0_graphing_functions.R
In 2DegreesInvesting/PACTA_analysis: Helpers for 'PACTA_analysis'
wrap_it <- function(x, len) {
sapply(x, function(y) paste(strwrap(y, len), collapse = "\n"), USE.NAMES = FALSE)
}
wrap_labels <- function(x, len) {
if (is.list(x)) {
lapply(x, wrap_it, len)
} else {
wrap_it(x, len)
}
}
comprss <- function(tx) {
tx[is.na(tx)] <- 0
div <- findInterval(tx, c(1, 1e3, 1e6, 1e9, 1e12))
div[div == 0] <- 1
labels <- paste(
"$", round(tx / 10^(3 * (div - 1)), 2),
c("", "K", "Mn", "Bn", "Tn")[div]
)
return(labels)
}
comprss_long <- function(tx) {
tx[is.na(tx)] <- 0
div <- findInterval(tx, c(1, 1e3, 1e6, 1e9, 1e12))
div[div == 0] <- 1
labels <- paste(
round(tx / 10^(3 * (div - 1)), 1),
c("", thousand, million, billion, trillion)[div]
)
return(labels)
}
perc <- function(x, format = "f", ...) {
x <- ifelse(x * 100 >= 10,
paste0(" ", formatC(100 * x, format = format, digits = 1, ...), "%"),
paste0(formatC(100 * x, format = format, digits = 2, ...), "%")
)
return(x)
}
shortened_company_names <- function(company_labels, length) {
for (i in 1:length(company_labels)) {
if (str_length(company_labels[[i]]) > length) {
new_name <- strtrim(company_labels[[i]], length)
company_labels[[i]] <- paste0(new_name, "...")
} else if (str_length(company_labels[[i]]) < length) {
for (j in 1:((length + 3) - str_length(company_labels[[i]]))) {
company_labels[[i]] <- paste0(" ", company_labels[[i]])
}
}
}
company_labels
}
set_initial_variables <- function() {
EQCombin <<- NA
CBCombin <<- NA
EQCompProdSnapshot <<- NA
CBCompProdSnapshot <<- NA
EQportmap <<- NA
CBportmap <<- NA
eq_market <<- NA
cb_market <<- NA
has_equity <<- FALSE
has_debt <<- FALSE
HasCarbonBudget <<- FALSE
RiskToPlot <<- NA
PlotChart <<- TRUE
chart_type <<- ""
tech_to_plot <<- ""
sector_to_plot <<- ""
BV.asset_type <<- ""
HasRenCapCB <<- F
HasRenCapEQ <<- F
HasCoalCapCB <<- F
HasCoalCapEQ <<- F
HasPower <<- FALSE
HasAuto <<- FALSE
HasOG <<- FALSE
HasCoal <<- FALSE
HasPowerCB <<- FALSE
HasAutoCB <<- FALSE
HasOGCB <<- FALSE
HasCoalCB <<- FALSE
HasPowerEQ <<- FALSE
HasAutoEQ <<- FALSE
HasOGEQ <<- FALSE
HasCoalEQ <<- FALSE
SovBondCov <<- NA
}
define_peers <- function() {
if (twodii_internal) {
if (file.exists(paste0(results_path, "/Equity_results_portfolio.rda"))) {
eq_peers <<- read_rds(paste0(results_path, "/Equity_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
} else {
warning("No Equity Peers File available. Setting inc_meta_portfolio to true MAY solve this. Alternatively there is no equity in any portfolio.")
}
if (file.exists(paste0(results_path, "/Bonds_results_portfolio.rda"))) {
cb_peers <<- read_rds(paste0(results_path, "/Bonds_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
} else {
warning("No bonds peers file available. Setting inc_meta_portfolio to true MAY solve this. Alternatively there are no bonds in any portfolio.")
}
} else {
if (project_name == "web_tool") {
eq_peers <<- NA
cb_peers <<- NA
} else {
eq_peers <<- read_rds(paste0(project_location_ext, "/", project_name, "/40_Results/Equity_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
cb_peers <<- read_rds(paste0(project_location_ext, "/", project_name, "/40_Results/Bonds_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
}
}
}
define_benchmarks <- function() {
if (twodii_internal) {
eq_market <<- read_rds(paste0(portcheck_v2_path, "/10_Projects/INDEX_2019/40_Results/Equity_results_portfolio.rda")) %>%
filter(portfolio_name == eq_market_ref)
cb_market <<- read_rds(paste0(portcheck_v2_path, "/10_Projects/INDEX_2019/40_Results/Bonds_results_portfolio.rda")) %>%
filter(portfolio_name == cb_market_ref)
} else {
# Set per project
eq_market <<- read_rds(paste0(data_location_ext, "/Fake_Index/Equity_results_portfolio.rda"))
cb_market <<- read_rds(paste0(data_location_ext, "/Fake_Index/Bonds_results_portfolio.rda"))
}
if (!start_year %in% eq_market$year) {
stop(paste0("Check that start years align. EQ Market Start year = ", min(eq_market$year), ". Report start year = ", start_year))
}
if (!start_year %in% cb_market$year) {
stop(paste0("Check that start years align. CB Market Start year = ", min(cb_market$year), ". Report start year = ", start_year))
}
}
results_call <- function() {
# Filtering Market should already happen here.
subgroup_overview <<- portfolio_overview[portfolio_overview$investor_name == investor_name_select & portfolio_overview$portfolio_name == portfolio_name_select & portfolio_overview$valid_input == TRUE, ]
define_benchmarks()
define_peers()
# RenewableAdditions <<- readRDS(paste0(analysis_inputs_path, "/RenewablesAdditionsData.rda"))
# CoalRetirements <<- readRDS(paste0(analysis_inputs_path, "/CoalRetirementsData.rda"))
# CarbonData <<- readRDS(paste0(analysis_inputs_path, "/CarbonCapexUpstream.rda"))
if (has_equity) {
EQCompProdSnapshot <<- read_rds(paste0(results_path, "/", investor_name_select, "/Equity_results_company.rda"))
EQCompProdSnapshot <<- EQCompProdSnapshot[EQCompProdSnapshot$portfolio_name == portfolio_name_select, ]
if (has_map) {
if (file.exists(paste0(results_path, "/", investor_name_select, "/Equity_results_map.rda"))) {
EQportmap <- readRDS(paste0(results_path, "/", investor_name_select, "/Equity_results_map.rda"))
EQportmap <- EQportmap %>% filter(portfolio_name == portfolio_name_select)
}
}
if (data_check(EQportmap)) {
EQportmap$scenario_geography <- ifelse(EQportmap$ald_sector == "Power", "GlobalAggregate", "Global")
}
EQportmap <<- EQportmap
EQTechData <<- read_rds(paste0(analysis_inputs_path, "/masterdata_ownership_datastore_technology_type_view.rda"))
EQOilShareData <<- EQTechData %>%
select(id, ald_sector, technology, technology_type, year, ald_production, ald_production_unit) %>%
filter(
year %in% seq(start_year, start_year + 5, 1),
ald_sector == "Oil&Gas"
) %>%
mutate(technology = if_else(technology == "Oil and Condensate", "Oil", technology))
# EQOilShareData <<- readRDS(paste0(analysis_inputs_path, "oil_and_gas_resource_type_rollup_ownership.rda"))
# EQOilShareData <<- readRDS(paste0(Location, "ReferenceData/oil_and_gas_resource_type_rollup_ownership.rda"))
}
if (has_debt == TRUE) {
CBCompProdSnapshot <<- readRDS(paste0(results_path, "/", investor_name_select, "/Bonds_results_company.rda"))
CBCompProdSnapshot <<- CBCompProdSnapshot[CBCompProdSnapshot$portfolio_name == portfolio_name_select, ]
if (has_map) {
if (file.exists(paste0(results_path, "/", investor_name_select, "/Bonds_results_map.rda"))) {
CBportmap <<- readRDS(paste0(results_path, "/", investor_name_select, "/Bonds_results_map.rda"))
CBportmap <<- CBportmap %>% filter(portfolio_name == portfolio_name_select)
}
if (data_check(CBportmap)) {
CBportmap$scenario_geography <- ifelse(CBportmap$ald_sector == "Power", "GlobalAggregate", "Global")
}
CBportmap <<- CBportmap
}
CBTechData <<- read_rds(paste0(analysis_inputs_path, "/masterdata_debt_datastore_technology_type_view.rda"))
CBOilShareData <<- CBTechData %>%
select(id, ald_sector, technology, technology_type, year, ald_production, ald_production_unit) %>%
filter(
year %in% seq(start_year, start_year + 5, 1),
ald_sector == "Oil&Gas"
) %>%
mutate(technology = if_else(technology == "Oil and Condensate", "Oil", technology))
}
if (has_sb) {
SB.Summary <<- read.csv(paste0(PROC.INPUT.PATH, "SovereignBondSummary.csv"), strip.white = T, stringsAsFactors = F)
}
if (inc_sda_approach) {
if (data_check(EQCombin)) {
max_market <- max(eq_market$year, na.rm = T)
max_year_data <- max(EQCombin$year, na.rm = T)
if (max_year_data == max_market) {
EQCombin <- sda_portfolio_target(eq_market, EQCombin,
start_year = start_year,
target_year = max_year_data
)
}
}
if (data_check(CBCombin)) {
max_market <- max(cb_market$year, na.rm = T)
max_year_data <- max(CBCombin$year, na.rm = T)
if (max_year_data == max_market) {
CBCombin <- sda_portfolio_target(cb_market, CBCombin,
start_year = start_year,
target_year = max_year_data
)
}
}
}
}
filter_by_parameters <- function(df, dfType, byscenario = TRUE, scenario.irrelevant = FALSE, by_equity_market = TRUE, ActorSectorOnly = FALSE, BV.asset_type = "") {
# This Df type should allow us to clear a few things up
if (data_check(df) == FALSE) {
print("No Results")
PlotChart <<- FALSE
} else {
PlotChart <<- TRUE
if ("asset_type" %in% colnames(df) & BV.asset_type != "") {
df <- df %>% filter(asset_type %in% BV.asset_type)
if (data_check(df) == FALSE) {
print("BV.asset_type Filter removed All Data")
}
}
if (tech_to_plot != "" & "technology" %in% colnames(df)) {
if (GraphType %in% c("CurrentTechnologyExposure", "FutureTechnologyExposure") & sector_to_plot %in% c("Oil&Gas", "Coal")) {
df <- df %>% filter(technology %in% c("Oil", "Gas", "Coal"))
if (data_check(df) == FALSE) {
print("Techology Filter removed All Data")
}
} else {
df <- df %>% filter(technology == tech_to_plot)
}
if (data_check(df) == FALSE) {
print("Techology Filter removed All Data")
}
}
if (RiskToPlot != "" & "RiskType" %in% colnames(df)) {
df <- df %>% filter(RiskType == RiskToPlot)
if (data_check(df) == FALSE) {
print("Risk Filter removed All Data")
}
}
# if (EquityMarketSelection != "" & "equity_market" %in% colnames(df) & by_equity_market == TRUE & data_check(df) == TRUE){df <- df %>% filter(equity_market == EquityMarketSelection)
if (EquityMarketSelection != "" & "equity_market" %in% colnames(df) & by_equity_market == TRUE & data_check(df) == TRUE) {
df <- df %>% filter(equity_market == EquityMarketSelection)
if (data_check(df) == FALSE) {
print("Equity Market Filter removed All Data")
}
}
if (!AccountingPrinciple %in% c("", "Mixed") & "allocation" %in% colnames(df) & data_check(df) == TRUE) { # AccountingPrinciple %in% df$allocation &
df <- df %>% filter(allocation == AccountingPrinciple)
if (data_check(df) == FALSE) {
print("Accounting Principle Filter removed All Data")
}
}
if (AccountingPrinciple == "Mixed" & "allocation" %in% colnames(df) & data_check(df) == TRUE) {
if (dfType == "EQ") {
df <- df %>% filter(allocation == "ownership_weight")
} else if (dfType == "CB") {
df <- df %>% filter(allocation == "portfolio_weight")
}
if (data_check(df) == FALSE) {
print("Accounting Principle Filter removed All Data")
}
}
if (ScenarioGeographyChoose != "" & "scenario_geography" %in% colnames(df) & data_check(df) == TRUE) {
if (ScenarioGeographyChoose == "GlobalAggregate") {
df1 <- df %>% filter(ald_sector == "Power", scenario_geography == "GlobalAggregate")
if (!data_check(df1)) {
df1 <- df %>% filter(ald_sector == "Power", scenario_geography == "Global")
}
df2 <- df %>% filter(ald_sector != "Power", scenario_geography == "Global")
df <- rbind(df1, df2)
} else {
df <- df %>% filter(scenario_geography == ScenarioGeographyChoose)
}
if (data_check(df) == FALSE) {
print("scenario_geography Filter removed All Data")
}
}
if (!sector_to_plot %in% c("", "All") & data_check(df) == TRUE) {
if (GraphType %in% c("CurrentTechnologyExposure", "FutureTechnologyExposure") & sector_to_plot %in% c("Oil&Gas", "Coal")) {
df <- df %>% filter(ald_sector %in% c("Oil&Gas", "Coal"))
} else {
df <- df %>% filter(ald_sector == sector_to_plot)
}
if (ActorSectorOnly == TRUE & !sector_to_plot %in% c("", "All") & data_check(df) == TRUE) {
df <- df %>% filter(financial_sector == sector_to_plot)
}
if (data_check(df) == FALSE) {
print("ald_sector Filter removed All Data")
}
}
# if (AccountingPrinciple != "" & "allocation" %in% colnames(df) & AccountingPrinciple %in% df$allocation & data_check(df) == TRUE){df <- df %>% filter(allocation == AccountingPrinciple)
# if (data_check(df) == FALSE){print("Accounting Principle Filter removed All Data")}}
if (byscenario == TRUE & Scenariochoose != "" & "scenario" %in% colnames(df) & Scenariochoose %in% df$scenario & data_check(df) == TRUE) {
df <- df %>% filter(scenario == Scenariochoose)
if (data_check(df) == FALSE) {
