tests/testthat/test-aggregates.R
In MatthewHeun/Recca: R Energy Conversion Chain Analysis
library(dplyr)
library(IEATools)
library(magrittr)
library(matsbyname)
library(matsindf)
library(tidyr)
test_that("primary_aggregates() works as expected", {
# Define primary industries
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
# Recca::primary_aggregates(p_industries = p_industries, by = "Total", aggregate_primary = "EX_total_agg.ktoe")
Recca::primary_aggregates(p_industries = p_industries,
notation = RCLabels::dash_notation,
by = "Total", aggregate_primary = "EX_total_agg.ktoe")
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$final) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$useful) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(98220)
# Primary PRODUCT aggregates
primary_product_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries, by = "Product",
aggregate_primary = "EX_product_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_product_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_product_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "E" & rownames == "Crude") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(50000, 3))
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "E" & rownames == "Crude") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(50000, 3))
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "E" & rownames == "NG") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(43000, 3))
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "X" & rownames == "Crude") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(53500)
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "X" & rownames == "NG") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(44720)
# Primary FLOW aggregates
primary_flow_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries, by = "Flow",
aggregate_primary = "EX_flow_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_flow_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_flow_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "E" & colnames == "Resources [of Crude]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(50000, 3))
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "E" & colnames == "Resources [of NG]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(43000, 3))
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "X" & colnames == "Resources [of Crude]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(53500)
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "X" & colnames == "Resources [of NG]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(44720)
})
test_that("primary_aggregates() works with leading pattern for names", {
# Define primary industries
p_industries <- c("Resources", "Resources")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries,
piece = "noun",
notation = RCLabels::of_notation,
pattern_type = "leading", by = "Total", aggregate_primary = "EX_total_agg.ktoe")
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$final) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$useful) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(98220)
})
test_that("primary_aggregates() works when R is folded into V", {
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
# Make it wide-by-matrices
tidyr::spread(key = matrix.name, value = matrix) %>%
dplyr::mutate(
V = matsbyname::sum_byname(R, V),
R = NULL
) %>%
Recca::primary_aggregates(p_industries = p_industries,
notation = RCLabels::dash_notation,
by = "Total",
aggregate_primary = "EX_total_agg.ktoe")
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$final) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
})
test_that("primary_aggregates() works for net and gross", {
# Define primary industries
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries,
notation = RCLabels::of_notation,
add_net_gross_cols = TRUE,
by = "Total")
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$final) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$final) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$useful) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$useful) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(98220)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(98220)
})
test_that("primary_aggregates() works with lists", {
# Define primary industries
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
psut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix)
# Make a list
arg_list <- list(R = psut$R[[1]], V = psut$V[[1]], Y = psut$Y[[1]])
# Calculate primary aggregates
p_aggs <- Recca::primary_aggregates(.sutdata = arg_list, p_industries = p_industries)
expect_equal(p_aggs$EX.p, 93000)
})
test_that("finaldemand_aggregates() of SUT data work as expected without units", {
# Define final demand sectors
fd_sectors <- c("Residential", "Transport", "Oil fields")
# Final demand TOTAL aggregates
final_demand_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Total",
net_aggregate_demand = "EX_total_net_agg.ktoe",
gross_aggregate_demand = "EX_total_gross_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_total_net_agg.ktoe, EX_total_gross_agg.ktoe)
final_demand_total_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E") %>%
dplyr::select("EX_total_gross_agg.ktoe") %>%
unlist() |>
unname() |>
expect_equal(74325)
final_demand_total_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E") %>%
dplyr::select("EX_total_net_agg.ktoe") %>%
unlist() |>
unname() |>
expect_equal(25915.380499999999)
final_demand_total_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "E") %>%
