# data wrangling functions
## libs
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,
here,
vroom,
ISOcodes)
#### load raw mrio results
read_raw <- function(peu_q_count = 10) {
dat_raw = vroom(here("analysis", "data", "derived", "si", paste0("mrio_results_eu_ntile_mapped_n_", peu_q_count, "_method2_ixi.csv")))
dat_raw
}
# get mrio results aggregated by country summarized by country quintile
get_country_summary_by_cquint_and_euntile <- function(peu_q_count = 10) {
dat_raw = read_raw(peu_q_count)
dat_summary_country_quintile = dat_raw %>%
group_by(year, iso2, quint, eu_q_rank) %>%
summarise(total_fd_me = sum(fd_me, na.rm = T),
# totals
total_adult_eq = first(adult_eq),
total_co2_kg = sum(co2_kg),
total_co2_dom_kg = sum(co2_dom_kg),
total_co2_eu_kg = sum(co2_eu_kg),
total_co2_noneu_kg = total_co2_kg - total_co2_dom_kg - total_co2_eu_kg,
total_co2eq_kg = sum(co2eq_kg),
total_co2eq_dom_kg = sum(co2eq_dom_kg),
total_co2eq_eu_kg = sum(co2eq_eu_kg),
total_co2eq_noneu_kg = total_co2eq_kg - total_co2eq_dom_kg - total_co2eq_eu_kg,
total_energy_use_tj = sum(energy_use_tj),
total_energy_use_dom_tj = sum(energy_use_dom_tj),
total_energy_use_eu_tj = sum(energy_use_eu_tj),
total_energy_use_noneu_tj = total_energy_use_tj -
total_energy_use_dom_tj -
total_energy_use_eu_tj,
#Per adult equivalent values
pae_fd_ke = total_fd_me*1000000/total_adult_eq*0.001,
pae_co2_t = total_co2_kg/total_adult_eq*0.001,
pae_co2_dom_t = total_co2_dom_kg/total_adult_eq*0.001,
pae_co2_eu_t = total_co2_eu_kg/total_adult_eq*0.001,
pae_co2_noneu_t = total_co2_noneu_kg/total_adult_eq*0.001,
pae_co2eq_t = total_co2eq_kg/total_adult_eq*0.001,
pae_co2eq_dom_t = total_co2eq_dom_kg/total_adult_eq*0.001,
pae_co2eq_eu_t = total_co2eq_eu_kg/total_adult_eq*0.001,
pae_co2eq_noneu_t = total_co2eq_noneu_kg/total_adult_eq*0.001,
pae_energy_use_gj = total_energy_use_tj*1000/total_adult_eq,
pae_energy_use_dom_gj = total_energy_use_dom_tj*1000/total_adult_eq,
pae_energy_use_eu_gj = total_energy_use_eu_tj*1000/total_adult_eq,
pae_energy_use_noneu_gj = total_energy_use_noneu_tj*1000/total_adult_eq,
# per euro intensities
pe_co2_kg = total_co2_kg/(total_fd_me*1000000),
pe_co2_dom_kg = total_co2_dom_kg/(total_fd_me*1000000),
pe_co2_eu_kg = total_co2_eu_kg/(total_fd_me*1000000),
pe_co2_noneu_kg = total_co2_noneu_kg/(total_fd_me*1000000),
pe_co2eq_kg = total_co2eq_kg/(total_fd_me*1000000),
pe_co2eq_dom_kg = total_co2eq_dom_kg/(total_fd_me*1000000),
pe_co2eq_eu_kg = total_co2eq_eu_kg/(total_fd_me*1000000),
pe_co2eq_noneu_kg = total_co2eq_noneu_kg/(total_fd_me*1000000),
pe_energy_use_mj = total_energy_use_tj/(total_fd_me),
pe_energy_use_dom_mj = total_energy_use_dom_tj/(total_fd_me),
pe_energy_use_eu_mj = total_energy_use_eu_tj/(total_fd_me),
pe_energy_use_noneu_mj = total_energy_use_noneu_tj/(total_fd_me)
)
dat_summary_country_quintile
}
# get mrio results aggregated by country summarized by eu quintile
get_country_summary_by_eu_ntile <- function(peu_q_count = 10) {
dat_summary_by_country_quintile = get_country_summary_by_cquint_and_euntile(peu_q_count)
dat_summary_by_eu_ntile = dat_summary_by_country_quintile %>%
group_by(year, iso2, eu_q_rank) %>%