print("scenario Filter removed All Data")
}
}
if (byscenario == TRUE & scenario.irrelevant == TRUE & "scenario" %in% colnames(df) & data_check(df) == TRUE) {
# df$scenario <- NULL
df <- df[df$scenario == unique(df$scenario)[1], ]
df$scenario <- "NA"
if (data_check(df) == FALSE) {
print("scenario Filter removed All Data")
}
}
if (PeerGroupSelection != "" & "investor_name" %in% colnames(df) & PeerGroupSelection %in% df$investor_name & data_check(df) == TRUE) {
df <- df %>% filter(investor_name == PeerGroupSelection)
if (data_check(df) == FALSE) {
print("Peer Group Filter removed All Data")
}
}
if (GraphType %in% c("FutureTechnologyExposure", "Map")) {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year == start_year + 5)
}
} else if (GraphType %in% c("CurrentTechnologyExposure")) {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year == start_year)
}
}
if (GraphType %in% "CompanyInformation") {
if (sector_to_plot %in% c("Oil&Gas", "CoalMining")) {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year %in% c(start_year, start_year + 5))
}
} else {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year == start_year + 5)
}
}
}
if (GraphType %in% c("PeerComparison")) {
if (PeerGroupSelection != "" & "portfolio_name" %in% colnames(df) & "PeerGroup" %in% colnames(df) & data_check(df) == TRUE) {
df <- df %>% filter(portfolio_name != "MetaPort")
if (data_check(df) == FALSE) {
print("Peer Group Sub-Filter removed All Data")
}
}
} else {
if (PeerGroupSelection != "" & "portfolio_name" %in% colnames(df) & "PeerGroup" %in% colnames(df) & data_check(df) == TRUE) {
df <- df %>% filter(portfolio_name == "MetaPort")
if (data_check(df) == FALSE) {
print("Peer Group Sub-Filter removed All Data")
}
}
}
if (data_check(df) == FALSE) {
PlotChart <<- FALSE
}
}
return(df)
} # finalize and adjust for future & current exposure
create_test_list <- function() {
# ID.COLS <- c("PortName","investor_name_select","Type") # removed Type as this is not existing
ID.COLS <- c("investor_name", "portfolio_name")
ID.COLS.LONG <- c("investor_name", "portfolio_name", "year", "allocation", "ald_sector", "technology", "scenario_geography")
# test_list <- NA
ald_sectors <- c("Automotive", "Coal", "OilGas", "Power")
if (file.exists(paste0(results_path, "/", investor_name_select, "/Equity_results_portfolio.rda"))) {
EQCombin <<- read_rds(paste0(results_path, "/", investor_name_select, "/Equity_results_portfolio.rda"))
EQCombin <<- EQCombin %>% filter(portfolio_name == portfolio_name_select)
# EQCombin
}
if (file.exists(paste0(results_path, "/", investor_name_select, "/Bonds_results_portfolio.rda"))) {
CBCombin <<- read_rds(paste0(results_path, "/", investor_name_select, "/Bonds_results_portfolio.rda"))
CBCombin <<- CBCombin %>% filter(portfolio_name == portfolio_name_select)
}
if (data_check(EQCombin) == T) {
# if (sector_to_plot %in% c("Shipping"))
Equity <- filter_by_parameters(EQCombin, "EQ", ActorSectorOnly = T) %>%
select(investor_name, portfolio_name, year, scenario, allocation, ald_sector, technology, scenario_geography, equity_market, plan_tech_prod) %>%
group_by(investor_name, portfolio_name, scenario_geography, allocation, equity_market, year, ald_sector, technology) %>%
summarise(Production = sum(plan_tech_prod, na.rm = T))
EquityTech <- Equity %>%
ungroup() %>%
select(investor_name, portfolio_name, technology, Production) %>%
group_by(investor_name, portfolio_name, technology) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
Equity <- Equity %>%
ungroup() %>%
select(investor_name, portfolio_name, ald_sector, Production) %>%
group_by(investor_name, portfolio_name, ald_sector) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
if (nrow(Equity) > 0) {
Equity$ald_sector <- ifelse(Equity$ald_sector == "Oil&Gas", "OilGas", Equity$ald_sector)
Equity$Indicator <- ifelse(Equity$ald_sectorProd > 0, TRUE, FALSE)
Equity$ald_sectorProd <- NULL
Equity$ald_sector <- paste0(Equity$ald_sector, ".EQ")
Equity <- Equity %>% spread(value = Indicator, key = ald_sector)
Equity <<- as.data.frame(Equity)
HasCoalCapEQ <<- "CoalCap" %in% EquityTech$technology
HasRenCapEQ <<- "RenewableCap" %in% EquityTech$technology
has_equity <<- TRUE
} else {
has_equity <<- FALSE
}
}
if (all(data_check(CBCombin) == T, chart_type %in% c("CB", "") | GraphType == "PercentageOfPortfolioAssessed")) {
Debt <- filter_by_parameters(CBCombin, "CB", by_equity_market = F, ActorSectorOnly = T) %>%
select(investor_name, portfolio_name, year, scenario, allocation, ald_sector, technology, scenario_geography, equity_market, plan_tech_prod) %>%
# filter(year == start_year) %>%
group_by(investor_name, portfolio_name, scenario_geography, allocation, equity_market, year, ald_sector, technology) %>%
summarise(Production = sum(plan_tech_prod, na.rm = T))
DebtTech <- Debt %>%
ungroup() %>%
select(investor_name, portfolio_name, technology, Production) %>%
group_by(investor_name, portfolio_name, technology) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
Debt <- Debt %>%
ungroup() %>%
select(investor_name, portfolio_name, ald_sector, Production) %>%
group_by(investor_name, portfolio_name, ald_sector) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
Debt$ald_sector <- ifelse(Debt$ald_sector == "Oil&Gas", "OilGas", Debt$ald_sector)
if (nrow(Debt) > 0) {
Debt$Indicator <- ifelse(Debt$ald_sectorProd > 0, TRUE, FALSE)
Debt$ald_sectorProd <- NULL
Debt$ald_sector <- paste0(Debt$ald_sector, ".CB")
Debt <- Debt %>% spread(value = Indicator, key = ald_sector)
Debt <<- as.data.frame(Debt)
HasCoalCapCB <<- "CoalCap" %in% DebtTech$technology
HasRenCapCB <<- "RenewableCap" %in% DebtTech$technology
# Debt$Indicator <<- ifelse(Debt$ > 0,1,0)
has_debt <<- TRUE
} else {
has_debt <<- FALSE
}
}
# missingcols <- rbind(paste0(ald_sectors,".EQ"),paste0(ald_sectors,".CB"))
if (has_debt == TRUE & has_equity == TRUE) {
test_list <- base::merge(Equity, Debt, by = ID.COLS)
test_list[is.na(test_list)] <- FALSE
colnames(test_list) <- gsub("\\.x", ".EQ", colnames(test_list))
colnames(test_list) <- gsub("\\.y", ".CB", colnames(test_list))
} else if (has_debt == FALSE & has_equity == TRUE) {
test_list <- Equity
# test_list$has_debt <- FALSE
} else if (has_debt == TRUE & has_equity == FALSE) {
test_list <- Debt
# test_list$has_equity <- FALSE
} else {
test_list <- NA
}
eqcols <- paste0(sector_list, ".EQ")
cbcols <- paste0(sector_list, ".CB")
missingeqcols <- setdiff(eqcols, colnames(test_list))
missingcbcols <- setdiff(cbcols, colnames(test_list))
test_list[missingeqcols] <- FALSE
test_list[missingcbcols] <- FALSE
if (is.data.frame(test_list)) {
test_list <- test_list %>%
filter(portfolio_name == portfolio_name_select)
}
return(test_list)
}
define_test_variables <- function(test_list) {
PortSummary <<- test_list[1, ]
# TestType <<- test_list[1,"Type"]
portfolio_name_selectLong <<- test_list[1, "portfolio_name_select"]
investor_name_selectLong <<- test_list[1, "investor_name_select"]
investor_name_select <<- gsub(" ", "", test_list[1, "investor_name_select"])
portfolio_name_select <<- gsub("[[:punct:]]", "", test_list[1, "portfolio_name"])
# portfolio_name_select <<- gsub(" ", "", test_list[1,"PortName"]) # KH: Adjusted this as i assume it wasn?t planned to overwrite portfolio_name_select
portfolio_name_select <<- gsub(" ", "", portfolio_name_select)
has_equity <<- test_list[1, "has_equity"]
has_debt <<- test_list[1, "has_debt"]
}
graph_values <- function() {
textfont <<- "Calibri"
windowsFonts(Calibri = windowsFont(textfont))
# orangeish
RenewablesColour <<- "#feedde"
HydroColour <<- "#fdbe85"
NuclearColour <<- "#fd8d3c"
GasCapColour <<- "#e6550d"
CoalCapColour <<- "#a63603"
# purpleish
ElectricColour <<- "#efedf5"
HybridColour <<- "#bcbddc"
ICEColour <<- "#756bb1"
# goldish
GasProdColour <<- "#D9DDD4" # "#F5F5F5" #D9DDD4
OilProdColour <<- "#BEBCAE" # "#BEA07B" #BEBCAE
CoalProdColour <<- "#8B7E66" # "#8C510A" #8B7E66
# blueish
FreightColor <<- "#6ba7e3"
PassengerColor <<- "#d6e7f7"
#
GColor <<- "#D73027"
FColor <<- "#FC8D59"
EColor <<- "#FEE08B"
DColor <<- "#FFFFBF"
CColor <<- "#D9EF8B"
Bcolor <<- "#91CF60"
AColor <<- "#1A9850"
# map
MAXRenewablesCap <<- "#a75105"
MAXHydroCap <<- "#9b4b03"
MAXNuclearCap <<- "#6b2e01"
MAXGasCap <<- "#260e02"
MAXCoalCap <<- "#290e01"
MAXElectric <<- "#6b5aa3"
MAXHybrid <<- "#464987"
MAXICE <<- "#3d3667"
MAXGas <<- "#737f65"
MAXOil <<- "#595647"
MAXCoal <<- "#423b30"
MINRenewablesCap <<- "#fde0c5"
MINHydroCap <<- "#fdcb9e"
MINNuclearCap <<- "#feb998"
MINGasCap <<- "#f5834b"
MINCoalCap <<- "#d84604"
MINElectric <<- "#e0ddec"
MINHybrid <<- "#bcbddc"
MINICE <<- "#a49dcc"
MINGas <<- "#D9DDD4"
MINOil <<- "#d5d3ca"
MINCoal <<- "#b9af9e"
# sector
energy <<- "#8B7E66"
pow <<- "#a63603"
trans <<- "#756bb1"
othr <<- "#9793c6"
notrelevant <<- "grey"
shipping <<- "#0F1e82" #
material <<- "#e46c00" #
# trajectory
area_6 <<- "#e07b73"
area_4_6 <<- "#fde291"
area_2_4 <<- "#FFFFCC"
area_2 <<- "#c3d69b"
area_1 <<- "#9cab7c"
eq_port <<- "#1056ff"
peer_group <<- "black"
eq_line <<- "#a63603" # same as CoalCapColor
cb_line <<- "#756bb1" # same as ICEColour
bv_line <<- "#265B9B"
# side-by-side techshare
Port <<- "#265B9B"
Benchmark <<- "#D9D9D9"
# moodys
HighRisk <<- area_6
MedRisk <<- area_4_6
LowRisk <<- area_2_4
# physical risk
pal_stacked_riskWS <<- c("#D73027", area_6, area_2, area_2_4) # water stress charts
pal_stacked_risks <<- c(area_6, area_2_4, area_2) # flood and wildfire charts
# text size
textsize <<- 8.5
textcolor <<- "#3D3D3C"
AxisColour <<- "#3D3D3C"
YourportColour <<- "#265b9b" # "#2e4b6e" #"#17224D"
IndexColour <<- "grey85"
Tar2DColourBar <<- "#b3de69"
Tar2DColour <<- "#a1c75e"
goodexpColour <<- "#1F891F"
badexpColour <<- "#ed1c24" # "#fb8072"
ReqCapColour <<- "grey55"
CurrCapColour <<- "grey75"
technology_order <<- c(
"Gas", "Oil", "Coal",
"RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap",
"Electric", "Hybrid", "ICE",
"Freight", "Passenger",
"A", "B", "C", "D", "E", "F", "G"
)
sector_order_short <<- c("FossilFuels", "Power", "Automotive")
technology_order_short <<- c(
"Gas", "Oil", "Coal",
"RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap",
"Electric", "Hybrid", "ICE"
)
goodtechs <<- c("RenewablesCap", "HydroCap", "NuclearCap", "Electric", "Hybrid")
badtechs <<- c("GasCap", "CoalCap", "ICE", "Gas", "Oil", "Coal")
ColourPalette <<- data.frame(
ald_sector = c(
"Fossil Fuels", "Fossil Fuels", "Fossil Fuels",
"Power", "Power", "Power", "Power", "Power",
"Automotive", "Automotive", "Automotive",
"Aviation", "Aviation",
"Shipping", "Shipping", "Shipping", "Shipping", "Shipping", "Shipping", "Shipping"
),
technology = technology_order,
Colours = c(
GasProdColour, OilProdColour, CoalProdColour,
RenewablesColour, HydroColour, NuclearColour, GasCapColour, CoalCapColour,
ElectricColour, HybridColour, ICEColour,
FreightColor, PassengerColor,
AColor, Bcolor, CColor, DColor, EColor, FColor, GColor
)
)
ColourPalette$technology <- factor(ColourPalette$technology, levels = technology_order)
ColourPalette <- arrange(ColourPalette, technology)
linesize <<- 1
ppi <<- 600
}
graph_name <- function(plotnumber, ParameterFile, explicit_filename = "") {
namelist <- ParameterFile[, !names(ParameterFile) %in% c("portfolio_name_select", "investor_name_select")]
namelist[is.na(namelist)] <- ""
namelist <- lapply(1:length(namelist), function(x) paste0(toupper(substr(as.character(namelist[[x]]), 1, 3)), "_"))
namelist <- namelist[1:6]
namelist <- c(namelist, start_year)
graphname <- do.call(paste0, namelist)
graphname <- paste0(graphname, ".png")
if (plotnumber == "00") {
graphname <- gsub(".png", "", graphname)
} else if (plotnumber != "99") {
graphname <- paste0(report_path, plotnumber, "_", explicit_filename, graphname)
} else {
graphname <- paste0(GRAPH.PATH, graphname)
}
return(graphname)