dplyr::select("EX_total_net_agg.ktoe") %>%
unlist() |>
unname() |>
expect_equal(5.007179166e+14)
# Final demand PRODUCT aggregates
final_demand_product_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Product",
net_aggregate_demand = "EX_product_net_agg.ktoe",
gross_aggregate_demand = "EX_product_gross_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_product_net_agg.ktoe, EX_product_gross_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_product_net_agg.ktoe, EX_product_gross_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_product_gross_agg.ktoe", rownames == "Crude [from Fields]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(2500)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_product_net_agg.ktoe", rownames == "Elect [from Grid]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(6000)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_product_gross_agg.ktoe", rownames == "LTH") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(20000)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_product_net_agg.ktoe", rownames == "MD [from Car engines]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(3000.4)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "E", matnames == "EX_product_net_agg.ktoe", rownames == "Freight [tonne-km/year]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(142916629629)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "X", matnames == "EX_product_gross_agg.ktoe", rownames == "Space heating [m3-K]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(7.500000e+10)
# Final demand SECTOR aggregates
final_demand_sector_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector",
net_aggregate_demand = "EX_sector_net_agg.ktoe",
gross_aggregate_demand = "EX_sector_gross_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_sector_net_agg.ktoe, EX_sector_gross_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_sector_net_agg.ktoe, EX_sector_gross_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_sector_gross_agg.ktoe", rownames == "Oil fields") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(2575)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_sector_net_agg.ktoe", rownames == "Residential") %>%
dplyr::select(matvals) %>%
unlist() |>
unname(31000)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_sector_gross_agg.ktoe", rownames == "Transport") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(21714.9805)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_sector_net_agg.ktoe", rownames == "Residential") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(4200.4)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "E", matnames == "EX_sector_gross_agg.ktoe", rownames == "Oil fields") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(75)
})
test_that("IEATools::primary_aggregates_IEA() works as expected on the UKEnergy2000tidy data frame", {
result <- UKEnergy2000tidy %>%
dplyr::mutate(
Method = "PCM"
) %>%
dplyr::group_by(Country, Method, Year, Energy.type, Last.stage) %>%
IEATools::primary_aggregates()
expect_equal(result[[Recca::aggregate_cols$aggregate_primary]], c(93000, 93000, 93000, 98220))
})
test_that("finaldemand_aggregates() works for sectors", {
fd_sectors <- c("Residential", "Transport", "Oil fields")
sut_result <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
dplyr::filter(Last.stage %in% c(IEATools::last_stages$final, IEATools::last_stages$useful)) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector")
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[1]][1,1], 31000)
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[1]][2,1], 40750)
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[2]][1,1], 4200.4)
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[2]][2,1], 21714.9805)
# Testing final demand by sector for the three fd_sectors at the final stage
# Oil fields
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]][1,1], 2575)
# Residential
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]][2,1], 31000)
# Transport
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]][3,1], 40750)
# Testing final demand by sector for the three fd_sectors at the useful stage
# Oil fields
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[2]][1,1], 75)
# Residential
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[2]][2,1], 4200.4)
# Transport
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[2]][3,1], 21714.9805)
})
test_that("finaldemand_aggregates() works with U_EIOU", {
fd_sectors <- c("Residential", "Transport", "Oil fields")
sut_result <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
dplyr::filter(Last.stage %in% c(IEATools::last_stages$final, IEATools::last_stages$useful)) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector")
expect_equal(matsbyname::getrownames_byname(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]]),
fd_sectors %>% sort())
})
test_that("region_aggregates() works as expected", {
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add another country, by duplicating GBR
mats <- dplyr::bind_rows(mats_GBR,
mats_GBR %>% dplyr::mutate(Country = "USA"),
mats_GBR %>% dplyr::mutate(Country = "FRA"))
agg_map <- list(EUR = c("GBR", "FRA"), AMR = "USA")
agg_df <- matsbyname::agg_map_to_agg_table(agg_map,
few_colname = "Continent",
many_colname = IEATools::iea_cols$country)
mats <- dplyr::left_join(mats, agg_df, by = IEATools::iea_cols$country)
# Try with an empty data frame
empty <- region_aggregates(mats[0, ],
many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
expect_equal(nrow(empty), 0)