summarise(total_fd_me = sum(total_fd_me),
# totals
total_adult_eq = sum(total_adult_eq),
total_co2_kg = sum(total_co2_kg),
total_co2_dom_kg = sum(total_co2_dom_kg),
total_co2_eu_kg = sum(total_co2_eu_kg),
total_co2_noneu_kg = total_co2_kg - total_co2_dom_kg - total_co2_eu_kg,
total_co2eq_kg = sum(total_co2eq_kg),
total_co2eq_dom_kg = sum(total_co2eq_dom_kg),
total_co2eq_eu_kg = sum(total_co2eq_eu_kg),
total_co2eq_noneu_kg = total_co2eq_kg - total_co2eq_dom_kg - total_co2eq_eu_kg,
total_energy_use_tj = sum(total_energy_use_tj),
total_energy_use_dom_tj = sum(total_energy_use_dom_tj),
total_energy_use_eu_tj = sum(total_energy_use_eu_tj),
total_energy_use_noneu_tj = total_energy_use_tj -
total_energy_use_dom_tj -
total_energy_use_eu_tj,
#Per adult equivalent values
pae_fd_ke = total_fd_me*1000000/total_adult_eq*0.001,
pae_co2_t = total_co2_kg/total_adult_eq*0.001,
pae_co2_dom_t = total_co2_dom_kg/total_adult_eq*0.001,
pae_co2_eu_t = total_co2_eu_kg/total_adult_eq*0.001,
pae_co2_noneu_t = total_co2_noneu_kg/total_adult_eq*0.001,
pae_co2eq_t = total_co2eq_kg/total_adult_eq*0.001,
pae_co2eq_dom_t = total_co2eq_dom_kg/total_adult_eq*0.001,
pae_co2eq_eu_t = total_co2eq_eu_kg/total_adult_eq*0.001,
pae_co2eq_noneu_t = total_co2eq_noneu_kg/total_adult_eq*0.001,
pae_energy_use_gj = total_energy_use_tj*1000/total_adult_eq,
pae_energy_use_dom_gj = total_energy_use_dom_tj*1000/total_adult_eq,
pae_energy_use_eu_gj = total_energy_use_eu_tj*1000/total_adult_eq,
pae_energy_use_noneu_gj = total_energy_use_noneu_tj*1000/total_adult_eq,
# per euro intensities
pe_co2_kg = total_co2_kg/(total_fd_me*1000000),
pe_co2_dom_kg = total_co2_dom_kg/(total_fd_me*1000000),
pe_co2_eu_kg = total_co2_eu_kg/(total_fd_me*1000000),
pe_co2_noneu_kg = total_co2_noneu_kg/(total_fd_me*1000000),
pe_co2eq_kg = total_co2eq_kg/(total_fd_me*1000000),
pe_co2eq_dom_kg = total_co2eq_dom_kg/(total_fd_me*1000000),
pe_co2eq_eu_kg = total_co2eq_eu_kg/(total_fd_me*1000000),
pe_co2eq_noneu_kg = total_co2eq_noneu_kg/(total_fd_me*1000000),
pe_energy_use_mj = total_energy_use_tj/(total_fd_me),
pe_energy_use_dom_mj = total_energy_use_dom_tj/(total_fd_me),
pe_energy_use_eu_mj = total_energy_use_eu_tj/(total_fd_me),
pe_energy_use_noneu_mj = total_energy_use_noneu_tj/(total_fd_me)
)
dat_summary_by_eu_ntile
}
# get mrio results aggregated by five sectors and summarized by country quintile
get_sector_summary_by_country_quintile <- function(peu_q_count = 10) {
dat_sectors_by_country_quintile = read_raw(peu_q_count) %>%
group_by(year, iso2, quint, eu_q_rank, coicop) %>%
summarise(total_fd_me = sum(fd_me),
# totals
total_adult_eq = first(adult_eq),
total_co2_kg = sum(co2_kg),
total_co2_dom_kg = sum(co2_dom_kg),
total_co2_eu_kg = sum(co2_eu_kg),
total_co2_noneu_kg = total_co2_kg - total_co2_dom_kg - total_co2_eu_kg,
total_co2eq_kg = sum(co2eq_kg),
total_co2eq_dom_kg = sum(co2eq_dom_kg),
total_co2eq_eu_kg = sum(co2eq_eu_kg),
total_co2eq_noneu_kg = total_co2eq_kg - total_co2eq_dom_kg - total_co2eq_eu_kg,
total_energy_use_tj = sum(energy_use_tj),
total_energy_use_dom_tj = sum(energy_use_dom_tj),
total_energy_use_eu_tj = sum(energy_use_eu_tj),
total_energy_use_noneu_tj = total_energy_use_tj -