}
# FIXME: There is another green_brown() in the repo create_interactive_report and
# they are not identical. Which one should win?
green_brown <- function(Tech) {
GreenTechs <- c("Electric", "Hybrid", "RenewablesCap", "HydroCap", "NuclearCap")
stopifnot(length(Tech) == 1L)
if (Tech %in% GreenTechs) {
TechIs <- "green"
} else {
TechIs <- "brown"
}
return(TechIs)
}
set_ald_sector <- function(Tech) {
ald_sector <- NA
ald_sector <- ifelse(grepl("Cap", Tech), "Power", ald_sector)
ald_sector <- ifelse(Tech %in% c("Electric", "ICE", "Hybrid", "FuelCell"), "Automotive", ald_sector)
ald_sector <- ifelse(Tech %in% c("Oil", "Gas"), "Oil&Gas", ald_sector)
ald_sector <- ifelse(Tech == "Coal", "Coal", ald_sector)
ald_sector
}
translate_labels <- function(Language) {
Translations <- read_excel(paste0("data/TranslationFileUTF8.xlsx"))
Translations <- Translations %>%
select(TextLabel, all_of(Language)) %>%
as.data.frame()
for (t in 1:nrow(Translations)) {
assign(x = Translations[t, "TextLabel"], value = Translations[t, Language], envir = .GlobalEnv)
}
}
ReportFigures <- function(explicit_filenames = F) {
explicit_filename <- ""
# Introduction
if (!explicit_filenames == F) {
explicit_filename <- "_scope_of_analysis_"
}
ScopeOfAnalysis("01", explicit_filename = explicit_filename) #
if (!explicit_filenames == F) {
explicit_filename <- "_climate_relevant_sectors_"
}
PercentageOfPortfolioAssessed("02", explicit_filename = explicit_filename) #
ScenarioGeographyChoose <- "Global"
if (!explicit_filenames == F) {
explicit_filename <- "_current_exposure_bonds_"
}
CarstenMetricChart("03", "CB", explicit_filename = explicit_filename) #
if (!explicit_filenames == F) {
explicit_filename <- "_current_expousre_equity_"
}
CarstenMetricChart("04", "EQ", explicit_filename = explicit_filename) #
ScenarioGeographyChoose <- "GlobalAggregate"
if (!explicit_filenames == F) {
explicit_filename <- "_future_tech_mix_bonds_"
}
TechnologyExposure("05", "CB", "All", start_year + 5, explicit_filename = explicit_filename) #
if (has_debt) {
# 5 year Trajectory
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_coal_bonds_"
}
FiveYearGrowthTrend("07", "CB", "CoalCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_renewables_bonds_"
}
FiveYearGrowthTrend("08", "CB", "RenewablesCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_coal_bonds_"
}
FiveYearGrowthTrend("09", "CB", "GasCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_hydro_bonds_"
}
FiveYearGrowthTrend("10", "CB", "HydroCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_oil_bonds_"
}
FiveYearGrowthTrend("11", "CB", "Oil", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_gas_bonds_"
}
FiveYearGrowthTrend("12", "CB", "Gas", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_coal_bonds_"
}
FiveYearGrowthTrend("13", "CB", "Coal", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_ice_bonds_"
}
FiveYearGrowthTrend("14", "CB", "ICE", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_electric_bonds_"
}
FiveYearGrowthTrend("15", "CB", "Electric", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_hybrid_bonds_"
}
FiveYearGrowthTrend("16", "CB", "Hybrid", LegendOn = F, explicit_filename = explicit_filename)
}
if (!explicit_filenames == F) {
explicit_filename <- "_future_tech_mix_equity_"
}
TechnologyExposure("06", "EQ", "All", start_year + 5, explicit_filename = explicit_filename) #
if (has_equity) {
# 5 year Trajectory
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_coal_equity_"
}
FiveYearGrowthTrend("17", "EQ", "CoalCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_renewables_equity_"
}
FiveYearGrowthTrend("18", "EQ", "RenewablesCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_hydro_equity_"
}
FiveYearGrowthTrend("19", "EQ", "GasCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_hydro_equity_"
}
FiveYearGrowthTrend("20", "EQ", "HydroCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_oil_equity_"
}
FiveYearGrowthTrend("21", "EQ", "Oil", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_gas_equity_"
}
FiveYearGrowthTrend("22", "EQ", "Gas", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_coal_equity_"
}
FiveYearGrowthTrend("23", "EQ", "Coal", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_ice_equity_"
}
FiveYearGrowthTrend("24", "EQ", "ICE", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_electric_equity_"
}
FiveYearGrowthTrend("25", "EQ", "Electric", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_hybrid_equity_"
}
FiveYearGrowthTrend("26", "EQ", "Hybrid", LegendOn = F, explicit_filename = explicit_filename)
}
if (IncOtherSectors == T) {
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_cement_"
}
CO2IntensityTrend("30", "Cement", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_steel_"
}
CO2IntensityTrend("31", "Steel", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_aviation_"
}
CO2IntensityTrend("32", "Aviation", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_shipping_"
}
ShippingChart("33", "All", start_year + 5, explicit_filename = explicit_filename) # All
}
if (has_map) {
if (!explicit_filenames == F) {
explicit_filename <- "_map_coal_bonds_"
}
MapChart("49", "CB", "Coal", start_year + 5, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_map_coal_equity_"
}
MapChart("50", "EQ", "Coal", start_year + 5, explicit_filename = explicit_filename)
}
if (has_debt) {
if (!explicit_filenames == F) {
explicit_filename <- "_companies_power_bonds_"
}
CompanyInformation("40", no_companies, "CB", "Power", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_auto_bonds_"
}
CompanyInformation("41", no_companies, "CB", "Automotive", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_built_out_bonds_"
}
OilGasBuildOut("42", no_companies, "CB", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_share_bonds_"
}
OilShare("43", no_companies, "CB", explicit_filename = explicit_filename)
# if (IncCoalRetirement){
# CoalRetirementChart("62",no_companies,"CB")
# RenewableAdditionsChart("64",no_companies,"CB")
# }
}
if (has_equity) {
if (IncPeersChart) {
PeerComparison(81, chart_type = "EQ")
}
if (!explicit_filenames == F) {
explicit_filename <- "_companies_power_equity_"
}
CompanyInformation("44", no_companies, "EQ", "Power", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_auto_equity_"
}
CompanyInformation("45", no_companies, "EQ", "Automotive", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_built_out_equity_"
}
OilGasBuildOut("46", no_companies, "EQ", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_share_equity_"
}
OilShare("47", no_companies, "EQ", explicit_filename = explicit_filename)
# CarbonBudget("48", no_companies, "EQ")
# if (IncCoalRetirement){
# CoalRetirementChart("63",no_companies,"EQ")
# RenewableAdditionsChart("65",no_companies,"EQ")
# }
}
}
##############
### Charts ###
##############
PercentageOfPortfolioAssessed <- function(plotnumber, explicit_filename = "") {
over <- SectorDataAnalysis()
over <- over[over$financial_sector != "Not Included", ]
# over$financial_sector <- factor(over$financial_sector, levels = c("Fossil Fuels","Power", "Automotive",
# "Cement & Steel","Aviation & Shipping"), ordered=TRUE)
over <- as.data.frame(over)
if (data_check(over)) {
over$financial_sector <- if_else(over$financial_sector == "Cement&Steel", "Cement & Steel", over$financial_sector)
over <- over %>% filter(!is.na(financial_sector))
## "steelblue" color below should be changed to whatever our Portfolio color is
over1 <- subset(over, portfolio_name == portfolio_name_select & asset_type %in% c("Equity", "Bonds"))
over1 <- over1 %>%
group_by(investor_name, portfolio_name, asset_type, financial_sector) %>%
summarise(ValueUSD = sum(valid_value_usd))
over1$asset_type <- gsub("Bonds", BondReference, over1$asset_type)
over1$asset_type <- gsub("Equity", "Equity", over1$asset_type)
over1$asset_type <- factor(over1$asset_type, levels = c("Equity", BondReference))
# if(BondReference == "Corporate bond") {
over1$asset_type <- dplyr::recode(over1$asset_type, "Bond" = wrap_labels(BondsTitle, 10), Equity = EquityTitle) #
# } else {
# over1$asset_type <- dplyr::recode(over1$asset_type, "Other Title" = " ", Equity = EquityTitle) #
# }
over1$financial_sector <- factor(over1$financial_sector, levels = c(
"Aviation & Shipping", "Cement & Steel",
"Automotive", "Power", "Fossil Fuels"
), ordered = TRUE)
over1$financial_sector <- dplyr::recode(over1$financial_sector,
"Fossil Fuels" = wrap_labels(S_FossilFuels, 15),
"Power" = wrap_labels(S_Power, 15),
"Automotive" = wrap_labels(S_Automotive, 15), #
"Cement & Steel" = wrap_labels(S_CementSteel, 15),
"Aviation & Shipping" = wrap_labels(S_AviationShipping, 15)
) #
over_horizontal_perc <- over1 %>%
group_by(asset_type) %>%
mutate(percfinancial_sector = ValueUSD / sum(ValueUSD))
# ymax<-max(aggregate(over1["ValueUSD"],by=over1["asset_type"],FUN=sum)$ValueUSD)
## version horizontal percentage
plot <- ggplot(over_horizontal_perc, aes(x = asset_type, y = percfinancial_sector, fill = financial_sector)) +
geom_bar(position = "stack", stat = "identity", width = 0.9) +
coord_flip() +
scale_fill_manual(name = "", values = c(shipping, pow, trans, material, energy), drop = FALSE) + #
# scale_x_discrete(name="Asset Type",drop=F, labels=c(BondsTitle, EquityTitle)) + #
scale_y_continuous(name = SectorShare, labels = percent, expand = c(0, 0)) +
guides(fill = guide_legend(nrow = 2, reverse = TRUE)) +
theme_barcharts() +
theme(
plot.title = element_text(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(size = textsize),
legend.spacing.y = unit(-22, "pt"),
legend.box.margin = unit(c(-5, 40, -10, 10), "pt"),
axis.line.x = element_blank(),
axis.line.y = element_line(size = 0.5),
axis.text.x = element_text(colour = textcolor, size = 11),
axis.text.y = element_text(colour = textcolor, size = 11),
axis.title.x = element_text(size = 11, margin = margin(3, 0, 0, 0, "pt")),
axis.title.y = element_blank(),
panel.grid.major.x = element_line(colour = "#989898", size = 0.2),
plot.margin = unit(c(-2, 20, 10, 2), "pt")
) +
ggtitle("\n")
} else {
label <- Therearenoclimaterelevantequityorbondholdingsintheportfolios #
plot <- no_chart(label)
}
ggsave(plot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 2.3, width = 4.5, dpi = ppi) # linewidth_in*.9
}
TechnologyExposure <- function(plotnumber, chart_type, sector_to_plot, plot_year, explicit_filename = "") { # PlotYr
Combin <- data.frame()
if (chart_type == "EQ" & has_equity == TRUE) {
Peers <- filter_by_parameters(eq_peers, "EQ")
Market <- filter_by_parameters(eq_market, "EQ")
Combin <- filter_by_parameters(EQCombin, "EQ")
} else if (chart_type == "CB" & has_debt == TRUE) {
Peers <- filter_by_parameters(cb_peers, "CB", by_equity_market = FALSE)
Market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
Combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
}
if (data_check(Combin) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
# print("format and bind data")
# Remove all portfolios other than Market, Average
Peers$Type <- "Peers"
Peers <- unique(Peers)
Peers <- Peers %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "Type", "plan_alloc_wt_tech_prod") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
Peers <- Peers %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Peers")
)
Peer_A <- subset(Peers, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Peer_P <- subset(Peers, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Peer_F <- subset(Peers, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Peers <- bind_rows(Peer_F, Peer_A, Peer_P)
if (plot_year == start_year + 5) {
TARGET <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>%
select("portfolio_name", "ald_sector", "technology", "scen_alloc_wt_tech_prod") %>% # "Type"
rename(WtProduction = scen_alloc_wt_tech_prod)
TARGET$Type <- "Target"
TARGET <- TARGET %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Target")
)
TARGET_A <- subset(TARGET, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
TARGET_P <- subset(TARGET, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
TARGET_F <- subset(TARGET, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
TARGET <- bind_rows(TARGET_F, TARGET_A, TARGET_P)
}
Combin$Type <- "Portfolio"
Production <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
Market$Type <- "Benchmark"
Market <- Market %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr & Type =="Portfolio") %>%
select("portfolio_name", "ald_sector", "technology", "scen_alloc_wt_tech_prod", "Type") %>% # "Type"
rename(WtProduction = scen_alloc_wt_tech_prod)
Market <- Market %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Benchmark")
)
Market_A <- subset(Market, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Market_P <- subset(Market, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Market_F <- subset(Market, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Market <- bind_rows(Market_F, Market_A, Market_P)
# Filter and select
# print("Filter and select data")
if (plot_year == start_year + 5) {
Production <- bind_rows(Production, Peers, TARGET, Market)
} else if (plot_year == start_year) {
Production <- bind_rows(Production, Peers, Market)
}
Production$ald_sector <- factor(Production$ald_sector, levels = c("Oil&Gas", "Coal", "Power", "Automotive"))
levels(Production$ald_sector)[levels(Production$ald_sector) == "Coal"] <- "Fossil Fuels"
levels(Production$ald_sector)[levels(Production$ald_sector) == "Oil&Gas"] <- "Fossil Fuels"
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive"))
# Aggregate and rename CarstenMetric_Port
ID.COLS <- c("ald_sector", "technology", "Type")
Production <- Production %>% gather(key = Metric, value = Value, "WtProduction")
Production <- aggregate(Production["Value"], by = Production[c(ID.COLS)], FUN = sum)