# It is important that the result NOT have the Continent column.
# If Continent is present, it will be NA when joined with other
# data frames that have successfully aggregated by region.
expect_equal(colnames(empty), setdiff(colnames(mats), "Continent"))
# Try when there is no Country column.
empty_no_country <- mats[0, ] %>%
dplyr::mutate(
"{Recca::psut_cols$country}" := NULL
)
expect_false(Recca::psut_cols$country %in% names(empty_no_country))
expect_true("Continent" %in% names(empty_no_country))
empty_after_region_agg <- empty_no_country |>
region_aggregates(many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
expect_false("Continent" %in% names(empty_after_region_agg))
expect_true(Recca::psut_cols$country %in% names(empty_after_region_agg))
# Now try with the data.
res <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
# Verify column names
expect_true(Recca::psut_cols$R %in% names(res))
expect_true(Recca::psut_cols$U_eiou %in% names(res))
expect_true(Recca::psut_cols$U_feed %in% names(res))
expect_true(Recca::psut_cols$V %in% names(res))
expect_true(Recca::psut_cols$Y %in% names(res))
expect_true(Recca::psut_cols$U %in% names(res))
expect_true(Recca::psut_cols$r_eiou %in% names(res))
expect_true(Recca::psut_cols$S_units %in% names(res))
# Check that the EUR rows are 2x the AMR rows
eur <- res %>% dplyr::filter(Country == "EUR")
amr <- res %>% dplyr::filter(Country == "AMR")
expect_equal(eur$R, matsbyname::hadamardproduct_byname(2, amr$R))
expect_equal(eur$U, matsbyname::hadamardproduct_byname(2, amr$U))
expect_equal(eur$U_EIOU, matsbyname::hadamardproduct_byname(2, amr$U_EIOU))
expect_equal(eur$U_feed, matsbyname::hadamardproduct_byname(2, amr$U_feed))
expect_equal(eur$V, matsbyname::hadamardproduct_byname(2, amr$V))
expect_equal(eur$Y, matsbyname::hadamardproduct_byname(2, amr$Y))
# But some of the matrices should be same
expect_equal(eur$S_units, amr$S_units)
expect_equal(eur$r_EIOU, amr$r_EIOU)
})
test_that("region_aggregates() works with WRLD not in aggregation_map", {
# This test approximates what happens when
# we have the WRLD country in with other countries
# but WRLD is not included in the aggregation map.
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add more countries, by duplicating GBR
mats <- dplyr::bind_rows(mats_GBR,
mats_GBR %>% dplyr::mutate(Country = "USA"),
mats_GBR %>% dplyr::mutate(Country = "FRA"),
mats_GBR %>% dplyr::mutate(Country = "WRLD"))
# Leave WRLD out of the agg_map.
agg_map <- list(Europe = c("GBR", "FRA"), NoAmr = "USA")
agg_df <- matsbyname::agg_map_to_agg_table(agg_map,
few_colname = "Continent",
many_colname = IEATools::iea_cols$country)
# Results in NA values for Continent column for WRLD country
mats <- dplyr::left_join(mats, agg_df, by = IEATools::iea_cols$country)
mats |>
dplyr::filter(.data[[Recca::psut_cols$country]] == "WRLD") |>
dplyr::select("Continent") |>
is.na() |>
all() |>
expect_true()
# Now do the aggregation.
# Unless we specify drop_na_few = TRUE,
# we'll get NA rows for WRLD.
res <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
res |>
dplyr::filter(is.na(.data[[Recca::psut_cols$country]])) |>
nrow() |>
expect_equal(4)