total_energy_use_dom_tj -
total_energy_use_eu_tj,
#Per adult equivalent values
pae_fd_ke = total_fd_me*1000000/total_adult_eq*0.001,
pae_co2_t = total_co2_kg/total_adult_eq*0.001,
pae_co2_dom_t = total_co2_dom_kg/total_adult_eq*0.001,
pae_co2_eu_t = total_co2_eu_kg/total_adult_eq*0.001,
pae_co2_noneu_t = total_co2_noneu_kg/total_adult_eq*0.001,
pae_co2eq_t = total_co2eq_kg/total_adult_eq*0.001,
pae_co2eq_dom_t = total_co2eq_dom_kg/total_adult_eq*0.001,
pae_co2eq_eu_t = total_co2eq_eu_kg/total_adult_eq*0.001,
pae_co2eq_noneu_t = total_co2eq_noneu_kg/total_adult_eq*0.001,
pae_energy_use_gj = total_energy_use_tj*1000/total_adult_eq,
pae_energy_use_dom_gj = total_energy_use_dom_tj*1000/total_adult_eq,
pae_energy_use_eu_gj = total_energy_use_eu_tj*1000/total_adult_eq,
pae_energy_use_noneu_gj = total_energy_use_noneu_tj*1000/total_adult_eq,
# per euro intensities
pe_co2_kg = total_co2_kg/(total_fd_me*1000000),
pe_co2_dom_kg = total_co2_dom_kg/(total_fd_me*1000000),
pe_co2_eu_kg = total_co2_eu_kg/(total_fd_me*1000000),
pe_co2_noneu_kg = total_co2_noneu_kg/(total_fd_me*1000000),
pe_co2eq_kg = total_co2eq_kg/(total_fd_me*1000000),
pe_co2eq_dom_kg = total_co2eq_dom_kg/(total_fd_me*1000000),
pe_co2eq_eu_kg = total_co2eq_eu_kg/(total_fd_me*1000000),
pe_co2eq_noneu_kg = total_co2eq_noneu_kg/(total_fd_me*1000000),
pe_energy_use_mj = total_energy_use_tj/(total_fd_me),
pe_energy_use_dom_mj = total_energy_use_dom_tj/(total_fd_me),
pe_energy_use_eu_mj = total_energy_use_eu_tj/(total_fd_me),
pe_energy_use_noneu_mj = total_energy_use_noneu_tj/(total_fd_me)
)
dat_sectors_by_country_quintile
}
# get mrio results aggregated by five sectors and summarized by country quintile
get_sector_summary_by_country_quintile_direct <- function(peu_q_count = 10) {
dat_sectors_by_country_quintile = read_raw(peu_q_count) %>%
## shift domestic use to new direct column for direct sectors
#mutate(co2eq_direct_kg = if_else(sector_id %in% c(164,165,166), co2eq_dom_kg, 0),
# co2_direct_kg = if_else(sector_id %in% c(164,165,166), co2_dom_kg, 0),
# energy_use_direct_tj = if_else(sector_id %in% c(164,165,166), energy_use_dom_tj, 0)) %>%
#mutate(co2eq_dom_kg = if_else(sector_id %in% c(164,165,166), 0, co2eq_dom_kg),
# co2_dom_kg = if_else(sector_id %in% c(164,165,166), 0, co2_dom_kg),
# energy_use_dom_tj = if_else(sector_id %in% c(164,165,166), 0, energy_use_dom_tj)) %>%
group_by(year, iso2, quint, eu_q_rank, coicop) %>%
summarise(total_fd_me = sum(fd_me),
# totals
total_adult_eq = first(adult_eq),
total_co2_kg = sum(co2_kg),
total_co2_dom_kg = sum(co2_dom_kg),
total_co2_eu_kg = sum(co2_eu_kg),
total_co2_direct_kg = sum(co2_direct_kg),
total_co2_noneu_kg = total_co2_kg - total_co2_dom_kg - total_co2_eu_kg - total_co2_direct_kg,
total_co2eq_kg = sum(co2eq_kg),
total_co2eq_dom_kg = sum(co2eq_dom_kg),
total_co2eq_eu_kg = sum(co2eq_eu_kg),
total_co2eq_direct_kg = sum(co2eq_direct_kg),
total_co2eq_noneu_kg = total_co2eq_kg - total_co2eq_dom_kg - total_co2eq_eu_kg -total_co2eq_direct_kg,
total_energy_use_tj = sum(energy_use_tj),
total_energy_use_dom_tj = sum(energy_use_dom_tj),
total_energy_use_eu_tj = sum(energy_use_eu_tj),
total_energy_use_direct_tj = sum(energy_use_direct_tj),