# Created an average for the peers (or even just use fill!)
# Transfer coal to gigajoule for tech share charts only!!!
Production$Value[Production$technology == "Coal"] <- Production$Value[Production$technology == "Coal"] * 29307600 / 365.25 / 1e6
# , Production$Type != "Market"
if (sector_to_plot != "All") {
technologyorder <- technology_order
colours <- as.vector(ColourPalette[["Colours"]])
names(colours) <- technologyorder
# labels <- gsub("Cap","",technologyorder)
labels <- dplyr::recode(technologyorder,
Gas = T_GasProd, Oil = T_OilProd, Coal = T_CoalProd, #
RenewablesCap = T_Renewables, HydroCap = T_HydroCap, NuclearCap = T_NuclearCap, GasCap = T_GasCap, CoalCap = T_CoalCap, #
Electric = x_Electric, Hybrid = x_Hybrid, ICE = x_ICE, "Freight Passenger" = FreightPassenger
)
names(labels) <- technologyorder
} else if (sector_to_plot == "All") {
technologyorder <- technology_order_short
colours <- as.vector(ColourPalette[["Colours"]])[1:11]
names(colours) <- technologyorder
# labels <- gsub("Cap","",technologyorder)
labels <- dplyr::recode(technologyorder,
Gas = T_GasProd, Oil = T_OilProd, Coal = wrap_labels(paste0(T_CoalProd, " "), 10), #
RenewablesCap = wrap_labels(T_Renewables, 10), HydroCap = wrap_labels(T_HydroCap, 15), NuclearCap = wrap_labels(T_NuclearCap, 15), GasCap = wrap_labels(T_GasCap, 15), CoalCap = wrap_labels(T_CoalCap, 15), #
Electric = wrap_labels(x_Electric, 10), Hybrid = wrap_labels(x_Hybrid, 10), ICE = wrap_labels(x_ICE, 10), "Freight Passenger" = FreightPassenger
)
names(labels) <- technologyorder
}
Production$technology <- factor(Production$technology, levels = technologyorder)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping"))
Production$Type <- wrap_labels(Production$Type, 20)
if (plot_year == start_year + 5) {
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Peers", "Target", "Benchmark"))
xlabels <- c(
Portf, wrap_labels(x_Peers, 10), ifelse(Language %in% c("FR", "ES"), paste(Portf, "\n", AlignedFEM), paste(Aligned, "\n", Portf)), #
ifelse(Language %in% c("FR", "ES"), paste(AlignMarket, "\n", AlignedMAS), paste(Aligned, "\n", AlignMarket))
) #
} else if (plot_year == start_year) {
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Peers", "Benchmark"))
xlabels <- c(Portf, wrap_labels(x_Peers, 9), ifelse(Language %in% c("FR", "ES"), paste(AlignMarket, "\n", Aligned), paste(Aligned, "\n", AlignMarket))) #
}
titles <- c("Fossil Fuel Production", "Power Capacity", "Automotive Production", "Aviation Production", "Shipping Production")
names(titles) <- c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")
Production <- subset(Production, select = c("Type", "ald_sector", "technology", "Value"))
plottheme <- stacked_bar_chart(Production, colours, labels) +
ylab(ShareofSectorProduction) +
scale_x_discrete(labels = xlabels) +
guides(fill = guide_legend(
keywidth = 0.1,
keyheight = 0.1,
default.unit = "inch"
)) +
theme(
plot.title = element_text(hjust = 0.5, colour = textcolor, size = 11, margin = unit(c(0, 0, 1, 0), "lines")),
legend.position = "bottom",
legend.box.margin = margin(-15, 0, 0, 0, unit = "pt"),
legend.title = element_blank(),
legend.text = element_text(size = 6.5), # , margin = margin(l = -0.5, r = -.5, unit = "pt")
axis.text.x = element_text(size = 6.5),
axis.text.y = element_text(size = 6.5)
)
if (sector_to_plot %in% c("Automotive", "Power", "Fossil Fuels", "Aviation", "Shipping")) {
# print("plotting")
dat <- subset(Production, ald_sector == sector_to_plot)
cmd <- plottheme %+% dat +
xlab("")
# print(p1)
# if (sector_to_plot == "Fossil Fuels"){
# sector_to_plot <- "FossilFuels"
# }
n <- 4
} else if (sector_to_plot %in% c("All", "")) {
dat <- subset(Production, ald_sector == "Automotive")
if (nrow(subset(dat, Type == "Portfolio")) > 0) {
p1 <- plottheme %+% dat +
ggtitle(AutomotiveProduction) #
} else {
dat <- rbind(dat, c(
"Type" = "Portfolio",
"ald_sector" = "Automotive",
"technology" = "ICE",
"Value" = 0
))
dat$Value <- as.numeric(dat$Value)
p1 <- plottheme %+% dat +
ggtitle(AutomotiveProduction) + #
geom_text(
data = subset(dat, Type == "Portfolio"),
aes(Type, y = .5, angle = 90, label = NoAutomotiveProduction), size = 2.5
) #
}
dat <- subset(Production, ald_sector == "Fossil Fuels")
if (nrow(subset(dat, Type == "Portfolio")) > 0) {
p2 <- plottheme %+% dat +
ggtitle(FossilFuelsProduction) #
} else {
dat <- rbind(dat, c(
"Type" = "Portfolio",
"ald_sector" = "Fossil Fuels",
"technology" = "OilProd",
"Value" = 0
))
dat$Value <- as.numeric(dat$Value)
p2 <- plottheme %+% dat +
ggtitle(FossilFuelsProduction) + #
geom_text(
data = subset(dat, Type == "Portfolio"),
aes(Type, y = 0.5, angle = 90, label = NoFossilFuelsProduction), size = 2.5
) #
}
dat <- subset(Production, ald_sector == "Power")
if (nrow(subset(dat, Type == "Portfolio")) > 0) {
p3 <- plottheme %+% dat +
ggtitle(C_Power) #
} else {
dat <- rbind(dat, c(
"Type" = "Portfolio",
"ald_sector" = "Power",
"technology" = "CoalCap",
"Value" = 0
))
dat$Value <- as.numeric(dat$Value)
p3 <- plottheme %+% dat +
ggtitle(C_Power) + #
geom_text(
data = subset(dat, Type == "Portfolio"),
aes(Type, y = .5, angle = 90, label = NoC_Power), size = 2.5
) #
}
# print("plotting")
n <- 8
cmd <- arrangeGrob(p2,
p3,
p1,
nrow = 1
)
# p3+theme(axis.text.y = element_blank(), axis.title.y = element_blank()),
# p1+theme(axis.text.y = element_blank(), axis.title.y = element_blank()), nrow=1)
# p3+theme(axis.text.y = element_text(color="white"), axis.title.y = element_text(color="white")),
# p1+theme(axis.text.y = element_text(color="white"), axis.title.y = element_text(color="white")), nrow=1)
dev.off()
}
} else {
Label <- YourPortfoliohasnoproductioninthissectorfortheselectedscenario_geographyandMarket #
cmd <- no_chart(Label)
n <- 4
}
ggsave(graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), # bg = "transparent",
plot = cmd, height = 3, width = n, dpi = ppi
)
}
FiveYearGrowthTrend <- function(plotnumber, chart_type, tech_to_plot, LegendOn = FALSE, explicit_filename = "") {
tech_to_plot <<- tech_to_plot
GoodBad <- green_brown(tech_to_plot)
if (GraphType %in% c("", "Report")) {
LegendOn <- FALSE
} else {
LegendOn <- TRUE
}
filternames <- c("Listed Market", "Bond Universe", "ListedEquity", "MetaPort", meta_investor_name)
PortNames <- portfolio_name_select
### Equity PRODUCTION
if (chart_type == "EQ") {
ALD_Market <- filter_by_parameters(eq_market, "EQ", byscenario = TRUE) %>% distinct()
ALD <- filter_by_parameters(EQCombin, "EQ", byscenario = FALSE) %>% filter(ald_sector != "Other")
if (data_check(ALD) == TRUE) {
ALD$asset_type <- "Equity"
ALD_Market <- ALD_Market %>%
filter(portfolio_name == eq_market_ref)
if (sum(ALD$plan_alloc_wt_tech_prod) > 0) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
}
}
if (chart_type == "CB") {
# ScenarioGeographyChoose <- "Global"
ALD_Market <- filter_by_parameters(cb_market, "CB", byscenario = TRUE, by_equity_market = FALSE)
# ScenarioGeographyChoose <- "GlobalAggregate"
ALD <- filter_by_parameters(CBCombin, "CB", byscenario = FALSE, by_equity_market = FALSE)
if (data_check(ALD) == TRUE) {
ALD$asset_type <- "Bonds"
if (sum(ALD$plan_alloc_wt_tech_prod, na.rm = T) > 0) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
}
}
if (PlotChart == TRUE) {
ALD$plan_alloc_wt_tech_prod <- ifelse(ALD$technology == "Oil", ALD$plan_alloc_wt_tech_prod * (1 / 6.12), ALD$plan_alloc_wt_tech_prod)
ALD$plan_alloc_wt_tech_prod <- ifelse(ALD$technology == "Gas", ALD$plan_alloc_wt_tech_prod * (1 / 0.0372), ALD$plan_alloc_wt_tech_prod)
ALD$scen_alloc_wt_tech_prod <- ifelse(ALD$technology == "Oil", ALD$scen_alloc_wt_tech_prod * (1 / 6.12), ALD$scen_alloc_wt_tech_prod)
ALD$scen_alloc_wt_tech_prod <- ifelse(ALD$technology == "Gas", ALD$scen_alloc_wt_tech_prod * (1 / 0.0372), ALD$scen_alloc_wt_tech_prod)
if (data_check(ALD_Market)) {
ALD_Market$investor_name <- "Market"
ALD_Market$plan_alloc_wt_tech_prod <- ifelse(ALD_Market$technology == "Oil", ALD_Market$plan_alloc_wt_tech_prod * (1 / 6.12), ALD_Market$plan_alloc_wt_tech_prod)
ALD_Market$plan_alloc_wt_tech_prod <- ifelse(ALD_Market$technology == "Gas", ALD_Market$plan_alloc_wt_tech_prod * (1 / 0.0372), ALD_Market$plan_alloc_wt_tech_prod)
}
TotalProduction <- sum(ALD$plan_tech_prod, na.rm = T)
ALD <- bind_rows(ALD, ALD_Market)
ALD <- subset(ALD, year >= start_year & year <= start_year + 5)
ALD <- ALD[ALD$technology == tech_to_plot, ]
table(ALD$asset_type, useNA = "always")
### Separate into CurrentPlans and scenario, get into same column headers
ALD <- subset(ALD, select = c(investor_name, portfolio_name, ald_sector, year, technology, scenario, plan_alloc_wt_tech_prod, scen_alloc_wt_tech_prod))
ALD.cp <- ALD %>%
select(investor_name, portfolio_name, ald_sector, scenario, year, technology, plan_alloc_wt_tech_prod) %>% # Tech.Type,
distinct() %>%
rename(Production = plan_alloc_wt_tech_prod) %>%
filter(
year %in% seq(start_year, start_year + 5),
scenario == Scenariochoose
) %>%
mutate(Line.Type = "CurrentPlan")
ALD.sc <- ALD %>%
select(investor_name, portfolio_name, scenario, ald_sector, year, technology, scen_alloc_wt_tech_prod) %>% # Tech.Type,
rename(Production = scen_alloc_wt_tech_prod) %>%
filter(
portfolio_name == portfolio_name_select,
year %in% seq(start_year, start_year + 5),
scenario %in% iea_scenario_list
) %>%
mutate(Line.Type = "scenario")
### Rename the scenarios from Green to Red - so the names are then independent
### Set the number of scenarios that are available
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPS", "NPSRTS", as.character(ALD.sc$scenario)) # New policy scenarios
ALD.sc$scenario <- ifelse(ALD.sc$scenario %in% c("450S", "SDS"), "SDS", as.character(ALD.sc$scenario)) # New policy scenarios
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPSProxy", "CPS", as.character(ALD.sc$scenario))
no.scenarios <- 0
if (n_distinct(ALD.sc$scenario) == 3) {
no.scenarios <- 3
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "SDS","Scen2",as.character(ALD.sc$scenario)) # 450S
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPSRTS","Scen3",as.character(ALD.sc$scenario)) # New policy scenarios
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPS","Scen4",as.character(ALD.sc$scenario)) # Current policy scenarios
}
if (n_distinct(ALD.sc$scenario) == 4) {
no.scenarios <- 4
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "B2DS","Scen1",as.character(ALD.sc$scenario)) # Below 2 degrees
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "SDS","Scen2",as.character(ALD.sc$scenario)) # 450S
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPSRTS","Scen3",as.character(ALD.sc$scenario)) # New policy scenarios
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPS","Scen4",as.character(ALD.sc$scenario)) # Current policy scenarios
}
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "B2DS", "Scen1", as.character(ALD.sc$scenario)) # Below 2 degrees
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "SDS", "Scen2", as.character(ALD.sc$scenario)) # 450S
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPSRTS", "Scen3", as.character(ALD.sc$scenario)) # New policy scenarios
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPS", "Scen4", as.character(ALD.sc$scenario)) # Current policy scenarios
### Remove additional scenarios
ALD.sc <- as.data.frame(ALD.sc)
ALD.sc <- ALD.sc[grepl("Scen", ALD.sc[, "scenario"]), ]
### Calculate GROWTH
ALD2 <- bind_rows(ALD.cp, ALD.sc)
### Normalisation of market to Portfolio
ALD.cp <- ALD2 %>% filter(Line.Type == "CurrentPlan")
var <- ifelse(ALD.cp[which(ALD.cp$portfolio_name == PortNames & ALD.cp$year == start_year & ALD.cp$technology == tech_to_plot), ]$Production == 0, 0,
ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$year == start_year & ALD.cp$technology == tech_to_plot), ]$Production / ALD.cp[which(ALD.cp$portfolio_name == PortNames & ALD.cp$year == start_year & ALD.cp$technology == tech_to_plot), ]$Production
)
# var <- 1
# ALD.cp[which(ALD.cp$investor_name_select=="Market" & ALD.cp$technology ==tech_to_plot),]$Production<- ifelse(var ==0,0,ALD.cp[which(ALD.cp$investor_name_select=="Market" & ALD.cp$technology ==tech_to_plot),]$Production/var)
if (data_check(ALD_Market)) {
if (var == 0) {
ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production <- 0
} else {
ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production <- ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production / var
}
}
if (sum(ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production) == 0) {
ALD.cp <- ALD.cp[ALD.cp$investor_name != "Market", ]
}
ALD.sc <- ALD2 %>% filter(Line.Type == "scenario")
ALD2 <- bind_rows(ALD.cp, ALD.sc)
ylims <- ALD2 %>%
filter(investor_name != "Market" | Line.Type != "scenario") %>%
summarise(min = min(Production), max = max(Production))
### Units
unitscaleval <- max(ylims$max, ALD.cp$Production)
unitscalefactor <- 1
green.unit <- c(
"Electric" = Vehicles,
"RenewablesCap" = "MW",
"NuclearCap" = "MW",
"HydroCap" = "MW",
"Hybrid" = Vehicles
)
brown.unit <- c(
"CoalCap" = "MW",
"GasCap" = "MW",
"OilCap" = "MW",
"Oil" = "bbl",
"Gas" = "m3",
"ICE" = Vehicles,
"Coal" = "t"
)
if (unitscaleval < 1e-3) {
unitscalefactor <- 1e-3
green.unit <- c(
"Electric" = paste0("1/1000 x ", Vehicles),
"RenewablesCap" = "W",
"NuclearCap" = "W",
