tests/testthat/test_supply_use.R
In earamendia/EROITools: EROITools
test_that("calc_total_use_by_product works",{
# Path to dummy AB data
A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
# Loading data
tidy_AB_data <- A_B_path %>%
IEATools::load_tidy_iea_df(unit_val = "ktoe") %>%
IEATools::specify_all() %>%
ECCTools::specify_elect_heat_renewables() %>%
ECCTools::specify_elect_heat_fossil_fuels() %>%
ECCTools::specify_elect_heat_nuclear() %>%
ECCTools::specify_other_elec_heat_production() %>%
ECCTools::specify_elect_heat_markets() %>%
IEATools::add_psut_matnames(R_includes_all_exogenous_flows = FALSE) %>%
ECCTools::stat_diffs_to_balancing() %>%
ECCTools::stock_changes_to_balancing()
# FIRST, WE TEST THE DTA APPROACH
# Calculating total use of each product
tidy_AB_dta <- tidy_AB_data %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE)
res_dta <- tidy_AB_dta %>%
calc_total_use_by_product()
# Testing the DTA approach
# Country A
res_dta %>%
dplyr::filter(Country == "A", Product == "Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(750)
res_dta %>%
dplyr::filter(Country == "A", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(200)
res_dta %>%
dplyr::filter(Country == "A", Product == "Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1750)
res_dta %>%
dplyr::filter(Country == "A", Product == "Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(2300)
res_dta %>%
dplyr::filter(Country == "A", Product == "Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(700)
# Country B
res_dta %>%
dplyr::filter(Country == "B", Product == "Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(650)
res_dta %>%
dplyr::filter(Country == "B", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(100)
res_dta %>%
dplyr::filter(Country == "B", Product == "Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
res_dta %>%
dplyr::filter(Country == "B", Product == "Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1200)
res_dta %>%
dplyr::filter(Country == "B", Product == "Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
# SECOND, WE TEST THE GMA APPROACH
tidy_AB_data_gma <- tidy_AB_data %>%
ECCTools::transform_to_gma()
tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
prepare_gma_for_shares()
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
res_gma <- tidy_AB_data_gma_prepared %>%
calc_total_use_by_product()
# Actual tests below:
# Country A
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(750)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(80)
res_gma %>%
dplyr::filter(Country == "A", Product == "{B}_Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(120)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1750)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(2300)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(700)
# Country B
res_gma %>%
dplyr::filter(Country == "B", product_without_origin == "Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(650)
res_gma %>%
dplyr::filter(Country == "B", product_without_origin == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(100)
res_gma %>%
dplyr::filter(Country == "B", product_without_origin == "Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
res_gma %>%
dplyr::filter(Country == "B", Product == "{B}_Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1200)
res_gma %>%
dplyr::filter(Country == "B", Product == "{A}_Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
# THIRD, QUICK TEST WITH NON-ENERGY USES
tidy_AB_data_non_energy <- tidy_AB_data %>%
tibble::add_row(
Country = "A",
Method = "PCM",
Energy.type = "E",
Last.stage = "Final",
Year = 2018,
Ledger.side = "Consumption",
Flow.aggregation.point = "Industry",
Flow = IEATools::non_energy_flows$non_energy_use_industry_transformation_energy,
Product = "Coke oven coke",
Unit = "ktoe",
E.dot = 300,
matnames = "Y"
)
# Calculating energy use by product excluding non-energy uses:
res_dta_excl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_total_use_by_product()
# Checking coke oven coke
res_dta_excl_non_energy %>%
dplyr::filter(Country == "A", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(200)
# Calculating energy use by product including non-energy uses:
res_dta_incl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_total_use_by_product(include_non_energy_uses = TRUE)
# Checking coke oven coke
res_dta_incl_non_energy %>%
dplyr::filter(Country == "A", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(500)
})
test_that("calc_primary_products_supply_by_group works",{
# Path to dummy AB data
A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
# Loading data
tidy_AB_data <- A_B_path %>%
IEATools::load_tidy_iea_df() %>%
IEATools::specify_all() %>%
ECCTools::specify_elect_heat_renewables() %>%
ECCTools::specify_elect_heat_fossil_fuels() %>%
ECCTools::specify_elect_heat_nuclear() %>%
ECCTools::specify_other_elec_heat_production() %>%
ECCTools::specify_elect_heat_markets() %>%