total_energy_use_noneu_tj = total_energy_use_tj -
total_energy_use_dom_tj -
total_energy_use_eu_tj - total_energy_use_direct_tj,
#Per adult equivalent values
pae_fd_ke = total_fd_me*1000000/total_adult_eq*0.001,
pae_co2_t = total_co2_kg/total_adult_eq*0.001,
pae_co2_dom_t = total_co2_dom_kg/total_adult_eq*0.001,
pae_co2_eu_t = total_co2_eu_kg/total_adult_eq*0.001,
pae_co2_direct_t = total_co2_direct_kg/total_adult_eq*0.001,
pae_co2_noneu_t = total_co2_noneu_kg/total_adult_eq*0.001,
pae_co2eq_t = total_co2eq_kg/total_adult_eq*0.001,
pae_co2eq_dom_t = total_co2eq_dom_kg/total_adult_eq*0.001,
pae_co2eq_eu_t = total_co2eq_eu_kg/total_adult_eq*0.001,
pae_co2eq_direct_t = total_co2eq_direct_kg/total_adult_eq*0.001,
pae_co2eq_noneu_t = total_co2eq_noneu_kg/total_adult_eq*0.001,
pae_energy_use_gj = total_energy_use_tj*1000/total_adult_eq,
pae_energy_use_dom_gj = total_energy_use_dom_tj*1000/total_adult_eq,
pae_energy_use_eu_gj = total_energy_use_eu_tj*1000/total_adult_eq,
pae_energy_use_direct_gj = total_energy_use_direct_tj*1000/total_adult_eq,
pae_energy_use_noneu_gj = total_energy_use_noneu_tj*1000/total_adult_eq,
# per euro intensities
pe_co2_kg = total_co2_kg/(total_fd_me*1000000),
pe_co2_dom_kg = total_co2_dom_kg/(total_fd_me*1000000),
pe_co2_eu_kg = total_co2_eu_kg/(total_fd_me*1000000),
pe_co2_direct_kg = total_co2_direct_kg/(total_fd_me*1000000),
pe_co2_noneu_kg = total_co2_noneu_kg/(total_fd_me*1000000),
pe_co2eq_kg = total_co2eq_kg/(total_fd_me*1000000),
pe_co2eq_dom_kg = total_co2eq_dom_kg/(total_fd_me*1000000),
pe_co2eq_eu_kg = total_co2eq_eu_kg/(total_fd_me*1000000),
pe_co2eq_direct_kg = total_co2eq_direct_kg/(total_fd_me*1000000),
pe_co2eq_noneu_kg = total_co2eq_noneu_kg/(total_fd_me*1000000),
pe_energy_use_mj = total_energy_use_tj/(total_fd_me),
pe_energy_use_dom_mj = total_energy_use_dom_tj/(total_fd_me),
pe_energy_use_eu_mj = total_energy_use_eu_tj/(total_fd_me),
pe_energy_use_direct_mj = total_energy_use_direct_tj/(total_fd_me),
pe_energy_use_noneu_mj = total_energy_use_noneu_tj/(total_fd_me)
)
dat_sectors_by_country_quintile
}
# get mrio results aggregated by five sectors and summarized by eu ntile
get_sector_summary_by_eu_ntile <- function(peu_q_count = 10) {
dat_sectors_by_country_quintile = get_sector_summary_by_country_quintile(peu_q_count)
dat_sectors_by_eu_ntile = dat_sectors_by_country_quintile %>%
group_by(year, eu_q_rank, coicop) %>%
summarise(total_fd_me = sum(total_fd_me),
# totals
total_adult_eq = sum(total_adult_eq),
total_co2_kg = sum(total_co2_kg),
total_co2_dom_kg = sum(total_co2_dom_kg),
total_co2_eu_kg = sum(total_co2_eu_kg),
total_co2_noneu_kg = total_co2_kg - total_co2_dom_kg - total_co2_eu_kg,
total_co2eq_kg = sum(total_co2eq_kg),
total_co2eq_dom_kg = sum(total_co2eq_dom_kg),
total_co2eq_eu_kg = sum(total_co2eq_eu_kg),
total_co2eq_noneu_kg = total_co2eq_kg - total_co2eq_dom_kg - total_co2eq_eu_kg,
total_energy_use_tj = sum(total_energy_use_tj),
total_energy_use_dom_tj = sum(total_energy_use_dom_tj),
total_energy_use_eu_tj = sum(total_energy_use_eu_tj),
total_energy_use_noneu_tj = total_energy_use_tj -
total_energy_use_dom_tj -
total_energy_use_eu_tj,