# Try with eliminating the NA rows
# by setting drop_na_few = TRUE.
res2 <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Continent",
drop_na_few = TRUE)
res2 |>
dplyr::filter(is.na(.data[[Recca::psut_cols$country]])) |>
nrow() |>
expect_equal(0)
})
test_that("region_aggregates() works when no aggregated country is present and drop_na_few = TRUE", {
mats_GBR <- UKEnergy2000mats |>
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add another country, by duplicating GBR
mats <- dplyr::bind_rows(mats_GBR,
mats_GBR |> dplyr::mutate(Country = "USA"),
mats_GBR |> dplyr::mutate(Country = "FRA"))
agg_map <- list(FoSUN = c("SUN", "RUS"))
agg_df <- matsbyname::agg_map_to_agg_table(agg_map,
few_colname = "Region",
many_colname = IEATools::iea_cols$country)
# Results in NA values for every entry in the region column
mats <- dplyr::left_join(mats, agg_df, by = IEATools::iea_cols$country)
# Now try to aggregate
res <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Region",
drop_na_few = TRUE)
expect_equal(nrow(res), 0)
expect_equal(names(res), setdiff(names(mats), "Region"))
})
test_that("despecified_aggregates() works as expected", {
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
res <- mats_GBR %>%
despecified_aggregates()
# Check that S_units was aggregated
expect_true(all(res$S_units_aggregated[[1]] == 1))
# Check a few numbers
# Aggregated Crude
expect_equal(res$U_aggregated[[1]][1, 1], 48000)
# Aggregated Petrol
expect_equal(res$U_aggregated[[1]][5, 8], 27000)
# Aggregated Diesel
expect_equal(res$U_aggregated[[1]][2, 2], 15850)
expect_equal(rownames(mats_GBR$U[[1]]),
c("Crude", "Crude [from Dist.]", "Crude [from Fields]", "Diesel", "Diesel [from Dist.]",
"Elect", "Elect [from Grid]", "NG", "NG [from Dist.]", "NG [from Wells]",
"Petrol", "Petrol [from Dist.]"))
expect_equal(rownames(res$U_aggregated[[1]]), c("Crude", "Diesel", "Elect", "NG", "Petrol"))
})
test_that("grouped_aggregates() works as expected", {
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
res <- mats_GBR %>%
grouped_aggregates(aggregation_map = list(`Oil and oil products` = c("Crude", "Diesel", "Petrol")),
pattern_type = "leading", margin = "Product")
expect_equal(colnames(res$R_aggregated[[1]]), c("NG", "Oil and oil products"))
expect_equal(rownames(res$U_aggregated[[1]]), c("Elect", "Elect [from Grid]", "NG", "NG [from Dist.]", "NG [from Wells]", "Oil and oil products"))
expect_equal(colnames(res$V_aggregated[[1]]), c("Elect", "Elect [from Grid]", "NG [from Dist.]", "NG [from Wells]", "Oil and oil products"))
expect_equal(rownames(res$Y_aggregated[[1]]), c("Elect [from Grid]", "NG [from Dist.]", "Oil and oil products"))
# Check some values
# Oil and oil products for Crude dist.
expect_equal(res$U_aggregated[[1]][6, 1], 48025)
# Oil and oil products for Transport
expect_equal(res$Y_aggregated[[1]][3, 2], 40750)
})
test_that("finaldemand_aggregates() works when there are no columns with fd_sectors", {
U_eiou <- matrix(1, dimnames = list("bogus_row", "bogus_col"))
Y <- matrix(1, dimnames = list("bogus_row", "bogus_col"))
res <- finaldemand_aggregates(fd_sectors = "fd_sector", piece = "noun",
notation = RCLabels::bracket_notation, pattern_type = "exact",
U_eiou = U_eiou, Y = Y, by = "Sector")
expect_equal(res$EX.fd_net, 0)
expect_false(is.matrix(res$EX.fd_net))
expect_equal(res$EX.fd_gross, 0)
expect_false(is.matrix(res$EX.fd_gross))
})
MatthewHeun/Recca documentation built on Feb. 9, 2024, 6:18 p.m.
