   USA;      GDP|MER;      billion US$2005/yr;   2010;     12990",
  "REMIND;   Baseline;   USA;      Population;   million;              2010;       310.4",
  "REMIND;   Baseline;   USA;      PE;           EJ/yr;                2010;        91.62",
  "REMIND;   Baseline;   CHN;      GDP|MER;      billion US$2005/yr;   2020;      8882",
  "REMIND;   Baseline;   CHN;      GDP|MER;      billion US$2005/yr;   2010;      4119",
  "REMIND;   Baseline;   CHN;      Population;   million;              2020;      1387",
  "REMIND;   Baseline;   CHN;      Population;   million;              2010;      1349"))

test_that(
  'calc_addVariable() results',
  {
    result <- tibble(
      model    = 'REMIND',
      scenario = 'Baseline',
      region   = rep(c('USA', 'CHN', 'CHN'), 5)[1:13],
      variable = c('PE', rep(c('GDP|MER', 'Population', 'GDPpC', 'ln GDPpC'),
                             each = 3)),
      unit     = c('EJ/yr', rep(c('billion US$2005/yr', 'million',
                                  'US$2005/cap', NA), each = 3)),
      period   = rep(c(2010L, 2010L, 2020L), 5)[1:13],
      result = c(91.62,
                 4119, 8882, 12990,
                 1349, 1387, 310.4,
                 3053.3728687917, 6403.74909877433, 41849.2268041237,
                 8.02400211721046, 8.76463889451935, 10.6418286003377))

    expect_equal(
      object = full_join(
        calc_addVariable(data, "GDPpC" = "`GDP|MER` / Population * 1e3",
                         "`ln GDPpC`" = "log(GDPpC)",
                         units = c("US$2005/cap", NA)),
        result,
        c('model', 'scenario', 'region', 'variable', 'unit', 'period')
      ) %>%
        filter(1e-14 < 1 - result / value),
      expected = data %>%
        mutate(result = 0) %>%
        filter('REMIND' != model)
    )

    expect_equal(
      object = full_join(
        calc_addVariable_(
          data,
          list("`GDPpC`" = c("`GDP|MER` / `Population` * 1e3", "US$2005/cap"),
               "`ln GDPpC`" = "log(`GDPpC`)")),
        result,
        c('model', 'scenario', 'region', 'variable', 'unit', 'period')
      ) %>%
        filter(1e-14 < 1 - result / value),
      expected = data %>%
        mutate(result = 0) %>%
        filter('REMIND' != model)
    )

    x <- tibble(scenario = c('A', 'A', 'B'),
                variable = c('X', 'Y', 'X'),
                unit     = 'U',
                period   = 2020,
                value    = c(1, 2, 4))
    expect_equal(
      object = x %>%
        calc_addVariable('Z' = 'X + Y', units = 'U', completeMissing = TRUE),
      expected = bind_rows(
        x,
        tibble(scenario = c('A', 'B'), variable = 'Z', unit = 'U', period = 2020,
               value = c(3, 4)))
    )
  })

test_that(
  'calc_addVariable() unit column substitution',
  {
    expect_equal(
      object = data %>%
        rename(foo = unit) %>%
        calc_addVariable("GDPpC" = "`GDP|MER` / Population * 1e3",
                         "`ln GDPpC`" = "log(GDPpC)",
                         units = c("US$2005/cap", NA),
                         unit = foo) %>%
        rename(unit = foo),
      expected = data %>%
        calc_addVariable("GDPpC" = "`GDP|MER` / Population * 1e3",
                         "`ln GDPpC`" = "log(GDPpC)",
                         units = c("US$2005/cap", NA))
    )
  })

test_that(
  'calc_addVariable() testing for duplicated data',
  {
    test_data <- quitte_example_data %>%
      filter(first(.data$model)    == .data$model,
             first(.data$scenario) == .data$scenario,
             first(.data$region)   == .data$region,
             first(.data$period)   == .data$period,
             .data$variable %in% c('PE', 'Population')) %>%
      `[`(c(1, 2, 2),)

    expect_error(
      object = calc_addVariable_(
        data = test_data,
        .dots = list('PE per capita' = c('PE / Population', unit = 'TJ/yr'))),
      regexp = 'Duplicate rows in data.frame.*')
  })

test_that(
  'name of new variable can be existing variable name',
  {
    expect_equal(
      object = data %>%
        calc_addVariable('`PE`' = '`PE` * 8760 * 3600 * 1e-9',
                         units = 'TWa') %>%
        arrange(!!!syms(colnames(data))),
      expected = data %>%
        mutate(value = ifelse('PE' == variable,
                              value * 8760 * 3600 * 1e-9,
                              value),
               unit = ifelse('PE' == variable,
                             'TWa',
                             unit)) %>%
        arrange(!!!syms(colnames(data)))
    )}
)

test_that(
  'missing unit columns work',
  {
    expect_equal(
      object = data %>%
        select(-unit) %>%
        calc_addVariable("GDPpC" = "`GDP|MER` / Population * 1e3"),
      expected = data %>%
        calc_addVariable("GDPpC" = "`GDP|MER` / Population * 1e3") %>%
        select(-unit)
    )}
)

test_that(
  'completly missing variables yield an error',
  {
    test_data <- tibble(variable = 'A', value = 1)

    expect_error(
      object = calc_addVariable(test_data, 'C' = 'A + B'),
      regexp = '1 variable is missing for the calculation of `C`:\n   `B`')

    expect_error(
      object = calc_addVariable(test_data, 'C' = 'A + B',
                                skip.missing.rhs = FALSE),
      regexp = '1 variable is missing for the calculation of `C`:\n   `B`')

    expect_warning(
      object = calc_addVariable(test_data, 'C' = 'A + B',
                                skip.missing.rhs = TRUE),
      regexp = '1 variable is missing for the calculation of `C`:\n   `B`')

    expect_identical(
      object = calc_addVariable(test_data, 'C' = 'A + B',
                                skip.missing.rhs = 'silent'),
      expected = test_data)
  })

test_that(
  'calc_addVariable() preserves column classes',
  {
    expect_equal(
      object = data %>%
        mutate(across(c('scenario', 'variable', 'unit'), factor)) %>%
        calc_addVariable('GDPpC' = '`GDP|MER` / Population * 1e3',
                         '`ln GDPpC`' = 'log(GDPpC)',
                         units = c('US$2005/cap', NA)),
      expected = data %>%
        calc_addVariable('GDPpC' = '`GDP|MER` / Population * 1e3',
                         '`ln GDPpC`' = 'log(GDPpC)',
                         units = c('US$2005/cap', NA)) %>%
        mutate(across(c('scenario', 'variable', 'unit'), factor))
    )
  })