#Per adult equivalent values
pae_fd_ke = total_fd_me*1000000/total_adult_eq*0.001,
pae_co2_t = total_co2_kg/total_adult_eq*0.001,
pae_co2_dom_t = total_co2_dom_kg/total_adult_eq*0.001,
pae_co2_eu_t = total_co2_eu_kg/total_adult_eq*0.001,
pae_co2_noneu_t = total_co2_noneu_kg/total_adult_eq*0.001,
pae_co2eq_t = total_co2eq_kg/total_adult_eq*0.001,
pae_co2eq_dom_t = total_co2eq_dom_kg/total_adult_eq*0.001,
pae_co2eq_eu_t = total_co2eq_eu_kg/total_adult_eq*0.001,
pae_co2eq_noneu_t = total_co2eq_noneu_kg/total_adult_eq*0.001,
pae_energy_use_gj = total_energy_use_tj*1000/total_adult_eq,
pae_energy_use_dom_gj = total_energy_use_dom_tj*1000/total_adult_eq,
pae_energy_use_eu_gj = total_energy_use_eu_tj*1000/total_adult_eq,
pae_energy_use_noneu_gj = total_energy_use_noneu_tj*1000/total_adult_eq,
# per euro intensities
pe_co2_kg = total_co2_kg/(total_fd_me*1000000),
pe_co2_dom_kg = total_co2_dom_kg/(total_fd_me*1000000),
pe_co2_eu_kg = total_co2_eu_kg/(total_fd_me*1000000),
pe_co2_noneu_kg = total_co2_noneu_kg/(total_fd_me*1000000),
pe_co2eq_kg = total_co2eq_kg/(total_fd_me*1000000),
pe_co2eq_dom_kg = total_co2eq_dom_kg/(total_fd_me*1000000),
pe_co2eq_eu_kg = total_co2eq_eu_kg/(total_fd_me*1000000),
pe_co2eq_noneu_kg = total_co2eq_noneu_kg/(total_fd_me*1000000),
pe_energy_use_mj = total_energy_use_tj/(total_fd_me),
pe_energy_use_dom_mj = total_energy_use_dom_tj/(total_fd_me),
pe_energy_use_eu_mj = total_energy_use_eu_tj/(total_fd_me),
pe_energy_use_noneu_mj = total_energy_use_noneu_tj/(total_fd_me)
)
dat_sectors_by_eu_ntile
}
# get mrio results aggregated by five sectors and summarized by eu ntile
get_sector_summary_by_eu_ntile_direct <- function(peu_q_count = 10) {
dat_sectors_by_country_quintile = get_sector_summary_by_country_quintile_direct(peu_q_count)
dat_sectors_by_eu_ntile = dat_sectors_by_country_quintile %>%
group_by(year, eu_q_rank, coicop) %>%
summarise(total_fd_me = sum(total_fd_me),
# totals
total_adult_eq = sum(total_adult_eq),
total_co2_kg = sum(total_co2_kg),
total_co2_dom_kg = sum(total_co2_dom_kg),
total_co2_eu_kg = sum(total_co2_eu_kg),
total_co2_direct_kg = sum(total_co2_direct_kg),
total_co2_noneu_kg = total_co2_kg - total_co2_dom_kg - total_co2_eu_kg - total_co2_direct_kg,
total_co2eq_kg = sum(total_co2eq_kg),
total_co2eq_dom_kg = sum(total_co2eq_dom_kg),
total_co2eq_eu_kg = sum(total_co2eq_eu_kg),
total_co2eq_direct_kg = sum(total_co2eq_direct_kg),
total_co2eq_noneu_kg = total_co2eq_kg - total_co2eq_dom_kg - total_co2eq_eu_kg - total_co2eq_direct_kg,
total_energy_use_tj = sum(total_energy_use_tj),
total_energy_use_dom_tj = sum(total_energy_use_dom_tj),
total_energy_use_eu_tj = sum(total_energy_use_eu_tj),
total_energy_use_direct_tj = sum(total_energy_use_direct_tj),
total_energy_use_noneu_tj = total_energy_use_tj -
total_energy_use_dom_tj -
total_energy_use_eu_tj - total_energy_use_direct_tj,
#Per adult equivalent values
pae_fd_ke = total_fd_me*1000000/total_adult_eq*0.001,
pae_co2_t = total_co2_kg/total_adult_eq*0.001,
pae_co2_dom_t = total_co2_dom_kg/total_adult_eq*0.001,
pae_co2_eu_t = total_co2_eu_kg/total_adult_eq*0.001,
pae_co2_direct_t = total_co2_direct_kg/total_adult_eq*0.001,