R Package Documentation
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library(dplyr)
library(IEATools)
library(magrittr)
library(matsbyname)
library(matsindf)
library(tidyr)
test_that("primary_aggregates() works as expected", {
# Define primary industries
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
# Recca::primary_aggregates(p_industries = p_industries, by = "Total", aggregate_primary = "EX_total_agg.ktoe")
Recca::primary_aggregates(p_industries = p_industries,
notation = RCLabels::dash_notation,
by = "Total", aggregate_primary = "EX_total_agg.ktoe")
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$final) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$useful) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(98220)
# Primary PRODUCT aggregates
primary_product_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries, by = "Product",
aggregate_primary = "EX_product_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_product_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_product_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "E" & rownames == "Crude") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(50000, 3))
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "E" & rownames == "Crude") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(50000, 3))
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "E" & rownames == "NG") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(43000, 3))
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "X" & rownames == "Crude") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(53500)
primary_product_aggregates_sut |>
dplyr::filter(Energy.type == "X" & rownames == "NG") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(44720)
# Primary FLOW aggregates
primary_flow_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries, by = "Flow",
aggregate_primary = "EX_flow_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_flow_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_flow_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "E" & colnames == "Resources [of Crude]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(50000, 3))
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "E" & colnames == "Resources [of NG]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(rep(43000, 3))
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "X" & colnames == "Resources [of Crude]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(53500)
primary_flow_aggregates_sut |>
dplyr::filter(Energy.type == "X" & colnames == "Resources [of NG]") |>
dplyr::select(matvals) |>
unlist() |>
unname() |>
expect_equal(44720)
})
test_that("primary_aggregates() works with leading pattern for names", {
# Define primary industries
p_industries <- c("Resources", "Resources")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries,
piece = "noun",
notation = RCLabels::of_notation,
pattern_type = "leading", by = "Total", aggregate_primary = "EX_total_agg.ktoe")
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$final) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$useful) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut |>
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(98220)
})
test_that("primary_aggregates() works when R is folded into V", {
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
# Make it wide-by-matrices
tidyr::spread(key = matrix.name, value = matrix) %>%
dplyr::mutate(
V = matsbyname::sum_byname(R, V),
R = NULL
) %>%
Recca::primary_aggregates(p_industries = p_industries,
notation = RCLabels::dash_notation,
by = "Total",
aggregate_primary = "EX_total_agg.ktoe")
primary_total_aggregates_sut |>
dplyr::filter(Last.stage == IEATools::last_stages$final) |>
dplyr::select("EX_total_agg.ktoe") |>
magrittr::extract2(1) |>
expect_equal(93000)
})
test_that("primary_aggregates() works for net and gross", {
# Define primary industries
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
primary_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::primary_aggregates(p_industries = p_industries,
notation = RCLabels::of_notation,
add_net_gross_cols = TRUE,
by = "Total")
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$final) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$final) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$useful) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Last.stage == IEATools::last_stages$useful) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "E", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(93000)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$net_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(98220)
primary_total_aggregates_sut %>%
dplyr::filter(Energy.type == "X", Last.stage == IEATools::last_stages$services) %>%
dplyr::select(Recca::aggregate_cols$gross_aggregate_primary) |>
magrittr::extract2(1) |>
expect_equal(98220)
})
test_that("primary_aggregates() works with lists", {
# Define primary industries
p_industries <- c("Resources [of Crude]", "Resources [of NG]")
# Primary TOTAL aggregates
psut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix)
# Make a list
arg_list <- list(R = psut$R[[1]], V = psut$V[[1]], Y = psut$Y[[1]])
# Calculate primary aggregates
p_aggs <- Recca::primary_aggregates(.sutdata = arg_list, p_industries = p_industries)
expect_equal(p_aggs$EX.p, 93000)
})
test_that("finaldemand_aggregates() of SUT data work as expected without units", {
# Define final demand sectors
fd_sectors <- c("Residential", "Transport", "Oil fields")
# Final demand TOTAL aggregates
final_demand_total_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Total",
net_aggregate_demand = "EX_total_net_agg.ktoe",
gross_aggregate_demand = "EX_total_gross_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_total_net_agg.ktoe, EX_total_gross_agg.ktoe)
final_demand_total_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E") %>%
dplyr::select("EX_total_gross_agg.ktoe") %>%
unlist() |>
unname() |>
expect_equal(74325)
final_demand_total_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E") %>%