"HydroCap" = "W",
"Hybrid" = paste0("1/1000 x ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "W",
"GasCap" = "W",
"OilCap" = "W",
"Oil" = paste0("1/1000 x ", "bbl"),
"Gas" = "L",
"ICE" = paste0("1/1000 x ", Vehicles),
"Coal" = "g"
)
}
if (unitscaleval > 1e3) {
unitscalefactor <- 1e3
green.unit <- c(
"Electric" = paste0(thousand, " ", Vehicles),
"RenewablesCap" = "GW",
"NuclearCap" = "GW",
"HydroCap" = "GW",
"Hybrid" = paste0(thousand, " ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "GW",
"GasCap" = "GW",
"OilCap" = "GW",
"Oil" = "Mbbl",
"Gas" = paste0(thousand, " m3"),
"ICE" = paste0(thousand, " ", Vehicles),
"Coal" = paste0(thousand, " t")
)
}
if (unitscaleval > 1e6) {
unitscalefactor <- 1e6
green.unit <- c(
"Electric" = paste0(million, " ", Vehicles),
"RenewablesCap" = "TW",
"NuclearCap" = "TW",
"HydroCap" = "TW",
"Hybrid" = paste0(million, " ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "TW",
"GasCap" = "TW",
"OilCap" = "TW",
"Oil" = "MMbbl",
"Gas" = paste0(million, " m3"),
"ICE" = paste0(million, " ", Vehicles),
"Coal" = paste0(million, " t")
)
}
if (unitscaleval > 1e9) {
unitscalefactor <- 1e9
green.unit <- c(
"Electric" = paste0(billion, " ", Vehicles),
"RenewablesCap" = "PW",
"NuclearCap" = "PW",
"HydroCap" = "PW",
"Hybrid" = paste0(billion, " ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "PW",
"GasCap" = "PW",
"Oil" = "Gbbl",
"Gas" = paste0(billion, " m3"),
"ICE" = paste0(billion, " ", Vehicles),
"Coal" = paste0(billion, " t")
)
}
ALD2$Production <- ALD2$Production / unitscalefactor
ylims <- ALD2 %>%
filter(investor_name != "Market" | Line.Type != "scenario") %>%
summarise(min = min(Production), max = max(Production))
### GET SCENARIOS INTO RIBBON FORMAT
ALD.sc <- ALD2 %>% filter(Line.Type == "scenario")
ALD.cp <- ALD2 %>% filter(Line.Type == "CurrentPlan")
ALD.sc.wide <- ALD.sc %>%
ungroup() %>%
select(investor_name, portfolio_name, scenario, ald_sector, technology, Line.Type, year, Production) %>% # Tech.Type,
spread(key = scenario, value = Production)
if (no.scenarios == 3) {
ALD.sc.wide <- ALD.sc.wide %>%
arrange(investor_name, portfolio_name, ald_sector, technology, Line.Type, year) %>% # Tech.Type,
group_by(investor_name, portfolio_name, ald_sector, technology, Line.Type) %>% # Tech.Type,
mutate(Green = ifelse(last(Scen2) > last(Scen3), 1, 0))
if ("Scen4" %in% colnames(ALD.sc.wide)) {
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line2 = ifelse(Green == 1, Scen4, Scen2),
Line3 = Scen3,
Line4 = ifelse(Green == 1, Scen2, Scen4)
)
}
if ("Scen1" %in% colnames(ALD.sc.wide) & green_brown(tech_to_plot) == "brown") {
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line1 = ifelse(Green == 1, Scen3, Scen1),
Line2 = Scen2,
Line3 = ifelse(Green == 1, Scen1, Scen3)
)
} else if ("Scen1" %in% colnames(ALD.sc.wide) & green_brown(tech_to_plot) == "green") {
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line2 = Scen3,
Line3 = Scen2,
Line4 = Scen1
)
}
} else if (no.scenarios == 4) {
ALD.sc.wide <- ALD.sc.wide %>%
arrange(investor_name, portfolio_name, ald_sector, technology, Line.Type, year) %>% # Tech.Type,
group_by(investor_name, portfolio_name, ald_sector, technology, Line.Type) %>% # Tech.Type,
mutate(Green = ifelse(last(Scen2) > last(Scen4), 1, 0))
if (green_brown(tech_to_plot) == "brown") {
if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 < ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen4 <- ALD.sc.wide$Scen4
} else if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 > ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
temp <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen4
ALD.sc.wide$Scen4 <- temp
}
} else if (green_brown(tech_to_plot) == "green") {
if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 > ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen4 <- ALD.sc.wide$Scen4
} else if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 < ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
temp <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen4
ALD.sc.wide$Scen4 <- temp
}
}
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line1 = ifelse(Green == 1, Scen4, Scen1),
Line2 = ifelse(Green == 1, Scen3, Scen2),
Line3 = ifelse(Green == 1, Scen2, Scen3),
Line4 = ifelse(Green == 1, Scen1, Scen4)
)
}
### IDENTIFY LIMITS of the Y axis
ymin <- min(ylims$min)
ymax <- max(ylims$max)
round.digits <- 1
if ((ymax - ymin) < 0.01) {
unit <- 0.001
round.digits <- 3
} else if ((ymax - ymin) >= 0.01 & (ymax - ymin) < 0.1) {
unit <- .01
round.digits <- 2
} else if ((ymax - ymin) >= 0.1 & (ymax - ymin) < 1) {
unit <- .1
} else if ((ymax - ymin) >= 1 & (ymax - ymin) < 10) {
unit <- 1
} else if ((ymax - ymin) >= 10 & (ymax - ymin) < 150) {
unit <- 10
} else if ((ymax - ymin) >= 150 & (ymax - ymin) < 1000) {
unit <- 100
} else if ((ymax - ymin) >= 1000 & (ymax - ymin) < 10000) {
unit <- 1000
} else if ((ymax - ymin) >= 10000 & (ymax - ymin) < 100000) {
unit <- 10000
}
MAX.Y <- ceiling(ymax / unit) * unit
MIN.Y <- floor(ymin / unit) * unit
NEXT.LINE <- paste0("Line", as.numeric(max(gsub("Line", "", colnames(ALD.sc.wide)[grep("Line", colnames(ALD.sc.wide))]))) + 1)
ALD.sc.wide[, NEXT.LINE] <- MAX.Y
ALD.sc.tall <- ALD.sc.wide %>%
select(-grep("Scen", colnames(ALD.sc.wide))) %>%
gather(key = "Target", value = "Value", -investor_name, -portfolio_name, -ald_sector, -technology, -Green, -Line.Type, -year)
ALD.sc.tall <- ALD.sc.tall %>%
group_by(investor_name, portfolio_name, ald_sector, technology, Line.Type, Green, year) %>% # Tech.Type
mutate(
lower = dplyr::lag(Value),
lower = ifelse(is.na(lower), MIN.Y, lower)
)
ALD.sc.tall <- as.data.frame(ALD.sc.tall)
ALD.cp <- as.data.frame(ALD.cp)
# if (no.scenarios == 4){ # WITH 1.75°
green.fill <- c(
"Line5" = area_1,
"Line4" = area_2,
"Line3" = area_2_4,
"Line2" = area_4_6,
"Line1" = area_6
)
brown.fill <- c(
"Line5" = area_6,
"Line4" = area_4_6,
"Line3" = area_2_4,
"Line2" = area_2,
"Line1" = area_1
)
green.labels <- c(
"Line5" = "1D",
"Line4" = "2D",
"Line3" = "2D-4D",
"Line2" = "4D-6D",
"Line1" = "6D"
)
brown.labels <- c(
"Line5" = "6D",
"Line4" = "4D-6D",
"Line3" = "2D-4D",
"Line2" = "2D",
"Line1" = "1D"
)
outputplot <- ggplot(data = subset(ALD.sc.tall, technology == tech_to_plot & ALD.sc.tall$portfolio_name == PortNames)) +
geom_ribbon(aes(ymin = lower, ymax = Value, x = year, fill = Target), alpha = 0.75) +
scale_fill_manual(labels = eval(parse(text = paste(GoodBad, ".labels", sep = ""))), values = eval(parse(text = paste(GoodBad, ".fill", sep = "")))) +
scale_x_continuous(name = Year, expand = c(0, 0), limits = c(start_year, start_year + 5.6)) +
scale_y_continuous(
name = paste0(WeightedProduction, " (", eval(parse(text = paste(GoodBad, ".unit", sep = "")))[tech_to_plot], ")"),
expand = c(0, 0),
breaks = round(seq(round(MIN.Y, digits = round.digits), round(MAX.Y, digits = round.digits), length.out = 5), digits = round.digits)
) +
theme_246() +
theme(legend.position = "none") +
coord_cartesian(ylim = c(round(MIN.Y, digits = round.digits), round(MAX.Y, digits = round.digits)))
if (chart_type == "CB") {
outputplot <- outputplot +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & portfolio_name == portfolio_name_select),
aes(x = year, y = Production), color = cb_line, size = .75
) +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & investor_name == "Market"),
aes(x = year, y = Production), color = cb_line, size = .75, linetype = "dashed"
)
}
if (chart_type == "EQ") {
outputplot <- outputplot +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & portfolio_name == portfolio_name_select),
aes(x = year, y = Production), color = eq_line, size = .75
) +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & investor_name == "Market"),
aes(x = year, y = Production), color = eq_line, size = .75, linetype = "dashed"
)
}
if (chart_type == "BV") {
outputplot <- outputplot +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & portfolio_name == portfolio_name_select),
aes(x = year, y = Production), color = bv_line, size = .75
) +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & investor_name == "Market"),
aes(x = year, y = Production), color = bv_line, size = .75, linetype = "dashed"
)
}
if (LegendOn == TRUE) {
rect_246 <- gList(
# <1?C
rectGrob(
x = 0.09, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_1)
),
# 1-2?C
rectGrob(
x = 0.21, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_2)
),
# 2-4?C
rectGrob(
x = 0.33, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_2_4)
),
# 4-6?C
rectGrob(
x = 0.45, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_4_6)
),
# >6?C
rectGrob(
x = 0.57, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_6)
)
)
arrows_246 <- gList(
linesGrob(
x = unit(0.15, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
),
linesGrob(
x = unit(0.27, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
),
linesGrob(
x = unit(0.39, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
),
linesGrob(
x = unit(0.51, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
)
)
if (chart_type == "CB") {
linecolour <- cb_line
Line_labels <- c(paste0(BondReference, " portfolio"), paste0(BondReference, " market"))
} else {
linecolour <- eq_line
Line_labels <- c("Equity portfolio", "Listed equity market")
}
portfolio_lines <- gList(
linesGrob(
x = unit(c(0.65, 0.735), "npc"), y = unit(0.68, "npc"),
gp = gpar(col = linecolour, lty = 1, lwd = 3)
),
linesGrob(
x = unit(c(0.65, 0.735), "npc"), y = unit(0.32, "npc"),
gp = gpar(col = linecolour, lty = 2, lwd = 3)
)
)
### Legend labels
# Labels
scenario_labels <- c("1.75C scenario", "2C scenario", "4C scenario", "6C scenario")
Legend_labels <- c("<1.75C", "<2C", "1.75 - 2C", "2 - 4C", "4 - 6C", "6C")
# Positions
scenario_labels_position <- c(0.15, 0.27, 0.39, 0.51)
Legend_labels_position <- c(0.09, 0.21, 0.33, 0.45, 0.57)
Line_labels_position <- c(0.68, 0.32)
# Label scenario and 2-4-6
label_text_246 <- gList(
textGrob(scenario_labels,
x = scenario_labels_position, y = 0.88,
just = "center", gp = gpar(fontsize = 5.8, col = textcolor)
),
textGrob(Legend_labels[c(1, 3:6)],
x = Legend_labels_position, y = 0.4,
just = "center", gp = gpar(fontsize = 7, col = textcolor)
),
textGrob(scenario_labels[2:4],
x = scenario_labels_position[2:4], y = 0.8,
just = "center", gp = gpar(fontsize = 5.8, col = textcolor)
),
textGrob(Legend_labels[c(2, 4:6)],
x = Legend_labels_position[2:5], y = 0.4,
just = "center", gp = gpar(fontsize = 7, col = textcolor)
)
)
# Label line
label_text_lines <- gList(
textGrob(Line_labels,
x = 0.745, y = Line_labels_position,
just = "left", gp = gpar(fontsize = 7, col = textcolor)
)
)
if (no.scenarios == 3) {
footer_246 <- grobTree(
rectGrob(gp = gpar(fill = "white", lwd = 0, col = "white")),
rect_246[2:5], arrows_246[2:4], label_text_246[3:4], portfolio_lines[1:2], label_text_lines[1]
)
# outputplot <- arrangeGrob(outputplot, footer_246, heights = unit(c(0.85, 0.15), c('npc', 'npc'))) #
outputplot <- arrangeGrob(outputplot, footer_246, heights = unit(c(0.85, 0.15), c("npc", "npc"))) #
} else if (no.scenarios == 4) {
footer_1246 <- grobTree(
rectGrob(gp = gpar(fill = "white", lwd = 0, col = "white")),
rect_246, arrows_246, label_text_246[1:2], portfolio_lines[1:2], label_text_lines[1]
)
# outputplot <- arrangeGrob(outputplot, footer_1246, heights = unit(c(0.85, 0.15), c('npc', 'npc'))) #
outputplot <- arrangeGrob(outputplot, footer_1246, heights = unit(c(0.85, 0.15), c("npc", "npc"))) #
}
}
} else {
Label <- YourportfoliohasnoproductionfortheselectedTechnologyScenarioGeographyandMarket
outputplot <- no_chart(Label)
}
ggsave(outputplot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 3.6, width = 4.6, dpi = ppi * 2) # ,bg="white"
tech_to_plot <<- ""
BV.asset_type <<- ""
}
CO2IntensityTrend <- function(plotnumber, sector_to_plot, LegendOn = TRUE, explicit_filename = "") {
PlotChart <- FALSE
Data <- NA
Data_debt <- NA
sector_to_plot <<- sector_to_plot
if (GraphType == "Report") {
LegendOn <- FALSE
} else {
LegendOn <- TRUE
}
if (has_equity == TRUE) {
Data <- filter_by_parameters(EQCombin, "EQ", byscenario = F)
if (data_check(Data) == TRUE) {
Data$Type <- "equity"
} else {
print("no EQ")
}
}
if (has_debt == TRUE) {
Data_debt <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE, byscenario = F)
if (data_check(Data_debt) == TRUE) {
Data_debt$Type <- BondReference
} else {
print("no CB")
}
}
if (data_check(Data) == TRUE & data_check(Data_debt) == TRUE) {
Data <- subset(Data, select = colnames(Data_debt))
Data <- rbind(Data, Data_debt)
Data <- subset(Data, year >= start_year & year <= start_year + 5)
} else if (data_check(Data) == FALSE & data_check(Data_debt) == TRUE) {
Data <- Data_debt
} else if (data_check(Data) == TRUE & data_check(Data_debt) == FALSE) {
Data <- Data
}
if (data_check(Data) == TRUE) {
Data <- Data[!is.na(Data$plan_sec_emissions_factor), ]
}
if (data_check(Data) == TRUE) {
Data <- Data[Data$ald_sector == sector_to_plot, ]
if (data_check(Data) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
}
if (PlotChart == TRUE) {
# print(PlotChart)
if (sector_to_plot == "Aviation") {
PlotData <- Data %>%
ungroup() %>%
select(portfolio_name, year, technology, plan_sec_emissions_factor, scen_sec_emissions_factor, Type) %>%
group_by(portfolio_name, year, Type) %>%