pae_co2_noneu_t = total_co2_noneu_kg/total_adult_eq*0.001,
pae_co2eq_t = total_co2eq_kg/total_adult_eq*0.001,
pae_co2eq_dom_t = total_co2eq_dom_kg/total_adult_eq*0.001,
pae_co2eq_eu_t = total_co2eq_eu_kg/total_adult_eq*0.001,
pae_co2eq_direct_t = total_co2eq_direct_kg/total_adult_eq*0.001,
pae_co2eq_noneu_t = total_co2eq_noneu_kg/total_adult_eq*0.001,
pae_energy_use_gj = total_energy_use_tj*1000/total_adult_eq,
pae_energy_use_dom_gj = total_energy_use_dom_tj*1000/total_adult_eq,
pae_energy_use_eu_gj = total_energy_use_eu_tj*1000/total_adult_eq,
pae_energy_use_direct_gj = total_energy_use_direct_tj*1000/total_adult_eq,
pae_energy_use_noneu_gj = total_energy_use_noneu_tj*1000/total_adult_eq,
# per euro intensities
pe_co2_kg = total_co2_kg/(total_fd_me*1000000),
pe_co2_dom_kg = total_co2_dom_kg/(total_fd_me*1000000),
pe_co2_eu_kg = total_co2_eu_kg/(total_fd_me*1000000),
pe_co2_direct_kg = total_co2_direct_kg/(total_fd_me*1000000),
pe_co2_noneu_kg = total_co2_noneu_kg/(total_fd_me*1000000),
pe_co2eq_kg = total_co2eq_kg/(total_fd_me*1000000),
pe_co2eq_dom_kg = total_co2eq_dom_kg/(total_fd_me*1000000),
pe_co2eq_eu_kg = total_co2eq_eu_kg/(total_fd_me*1000000),
pe_co2eq_direct_kg = total_co2eq_direct_kg/(total_fd_me*1000000),
pe_co2eq_noneu_kg = total_co2eq_noneu_kg/(total_fd_me*1000000),
pe_energy_use_mj = total_energy_use_tj/(total_fd_me),
pe_energy_use_dom_mj = total_energy_use_dom_tj/(total_fd_me),
pe_energy_use_eu_mj = total_energy_use_eu_tj/(total_fd_me),
pe_energy_use_direct_mj = total_energy_use_direct_tj/(total_fd_me),
pe_energy_use_noneu_mj = total_energy_use_noneu_tj/(total_fd_me)
)
dat_sectors_by_eu_ntile
}
# pivot results and add indicator type and eu ntile columns
pivot_results_longer <- function(pdat_in, pcols_exclude) {
## get iso3 codes to attach
country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
# pivot longer (exlcuding paramter columns) and attach indicator names
dat_out = pdat_in %>%
pivot_longer(cols = -all_of(pcols_exclude),
names_to = "indicator",
values_to = "value") %>%
mutate(indicator_type = case_when(grepl("total_", indicator) ~ "total",
grepl("pae_", indicator) ~ "per_ae",
grepl("pe_", indicator) ~ "intensity",
TRUE ~ "other"))
# if iso2 country code is present, add iso3
if("iso2" %in% colnames(dat_out)) {
dat_out = dat_out %>%
left_join(country_codes, by="iso2")
}
# if the eu_q_rank column exists, provide a factor column
if("eu_q_rank" %in% colnames(dat_out)) {
dat_out = dat_out %>%
mutate(eu_ntile_name = if_else(eu_q_rank<10,
paste0("Q0",eu_q_rank),
paste0("Q",eu_q_rank)))
}
dat_out
}
pivot_results_longer_adorn <- function(pdat_in, pcols_exclude) {
dat_out = pivot_results_longer(pdat_in, pcols_exclude) %>%
mutate(indicator_name = case_when(
# per adult equivalent indicators
indicator == "pae_co2_t" ~ "CO2 footprint (t/ae)",
indicator == "pae_co2_dom_t" ~ "CO2 fp dom (t/ae)",
indicator == "pae_co2_eu_t" ~ "CO2 fp EU (t/ae)",
indicator == "pae_co2_noneu_t" ~ "CO2 fp non-EU (t/ae)",
indicator == "pae_co2eq_t" ~ "CO2eq footprint (t/ae)",
indicator == "pae_co2eq_dom_t" ~ "CO2eq fp dom (t/ae)",
indicator == "pae_co2eq_eu_t" ~ "CO2eq fp EU (t/ae)",
indicator == "pae_co2eq_noneu_t" ~ "CO2eq fp non-EU (t/ae)",