dplyr::select("EX_total_net_agg.ktoe") %>%
unlist() |>
unname() |>
expect_equal(25915.380499999999)
final_demand_total_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "E") %>%
dplyr::select("EX_total_net_agg.ktoe") %>%
unlist() |>
unname() |>
expect_equal(5.007179166e+14)
# Final demand PRODUCT aggregates
final_demand_product_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Product",
net_aggregate_demand = "EX_product_net_agg.ktoe",
gross_aggregate_demand = "EX_product_gross_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_product_net_agg.ktoe, EX_product_gross_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_product_net_agg.ktoe, EX_product_gross_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_product_gross_agg.ktoe", rownames == "Crude [from Fields]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(2500)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_product_net_agg.ktoe", rownames == "Elect [from Grid]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(6000)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_product_gross_agg.ktoe", rownames == "LTH") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(20000)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_product_net_agg.ktoe", rownames == "MD [from Car engines]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(3000.4)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "E", matnames == "EX_product_net_agg.ktoe", rownames == "Freight [tonne-km/year]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(142916629629)
final_demand_product_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "X", matnames == "EX_product_gross_agg.ktoe", rownames == "Space heating [m3-K]") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(7.500000e+10)
# Final demand SECTOR aggregates
final_demand_sector_aggregates_sut <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector",
net_aggregate_demand = "EX_sector_net_agg.ktoe",
gross_aggregate_demand = "EX_sector_gross_agg.ktoe") %>%
dplyr::select(Country, Year, Last.stage, Energy.type, EX_sector_net_agg.ktoe, EX_sector_gross_agg.ktoe) %>%
tidyr::gather(key = "matnames", value = "matvals", EX_sector_net_agg.ktoe, EX_sector_gross_agg.ktoe) %>%
matsindf::expand_to_tidy(drop = 0)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_sector_gross_agg.ktoe", rownames == "Oil fields") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(2575)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Final", Energy.type == "E", matnames == "EX_sector_net_agg.ktoe", rownames == "Residential") %>%
dplyr::select(matvals) %>%
unlist() |>
unname(31000)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_sector_gross_agg.ktoe", rownames == "Transport") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(21714.9805)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Useful", Energy.type == "E", matnames == "EX_sector_net_agg.ktoe", rownames == "Residential") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(4200.4)
final_demand_sector_aggregates_sut %>%
dplyr::filter(Last.stage == "Services", Energy.type == "E", matnames == "EX_sector_gross_agg.ktoe", rownames == "Oil fields") %>%
dplyr::select(matvals) %>%
unlist() |>
unname() |>
expect_equal(75)
})
test_that("IEATools::primary_aggregates_IEA() works as expected on the UKEnergy2000tidy data frame", {
result <- UKEnergy2000tidy %>%
dplyr::mutate(
Method = "PCM"
) %>%
dplyr::group_by(Country, Method, Year, Energy.type, Last.stage) %>%
IEATools::primary_aggregates()
expect_equal(result[[Recca::aggregate_cols$aggregate_primary]], c(93000, 93000, 93000, 98220))
})
test_that("finaldemand_aggregates() works for sectors", {
fd_sectors <- c("Residential", "Transport", "Oil fields")
sut_result <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
dplyr::filter(Last.stage %in% c(IEATools::last_stages$final, IEATools::last_stages$useful)) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector")
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[1]][1,1], 31000)
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[1]][2,1], 40750)
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[2]][1,1], 4200.4)
expect_equal(sut_result[[Recca::aggregate_cols$net_aggregate_demand]][[2]][2,1], 21714.9805)
# Testing final demand by sector for the three fd_sectors at the final stage
# Oil fields
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]][1,1], 2575)
# Residential
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]][2,1], 31000)
# Transport
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]][3,1], 40750)
# Testing final demand by sector for the three fd_sectors at the useful stage
# Oil fields
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[2]][1,1], 75)
# Residential
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[2]][2,1], 4200.4)
# Transport
expect_equal(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[2]][3,1], 21714.9805)
})
test_that("finaldemand_aggregates() works with U_EIOU", {
fd_sectors <- c("Residential", "Transport", "Oil fields")
sut_result <- UKEnergy2000mats %>%
tidyr::spread(key = matrix.name, value = matrix) %>%
dplyr::filter(Last.stage %in% c(IEATools::last_stages$final, IEATools::last_stages$useful)) %>%
Recca::finaldemand_aggregates(fd_sectors = fd_sectors, by = "Sector")
expect_equal(matsbyname::getrownames_byname(sut_result[[Recca::aggregate_cols$gross_aggregate_demand]][[1]]),
fd_sectors %>% sort())
})
test_that("region_aggregates() works as expected", {
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add another country, by duplicating GBR
mats <- dplyr::bind_rows(mats_GBR,
mats_GBR %>% dplyr::mutate(Country = "USA"),
mats_GBR %>% dplyr::mutate(Country = "FRA"))
agg_map <- list(EUR = c("GBR", "FRA"), AMR = "USA")
agg_df <- matsbyname::agg_map_to_agg_table(agg_map,
few_colname = "Continent",
many_colname = IEATools::iea_cols$country)
mats <- dplyr::left_join(mats, agg_df, by = IEATools::iea_cols$country)
# Try with an empty data frame
empty <- region_aggregates(mats[0, ],
many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
expect_equal(nrow(empty), 0)
