tests/testthat/test-tables.R
In MatthewHeun/IEATools: Tools for Working with International Energy Agency Data
test_that("tidy_fu_allocation_table() works", {
fu_table <- load_fu_allocation_data()
years <- year_cols(fu_table, return_names = TRUE)
tidy <- tidy_fu_allocation_table(fu_table)
expect_null(tidy[[IEATools::template_cols$maximum_values]])
for (yr in years) {
expect_null(tidy[[yr]])
}
tidy %>%
dplyr::filter(.data[[IEATools::template_cols$quantity]] %in% c(IEATools::iea_cols$e_dot, IEATools::template_cols$e_dot_perc)) %>%
nrow() %>%
expect_equal(0)
# Test that the original is returned if it is already tidy
tidy2 <- tidy_fu_allocation_table(tidy)
expect_equal(tidy2, tidy)
})
test_that("complete_fu_allocation_table works as expected", {
# In this test, the 2nd exemplar isn't needed, and the code should
# break out of the for loop.
# The strategy here will be to use Ghana's FU allocation table
# with South Africa's as an exemplar.
# We'll remove a row from Ghana's table and make sure South Africa's is inserted.
fu_table <- load_fu_allocation_data()
fu_table_GHA <- fu_table %>%
dplyr::filter(Country == "GHA") %>%
# Delete rows from Ghana's table for Residential consumption of PSBs
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential))
# Verify that those PSB rows have been deleted.
fu_table_GHA %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF")
# Verify that the ZAF table has rows for Residential consumption of PSBs.
fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_gt(0)
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df(apply_fixes = FALSE) %>%
specify_all()
# Now send the data into complete_fu_allocation_table()
completed <- complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$c_source]] == "ZAF") %>%
nrow() %>%
expect_equal(4)
# We should have four Ghana rows for PSBs in Residential
completed %>%
dplyr::filter(Country == "GHA",
.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(4)
# But their source should not be Ghana.
completed %>%
dplyr::filter(.data[[IEATools::template_cols$c_source]] == "GHA",
.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
# Try with a missing allocation table.
expect_error(complete_fu_allocation_table(country_to_complete = "GHA",
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data),
'argument "fu_allocation_table" is missing, with no default')
# Try with missing exemplars
expect_error(complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
tidy_specified_iea_data = tidy_specified_iea_data),
'argument "exemplar_fu_allocation_tables" is missing, with no default')
# Try with missing IEA data
expect_error(complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF)),
'argument "tidy_specified_iea_data" is missing, with no default')
})
test_that("complete_fu_allocation_table() works with a tidy incomplete fu table", {
fu_table <- load_fu_allocation_data()
tidy_fu_table_GHA <- fu_table %>%
dplyr::filter(Country == "GHA") %>%
# Delete rows from Ghana's table for Residential consumption of PSBs
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential)) %>%
# Tidy it
tidy_fu_allocation_table()
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF")
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df(apply_fixes = FALSE) %>%
specify_all()
# Now send the data into complete_fu_allocation_table()
completed <- complete_fu_allocation_table(fu_allocation_table = tidy_fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data)
# Ensure that the correct rows have been picked up for GHA from ZAF.
completed %>%
dplyr::filter(.data[[IEATools::template_cols$c_source]] == "ZAF") %>%
nrow() %>%
expect_equal(4)
})
test_that("complete_fu_allocation_table() works with an empty tidy fu table", {
# We have a bug where an empty (no rows) tidy data frame sent into complete_fu_allocation_table() causes an error.
# This test makes sure that the bug is solved and stays fixed.
fu_table <- load_fu_allocation_data()
tidy_fu_table_GHA <- fu_table %>%
# Keep only GHA rows.
dplyr::filter(Country == "GHA") %>%
# Tidy it
tidy_fu_allocation_table()
# Delete all rows from Ghana's table
empty_fu_table_GHA <- tidy_fu_table_GHA[0, ]
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF")
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df() %>%
specify_all()