IEATools::add_psut_matnames(R_includes_all_exogenous_flows = FALSE) %>%
ECCTools::stat_diffs_to_balancing() %>%
ECCTools::stock_changes_to_balancing()
# FIRST, WE TEST THE DTA APPROACH
# Calculating total use of each product
tidy_AB_dta <- tidy_AB_data %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE)
res_dta <- tidy_AB_dta %>%
calc_primary_products_supply_by_group()
# Testing
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(5000)
# Doesn't quite match with the ECC diagram in ECCTools package description because
# 1) Have added 500 oil and gas extraction for creating unbalances
# 2) Natural gas also created by the oil refineries in our example to add challenge
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(12600)
res_dta %>%
dplyr::filter(Country == "B") %>%
nrow() %>%
expect_equal(0)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(17600)
# SECOND, WE TEST THE GMA APPROACH
# Calculating total use of each product
tidy_AB_data_gma <- tidy_AB_data %>%
ECCTools::transform_to_gma()
tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
prepare_gma_for_shares()
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
res_gma <- tidy_AB_data_gma_prepared %>%
calc_primary_products_supply_by_group()
# Testing
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(5000)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(12600)
res_gma %>%
dplyr::filter(Country == "B") %>%
nrow() %>%
expect_equal(0)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(17600)
})
test_that("calc_all_products_use_by_group works",{
# Path to dummy AB data
A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
# Loading data
tidy_AB_data <- A_B_path %>%
IEATools::load_tidy_iea_df(unit_val = "ktoe") %>%
IEATools::specify_all() %>%
ECCTools::specify_elect_heat_renewables() %>%
ECCTools::specify_elect_heat_fossil_fuels() %>%
ECCTools::specify_elect_heat_nuclear() %>%
ECCTools::specify_other_elec_heat_production() %>%
ECCTools::specify_elect_heat_markets() %>%
IEATools::add_psut_matnames(R_includes_all_exogenous_flows = FALSE) %>%
ECCTools::stat_diffs_to_balancing() %>%
ECCTools::stock_changes_to_balancing()
# FIRST, WE TEST THE DTA APPROACH
# Calculating total energy use by product group
tidy_AB_dta <- tidy_AB_data %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE)
res_dta <- tidy_AB_dta %>%
calc_all_products_use_by_group()
# Testing
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(950)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4050)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4750)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(700)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(750)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(2200)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3200)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(1000)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(5700)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3950)
# SECOND, WE TEST THE GMA APPROACH
# Calculating total energy use by product group
tidy_AB_data_gma <- tidy_AB_data %>%
ECCTools::transform_to_gma()
tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
prepare_gma_for_shares()
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
res_gma <- tidy_AB_data_gma_prepared %>%
calc_all_products_use_by_group()
# Testing
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(950)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4050)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4750)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(700)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(750)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(2200)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3200)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(1000)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(5700)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3950)
# THIRD,TRYING WITH NON-ENERGY USES FLOWS
# Adding a non-energy use flow
tidy_AB_data_non_energy <- tidy_AB_data %>%
tibble::add_row(
Country = "A",
Method = "PCM",
Energy.type = "E",
Last.stage = "Final",
Year = 2018,
Ledger.side = "Consumption",
Flow.aggregation.point = "Industry",
Flow = IEATools::non_energy_flows$non_energy_use_industry_transformation_energy,
Product = "Coke oven coke",
Unit = "ktoe",
E.dot = 300,
matnames = "Y"
)
# Calculating energy use by product excluding non-energy uses:
res_dta_excl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_all_products_use_by_group()
# Checking coal products
res_dta_excl_non_energy %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(950)
# Calculating energy use by product including non-energy uses:
res_dta_incl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_all_products_use_by_group(include_non_energy_uses = TRUE)
# Checking coal products
res_dta_incl_non_energy %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(1250)
# Testing
res_dta_incl_non_energy %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(6000)