# It is important that the result NOT have the Continent column.
# If Continent is present, it will be NA when joined with other
# data frames that have successfully aggregated by region.
expect_equal(colnames(empty), setdiff(colnames(mats), "Continent"))
# Try when there is no Country column.
empty_no_country <- mats[0, ] %>%
dplyr::mutate(
"{Recca::psut_cols$country}" := NULL
)
expect_false(Recca::psut_cols$country %in% names(empty_no_country))
expect_true("Continent" %in% names(empty_no_country))
empty_after_region_agg <- empty_no_country |>
region_aggregates(many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
expect_false("Continent" %in% names(empty_after_region_agg))
expect_true(Recca::psut_cols$country %in% names(empty_after_region_agg))
# Now try with the data.
res <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
# Verify column names
expect_true(Recca::psut_cols$R %in% names(res))
expect_true(Recca::psut_cols$U_eiou %in% names(res))
expect_true(Recca::psut_cols$U_feed %in% names(res))
expect_true(Recca::psut_cols$V %in% names(res))
expect_true(Recca::psut_cols$Y %in% names(res))
expect_true(Recca::psut_cols$U %in% names(res))
expect_true(Recca::psut_cols$r_eiou %in% names(res))
expect_true(Recca::psut_cols$S_units %in% names(res))
# Check that the EUR rows are 2x the AMR rows
eur <- res %>% dplyr::filter(Country == "EUR")
amr <- res %>% dplyr::filter(Country == "AMR")
expect_equal(eur$R, matsbyname::hadamardproduct_byname(2, amr$R))
expect_equal(eur$U, matsbyname::hadamardproduct_byname(2, amr$U))
expect_equal(eur$U_EIOU, matsbyname::hadamardproduct_byname(2, amr$U_EIOU))
expect_equal(eur$U_feed, matsbyname::hadamardproduct_byname(2, amr$U_feed))
expect_equal(eur$V, matsbyname::hadamardproduct_byname(2, amr$V))
expect_equal(eur$Y, matsbyname::hadamardproduct_byname(2, amr$Y))
# But some of the matrices should be same
expect_equal(eur$S_units, amr$S_units)
expect_equal(eur$r_EIOU, amr$r_EIOU)
})
test_that("region_aggregates() works with WRLD not in aggregation_map", {
# This test approximates what happens when
# we have the WRLD country in with other countries
# but WRLD is not included in the aggregation map.
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add more countries, by duplicating GBR
mats <- dplyr::bind_rows(mats_GBR,
mats_GBR %>% dplyr::mutate(Country = "USA"),
mats_GBR %>% dplyr::mutate(Country = "FRA"),
mats_GBR %>% dplyr::mutate(Country = "WRLD"))
# Leave WRLD out of the agg_map.
agg_map <- list(Europe = c("GBR", "FRA"), NoAmr = "USA")
agg_df <- matsbyname::agg_map_to_agg_table(agg_map,
few_colname = "Continent",
many_colname = IEATools::iea_cols$country)
# Results in NA values for Continent column for WRLD country
mats <- dplyr::left_join(mats, agg_df, by = IEATools::iea_cols$country)
mats |>
dplyr::filter(.data[[Recca::psut_cols$country]] == "WRLD") |>
dplyr::select("Continent") |>
is.na() |>
all() |>
expect_true()