# Now send the IEA data and the empty GHA FU allocation data frame data into complete_fu_allocation_table(),
# This call triggers the error we are trying to solve.
# But it causes a different error, as ZAF doesn't have all the rows for GHA.
# Here, we just make sure we see the error we expect.
expect_error(complete_fu_allocation_table(fu_allocation_table = empty_fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data),
"Didn't complete FU Allocation table for the following final energy flows:\nGHA, 1971, Industry, Non-ferrous metals, Electricity;")
})
test_that("complete_fu_allocation_table() works with 2 exemplars", {
# In this test, we set up an allocation table that has two missing pieces.
# The two missing pieces are obtained from 2 exemplars, one missing piece from each.
fu_table <- load_fu_allocation_data()
fu_table_GHA <- fu_table %>%
dplyr::filter(Country == "GHA") %>%
# Delete rows from Ghana's table for Residential consumption of PSBs
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential)) %>%
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use &
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity &
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants))
# Ensure that we removed the correct rows from Ghana.
fu_table_GHA %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
fu_table_GHA %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use &
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity &
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants) %>%
nrow() %>%
expect_equal(0)
# Now make two exemplars from South Africa, one with country ZAF, the other whose country is renamed to "World".
# South Africa has EIOU Electricity consumed by Main activity producer electricity plants but NO Residential PSB consumption.
# World has Residential PSB consumption but NO EIOU Electricity consumed by Main activity producer electricity plants.
# Ghana will pick up EIOU Electricity consumed by Main activity producer electricity plants from South Africa.
# Ghana will pick up Residential PSB consumption from World.
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF") %>%
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential))
# Ensure we deleted correct rows from this table.
fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
# Second exemplar
fu_table_World <- fu_table %>%
dplyr::filter(Country == "ZAF") %>%
dplyr::mutate(
Country = "World"
) %>%
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use &
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity &
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants))
# Ensure we removed the correct rows from the World exemplar.
fu_table_World %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use,
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity,
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants) %>%
nrow() %>%
expect_equal(0)
fu_table_World %>%
magrittr::extract2(IEATools::iea_cols$country) %>%
unique() %>%
expect_equal("World")
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df() %>%
specify_all()
# Now run Ghana through the completion process.
completed <- complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_World),
tidy_specified_iea_data = tidy_specified_iea_data)
# Check that Ghana obtained Residential PSB consumption from World.
completed %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
magrittr::extract2(IEATools::template_cols$c_source) %>%
unique() %>%
expect_equal("World")
# Check that Ghana obtained EIOU Electricity consumed by Main activity producer electricity plants from South Africa.
completed %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use,
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity,
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants) %>%
magrittr::extract2(IEATools::template_cols$c_source) %>%
expect_equal(c("ZAF", "ZAF"))
# Try again without enough information to complete the FU Allocation table.
# Create this situation by dropping the fu_table_World from the list of exemplars.
# In this situation, we cannot allocate the Residential PSB rows for Ghana.
# This attempt should emit a warning.
expect_error(complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
exemplar_fu_allocation_tables = fu_table_ZAF,
tidy_specified_iea_data = tidy_specified_iea_data),
"Didn't complete FU Allocation table for the following final energy flows:\nGHA, 1971, Other, Residential, Primary solid biofuels;\nGHA, 2000, Other, Residential, Primary solid biofuels.\nPlease check the FU allocation table for typos or misspellings.")
})
test_that("fu_allocation_table_completed() works as expected", {
iea_data <- load_tidy_iea_df(apply_fixes = FALSE) %>%
specify_all()
fu_allocations <- load_fu_allocation_data()
expect_true(fu_allocation_table_completed(fu_allocations, iea_data))
# Remove a row from fu_allocations. Now the test for completion should fail.
# In fact, remove a row that has a 1 in it.
expect_false(fu_allocation_table_completed(fu_allocations[-3, ], iea_data))
# Now remove a row that has a fraction.
expect_error(fu_allocation_table_completed(fu_allocations[-8, ], iea_data),
"Not all final energy was allocated to final-to-useful machines to within 1e-09. Problematic final energy flows and their errors are: GHA, 1971, Electricity, Main activity producer electricity plants, -0.5; GHA, 2000, Electricity, Main activity producer electricity plants, -0.5. Please check the FU allocation file for allocations that don't sum to 1.")