res_dta_incl_non_energy %>%
dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3950)
})
# test_that("calc_primary_ff_supply works",{
#
# # Path to dummy AB data
# A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
# #"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
#
# # Loading data
# tidy_AB_data <- A_B_path %>%
# IEATools::load_tidy_iea_df() %>%
# IEATools::specify_all() %>%
# ECCTools::specify_elect_heat_renewables() %>%
# ECCTools::specify_elect_heat_fossil_fuels() %>%
# ECCTools::specify_elect_heat_nuclear() %>%
# ECCTools::specify_other_elec_heat_production() %>%
# ECCTools::specify_elect_heat_markets() %>%
# IEATools::add_psut_matnames() %>%
# ECCTools::stat_diffs_to_balancing() %>%
# ECCTools::stock_changes_to_balancing()
#
#
# # FIRST, WE TEST THE DTA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_dta <- tidy_AB_data %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE)
#
# res_dta <- tidy_AB_dta %>%
# calc_primary_ff_supply()
#
# # Testing
# res_dta %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Supply") %>%
# expect_equal(17600)
#
# res_dta %>%
# dplyr::filter(Country == "B") %>%
# nrow() %>%
# expect_equal(0)
#
#
# # SECOND, WE TEST THE GMA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_data_gma <- tidy_AB_data %>%
# ECCTools::transform_to_gma()
#
# tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
#
# res_gma <- tidy_AB_data_gma_prepared %>%
# calc_primary_ff_supply()
#
# # Testing
# res_gma %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Supply") %>%
# expect_equal(17600)
#
# res_gma %>%
# dplyr::filter(Country == "B") %>%
# nrow() %>%
# expect_equal(0)
# })
# test_that("calc_ff_use works",{
#
# # Path to dummy AB data
# A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#
# # Loading data
# tidy_AB_data <- A_B_path %>%
# IEATools::load_tidy_iea_df() %>%
# IEATools::specify_all() %>%
# ECCTools::specify_elect_heat_renewables() %>%
# ECCTools::specify_elect_heat_fossil_fuels() %>%
# ECCTools::specify_elect_heat_nuclear() %>%
# ECCTools::specify_other_elec_heat_production() %>%
# ECCTools::specify_elect_heat_markets() %>%
# IEATools::add_psut_matnames() %>%
# ECCTools::stat_diffs_to_balancing() %>%
# ECCTools::stock_changes_to_balancing()
#
#
# # FIRST, WE TEST THE DTA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_dta <- tidy_AB_data %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE)
#
# res_dta <- tidy_AB_dta %>%
# calc_ff_use()
#
# # Testing
# res_dta %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(5700)
#
# res_dta %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
#
#
# # SECOND, WE TEST THE GMA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_data_gma <- tidy_AB_data %>%
# ECCTools::transform_to_gma()
#
# tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
#
# res_gma <- tidy_AB_data_gma_prepared %>%
# calc_ff_use()
#
# # Testing
# res_gma %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(5700)
#
# res_gma %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
#
#
# # THIRD,TRYING WITH NON-ENERGY USES FLOWS
#
# # Adding a non-energy use flow
# tidy_AB_data_non_energy <- tidy_AB_data %>%
# tibble::add_row(
# Country = "A",
# Method = "PCM",
# Energy.type = "E",
# Last.stage = "Final",
# Year = 2018,
# Ledger.side = "Consumption",
# Flow.aggregation.point = "Industry",
# Flow = IEATools::non_energy_flows$non_energy_use_industry_transformation_energy,
# Product = "Coke oven coke",
# Unit = "ktoe",
# E.dot = 300,
# matnames = "Y"
# )
#
# # Calculating energy use by product excluding non-energy uses:
# res_dta_excl_non_energy <- tidy_AB_data_non_energy %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE) %>%
# calc_ff_use()
#
# # Testing
# res_dta_excl_non_energy %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(5700)
#
# res_dta_excl_non_energy %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
#
# # Calculating energy use by product including non-energy uses:
# res_dta_incl_non_energy <- tidy_AB_data_non_energy %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE) %>%
# calc_ff_use(include_non_energy_uses = TRUE)
#
# # Testing
# res_dta_incl_non_energy %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(6000)
#
# res_dta_incl_non_energy %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
# })
earamendia/EROITools documentation built on May 19, 2023, 10:30 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
test_that("calc_total_use_by_product works",{
# Path to dummy AB data
A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
# Loading data
tidy_AB_data <- A_B_path %>%
IEATools::load_tidy_iea_df(unit_val = "ktoe") %>%
IEATools::specify_all() %>%
ECCTools::specify_elect_heat_renewables() %>%
ECCTools::specify_elect_heat_fossil_fuels() %>%
ECCTools::specify_elect_heat_nuclear() %>%
ECCTools::specify_other_elec_heat_production() %>%
ECCTools::specify_elect_heat_markets() %>%
IEATools::add_psut_matnames(R_includes_all_exogenous_flows = FALSE) %>%
