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8b_prepare_footprints.R
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8b_prepare_footprints.R
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#'
#'
#'
#' @author Simon Schulte
#' Date: 2022-09-12 14:23:28
#'
#' Content:
#'
############################################################################## #
##### load packages ############################################################
############################################################################## #
library(data.table)
library(tidyverse)
library(units)
library(ggforce)
library(mRio)
library(pbmcapply)
library(testthat)
library(svMisc)
library(pbapply)
library(arrow)
#library('RPostgreSQL')
############################################################################## #
##### settings #################################################################
############################################################################## #
source(file.path('src', 'functions.R'))
# read config and setup log script
config <- setup_config_and_log()
path2output <- config$path2output
theme_set(theme_bw())
n_cores <- config$n_cores
RhpcBLASctl::blas_set_num_threads(4)
RhpcBLASctl::blas_get_num_procs()
############################################################################## #
##### functions #############################################################
############################################################################## #
calculate_summary_statistics2 <- function(dt) {
out <- dt[, list(
mean = mean(value),
median = median(value),
sd = sd(value),
cv = sd(value) / mean(value),
CI97.5 = quantile(value, probs = 0.975),
CI2.5 = quantile(value, probs = 0.025)
), by = key(dt)]
out[, mean_rel := mean / sum(mean), by = gas]
return(out[])
}
############################################################################## #
##### load data #############################################################
############################################################################## #
# meta data
meta <- parse_EB3_metadata(config$path2exiobaseIOT)
F_rownames <- readRDS(file.path(path2output, 'convert_to_matrix_rownames.RData'))
# 1. national footprints footprint =============================================
fp_detailed <- read_feather(file.path(path2output, 'calculate_footprints_national.feather'))
# rows
fp_detailed <- merge(fp_detailed, F_rownames, by.x = 'i', by.y = 'row', all.x = TRUE,
sort = FALSE)
# columns
EB_regions <- data.table(
EB_region = meta$indices_Y$colnames$region %>% unique,
j = 1:49)
fp_detailed <- merge(fp_detailed, EB_regions, by = 'j')
# summary statistics
fp_by_reg <- fp_detailed[, list(value = sum(value)), by = .(EB_region, gas, run)]
setkey(fp_by_reg, EB_region, gas)
fp_by_reg_crf <- fp_detailed[, list(value = sum(value)),
by = .(EB_region, gas, category_code2, run)]
setkey(fp_by_reg_crf, EB_region, gas, category_code2)
fp_by_reg2 <- calculate_summary_statistics2(fp_by_reg)
fp_by_reg_crf2 <- calculate_summary_statistics2(fp_by_reg_crf)
# save results
save_results(fp_by_reg2, suffix = '_by_region', type = '.feather')
save_results(fp_by_reg_crf2, suffix = '_by_region_and_CRF', type = '.feather')
rm(fp_by_reg, fp_by_reg2, fp_by_reg_crf, fp_by_reg_crf2)
# 2. sector footprints footprint =============================================
fp_detailed <- read_feather(file.path(path2output,
'calculate_footprints_multiplier.feather'))
# rows
fp_detailed <- merge(fp_detailed, F_rownames[, .(row, gas)],
by.x = 'i', by.y = 'row', all.x = TRUE,
sort = FALSE)
# sum by gas (no detail on CRF needed at sector level)
fp_detailed <- fp_detailed[, list(value = sum(value)),
by = .(j, gas, run)]
# summary statistics
setkey(fp_detailed, j, gas)
fp_by_sec <- calculate_summary_statistics2(fp_detailed)
# columns
EB_sectors <- merge(meta$indices_S$colnames[, j := 1:.N],
meta$industries[, .(Name, CodeNr)],
by.x = 'sector', by.y = 'Name',
sort = FALSE)
EB_sectors <- EB_sectors[, .(j, region, CodeNr)]
setnames(EB_sectors,c('region', 'CodeNr'), c("EB_region", 'industry_code'))
fp_by_sec <- merge(fp_by_sec, EB_sectors, by = 'j')
# save results
save_results(fp_by_sec, suffix = '_by_sector', type = '.feather')
# end ==========================================================================