# Try with a wide IEAData data frame.
expect_true(fu_allocation_table_completed(fu_allocations, iea_data %>%
tidyr::pivot_wider(names_from = IEATools::iea_cols$year,
values_from = IEATools::iea_cols$e_dot)))
})
test_that("tidy_eta_fu_table() works", {
eta_table <- load_eta_fu_data()
years <- year_cols(eta_table, return_names = TRUE)
tidy <- tidy_eta_fu_table(eta_table)
expect_null(tidy[[IEATools::template_cols$maximum_values]])
for (yr in years) {
expect_null(tidy[[yr]])
}
tidy %>%
dplyr::filter(.data[[IEATools::template_cols$quantity]] %in% c(IEATools::template_cols$e_dot_machine, IEATools::template_cols$e_dot_machine_perc)) %>%
nrow() %>%
expect_equal(0)
# Test that the original is returned if it is already tidy
tidy2 <- tidy_eta_fu_table(tidy)
expect_equal(tidy2, tidy)
})
test_that("complete_eta_fu_table works as expected", {
eta_fu_table <- load_eta_fu_data()
eta_fu_table_GHA <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA")
eta_fu_table_ZAF <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
fu_allocation_table <- load_fu_allocation_data() %>%
tidy_fu_allocation_table()
fu_allocation_table_GHA <- fu_allocation_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA")
fu_allocation_table_ZAF <- fu_allocation_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
# Use ZAF as an exemplar for GHA.
# Check that the machines are present, initially.
eta_fu_table_GHA %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles") %>%
nrow() %>%
expect_equal(4)
eta_fu_table_GHA %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons") %>%
nrow() %>%
expect_equal(4)
# Eliminate 2 machines (Automobiles and Irons) from GHA and see if their efficiencies get picked up from ZAF and World.
eta_fu_table_GHA_incomplete <- eta_fu_table_GHA %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] != "Automobiles",
.data[[IEATools::template_cols$machine]] != "Irons")
# Ensure that we eliminated the desired rows
eta_fu_table_GHA_incomplete %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] %in% c("Automobiles", "Irons")) %>%
nrow() %>%
expect_equal(0)
# Make exemplar tables from ZAF.
# The first exemplar (ZAF) will have Automobiles but not Irons.
exemplar_ZAF <- eta_fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] != "Irons")
# Verify we deleted the right rows.
exemplar_ZAF %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons") %>%
nrow() %>%
expect_equal(0)
# The second exemplar (World) will have Irons, but not Automobiles.
exemplar_World <- eta_fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] != "Automobiles") %>%
dplyr::mutate(
"{IEATools::iea_cols$country}" := "World"
)
# Verify we deleted the right rows.
exemplar_World %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles") %>%
nrow() %>%
expect_equal(0)
# Try to complete with ONLY ZAF.
# Should find that a warning is emitted, because we can't find Irons
expect_error(complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA_incomplete,
exemplar_eta_fu_tables = exemplar_ZAF,
fu_allocation_table = fu_allocation_table_GHA),
"Irons")
# Now call the completion function to pick up Automobiles from ZAF and Irons from World
completed <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA_incomplete,
exemplar_eta_fu_tables = list(exemplar_ZAF, exemplar_World),
fu_allocation_table = fu_allocation_table_GHA)
# Check that we got Automobiles from ZAF
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$eta_fu_source]] == "ZAF") %>%
nrow() %>%
expect_equal(4)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.31)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(1)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(1)
# Check that we got Irons from World
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$eta_fu_source]] == "World") %>%
nrow() %>%
expect_equal(4)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.35)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3698615661)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3698615661)