filter(year <= start_year + 5) %>%
summarise(
plan_emission_factor = mean(plan_sec_emissions_factor, na.rm = T) * 1000,
scen_emission_factor = mean(scen_sec_emissions_factor, na.rm = T) * 1000
)
Ref.Data <- PlotData %>%
ungroup() %>%
filter(year == start_year) %>%
rename(Ref.Scen.EF = scen_emission_factor) %>%
select(Type, Ref.Scen.EF)
PlotData <- PlotData %>% left_join(Ref.Data, by = "Type")
PlotData <- PlotData %>%
group_by(year, Type) %>%
mutate(scen_emission_factor = scen_emission_factor * plan_emission_factor / Ref.Scen.EF) %>%
select(-Ref.Scen.EF)
} else {
PlotData <- Data %>%
ungroup() %>%
select(portfolio_name, year, technology, plan_sec_emissions_factor, scen_sec_emissions_factor, Type) %>%
group_by(portfolio_name, year, Type) %>%
filter(year <= start_year + 5) %>%
summarise(
plan_emission_factor = mean(plan_sec_emissions_factor, na.rm = T),
scen_emission_factor = mean(scen_sec_emissions_factor, na.rm = T)
)
Ref.Data <- PlotData %>%
ungroup() %>%
filter(year == start_year) %>%
rename(Ref.Scen.EF = scen_emission_factor) %>%
select(Type, Ref.Scen.EF)
PlotData <- PlotData %>% left_join(Ref.Data, by = "Type")
PlotData <- PlotData %>%
group_by(year, Type) %>%
mutate(scen_emission_factor = scen_emission_factor * plan_emission_factor / Ref.Scen.EF) %>%
select(-Ref.Scen.EF)
}
# print(nrow(PlotData))
OtherSectorUnits <- data.frame("ald_sector" = c("Cement", "Steel", "Aviation"), "Units" = c(
paste0("t CO2 / t ", L_Cement), #
paste0("t CO2 / t ", L_Steel), #
paste0("CO2/", x_passenger, "km")
)) #
year_lab <- Year # "year" #GT["year"][[1]]
ylabel <- paste0(EmissionsIntensity, OtherSectorUnits$Units[OtherSectorUnits$ald_sector == sector_to_plot], ")") #
df <- PlotData
colourdf <- data.frame("Colour" = c(eq_line, cb_line), "TargetCol" = c(area_2, area_2), "Type" = c("equity", BondReference))
df <- base::merge(df, colourdf, by = "Type")
outputplot <- ggplot() +
geom_line(data = df, aes(x = year, y = scen_emission_factor, colour = Type, group = Type), size = 1.5, linetype = 1, colour = area_2) +
annotate(geom = "point", y = df$plan_emission_factor[df$year == start_year], x = df$year[df$year == start_year], size = 5, colour = unique(df$Colour), fill = unique(df$Colour), shape = 22)
outputplot <- outputplot +
# scale_fill_identity(name = "", guide = 'legend',labels = c("Exposure gap","Current capacity + planned additions")) +
scale_colour_manual(name = "", guide = "legend", values = unique(df$Colour), labels = unique(df$Type)) +
xlab(year_lab) + ylab(ylabel) + # Set axis labels
# legend(values=legelabels)+
scale_x_continuous(breaks = seq(start_year, max(df$year), 1), expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
expand_limits(x = c(start_year - 0.5, start_year + 5.5), y = c(.7 * min(df[, c("plan_emission_factor", "scen_emission_factor")], na.rm = TRUE), 1.3 * max(df[, c("scen_emission_factor")], na.rm = TRUE))) +
theme_linecharts() #+
# theme(axis.line.x = element_line(colour = "red",size=0.5))
if (LegendOn == TRUE) {
# Count number of sectors present
nosectors <- length(unique(df$Type))
if (nosectors == 1) {
chartsectors <- unique(df$Type)
if (chartsectors == "equity") {
boxcol <- eq_line
} else if (chartsectors == "Corporate bond") {
boxcol <- cb_line
}
}
if (nosectors == 2) {
y1 <- 0.8
y2 <- 0.5
y3 <- 0.2
} else {
y3 <- 0.4
y2 <- 0.8
}
boxdim <- c(0.03, 0.2)
### Target emissions intensity lines
Emission_lines <- gList(
linesGrob(
x = unit(c(0.03, 0.13), "npc"), y = unit(y3, "npc"),
gp = gpar(col = area_2, lty = 1, lwd = 3)
)
)
### Current Portfolio Emission Intensity
if (nosectors == 2) {
Emission_rectangle <- gList(
rectGrob(
x = 0.07, y = unit(y2, "npc"), width = boxdim[1], height = boxdim[2],
gp = gpar(col = NA, fill = eq_line)
),
rectGrob(
x = 0.07, y = unit(y1, "npc"), width = boxdim[1], height = boxdim[2],
gp = gpar(col = NA, fill = cb_line)
)
)
} else {
Emission_rectangle <- gList(
rectGrob(
x = 0.07, y = unit(y2, "npc"), width = boxdim[1], height = boxdim[2],
gp = gpar(col = NA, fill = boxcol)
)
)
}
### legend labels
# labels
if (nosectors == 2) {
Emission_labels <- c(
paste0("Current emissions intensity for your", tolower(BondReference), "portfolio"),
"Current emissions intensity for your equity portfolio",
"Target emissions intensity for the relevant portfolio"
)
Emission_labels_position <- c(y1, y2, y3)
} else {
Emission_labels <- c(
paste0("Current emissions intensity for your ", chartsectors, " portfolio"),
"Target emissions intensity for the portfolio"
)
Emission_labels_position <- c(y2, y3)
}
# positions
# all labels together
Emission_label_text <-
textGrob(Emission_labels,
x = 0.15, y = Emission_labels_position,
just = "left", gp = gpar(fontsize = 8, col = textcolor, family = textfont)
)
# all legend objects togeter
# footer_Emission = grobTree(rectGrob(gp = gpar(fill = 'transparent', lwd = 0, colour = "white")),
# Emission_lines, Emission_rectangle, Emission_label_text) # one footer too much
footer_Emission <- grobTree(Emission_lines, Emission_rectangle, Emission_label_text)
# outputplot <- arrangeGrob(footer_Emission, heights = unit(c(.15), c('npc')))
# legend with "a" - a ggplot object
outputplot <- arrangeGrob(outputplot, footer_Emission, heights = unit(c(0.85, 0.15), c("npc", "npc")))
}
} else {
if (sector_to_plot == "Cement") {
Label <- paste0(TheportfoliohasnocompaniesintheCementsector) #
} else if (sector_to_plot == "Steel") {
Label <- paste0(TheportfoliohasnocompaniesintheSteelsector) #
} else if (sector_to_plot == "Aviation") {
Label <- paste0(TheportfoliohasnocompaniesintheAviationsector) #
} else { # (sector_to_plot == "Shipping")
Label <- paste0(TheportfoliohasnocompaniesintheShippingsector) #
}
outputplot <- no_chart(Label)
}
ggsave(filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", outputplot, height = 3, width = 4, dpi = ppi) # ,bg="transparent"
sector_to_plot <<- ""
# return()
}
CompanyInformation <- function(plotnumber, companiestoprint, chart_type, sector_to_plot, explicit_filename = "") {
sector_to_plot <<- sector_to_plot
PlotChart <- F
if (chart_type == "EQ" & has_equity == T) {
market <- filter_by_parameters(eq_market, "EQ")
combin <- filter_by_parameters(EQCombin, "EQ")
CompProdSS <- filter_by_parameters(EQCompProdSnapshot, "EQ", ActorSectorOnly = T)
}
if (chart_type == "CB" & has_debt == T) {
market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
CompProdSS <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = FALSE, ActorSectorOnly = T)
}
if (data_check(CompProdSS) == T) {
PlotChart <- T
}
if (PlotChart == TRUE) {
if (!"Entity.Name" %in% colnames(CompProdSS) & chart_type == "CB") {
CompProdSS$Entity.Name <- CompProdSS$id
}
if (!"Entity.Name" %in% colnames(CompProdSS) & chart_type == "EQ") {
CompProdSS$Entity.Name <- CompProdSS$company_name
}
# Portfolio (Weighted by the AUM)
Portfoliomix <- subset(combin, year == start_year + 5, select = c("technology", "plan_alloc_wt_tech_prod"))
Portfoliomix <- unique(Portfoliomix) # KLAUS: Added this line to avoid duplicate lines!
Portfoliomix$Classification <- "Portfolio"
Portfoliomix$Name <- "Portfolio"
Portfoliomix <- subset(Portfoliomix, select = c("Name", "Classification", "technology", "plan_alloc_wt_tech_prod"))
Portfoliomix$plan_alloc_wt_tech_prod <- Portfoliomix$plan_alloc_wt_tech_prod
colnames(Portfoliomix) <- c("Name", "Classification", "technology", "tech_share")
# Add 2D Target (Global Market under 2D scenario)
Targetmix <- subset(combin, year == start_year + 5, select = c("technology", "scen_alloc_wt_tech_prod")) # Scen.WtProduction needs to be adjusted to BenchmarkPortfolio (Trajectory vs Market approach) - will address this in FilterByParameters
Targetmix <- unique(Targetmix) # KLAUS: Added this line to avoid duplicate lines!
Targetmix$Classification <- "Portfolio"
Targetmix$Name <- "Aligned Portfolio"
Targetmix <- subset(Targetmix, select = c("Name", "Classification", "technology", "scen_alloc_wt_tech_prod"))
Targetmix$scen_alloc_wt_tech_prod <- Targetmix$scen_alloc_wt_tech_prod
colnames(Targetmix) <- c("Name", "Classification", "technology", "tech_share")
# Add Benchmark / Global Market
Marketmix <- NA
if (data_check(market)) {
Marketmix <- subset(market, year == start_year + 5, select = c("technology", "scen_alloc_wt_tech_prod"))
Marketmix <- unique(Marketmix)
Marketmix$Classification <- "Portfolio"
Marketmix$Name <- "Aligned Market"
Marketmix <- subset(Marketmix, select = c("Name", "Classification", "technology", "scen_alloc_wt_tech_prod"))
Marketmix$scen_alloc_wt_tech_prod <- Marketmix$scen_alloc_wt_tech_prod
colnames(Marketmix) <- c("Name", "Classification", "technology", "tech_share")
}
PortfolioData <- rbind(Marketmix, Targetmix, Portfoliomix)
# PortfolioData <- rbind(Targetmix, Portfoliomix)
# Percentage share of each technology for each company in the portfolio
Companies <- subset(CompProdSS, year == start_year + 5, select = c("Entity.Name", "technology", "plan_tech_prod", "plan_sec_prod", "port_weight"))
Companies <- Companies %>%
filter(plan_sec_prod > 0) %>%
select(-plan_sec_prod)
Companies <- unique(Companies)
Companies$tech_share <- Companies$plan_tech_prod
Companies$Classification <- "Companies"
# Companies$Name <- paste0(substr(Companies$Name, 1, 15),"...")
Companies <- subset(Companies, select = c("Entity.Name", "Classification", "technology", "tech_share", "port_weight"))
colnames(Companies) <- c("Name", "Classification", "technology", "tech_share", "PortWeight")
Companies$Name <- as.character(Companies$Name)
Companies$Name <- ifelse(is.na(Companies$Name), "No Name", Companies$Name)
colors <- as.vector(ColourPalette$Colours[ColourPalette$ald_sector %in% sector_to_plot])
if (sector_to_plot == "Power") {
techorder <- c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap")
tech_labels <- c(T_Renewables, T_HydroCap, T_Nuclear, T_GasCap, T_CoalCap) #
# colors <- as.vector(ColourPalette$Colours[ColourPalette$ald_sector %in% sector_to_plot])
} else if (sector_to_plot == "Automotive") {
techorder <- c("Electric", "Hybrid", "ICE")
tech_labels <- c(x_Electric, x_Hybrid, x_ICE) #
} else if (sector_to_plot == "CoalMining") {
techorder <- c("Coal")
tech_labels <- c(T_CoalProd) #
colors <- as.vector(ColourPalette$Colours[ColourPalette$technology %in% "Coal"])
} else if (sector_to_plot == "OilGas") {
techorder <- c("Gas", "Oil")
tech_labels <- c(T_GasProd, T_OilProd) #
colors <- as.vector(ColourPalette$Colours[ColourPalette$technology %in% tech_labels])
} else if (sector_to_plot == "Shipping") {
tech_labels <- c("GHG_A", "GHG_B", "GHG_C", "GHG_D", "GHG_E", "GHG_F", "GHG_G")
# colors <- as.vector(ColourPalette$Colours[12:13])
} else if (sector_to_plot == "Steel") {
tech_labels <- c(ElectricArc, BlastFurnace) #
# colors <- as.vector(ColourPalette$Colours[14:20])
}
PortfolioData <- filter(PortfolioData, technology %in% techorder)
Companies <- Companies %>%
filter(technology %in% techorder) %>%
arrange(-PortWeight)
Companies <- Companies %>%
filter(Name %in% unique(Companies$Name)[1:min(companiestoprint, length(unique(Companies$Name)))])
dummy <- data.frame(
c("Name", ""),
c("Classification", NA),
c("technology", NA),
c("tech_share", 0)
)
colnames(dummy) <- as.character(unlist(dummy[1, ]))
dummy <- dummy[-1, ]
dummy$tech_share <- as.numeric(dummy$tech_share)
AllData <- rbind(
PortfolioData,
dummy,
select(Companies, -PortWeight)
)
AllData <- unique(AllData)
AllData$Name <- factor(AllData$Name, levels = rev(unique(c(PortfolioData$Name, "", Companies$Name))))
AllData$Name <- dplyr::recode(AllData$Name,
"Aligned Market" = AlignedMarket, #
"Aligned Portfolio" = AlignedPortfolio, #
Portfolio = Portf
) #
AllData$technology <- factor(AllData$technology, levels = rev(techorder))
names(colors) <- techorder
names(tech_labels) <- techorder
Companies <- unique(select(Companies, Name, PortWeight, technology))
PortfolioData <- unique(select(PortfolioData, Name))
PortfolioData$Name <- dplyr::recode(PortfolioData$Name,
"Aligned Market" = AlignedMarket,
"Aligned Portfolio" = AlignedPortfolio,
Portfolio = Portf
)
bar_labels <- c(PortfolioData$Name, "", Companies$Name)
# company_labels <- trim(Companies$Name)
company_labels <- Companies$Name
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
bar_labels <- c(PortfolioData$Name, "", company_labels)
Companies_PortWeight <- Companies %>%
group_by(Name) %>%
summarise(PortWeight = mean(PortWeight))
PortPlot <- stacked_bar_chart(AllData, colors, tech_labels) +
geom_text(
data = Companies_PortWeight,
aes(x = Name, y = 1),
label = perc(Companies_PortWeight$PortWeight),
hjust = -1., color = textcolor, size = 10 * (5 / 14),
family = textfont
) +
geom_text(
data = Companies,
aes(x = "", y = 1),
label = x_Weight, #
hjust = -0.9, color = textcolor, size = 10 * (5 / 14),
family = textfont
) +
xlab("") +
coord_flip() +
theme(
legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(margin = margin(l = 0.15, r = 0.35, unit = "cm")),
plot.margin = unit(c(1, 8, 0, 0), "lines")
)
gt <- ggplot_gtable(ggplot_build(PortPlot))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
if (sector_to_plot == "Fossil Fuels") {
sector_to_plot <- "FossilFuels"
}
bar_size <- 8 / 15
# height <- min(4+companiestoprint,n_distinct(AllData$Name))*bar_size + 2
if (sector_to_plot == "Fossil Fuels") {
sector_to_plot <- "FossilFuels"