ECCTools::stat_diffs_to_balancing() %>%
ECCTools::stock_changes_to_balancing()
# FIRST, WE TEST THE DTA APPROACH
# Calculating total use of each product
tidy_AB_dta <- tidy_AB_data %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE)
res_dta <- tidy_AB_dta %>%
calc_total_use_by_product()
# Testing the DTA approach
# Country A
res_dta %>%
dplyr::filter(Country == "A", Product == "Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(750)
res_dta %>%
dplyr::filter(Country == "A", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(200)
res_dta %>%
dplyr::filter(Country == "A", Product == "Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1750)
res_dta %>%
dplyr::filter(Country == "A", Product == "Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(2300)
res_dta %>%
dplyr::filter(Country == "A", Product == "Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(700)
# Country B
res_dta %>%
dplyr::filter(Country == "B", Product == "Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(650)
res_dta %>%
dplyr::filter(Country == "B", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(100)
res_dta %>%
dplyr::filter(Country == "B", Product == "Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
res_dta %>%
dplyr::filter(Country == "B", Product == "Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1200)
res_dta %>%
dplyr::filter(Country == "B", Product == "Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
# SECOND, WE TEST THE GMA APPROACH
tidy_AB_data_gma <- tidy_AB_data %>%
ECCTools::transform_to_gma()
tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
prepare_gma_for_shares()
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
res_gma <- tidy_AB_data_gma_prepared %>%
calc_total_use_by_product()
# Actual tests below:
# Country A
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(750)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(80)
res_gma %>%
dplyr::filter(Country == "A", Product == "{B}_Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(120)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1750)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(2300)
res_gma %>%
dplyr::filter(Country == "A", Product == "{A}_Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(700)
# Country B
res_gma %>%
dplyr::filter(Country == "B", product_without_origin == "Blast furnace gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(650)
res_gma %>%
dplyr::filter(Country == "B", product_without_origin == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(100)
res_gma %>%
dplyr::filter(Country == "B", product_without_origin == "Kerosene type jet fuel excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
res_gma %>%
dplyr::filter(Country == "B", Product == "{B}_Motor gasoline excl. biofuels") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1200)
res_gma %>%
dplyr::filter(Country == "B", Product == "{A}_Natural gas") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(1000)
# THIRD, QUICK TEST WITH NON-ENERGY USES
tidy_AB_data_non_energy <- tidy_AB_data %>%
tibble::add_row(
Country = "A",
Method = "PCM",
Energy.type = "E",
Last.stage = "Final",
Year = 2018,
Ledger.side = "Consumption",
Flow.aggregation.point = "Industry",
Flow = IEATools::non_energy_flows$non_energy_use_industry_transformation_energy,
Product = "Coke oven coke",
Unit = "ktoe",
E.dot = 300,
matnames = "Y"
)
# Calculating energy use by product excluding non-energy uses:
res_dta_excl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_total_use_by_product()
# Checking coke oven coke
res_dta_excl_non_energy %>%
dplyr::filter(Country == "A", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(200)
# Calculating energy use by product including non-energy uses:
res_dta_incl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_total_use_by_product(include_non_energy_uses = TRUE)
# Checking coke oven coke
res_dta_incl_non_energy %>%
dplyr::filter(Country == "A", Product == "Coke oven coke") %>%
magrittr::extract2("Total_Product_Use") %>%
expect_equal(500)
})
test_that("calc_primary_products_supply_by_group works",{
# Path to dummy AB data
A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
# Loading data
tidy_AB_data <- A_B_path %>%
IEATools::load_tidy_iea_df() %>%
IEATools::specify_all() %>%
ECCTools::specify_elect_heat_renewables() %>%
ECCTools::specify_elect_heat_fossil_fuels() %>%
ECCTools::specify_elect_heat_nuclear() %>%
ECCTools::specify_other_elec_heat_production() %>%
ECCTools::specify_elect_heat_markets() %>%
IEATools::add_psut_matnames(R_includes_all_exogenous_flows = FALSE) %>%
ECCTools::stat_diffs_to_balancing() %>%
ECCTools::stock_changes_to_balancing()
# FIRST, WE TEST THE DTA APPROACH
# Calculating total use of each product
tidy_AB_dta <- tidy_AB_data %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE)
res_dta <- tidy_AB_dta %>%
calc_primary_products_supply_by_group()
# Testing
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(5000)