# Try using two eta_fu_table_ZAF's as the exemplars.
# It should get everything it needs from the first one, thereafter hitting the break statement
completed2 <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA_incomplete,
exemplar_eta_fu_tables = list(eta_fu_table_ZAF, eta_fu_table_ZAF),
fu_allocation_table = fu_allocation_table_GHA,
which_quantity = "eta.fu")
# Check that completed2 has obtained both Automobiles and Irons from ZAF.
completed2 %>%
dplyr::filter(.data[[IEATools::template_cols$eta_fu_source]] == "ZAF",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu) %>%
nrow() %>%
expect_equal(4)
completed2 %>%
dplyr::filter(.data[[IEATools::template_cols$eta_fu_source]] == "ZAF",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu) %>%
magrittr::extract2(IEATools::template_cols$machine) %>%
expect_equal(c("Automobiles", "Automobiles", "Irons", "Irons"))
})
test_that("complete_eta_fu_table() returns an empty data frame when the fu_allocation_table has zero rows", {
eta_fu_table <- load_eta_fu_data()
eta_fu_table_GHA <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA")
eta_fu_table_ZAF <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
fu_allocation_table <- load_fu_allocation_data() %>%
tidy_fu_allocation_table()
fu_allocation_table_GHA <- fu_allocation_table[c(), ]
result <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA,
exemplar_eta_fu_tables = eta_fu_table_ZAF,
fu_allocation_table = fu_allocation_table_GHA)
expect_equal(nrow(result), 0)
expect_true(IEATools::template_cols$eta_fu_source %in% names(result))
})
test_that("eta_fu_table_completed() works as expected", {
fu_allocations <- load_fu_allocation_data()
fu_efficiencies <- load_eta_fu_data()
expect_true(eta_fu_table_completed(fu_efficiencies, fu_allocations))
# Remove a couple rows to get a FALSE return value.
expect_false(eta_fu_table_completed(fu_efficiencies[-3, ], fu_allocations))
# Try with a regular allocations data frame
expect_true(eta_fu_table_completed(fu_efficiencies, fu_allocations))
# Try with a tall efficiencies data frame
expect_true(eta_fu_table_completed(fu_efficiencies %>% tidyr::pivot_longer(cols = year_cols(fu_efficiencies),
names_to = IEATools::iea_cols$year,
values_to = IEATools::template_cols$.values),
fu_allocations))
# Try with a NULL fu_efficiencies
expect_false(eta_fu_table_completed(machines_that_need_quantities = fu_allocations))
expect_true(!is.null(eta_fu_table_completed(machines_that_need_quantities = fu_allocations) %>%
attr("unallocated_rows")))
})
test_that("complete_eta_fu_table() works when we have more allocation data than efficiency data", {
eta_fu_table <- load_eta_fu_data()
eta_fu_table_GHA <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA",
# Eliminate rows so we invoke the exemplar process
.data[[IEATools::template_cols$machine]] != "Irons") %>%
tidy_eta_fu_table() %>%
# Now empty it out
magrittr::extract(c(), )
eta_fu_table_ZAF <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
fu_allocation_table <- load_fu_allocation_data() %>%
tidy_fu_allocation_table()
fu_allocation_table_GHA <- fu_allocation_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA") %>%
# Eliminate some machines in GHA that are not present in the exemplar (ZAF).
dplyr::filter(! .data[[IEATools::template_cols$machine]] %in%
c("Boat engines", "LPG stoves", "Kerosene stoves",
"Diesel cars", "Diesel trains", "Electric stoves", "Non-energy"))
# Try to complete the GHA table
result <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA,
exemplar_eta_fu_tables = eta_fu_table_ZAF,
fu_allocation_table = fu_allocation_table_GHA)
# Verify that we successfully received a result
expect_true(! is.null(result))
expect_equal(nrow(result), 58)
})
MatthewHeun/IEATools documentation built on Feb. 6, 2024, 3:29 p.m.
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test_that("tidy_fu_allocation_table() works", {
fu_table <- load_fu_allocation_data()
years <- year_cols(fu_table, return_names = TRUE)
tidy <- tidy_fu_allocation_table(fu_table)
expect_null(tidy[[IEATools::template_cols$maximum_values]])
for (yr in years) {
expect_null(tidy[[yr]])
}
tidy %>%
dplyr::filter(.data[[IEATools::template_cols$quantity]] %in% c(IEATools::iea_cols$e_dot, IEATools::template_cols$e_dot_perc)) %>%
nrow() %>%
expect_equal(0)
# Test that the original is returned if it is already tidy
tidy2 <- tidy_fu_allocation_table(tidy)
expect_equal(tidy2, tidy)
})