}
height <- max(2.6, length(unique(Companies$Name)) * .3)
} else {
if (sector_to_plot == "Automotive") {
Label <- paste0(YourPortfoliohasnocompaniesintheAutomotivesector) #
} else if (sector_to_plot == "CoalMining") {
Label <- paste0(YourPortfoliohasnocompaniesintheCoalMiningsector) #
} else if (sector_to_plot == "OliGas") {
Label <- paste0(YourPortfoliohasnocompaniesintheOilGassector) #
} else if (sector_to_plot == "Power") {
Label <- paste0(YourPortfoliohasnocompaniesinthePowersector) #
} else if (sector_to_plot == "Shipping") {
Label <- paste0(YourPortfoliohasnocompaniesintheShippingsector) #
} else if (sector_to_plot == "Steel") {
Label <- paste0(YourPortfoliohasnocompaniesintheSteelsector) #
}
gt <- no_chart(Label)
height <- 3
}
ggsave(gt,
filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), # bg = "transparent",
height = height, width = 10, dpi = ppi
)
sector_to_plot <<- ""
BV.asset_type <<- ""
}
OilShare <- function(plotnumber, companiestoprint, chart_type, explicit_filename = "") {
tech_to_plot <<- "Oil"
chart_type <<- chart_type
OilData <- NA
if (chart_type == "EQ") {
comps <- filter_by_parameters(EQCompProdSnapshot, "EQ") %>% ungroup()
# comps$id<-comps$bloomberg_id
# comps$bloomberg_id <-NULL
# comps$id <-as.integer(comps$id)
if (data_check(comps)) {
OilData <- EQOilShareData
OilData <- OilData[OilData$id %in% comps$id, ]
OilData <- OilData[OilData$technology == "Oil", ]
OilData <- OilData[OilData$year == start_year + 5, ]
OilData <- OilData %>%
group_by(id, year) %>%
mutate(SecOilProd = sum(ald_production)) %>%
filter(SecOilProd > 0) %>%
select(-SecOilProd) %>%
ungroup()
if (data_check(OilData)) {
OilData$Type <- "equity"
OilData$ID <- OilData$id
OilData$bloomberg_id <- NULL
}
OilData <- left_join(comps %>% select(technology, company_name, id, port_weight), OilData, by = c("technology" = "technology", "id" = "id"))
}
}
if (chart_type == "CB") {
comps <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F)
# comps$id <- comps$corp_bond_ticker
if (data_check(comps)) {
OilData <- CBOilShareData %>%
ungroup() %>%
filter(
technology == "Oil",
year == start_year + 5,
id %in% comps$id
)
OilData <- OilData %>%
group_by(id, year) %>%
mutate(SecOilProd = sum(ald_production)) %>%
filter(SecOilProd > 0) %>%
select(-SecOilProd) %>%
ungroup()
if (data_check(OilData)) {
OilData <- OilData %>% mutate(
Type = "corporate bond",
ID = id,
company_name = ID,
company_lvl_production = ald_production
)
OilData <- left_join(comps %>% select(technology, id, port_weight), OilData, by = c("technology" = "technology", "id" = "id"))
}
}
}
if (data_check(OilData)) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart) {
OilCompanies <- subset(OilData, select = c("ald_production", "port_weight", "technology_type", "company_name"))
OilCompanies1 <- OilCompanies %>%
group_by(company_name, technology_type, port_weight) %>%
summarise("Oilsum" = sum(ald_production)) %>%
ungroup()
OilCompanies2 <- OilCompanies1 %>%
group_by(company_name) %>%
summarise("Oiltotal" = sum(Oilsum))
OilCompanies <- left_join(OilCompanies1, OilCompanies2, by = c("company_name"))
OilCompanies$OilShare <- (OilCompanies$Oilsum / OilCompanies$Oiltotal)
OilCompanies$Classification <- "Companies"
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Conventional Gas", "Other & Unknown", OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Unconventional Gas", "Other & Unknown", OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "-", "Other & Unknown", OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Oil Sands", OilSands, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Heavy Oil", HeavyOil, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Conventional Oil", ConventionalOil, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Unconventional Oil", UnconventionalOil, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Other & Unknown", OtherandUnknown, OilCompanies$technology_type)
OilCompanies$technology_type <- as.factor(OilCompanies$technology_type)
# levels(OilCompanies$technology_type)[levels(OilCompanies$technology_type)=="Conventional Gas"] <- "Other & Unknown"
# levels(OilCompanies$technology_type)[levels(OilCompanies$technology_type)=="Unconventional Gas"] <- "Other & Unknown"
# levels(OilCompanies$technology_type)[levels(OilCompanies$technology_type)=="-"] <- "Other & Unknown"
# techorder <- c("Oil Sands","Heavy Oil","Conventional Oil","Unconventional Oil","Other & Unknown")
techorder <- c(OilSands, HeavyOil, ConventionalOil, UnconventionalOil, OtherandUnknown)
tech_labels <- techorder
colors <- c("#72755e", "#8d9176", "#a5a792", "#bcbeae", "#d3d5ca")
OilCompanies$technology_type <- factor(OilCompanies$technology_type, levels = techorder)
OilCompanies <- OilCompanies %>%
filter(technology_type %in% techorder) %>%
arrange(-port_weight)
# LIne to aggregate to portfolio level
# aggregated all companies by oil type to get the portfolio oilshare
# OilCompanies_P<-OilCompanies %>%
# group_by(technology_type) %>%
# summarise(OilShare=sum(OilShare)) %>%
# ungroup()%>%
# mutate(company_name="Portfolio",
# Classification="Portfolio")
# colnames(OilCompanies_P)[which(names(OilCompanies_P) == "technology_type")] <- "Oil.Type"
OilCompanies <- OilCompanies %>%
filter(company_name %in% unique(OilCompanies$company_name)[1:min(companiestoprint, length(unique(OilCompanies$company_name)))])
colnames(OilCompanies)[which(names(OilCompanies) == "technology_type")] <- "Oil.Type"
OilCompanies <- subset(OilCompanies, select = c("Oil.Type", "company_name", "OilShare", "Classification", "port_weight"))
dummy <- data.frame(
c("Oil.Type", NA),
c("company_name", ""),
c("OilShare", 0),
c("Classification", NA),
c("port_weight", NA)
)
colnames(dummy) <- as.character(unlist(dummy[1, ]))
dummy <- dummy[-1, ]
dummy$OilShare <- as.numeric(dummy$OilShare)
OilCompany <- rbind(OilCompanies, dummy)
OilCompany$Oil.Type <- factor(OilCompany$Oil.Type, levels = techorder)
OilCompany$port_weight <- as.numeric(OilCompany$port_weight)
OilCompany <- as.data.frame(OilCompany)
# OilCompany$company_name <- factor(OilCompany$company_name, levels=(unique(c("",OilCompany$company_name))))
names(colors) <- techorder
names(tech_labels) <- techorder
# company_labels <- c(trimws(unique(OilCompanies[!is.na(OilCompanies$Oil.Type),]$company_name))," ","Portfolio")
Oil <- na.omit(OilCompany[, c("company_name", "port_weight")])
OilCompany <- OilCompany %>% arrange(port_weight)
company_labels <- trimws(unique(OilCompany[!is.na(OilCompany$Oil.Type), ]$company_name))
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
bar_labels <- c(company_labels, " ") # company_labels#
OilCompany <- OilCompany %>% filter(!is.na(port_weight))
PortPlot <- ggplot(data = OilCompany, aes(x = company_name, y = OilShare, fill = Oil.Type)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colors, labels = rev(paste(tech_labels, " ")), breaks = rev(techorder)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
scale_x_discrete(labels = bar_labels) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts() +
geom_text(
data = OilCompany,
aes(x = company_name, y = 1),
label = perc(OilCompany$port_weight),
hjust = -1, color = textcolor, size = 8 * (5 / 14),
family = textfont
) +
# geom_text(aes(x="",y=1),
# label = x_Weight,
# hjust = -0.5, color = textcolor, size=8*(5/14),
# family = textfont)+
xlab("") +
ylab("tech_share") +
coord_flip() +
theme(
legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(1, 6, 0, 0), "lines"), axis.line.x = element_line(colour = textcolor, size = 0.5)
) +
guides(fill = guide_legend(ncol = 5, keywidth = 1))
gt <- ggplot_gtable(ggplot_build(PortPlot))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
bar_size <- 4 / 15
height <- min(1 + companiestoprint, n_distinct(OilCompanies$company_name)) * bar_size + .86
} else {
if (chart_type == "EQ") {
Type <- oil_equity
} else if (chart_type == "CB") {
Type <- oil_bond
} #
Label <- paste0(Nooilproductionbreakdowninyour, " ", Type, portfo) #
gt <- no_chart(Label) #+
height <- 3
}
ggsave(gt, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = height, width = 10, dpi = ppi)
tech_to_plot <<- ""
chart_type <<- ""
BV.asset_type <<- ""
}
MapChart <- function(plotnumber, chart_type, tech_to_plot, plot_year, explicit_filename = "") {
Power <- data.frame()
tech_to_plot <<- tech_to_plot
# ScenarioGeographyChoose <<- "Global"
mapfilename <- ""
if (chart_type == "EQ" & has_equity == TRUE) {
Power <- filter_by_parameters(EQportmap, "EQ", byscenario = F)
mapfilename <- paste0(results_path, "/", investor_name_select, "/", "Equiry_results_map.rda")
}
if (chart_type == "CB" & has_debt == TRUE) {
Power <- filter_by_parameters(CBportmap, "CB", byscenario = F, by_equity_market = FALSE)
mapfilename <- paste0(results_path, "/", investor_name_select, "/", "Bonds_results_map.rda")
}
if (data_check(Power)) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (!file.exists(mapfilename)) {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
# tech_to_plot <- gsub("Cap","",tech_to_plot)
Power <- as.data.frame(Power)
# Power$technology<-gsub("RenewablesCap","Renewables",Power$technology)
# Power$technology<-gsub("NuclearCap","Nuclear",Power$technology)
# Power$technology<-gsub("HydroCap","Hydro",Power$technology)
Power$technology <- as.factor(Power$technology)
Power <- subset(Power, year == plot_year)
Power <- Power %>%
group_by(ald_location, technology) %>%
summarise(Production = sum(plan_alloc_wt_tech_prod)) %>%
ungroup() %>%
filter(technology == tech_to_plot) %>%
arrange(desc(Production))
Power <- as.data.frame(Power)
UNIT <- c("MW", "MW", "MW", "MW", "MW", Vehicles, Vehicles, Vehicles, "m3", "bbl", MetricTons) #
names(UNIT) <- c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap", "Electric", "Hybrid", "ICE", "Gas", "Oil", "Coal")
if (sector_to_plot %in% c("Power")) {
capprod <- Power_Unit
} else {
capprod <- xProduction
} #
# if (nrow(Power)>0){
tech_map <- joinCountryData2Map(Power, joinCode = "ISO2", nameJoinColumn = "ald_location")
tech_map_poly <- fortify(tech_map) # extract polygons
tech_map_poly <- base::merge(tech_map_poly, tech_map@data, by.x = "id", by.y = "ADMIN", all.x = T)
tech_map_poly <- tech_map_poly %>% arrange(id, order)
outputplot <- ggplot() +
coord_map(xlim = c(-180, 180), ylim = c(-60, 75)) +
geom_polygon(data = tech_map_poly, aes(long, lat,
group = group,
fill = Production
), size = 0.3) +
scale_fill_gradient(
name = paste0(capprod, " (", UNIT[tech_to_plot], ")"),
# breaks=floor(seq(0,max(Power$Production),length.out = 3)),
low = eval(parse(text = paste("MIN", tech_to_plot, sep = ""))),
high = eval(parse(text = paste("MAX", tech_to_plot, sep = ""))),
labels = comma
) +
theme_bw() +
xlab(NULL) +
ylab(NULL) +
guides(fill = guide_colorbar(
keywidth = 0.7, keyheight = 0.7,
reverse = F, title.position = "top"
)) +
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
legend.position = "right",
legend.title.align = 0.5,
legend.direction = "vertical",
legend.key.width = unit(1, "cm"),
legend.key.height = unit(0.8, "cm")
)
#
# mapWorld <- borders("world", colour="#E7E7E7", fill="#1a0000", alpha = .5)
# outputplot<- ggplot()+mapWorld+
# geom_point(aes(x=Power$Longitude, y=Power$Latitude, size=Power$Production), alpha = .9, color="#000033")+
# labs(size = "Production")+
# scale_size_continuous(breaks = round(unname(quantile(Power$Production)),digits = 0))+
# theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# axis.ticks.x=element_blank(),
# axis.title.y=element_blank(),
# axis.text.y=element_blank(),
# axis.ticks.y=element_blank())
} else {
Label <- YourportfoliohasnoproductionfortheselectedTechnologyScenarioGeographyandMarket #
outputplot <- no_chart(Label)
}
ggsave(plot = outputplot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 5, width = 10, dpi = ppi) # linewidth_in*.9
tech_to_plot <<- ""
BV.asset_type <<- ""
}
PeerComparison <- function(plotnumber, VariableToPlot = "plan_carsten", Grouping.Level = "portfolio_name", chart_type, BV.asset_type = "") {
BV.asset_type <<- BV.asset_type
# Title <- paste0("Percent of ", ifelse(chart_type=="CB",BondReference,"Equity")," Portfolio Value")
browntechs <- data.frame("ald_sector" = c("Automotive", "Power", "Power", "OilGas", "OilGas", "CoalMining"), "technology" = c("ICE", "CoalCap", "GasCap", "Oil", "Gas", "Coal"))
if (chart_type == "EQ") {
Title <- PercentofEquityPortfolioValue #
Batch <- filter_by_parameters(eq_peersSep, "EQ", scenario.irrelevant = T)
combin <- filter_by_parameters(EQCombin, "EQ", scenario.irrelevant = T)
} else if (chart_type == "CB") {
Title <- PercentofCorporateBondsPortfolioValue #
Batch <- filter_by_parameters(cb_peersSep, "CB", by_equity_market = FALSE, scenario.irrelevant = T)
combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE, scenario.irrelevant = T)
} else if (chart_type == "BV") {
if (BV.asset_type == "BV_Equity") {
Title <- PercentofEquityPortfolioValue #
} else if (BV.asset_type == "BV_Bonds") {
Title <- PercentofCorporateBondsPortfolioValue #
}
Batch <- filter_by_parameters(BVPeersSep, "BV", by_equity_market = FALSE, scenario.irrelevant = T, BV.asset_type = BV.asset_type)
combin <- filter_by_parameters(BVCombin, "BV", by_equity_market = FALSE, scenario.irrelevant = T, BV.asset_type = BV.asset_type)