# Now do the aggregation.
# Unless we specify drop_na_few = TRUE,
# we'll get NA rows for WRLD.
res <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Continent")
res |>
dplyr::filter(is.na(.data[[Recca::psut_cols$country]])) |>
nrow() |>
expect_equal(4)
# Try with eliminating the NA rows
# by setting drop_na_few = TRUE.
res2 <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Continent",
drop_na_few = TRUE)
res2 |>
dplyr::filter(is.na(.data[[Recca::psut_cols$country]])) |>
nrow() |>
expect_equal(0)
})
test_that("region_aggregates() works when no aggregated country is present and drop_na_few = TRUE", {
mats_GBR <- UKEnergy2000mats |>
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
# Add another country, by duplicating GBR
mats <- dplyr::bind_rows(mats_GBR,
mats_GBR |> dplyr::mutate(Country = "USA"),
mats_GBR |> dplyr::mutate(Country = "FRA"))
agg_map <- list(FoSUN = c("SUN", "RUS"))
agg_df <- matsbyname::agg_map_to_agg_table(agg_map,
few_colname = "Region",
many_colname = IEATools::iea_cols$country)
# Results in NA values for every entry in the region column
mats <- dplyr::left_join(mats, agg_df, by = IEATools::iea_cols$country)
# Now try to aggregate
res <- region_aggregates(mats,
many_colname = IEATools::iea_cols$country,
few_colname = "Region",
drop_na_few = TRUE)
expect_equal(nrow(res), 0)
expect_equal(names(res), setdiff(names(mats), "Region"))
})
test_that("despecified_aggregates() works as expected", {
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
res <- mats_GBR %>%
despecified_aggregates()
# Check that S_units was aggregated
expect_true(all(res$S_units_aggregated[[1]] == 1))
# Check a few numbers
# Aggregated Crude
expect_equal(res$U_aggregated[[1]][1, 1], 48000)
# Aggregated Petrol
expect_equal(res$U_aggregated[[1]][5, 8], 27000)
# Aggregated Diesel
expect_equal(res$U_aggregated[[1]][2, 2], 15850)
expect_equal(rownames(mats_GBR$U[[1]]),
c("Crude", "Crude [from Dist.]", "Crude [from Fields]", "Diesel", "Diesel [from Dist.]",
"Elect", "Elect [from Grid]", "NG", "NG [from Dist.]", "NG [from Wells]",
"Petrol", "Petrol [from Dist.]"))
expect_equal(rownames(res$U_aggregated[[1]]), c("Crude", "Diesel", "Elect", "NG", "Petrol"))
})
test_that("grouped_aggregates() works as expected", {
mats_GBR <- UKEnergy2000mats %>%
tidyr::pivot_wider(names_from = matrix.name, values_from = matrix)
res <- mats_GBR %>%
grouped_aggregates(aggregation_map = list(`Oil and oil products` = c("Crude", "Diesel", "Petrol")),
pattern_type = "leading", margin = "Product")
expect_equal(colnames(res$R_aggregated[[1]]), c("NG", "Oil and oil products"))
expect_equal(rownames(res$U_aggregated[[1]]), c("Elect", "Elect [from Grid]", "NG", "NG [from Dist.]", "NG [from Wells]", "Oil and oil products"))
expect_equal(colnames(res$V_aggregated[[1]]), c("Elect", "Elect [from Grid]", "NG [from Dist.]", "NG [from Wells]", "Oil and oil products"))
expect_equal(rownames(res$Y_aggregated[[1]]), c("Elect [from Grid]", "NG [from Dist.]", "Oil and oil products"))
# Check some values
# Oil and oil products for Crude dist.
expect_equal(res$U_aggregated[[1]][6, 1], 48025)
# Oil and oil products for Transport
expect_equal(res$Y_aggregated[[1]][3, 2], 40750)
})
test_that("finaldemand_aggregates() works when there are no columns with fd_sectors", {
U_eiou <- matrix(1, dimnames = list("bogus_row", "bogus_col"))
Y <- matrix(1, dimnames = list("bogus_row", "bogus_col"))
res <- finaldemand_aggregates(fd_sectors = "fd_sector", piece = "noun",
notation = RCLabels::bracket_notation, pattern_type = "exact",
U_eiou = U_eiou, Y = Y, by = "Sector")
expect_equal(res$EX.fd_net, 0)
expect_false(is.matrix(res$EX.fd_net))
expect_equal(res$EX.fd_gross, 0)
expect_false(is.matrix(res$EX.fd_gross))
})
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