test_that("complete_fu_allocation_table works as expected", {
# In this test, the 2nd exemplar isn't needed, and the code should
# break out of the for loop.
# The strategy here will be to use Ghana's FU allocation table
# with South Africa's as an exemplar.
# We'll remove a row from Ghana's table and make sure South Africa's is inserted.
fu_table <- load_fu_allocation_data()
fu_table_GHA <- fu_table %>%
dplyr::filter(Country == "GHA") %>%
# Delete rows from Ghana's table for Residential consumption of PSBs
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential))
# Verify that those PSB rows have been deleted.
fu_table_GHA %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF")
# Verify that the ZAF table has rows for Residential consumption of PSBs.
fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_gt(0)
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df(apply_fixes = FALSE) %>%
specify_all()
# Now send the data into complete_fu_allocation_table()
completed <- complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$c_source]] == "ZAF") %>%
nrow() %>%
expect_equal(4)
# We should have four Ghana rows for PSBs in Residential
completed %>%
dplyr::filter(Country == "GHA",
.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(4)
# But their source should not be Ghana.
completed %>%
dplyr::filter(.data[[IEATools::template_cols$c_source]] == "GHA",
.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
# Try with a missing allocation table.
expect_error(complete_fu_allocation_table(country_to_complete = "GHA",
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data),
'argument "fu_allocation_table" is missing, with no default')
# Try with missing exemplars
expect_error(complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
tidy_specified_iea_data = tidy_specified_iea_data),
'argument "exemplar_fu_allocation_tables" is missing, with no default')
# Try with missing IEA data
expect_error(complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF)),
'argument "tidy_specified_iea_data" is missing, with no default')
})
test_that("complete_fu_allocation_table() works with a tidy incomplete fu table", {
fu_table <- load_fu_allocation_data()
tidy_fu_table_GHA <- fu_table %>%
dplyr::filter(Country == "GHA") %>%
# Delete rows from Ghana's table for Residential consumption of PSBs
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential)) %>%
# Tidy it
tidy_fu_allocation_table()
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF")
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df(apply_fixes = FALSE) %>%
specify_all()
# Now send the data into complete_fu_allocation_table()
completed <- complete_fu_allocation_table(fu_allocation_table = tidy_fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data)
# Ensure that the correct rows have been picked up for GHA from ZAF.
completed %>%
dplyr::filter(.data[[IEATools::template_cols$c_source]] == "ZAF") %>%
nrow() %>%
expect_equal(4)
})
test_that("complete_fu_allocation_table() works with an empty tidy fu table", {
# We have a bug where an empty (no rows) tidy data frame sent into complete_fu_allocation_table() causes an error.
# This test makes sure that the bug is solved and stays fixed.
fu_table <- load_fu_allocation_data()
tidy_fu_table_GHA <- fu_table %>%
# Keep only GHA rows.
dplyr::filter(Country == "GHA") %>%
# Tidy it
tidy_fu_allocation_table()
# Delete all rows from Ghana's table
empty_fu_table_GHA <- tidy_fu_table_GHA[0, ]
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF")
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df() %>%
specify_all()
# Now send the IEA data and the empty GHA FU allocation data frame data into complete_fu_allocation_table(),
# This call triggers the error we are trying to solve.
# But it causes a different error, as ZAF doesn't have all the rows for GHA.
# Here, we just make sure we see the error we expect.
expect_error(complete_fu_allocation_table(fu_allocation_table = empty_fu_table_GHA,
country_to_complete = "GHA",
# Give 2 exemplars so that we stress test the loop.
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_ZAF),
tidy_specified_iea_data = tidy_specified_iea_data),
"Didn't complete FU Allocation table for the following final energy flows:\nGHA, 1971, Industry, Non-ferrous metals, Electricity;")
})
test_that("complete_fu_allocation_table() works with 2 exemplars", {
# In this test, we set up an allocation table that has two missing pieces.
# The two missing pieces are obtained from 2 exemplars, one missing piece from each.
fu_table <- load_fu_allocation_data()
fu_table_GHA <- fu_table %>%
dplyr::filter(Country == "GHA") %>%
# Delete rows from Ghana's table for Residential consumption of PSBs
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential)) %>%
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use &
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity &
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants))
# Ensure that we removed the correct rows from Ghana.
fu_table_GHA %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
fu_table_GHA %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use &
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity &
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants) %>%
nrow() %>%
expect_equal(0)
# Now make two exemplars from South Africa, one with country ZAF, the other whose country is renamed to "World".
# South Africa has EIOU Electricity consumed by Main activity producer electricity plants but NO Residential PSB consumption.