indicator == "pae_energy_use_gj" ~ "Energy footprint (GJ/ae)",
indicator == "pae_energy_use_dom_gj" ~ "Energy fp dom (GJ/ae)",
indicator == "pae_energy_use_eu_gj" ~ "Energy fp EU (GJ/ae)",
indicator == "pae_energy_use_noneu_gj" ~ "Energy fp non-EU (GJ/ae)",
indicator == "pae_fd_ke" ~ "Expenditure (k€/ae)",
# intensity indicators
indicator == "pe_co2_kg" ~ "CO2 intensity (kg/€)",
indicator == "pe_co2_dom_kg" ~ "CO2 intensity dom (kg/€)",
indicator == "pe_co2_eu_kg" ~ "CO2 intensity EU (kg/€)",
indicator == "pe_co2_noneu_kg" ~ "CO2 intensity non-EU (kg/€)",
indicator == "pe_co2eq_kg" ~ "CO2eq intensity (kg/€)",
indicator == "pe_co2eq_dom_kg" ~ "CO2eq intensity dom (kg/€)",
indicator == "pe_co2eq_eu_kg" ~ "CO2eq intensity EU (kg/€)",
indicator == "pe_co2eq_noneu_kg" ~ "CO2eq intensity non-EU (kg/€)",
indicator == "pe_energy_use_mj" ~ "Energy intensity (MJ/€)",
indicator == "pe_energy_use_dom_mj" ~ "Energy intensity dom (MJ/€)",
indicator == "pe_energy_use_eu_mj" ~ "Energy intensity EU (MJ/€)",
indicator == "pe_energy_use_noneu_mj" ~ "Energy intensity non-EU (MJ/€)"
))
}
get_eu_ntile_summary_long_adorned <- function(peu_q_count) {
pdat_totals_eu_ntiles = get_country_summary_by_eu_ntile(peu_q_count) %>%
group_by(year, eu_q_rank) %>%
summarise(across(c(total_fd_me,
total_adult_eq,
total_co2_kg,
total_co2_dom_kg,
total_co2_eu_kg,
total_co2_noneu_kg,
total_co2eq_kg,
total_co2eq_dom_kg,
total_co2eq_eu_kg,
total_co2eq_noneu_kg,
total_energy_use_tj,
total_energy_use_dom_tj,
total_energy_use_eu_tj,
total_energy_use_noneu_tj
), sum)) %>%
pivot_results_longer(c("year", "eu_q_rank") ) %>%
mutate(value = case_when(indicator == "total_fd_me" ~ value/1000000,
indicator == "total_adult_eq" ~ value/1000000,
indicator == "total_co2_kg" ~ value/1000000000,
indicator == "total_co2_dom_kg" ~ value/1000000000,
indicator == "total_co2_eu_kg" ~ value/1000000000,
indicator == "total_co2_noneu_kg" ~ value/1000000000,
indicator == "total_co2eq_kg" ~ value/1000000000,
indicator == "total_co2eq_dom_kg" ~ value/1000000000,
indicator == "total_co2eq_eu_kg" ~ value/1000000000,
indicator == "total_co2eq_noneu_kg" ~ value/1000000000,
indicator == "total_energy_use_tj" ~ value/1000000,
indicator == "total_energy_use_dom_tj" ~ value/1000000,
indicator == "total_energy_use_eu_tj" ~ value/1000000,
indicator == "total_energy_use_noneu_tj" ~ value/1000000)) %>%
mutate(indicator_name = case_when(indicator == "total_fd_me" ~ "Expenditure (trn €)",
indicator == "total_adult_eq" ~ "Aeq Population (mio)",
indicator == "total_co2_kg" ~ "CO2 footprint (Mt)",
indicator == "total_co2_dom_kg" ~ "CO2 fp dom (Mt)",
indicator == "total_co2_eu_kg" ~ "CO2 fp EU (Mt)",
indicator == "total_co2_noneu_kg" ~ "CO2 fp non-EU (Mt)",
indicator == "total_co2eq_kg" ~ "CO2eq footprint (Mt)",
indicator == "total_co2eq_dom_kg" ~ "CO2eq fp dom (Mt)",
indicator == "total_co2eq_eu_kg" ~ "CO2eq fp EU (Mt)",
indicator == "total_co2eq_noneu_kg" ~ "CO2eq fp non-EU (Mt)",
indicator == "total_energy_use_tj" ~ "Energy footprint (EJ)",
indicator == "total_energy_use_dom_tj" ~ "Energy fp dom (EJ)",
indicator == "total_energy_use_eu_tj" ~ "Energy fp EU (EJ)",