# Doesn't quite match with the ECC diagram in ECCTools package description because
# 1) Have added 500 oil and gas extraction for creating unbalances
# 2) Natural gas also created by the oil refineries in our example to add challenge
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(12600)
res_dta %>%
dplyr::filter(Country == "B") %>%
nrow() %>%
expect_equal(0)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(17600)
# SECOND, WE TEST THE GMA APPROACH
# Calculating total use of each product
tidy_AB_data_gma <- tidy_AB_data %>%
ECCTools::transform_to_gma()
tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
prepare_gma_for_shares()
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
res_gma <- tidy_AB_data_gma_prepared %>%
calc_primary_products_supply_by_group()
# Testing
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(5000)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(12600)
res_gma %>%
dplyr::filter(Country == "B") %>%
nrow() %>%
expect_equal(0)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Supply") %>%
expect_equal(17600)
})
test_that("calc_all_products_use_by_group works",{
# Path to dummy AB data
A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
# Loading data
tidy_AB_data <- A_B_path %>%
IEATools::load_tidy_iea_df(unit_val = "ktoe") %>%
IEATools::specify_all() %>%
ECCTools::specify_elect_heat_renewables() %>%
ECCTools::specify_elect_heat_fossil_fuels() %>%
ECCTools::specify_elect_heat_nuclear() %>%
ECCTools::specify_other_elec_heat_production() %>%
ECCTools::specify_elect_heat_markets() %>%
IEATools::add_psut_matnames(R_includes_all_exogenous_flows = FALSE) %>%
ECCTools::stat_diffs_to_balancing() %>%
ECCTools::stock_changes_to_balancing()
# FIRST, WE TEST THE DTA APPROACH
# Calculating total energy use by product group
tidy_AB_dta <- tidy_AB_data %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE)
res_dta <- tidy_AB_dta %>%
calc_all_products_use_by_group()
# Testing
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(950)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4050)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4750)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(700)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(750)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(2200)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3200)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(1000)
res_dta %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(5700)
res_dta %>%
dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3950)
# SECOND, WE TEST THE GMA APPROACH
# Calculating total energy use by product group
tidy_AB_data_gma <- tidy_AB_data %>%
ECCTools::transform_to_gma()
tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
prepare_gma_for_shares()
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
res_gma <- tidy_AB_data_gma_prepared %>%
calc_all_products_use_by_group()
# Testing
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(950)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4050)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(4750)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(700)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(750)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Oil products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(2200)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Oil and gas products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3200)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "Natural gas") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(1000)
res_gma %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(5700)
res_gma %>%
dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3950)
# THIRD,TRYING WITH NON-ENERGY USES FLOWS
# Adding a non-energy use flow
tidy_AB_data_non_energy <- tidy_AB_data %>%
tibble::add_row(
Country = "A",
Method = "PCM",
Energy.type = "E",
Last.stage = "Final",
Year = 2018,
Ledger.side = "Consumption",
Flow.aggregation.point = "Industry",
Flow = IEATools::non_energy_flows$non_energy_use_industry_transformation_energy,
Product = "Coke oven coke",
Unit = "ktoe",
E.dot = 300,
matnames = "Y"
)
# Calculating energy use by product excluding non-energy uses:
res_dta_excl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_all_products_use_by_group()
# Checking coal products
res_dta_excl_non_energy %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(950)
# Calculating energy use by product including non-energy uses:
res_dta_incl_non_energy <- tidy_AB_data_non_energy %>%
ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
select_dta_observations = FALSE) %>%
calc_all_products_use_by_group(include_non_energy_uses = TRUE)
# Checking coal products
res_dta_incl_non_energy %>%
dplyr::filter(Country == "A", Product.Group == "Coal products") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(1250)
# Testing
res_dta_incl_non_energy %>%
dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(6000)
res_dta_incl_non_energy %>%
dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
magrittr::extract2("Total_Group_Use") %>%
expect_equal(3950)
})
# test_that("calc_primary_ff_supply works",{
#
# # Path to dummy AB data
# A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
# #"inst/extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv"
#
# # Loading data
# tidy_AB_data <- A_B_path %>%
# IEATools::load_tidy_iea_df() %>%
# IEATools::specify_all() %>%
# ECCTools::specify_elect_heat_renewables() %>%
# ECCTools::specify_elect_heat_fossil_fuels() %>%
# ECCTools::specify_elect_heat_nuclear() %>%
# ECCTools::specify_other_elec_heat_production() %>%
# ECCTools::specify_elect_heat_markets() %>%
# IEATools::add_psut_matnames() %>%
# ECCTools::stat_diffs_to_balancing() %>%
# ECCTools::stock_changes_to_balancing()
#
#
# # FIRST, WE TEST THE DTA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_dta <- tidy_AB_data %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE)
#
# res_dta <- tidy_AB_dta %>%
# calc_primary_ff_supply()
#
# # Testing
# res_dta %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Supply") %>%
# expect_equal(17600)
#
# res_dta %>%
# dplyr::filter(Country == "B") %>%
# nrow() %>%
# expect_equal(0)
#
#
# # SECOND, WE TEST THE GMA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_data_gma <- tidy_AB_data %>%
# ECCTools::transform_to_gma()
#
# tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
#
# res_gma <- tidy_AB_data_gma_prepared %>%
# calc_primary_ff_supply()
#
# # Testing
# res_gma %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Supply") %>%
# expect_equal(17600)
#
# res_gma %>%
# dplyr::filter(Country == "B") %>%
# nrow() %>%
# expect_equal(0)
# })
# test_that("calc_ff_use works",{
#
# # Path to dummy AB data
# A_B_path <- system.file("extdata/A_B_data_full_2018_format_stat_diffs_stock_changes.csv", package = "EROITools")
#
# # Loading data
# tidy_AB_data <- A_B_path %>%
# IEATools::load_tidy_iea_df() %>%
# IEATools::specify_all() %>%
# ECCTools::specify_elect_heat_renewables() %>%
# ECCTools::specify_elect_heat_fossil_fuels() %>%
# ECCTools::specify_elect_heat_nuclear() %>%
# ECCTools::specify_other_elec_heat_production() %>%
# ECCTools::specify_elect_heat_markets() %>%
# IEATools::add_psut_matnames() %>%
# ECCTools::stat_diffs_to_balancing() %>%
# ECCTools::stock_changes_to_balancing()
#
#
# # FIRST, WE TEST THE DTA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_dta <- tidy_AB_data %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE)
#
# res_dta <- tidy_AB_dta %>%
# calc_ff_use()
#
# # Testing
# res_dta %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(5700)
#
# res_dta %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
#
#
# # SECOND, WE TEST THE GMA APPROACH
#
# # Calculating total energy use by product group
# tidy_AB_data_gma <- tidy_AB_data %>%
# ECCTools::transform_to_gma()
#
# tidy_AB_data_gma_prepared <- tidy_AB_data_gma %>%
# dplyr::mutate(
# Country = stringr::str_extract(Flow, "\\{.*\\}") %>%
# stringr::str_remove("\\{") %>%
# stringr::str_remove("\\}"),
# Flow = stringr::str_remove(Flow, "\\{.*\\}_"),
# product_without_origin = stringr::str_remove(Product, "\\{.*\\}_"),
# )
#
# res_gma <- tidy_AB_data_gma_prepared %>%
# calc_ff_use()
#
# # Testing
# res_gma %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(5700)
#
# res_gma %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
#
#
# # THIRD,TRYING WITH NON-ENERGY USES FLOWS
#
# # Adding a non-energy use flow
# tidy_AB_data_non_energy <- tidy_AB_data %>%
# tibble::add_row(
# Country = "A",
# Method = "PCM",
# Energy.type = "E",
# Last.stage = "Final",
# Year = 2018,
# Ledger.side = "Consumption",
# Flow.aggregation.point = "Industry",
# Flow = IEATools::non_energy_flows$non_energy_use_industry_transformation_energy,
# Product = "Coke oven coke",
# Unit = "ktoe",
# E.dot = 300,
# matnames = "Y"
# )
#
# # Calculating energy use by product excluding non-energy uses:
# res_dta_excl_non_energy <- tidy_AB_data_non_energy %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE) %>%
# calc_ff_use()
#
# # Testing
# res_dta_excl_non_energy %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(5700)
#
# res_dta_excl_non_energy %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
#
# # Calculating energy use by product including non-energy uses:
# res_dta_incl_non_energy <- tidy_AB_data_non_energy %>%
# ECCTools::transform_to_dta(requirement_matrices_list = c("U_feed"),
# select_dta_observations = FALSE) %>%
# calc_ff_use(include_non_energy_uses = TRUE)
#
# # Testing
# res_dta_incl_non_energy %>%
# dplyr::filter(Country == "A", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(6000)
#
# res_dta_incl_non_energy %>%
# dplyr::filter(Country == "B", Product.Group == "All fossil fuels") %>%
# magrittr::extract2("Total_Group_Use") %>%
# expect_equal(3950)
# })
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.