# World has Residential PSB consumption but NO EIOU Electricity consumed by Main activity producer electricity plants.
# Ghana will pick up EIOU Electricity consumed by Main activity producer electricity plants from South Africa.
# Ghana will pick up Residential PSB consumption from World.
fu_table_ZAF <- fu_table %>%
dplyr::filter(Country == "ZAF") %>%
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other &
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels &
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential))
# Ensure we deleted correct rows from this table.
fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
nrow() %>%
expect_equal(0)
# Second exemplar
fu_table_World <- fu_table %>%
dplyr::filter(Country == "ZAF") %>%
dplyr::mutate(
Country = "World"
) %>%
dplyr::filter(!(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use &
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity &
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants))
# Ensure we removed the correct rows from the World exemplar.
fu_table_World %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use,
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity,
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants) %>%
nrow() %>%
expect_equal(0)
fu_table_World %>%
magrittr::extract2(IEATools::iea_cols$country) %>%
unique() %>%
expect_equal("World")
# Get the IEA data for GHA and ZAF and specify it.
tidy_specified_iea_data <- load_tidy_iea_df() %>%
specify_all()
# Now run Ghana through the completion process.
completed <- complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
exemplar_fu_allocation_tables = list(fu_table_ZAF, fu_table_World),
tidy_specified_iea_data = tidy_specified_iea_data)
# Check that Ghana obtained Residential PSB consumption from World.
completed %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_flows$other,
.data[[IEATools::template_cols$ef_product]] == IEATools::biofuels_and_waste_products$primary_solid_biofuels,
.data[[IEATools::template_cols$destination]] == IEATools::other_flows$residential) %>%
magrittr::extract2(IEATools::template_cols$c_source) %>%
unique() %>%
expect_equal("World")
# Check that Ghana obtained EIOU Electricity consumed by Main activity producer electricity plants from South Africa.
completed %>%
dplyr::filter(.data[[IEATools::iea_cols$flow_aggregation_point]] == IEATools::tfc_compare_flows$energy_industry_own_use,
.data[[IEATools::template_cols$ef_product]] == IEATools::electricity_products$electricity,
.data[[IEATools::template_cols$destination]] == IEATools::transformation_processes$main_activity_producer_electricity_plants) %>%
magrittr::extract2(IEATools::template_cols$c_source) %>%
expect_equal(c("ZAF", "ZAF"))
# Try again without enough information to complete the FU Allocation table.
# Create this situation by dropping the fu_table_World from the list of exemplars.
# In this situation, we cannot allocate the Residential PSB rows for Ghana.
# This attempt should emit a warning.
expect_error(complete_fu_allocation_table(fu_allocation_table = fu_table_GHA,
country_to_complete = "GHA",
exemplar_fu_allocation_tables = fu_table_ZAF,
tidy_specified_iea_data = tidy_specified_iea_data),
"Didn't complete FU Allocation table for the following final energy flows:\nGHA, 1971, Other, Residential, Primary solid biofuels;\nGHA, 2000, Other, Residential, Primary solid biofuels.\nPlease check the FU allocation table for typos or misspellings.")
})
test_that("fu_allocation_table_completed() works as expected", {
iea_data <- load_tidy_iea_df(apply_fixes = FALSE) %>%
specify_all()
fu_allocations <- load_fu_allocation_data()
expect_true(fu_allocation_table_completed(fu_allocations, iea_data))
# Remove a row from fu_allocations. Now the test for completion should fail.
# In fact, remove a row that has a 1 in it.
expect_false(fu_allocation_table_completed(fu_allocations[-3, ], iea_data))
# Now remove a row that has a fraction.
expect_error(fu_allocation_table_completed(fu_allocations[-8, ], iea_data),
"Not all final energy was allocated to final-to-useful machines to within 1e-09. Problematic final energy flows and their errors are: GHA, 1971, Electricity, Main activity producer electricity plants, -0.5; GHA, 2000, Electricity, Main activity producer electricity plants, -0.5. Please check the FU allocation file for allocations that don't sum to 1.")