indicator == "total_energy_use_noneu_tj" ~ "Energy fp non-EU (EJ)"
)) %>%
ungroup() %>%
mutate(indicator_name = factor(indicator_name,
levels=c("CO2 footprint (Mt)",
"CO2eq footprint (Mt)",
"Energy footprint (EJ)",
"CO2 fp dom (Mt)",
"CO2eq fp dom (Mt)",
"Energy fp dom (EJ)",
"CO2 fp EU (Mt)",
"CO2eq fp EU (Mt)",
"Energy fp EU (EJ)",
"CO2 fp non-EU (Mt)",
"CO2eq fp non-EU (Mt)",
"Energy fp non-EU (EJ)",
"Expenditure (trn €)",
"Aeq Population (mio)"
)))
pdat_totals_eu_ntiles
}
get_eu_ntile_summary_relative_long_adorned <- function(peu_q_count) {
pdat_relative_eu_ntile = get_country_summary_by_eu_ntile(peu_q_count) %>%
group_by(year, eu_q_rank) %>%
summarise(across(c(total_fd_me,
total_co2_kg,
total_co2_dom_kg,
total_co2_eu_kg,
total_co2_noneu_kg,
total_co2eq_kg,
total_co2eq_dom_kg,
total_co2eq_eu_kg,
total_co2eq_noneu_kg,
total_energy_use_tj,
total_energy_use_dom_tj,
total_energy_use_eu_tj,
total_energy_use_noneu_tj,
), sum)) %>%
group_by(year) %>%
mutate(total_fd_me = total_fd_me/sum(total_fd_me)*100,
total_co2_kg = total_co2_kg/sum(total_co2_kg)*100,
total_co2_dom_kg = total_co2_dom_kg/sum(total_co2_dom_kg)*100,
total_co2_eu_kg = total_co2_eu_kg/sum(total_co2_eu_kg)*100,
total_co2_noneu_kg = total_co2_noneu_kg/sum(total_co2_noneu_kg)*100,
total_co2eq_kg = total_co2eq_kg/sum(total_co2eq_kg)*100,
total_co2eq_dom_kg = total_co2eq_dom_kg/sum(total_co2eq_dom_kg)*100,
total_co2eq_eu_kg = total_co2eq_eu_kg/sum(total_co2eq_eu_kg)*100,
total_co2eq_noneu_kg = total_co2eq_noneu_kg/sum(total_co2eq_noneu_kg)*100,
total_energy_use_tj = total_energy_use_tj/sum(total_energy_use_tj)*100,
total_energy_use_dom_tj = total_energy_use_dom_tj/sum(total_energy_use_dom_tj)*100,
total_energy_use_eu_tj = total_energy_use_eu_tj/sum(total_energy_use_eu_tj)*100,
total_energy_use_noneu_tj = total_energy_use_noneu_tj/sum(total_energy_use_noneu_tj)*100
) %>%
pivot_longer(-c(year,eu_q_rank), names_to = "indicator", values_to = "value") %>%
mutate(euro_quint = if_else(eu_q_rank<10,
paste0("Q0",eu_q_rank),
paste0("Q",eu_q_rank))) %>%
mutate(indicator = case_when(indicator == "total_fd_me" ~ "Expenditure (%)",
indicator == "total_co2_kg" ~ "CO2 footprint (%)",
indicator == "total_co2_dom_kg" ~ "CO2 fp dom (%)",
indicator == "total_co2_eu_kg" ~ "CO2 fp EU (%)",
indicator == "total_co2_noneu_kg" ~ "CO2 fp non-EU (%)",
indicator == "total_co2eq_kg" ~ "CO2eq footprint (%)",
indicator == "total_co2eq_dom_kg" ~ "CO2eq fp dom (%)",
indicator == "total_co2eq_eu_kg" ~ "CO2eq fp EU (%)",
indicator == "total_co2eq_noneu_kg" ~ "CO2eq fp non-EU (%)",
indicator == "total_energy_use_tj" ~ "Energy footprint (%)",
indicator == "total_energy_use_dom_tj" ~ "Energy fp dom (%)",
indicator == "total_energy_use_eu_tj" ~ "Energy fp EU (%)",
indicator == "total_energy_use_noneu_tj" ~ "Energy fp non-EU (%)")) %>%
ungroup() %>%
mutate(indicator = factor(indicator,
levels=c("CO2 footprint (%)",
"CO2eq footprint (%)",
"Energy footprint (%)",
"CO2 fp dom (%)",
"CO2eq fp dom (%)",
"Energy fp dom (%)",
"CO2 fp EU (%)",
"CO2eq fp EU (%)",
"Energy fp EU (%)",
"CO2 fp non-EU (%)",
"CO2eq fp non-EU (%)",
"Energy fp non-EU (%)",
"Expenditure (%)"
)))
pdat_relative_eu_ntile
}