# Try with a wide IEAData data frame.
expect_true(fu_allocation_table_completed(fu_allocations, iea_data %>%
tidyr::pivot_wider(names_from = IEATools::iea_cols$year,
values_from = IEATools::iea_cols$e_dot)))
})
test_that("tidy_eta_fu_table() works", {
eta_table <- load_eta_fu_data()
years <- year_cols(eta_table, return_names = TRUE)
tidy <- tidy_eta_fu_table(eta_table)
expect_null(tidy[[IEATools::template_cols$maximum_values]])
for (yr in years) {
expect_null(tidy[[yr]])
}
tidy %>%
dplyr::filter(.data[[IEATools::template_cols$quantity]] %in% c(IEATools::template_cols$e_dot_machine, IEATools::template_cols$e_dot_machine_perc)) %>%
nrow() %>%
expect_equal(0)
# Test that the original is returned if it is already tidy
tidy2 <- tidy_eta_fu_table(tidy)
expect_equal(tidy2, tidy)
})
test_that("complete_eta_fu_table works as expected", {
eta_fu_table <- load_eta_fu_data()
eta_fu_table_GHA <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA")
eta_fu_table_ZAF <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
fu_allocation_table <- load_fu_allocation_data() %>%
tidy_fu_allocation_table()
fu_allocation_table_GHA <- fu_allocation_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA")
fu_allocation_table_ZAF <- fu_allocation_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
# Use ZAF as an exemplar for GHA.
# Check that the machines are present, initially.
eta_fu_table_GHA %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles") %>%
nrow() %>%
expect_equal(4)
eta_fu_table_GHA %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons") %>%
nrow() %>%
expect_equal(4)
# Eliminate 2 machines (Automobiles and Irons) from GHA and see if their efficiencies get picked up from ZAF and World.
eta_fu_table_GHA_incomplete <- eta_fu_table_GHA %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] != "Automobiles",
.data[[IEATools::template_cols$machine]] != "Irons")
# Ensure that we eliminated the desired rows
eta_fu_table_GHA_incomplete %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] %in% c("Automobiles", "Irons")) %>%
nrow() %>%
expect_equal(0)
# Make exemplar tables from ZAF.
# The first exemplar (ZAF) will have Automobiles but not Irons.
exemplar_ZAF <- eta_fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] != "Irons")
# Verify we deleted the right rows.
exemplar_ZAF %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons") %>%
nrow() %>%
expect_equal(0)
# The second exemplar (World) will have Irons, but not Automobiles.
exemplar_World <- eta_fu_table_ZAF %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] != "Automobiles") %>%
dplyr::mutate(
"{IEATools::iea_cols$country}" := "World"
)
# Verify we deleted the right rows.
exemplar_World %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles") %>%
nrow() %>%
expect_equal(0)
# Try to complete with ONLY ZAF.
# Should find that a warning is emitted, because we can't find Irons
expect_error(complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA_incomplete,
exemplar_eta_fu_tables = exemplar_ZAF,
fu_allocation_table = fu_allocation_table_GHA),
"Irons")
# Now call the completion function to pick up Automobiles from ZAF and Irons from World
completed <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA_incomplete,
exemplar_eta_fu_tables = list(exemplar_ZAF, exemplar_World),
fu_allocation_table = fu_allocation_table_GHA)
# Check that we got Automobiles from ZAF
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$eta_fu_source]] == "ZAF") %>%
nrow() %>%
expect_equal(4)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.31)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(1)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Automobiles",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(1)
# Check that we got Irons from World
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$eta_fu_source]] == "World") %>%
nrow() %>%
expect_equal(4)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.35)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 1971) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3698615661)
completed %>%
dplyr::filter(.data[[IEATools::template_cols$machine]] == "Irons",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$phi_u,
.data[[IEATools::iea_cols$year]] == 2000) %>%
magrittr::extract2(IEATools::template_cols$.values) %>%
expect_equal(0.3698615661)
# Try using two eta_fu_table_ZAF's as the exemplars.
# It should get everything it needs from the first one, thereafter hitting the break statement
completed2 <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA_incomplete,
exemplar_eta_fu_tables = list(eta_fu_table_ZAF, eta_fu_table_ZAF),
fu_allocation_table = fu_allocation_table_GHA,
which_quantity = "eta.fu")
# Check that completed2 has obtained both Automobiles and Irons from ZAF.
completed2 %>%
dplyr::filter(.data[[IEATools::template_cols$eta_fu_source]] == "ZAF",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu) %>%
nrow() %>%
expect_equal(4)
completed2 %>%
dplyr::filter(.data[[IEATools::template_cols$eta_fu_source]] == "ZAF",
.data[[IEATools::template_cols$quantity]] == IEATools::template_cols$eta_fu) %>%
magrittr::extract2(IEATools::template_cols$machine) %>%
expect_equal(c("Automobiles", "Automobiles", "Irons", "Irons"))
})
test_that("complete_eta_fu_table() returns an empty data frame when the fu_allocation_table has zero rows", {
eta_fu_table <- load_eta_fu_data()
eta_fu_table_GHA <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA")
eta_fu_table_ZAF <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
fu_allocation_table <- load_fu_allocation_data() %>%
tidy_fu_allocation_table()
fu_allocation_table_GHA <- fu_allocation_table[c(), ]
result <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA,
exemplar_eta_fu_tables = eta_fu_table_ZAF,
fu_allocation_table = fu_allocation_table_GHA)
expect_equal(nrow(result), 0)
expect_true(IEATools::template_cols$eta_fu_source %in% names(result))
})
test_that("eta_fu_table_completed() works as expected", {
fu_allocations <- load_fu_allocation_data()
fu_efficiencies <- load_eta_fu_data()
expect_true(eta_fu_table_completed(fu_efficiencies, fu_allocations))
# Remove a couple rows to get a FALSE return value.
expect_false(eta_fu_table_completed(fu_efficiencies[-3, ], fu_allocations))
# Try with a regular allocations data frame
expect_true(eta_fu_table_completed(fu_efficiencies, fu_allocations))
# Try with a tall efficiencies data frame
expect_true(eta_fu_table_completed(fu_efficiencies %>% tidyr::pivot_longer(cols = year_cols(fu_efficiencies),
names_to = IEATools::iea_cols$year,
values_to = IEATools::template_cols$.values),
fu_allocations))
# Try with a NULL fu_efficiencies
expect_false(eta_fu_table_completed(machines_that_need_quantities = fu_allocations))
expect_true(!is.null(eta_fu_table_completed(machines_that_need_quantities = fu_allocations) %>%
attr("unallocated_rows")))
})
test_that("complete_eta_fu_table() works when we have more allocation data than efficiency data", {
eta_fu_table <- load_eta_fu_data()
eta_fu_table_GHA <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA",
# Eliminate rows so we invoke the exemplar process
.data[[IEATools::template_cols$machine]] != "Irons") %>%
tidy_eta_fu_table() %>%
# Now empty it out
magrittr::extract(c(), )
eta_fu_table_ZAF <- eta_fu_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "ZAF")
fu_allocation_table <- load_fu_allocation_data() %>%
tidy_fu_allocation_table()
fu_allocation_table_GHA <- fu_allocation_table %>%
dplyr::filter(.data[[IEATools::iea_cols$country]] == "GHA") %>%
# Eliminate some machines in GHA that are not present in the exemplar (ZAF).
dplyr::filter(! .data[[IEATools::template_cols$machine]] %in%
c("Boat engines", "LPG stoves", "Kerosene stoves",
"Diesel cars", "Diesel trains", "Electric stoves", "Non-energy"))
# Try to complete the GHA table
result <- complete_eta_fu_table(eta_fu_table = eta_fu_table_GHA,
exemplar_eta_fu_tables = eta_fu_table_ZAF,
fu_allocation_table = fu_allocation_table_GHA)
# Verify that we successfully received a result
expect_true(! is.null(result))
expect_equal(nrow(result), 58)
})
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