}
combin <- as.data.frame(combin)
Batch <- as.data.frame(Batch)
# Batch <- Batch %>% filter(investor_name != investor_name_select)
MetricCol <- VariableToPlot
df <- bind_rows(combin, Batch)
df <- as.data.frame(df)
df <- subset(df, year %in% c(start_year, 2018) & ald_sector %in% c("Oil&Gas", "Coal"), select = c("investor_name", "portfolio_name", "ald_sector", "technology", MetricCol))
df <- df[!is.na(df[, MetricCol]), ]
if (investor_name_select %in% df$investor_name) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
ID.COLS <- Grouping.Level
BarColors <- energy
Labels <- FossilFuelsSector #
port.value <- sum(filter(df, portfolio_name == portfolio_name_select & investor_name == investor_name_select)[MetricCol])
portfolio_label <- paste0(
ThisPortfolio, " ", #
unique(round(100 * (port.value), 2)), "%"
)
dfagg <- aggregate(eval(parse(text = MetricCol)) ~ eval(parse(text = ID.COLS)) + ald_sector, data = df, sum)
colnames(dfagg) <- c("ID.COL", "ald_sector", "Metric")
dfagg$Metric <- as.numeric(dfagg$Metric)
dfagg <- as.data.frame(dfagg)
dfagg <- dfagg %>%
group_by(ID.COL) %>%
summarise(Metricsum = sum(Metric))
x_length <- length(unique(dfagg$ID.COL))
xlabel <- ""
yaxislabel <- paste0(wrap_labels(ExposureoftheportfoliotoFossilFuels, 30)) #
if (range(dfagg$Metricsum)[1] >= 0 & range(dfagg$Metricsum)[2] >= 0) {
lim <- c(0, range(dfagg$Metricsum)[2] + 0.005)
} else if (range(dfagg$Metricsum)[1] < 0 & range(dfagg$Metricsum)[2] >= 0) {
lim <- c(range(dfagg$Metricsum)[1] - 0.005, range(dfagg$Metricsum)[2] + 0.005)
} else if (range(dfagg$Metricsum)[1] < 0 & range(dfagg$Metricsum)[2] < 0) {
lim <- c(range(dfagg$Metricsum)[1] - 0.005, 0)
}
dfagg <- dfagg %>% arrange(-Metricsum)
dfagg$ID.COL <- factor(dfagg$ID.COL, levels = dfagg$ID.COL[rev(order(dfagg$Metricsum))])
if (ID.COLS == "portfolio_name") {
x_coord <- which(dfagg$ID.COL == investor_name_select)
}
if (ID.COLS == "portfolio_name") {
x_coord <- which(dfagg$ID.COL == portfolio_name_select)
}
is_left <- x_coord / x_length < .50
is_first <- if_else(x_coord == 1, .005, 0)
distribution_plot <- ggplot(dfagg) +
geom_bar(aes(x = ID.COL, y = Metricsum),
stat = "identity", width = 1
) +
annotate("text",
x = x_coord, y = port.value,
label = portfolio_label,
hjust = ifelse(is_left, -.05, -0.05), # 1.05
vjust = -0.3,
size = textsize * (5 / 14)
) +
scale_fill_manual(values = BarColors, labels = Labels) + # , breaks=c(MetricC) #
scale_y_continuous(
limits = lim,
expand = expand_scale(add = c(0, 0)),
name = yaxislabel,
labels = percent
) +
scale_x_discrete(labels = NULL, expand = c(0, 0)) +
coord_cartesian(ylim = c(0, lim[2]), clip = "off") +
theme_distribution() +
geom_hline(yintercept = 0, size = 1) +
xlab(xlabel) +
geom_vline(xintercept = x_coord, linetype = 2) +
theme(
legend.position = "none",
plot.margin = unit(c(1, 0, 0, 0), "cm")
)
# gt <- ggplot_gtable(ggplot_build(distribution_plot))
# gt$layout$clip[gt$layout$name == "panel"] <- "off"
# grid.draw(gt)
} else {
if (sector_to_plot == "Power") {
SectorToInsert <- PowerSector #
} else if (sector_to_plot == "Fossil Fuels") {
SectorToInsert <- FossilFuelsSector #
} else {
SectorToInsert <- " "
}
Label <- paste0(Yourportfoliocontainsnoholdingsinthe, " ", SectorToInsert, " ", sectorintheselectedMarket) #
distribution_plot <- no_chart(Label)
}
ggsave(plot = distribution_plot, filename = graph_name(plotnumber, ParameterFile), bg = "white", height = 2.8, width = 10, dpi = ppi)
BV.asset_type <<- ""
}
ShippingChart <- function(plotnumber, chart_type, plot_year, sector_to_plot = "Shipping", explicit_filename = "") {
# print(chart_type)
# print(sector_to_plot)
# chart_type <-"All"
Combin <- data.frame()
if (GraphType == "Report") {
LegendOn <- FALSE
} else {
LegendOn <- TRUE
}
if (chart_type == "EQ" & has_equity == TRUE) {
Peers <- filter_by_parameters(eq_peers, "EQ")
Market <- filter_by_parameters(eq_market, "EQ")
Combin <- filter_by_parameters(EQCombin, "EQ")
} else if (chart_type == "CB" & has_debt == TRUE) {
Peers <- filter_by_parameters(cb_peers, "CB", by_equity_market = FALSE)
Market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
Combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
} else if (chart_type %in% c("All", "")) {
sector_to_plot <<- "Shipping"
Market_e <- filter_by_parameters(eq_market, "EQ")
if (data_check(Market_e) == TRUE) {
Market_e$Type <- "Equity Market"
}
Market_b <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
if (data_check(Market_b) == TRUE) {
Market_b$Type <- "Bond Market"
}
Combin_e <- filter_by_parameters(EQCombin, "EQ")
if (data_check(Combin_e) == TRUE) {
Combin_e$Type <- "Equity Portfolio"
}
Combin_b <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
if (data_check(Combin_b) == TRUE) {
Combin_b$Type <- "Bond Portfolio"
}
if (has_equity & has_debt) {
Market <- bind_rows(Market_e, Market_b)
} else if (has_debt & (has_equity == FALSE)) {
Market <- Market_b
} else if (has_equity & (has_debt == FALSE)) {
Market <- Market_e
}
if (has_equity & has_debt) {
Combin <- bind_rows(Combin_e, Combin_b)
} else if (has_debt & (has_equity == FALSE)) {
Combin <- Combin_b
} else if (has_equity & (has_debt == FALSE)) {
Combin <- Combin_e
}
}
# print(unique(Combin$ald_sector))
if (data_check(Combin) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
if (!chart_type %in% c("All", "")) {
if (data_check(Peers)) {
Peers$Type <- "Peers"
} else {
Type <- c()
Type <- as.character(Type)
Peers[, "Type"] <- Type
}
Peers <- unique(Peers)
Peers <- Peers %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "Type", "plan_alloc_wt_tech_prod") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
Peers <- Peers %>%
complete(
Type = c("Peers"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
Combin$Type <- "Portfolio"
if (data_check(Market)) {
Market$Type <- "Market"
} else {
Type <- c()
Type <- as.character(Type)
Market[, "Type"] <- Type
}
}
Combin <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
if (chart_type %in% c("All", "")) {
Combin <- Combin %>%
complete(
Type = c("Equity Portfolio", "Bond Portfolio"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
}
if (chart_type %in% c("All", "") && (data_check(Market) == FALSE)) {
Type <- c()
Type <- as.character(Type)
Market[, "Type"] <- Type
}
Market <- Market %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr & Type =="Portfolio") %>%
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>% # "Type"
rename(WtProduction = plan_alloc_wt_tech_prod)
if (chart_type %in% c("All", "")) {
Market <- Market %>%
complete(
Type = c("Equity Market", "Bond Market"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
} else {
Market <- Market %>%
complete(
Type = c("Market"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
}
if (!chart_type %in% c("All", "")) {
Production <- bind_rows(Combin, Peers, Market)
} else if (chart_type %in% c("All", "")) {
Production <- bind_rows(Combin, Market)
}
# print(unique(Production$Type))
Production$ald_sector <- as.factor(Production$ald_sector)
levels(Production$ald_sector)[levels(Production$ald_sector) == "Coal"] <- "Fossil Fuels"
levels(Production$ald_sector)[levels(Production$ald_sector) == "Oil&Gas"] <- "Fossil Fuels"
# Aggregate and rename CarstenMetric_Port
ID.COLS <- c("ald_sector", "technology", "Type")
Production <- Production %>% gather(key = Metric, value = Value, "WtProduction")
Production <- aggregate(Production["Value"], by = Production[c(ID.COLS)], FUN = sum)
colours <- as.vector(ColourPalette[["Colours"]])
names(colours) <- technology_order
labels <- c(
"Renewables", "Hydro", "Nuclear", "Gas", "Coal", "Electric", "Hybrid", "ICE", "Gas", "Oil", "Coal",
"Freight", "Passenger", "A", "B", "C", "D", "E", "F", "G"
)
names(labels) <- technology_order
Production$technology <- factor(Production$technology, levels = technology_order)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")) #
Production$Type <- wrap_labels(Production$Type, 20)
if (!chart_type %in% c("All", "")) {
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Peers", "Market"))
xlabels <- c(Portf, wrap_labels(x_Peers, 10), x_Benchmark) #
} else if (chart_type %in% c("All", "")) {
Production$Type <- factor(Production$Type, levels = c("Bond Portfolio", "Equity Portfolio", "Bond Market", "Equity Market"))
xlabels <- c(
paste0(wrap_labels(BondPortfolio, 10)), #
paste0(wrap_labels(EquityPortfollio, 10)), #
paste0(wrap_labels(BondMarket, 10)), #
paste0(wrap_labels(EquityMarket, 10))
) #
}
Production <- subset(Production, select = c("Type", "ald_sector", "technology", "Value"))
shippingchart <- stacked_bar_chart(Production, colours, labels) +
ylab(ShareofSectorProduction) + #
scale_x_discrete(labels = xlabels, expand = c(0, 0)) +
theme(plot.margin = unit(c(0.2, 0.2, 0.2, 0), "cm"))
if (LegendOn == TRUE) {
shippingchart <- shippingchart +
theme(
plot.title = element_text(hjust = 0.5, colour = textcolor, size = 11, margin = unit(c(0, 0, 1, 0), "lines")),
legend.position = "bottom",
legend.title = element_blank()
)
}
} else {
Label <- Theportfoliohasnocompaniesintheshippingsector #
shippingchart <- no_chart(Label)
}
ggsave(shippingchart, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 3, width = 4, dpi = ppi)
sector_to_plot <<- ""
}
steel_chart <- function(plotnumber) {
if (chart_type == "EQ") {
ProdSnapshot <- filter_by_parameters(EQCompProdSnapshot, "EQ")
}
if (chart_type == "CB") {
ProdSnapshot <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = FALSE)
}
ProdSnapshot <- subset(ProdSnapshot, year = start_year + 5)
steel <- base::merge(ProdSnapshot, SteelData, by.x = "Name", by.y = "Company") # import steel data
nosteel <- nrow(steel)
if (nosteel > 0) {
# categorize high c vs. low c
# Classification<- ifelse()
steel <- steel %>%
group_by(Classification) %>%
summarize(tot = sum(tech_share)) %>%
ungroup() %>%
mutate(per = tech_share / tot)
plottheme <- ggplot(
data = steel, aes_string(x = asset_type, y = per, fill = Classification),
show.guide = TRUE
) +
geom_bar(stat = "identity", position = "fill", width = .6) +
# geom_hline(yintercept = c(.25,.50,.75), color="white")+
scale_fill_manual(values = colors, labels = legend_labels, breaks = names(legend_labels)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts()
} else {
Label <- YourPortfolioincludesnocompanieswithsteelproductionintheselectedscenario_geographyandMarket #
outputplot <- no_chart(Label)
}
ggsave(filename = graph_name(plotnumber, ParameterFile), bg = "white", height = 3.6, width = 3.6, plot = outputplot, dpi = ppi)
}
SectorDataAnalysis <- function() {
over <- subgroup_overview
over <- subset(over, over$portfolio_name == portfolio_name_select & valid_input == 1)
over$financial_sector <- ifelse(over$financial_sector %in% c("Coal", "Oil&Gas"), "Fossil Fuels", over$financial_sector)
over$financial_sector <- ifelse(over$financial_sector %in% c("Aviation", "Shipping"), "Aviation & Shipping", over$financial_sector)
over$financial_sector <- ifelse(over$financial_sector %in% c("Steel", "Cement", "Cement & Steel"), "Cement & Steel", over$financial_sector)
over$financial_sector <- ifelse(over$financial_sector %in% c("Other"), "Not Included", over$financial_sector)
return(over)
}
ScopeOfAnalysis <- function(plotnumber, explicit_filename = "") {
over <- SectorDataAnalysis()
Bond_translation <- if (BondReference == "Corporate bond") {
paste0(wrap_labels(BondsTitle, 10)) #
} else {
wrap_labels(Corporate_Bond_1, 10) ## place for other category!!!
}
over$asset_type <- gsub("Funds", "Others", over$asset_type) #
over$asset_type <- gsub("Bonds", Bond_translation, over$asset_type) #
over$asset_type <- gsub("Equity", EquityTitle, over$asset_type) #
over$asset_type <- gsub("Others", OthersTitle, over$asset_type) #
over$Sector.All <- ifelse(over$financial_sector == "Not Included", "Sectors Not in Scope", "Scope of the Analysis")
over <- over %>%
ungroup() %>%
select(Sector.All, asset_type, valid_value_usd) %>%
group_by(Sector.All, asset_type) %>%
summarise(valid_value_usd = sum(valid_value_usd))
if (data_check(over) == F) {
over <- over %>%
ungroup() %>%
add_row(Sector.All = "Power", asset_type = Bond_translation, valid_value_usd = 0)
}
over <- over %>%
complete(
asset_type = c(Bond_translation, EquityTitle, OthersTitle), #
Sector.All = c(
"Sectors Not in Scope",
# "Other Climate Sectors",
"Scope of the Analysis"
),
fill = list(valid_value_usd = 0)
) %>%
unique()
over <- as.data.frame(over)
orderofchart <- c(Bond_translation, EquityTitle, OthersTitle) #
over$asset_type <- factor(over$asset_type, levels = orderofchart)
over$Sector.All <- factor(over$Sector.All, levels = c(
"Sectors Not in Scope",
# "Other Climate Sectors",
"Scope of the Analysis"
), ordered = TRUE)
tot <- over %>%
group_by(asset_type) %>%
summarise(s = sum(valid_value_usd))
# for NEW VERSION horizontal chart
over_horizontal_chart <- over %>% filter(asset_type != OthersTitle)
annotate.position <- over_horizontal_chart %>%
group_by(asset_type) %>%
summarize(sumValueUSD = sum(valid_value_usd)) %>%
summarize(maxsumValueUSD = max(sumValueUSD))
annotate.label <- over_horizontal_chart %>% filter(Sector.All == "Scope of the Analysis")
number_cifer <- nchar(round(annotate.position$maxsumValueUSD, digits = 0))
breaks_by <- function(number_cifer) {
10^(number_cifer - 1)
}
breaks_interval <- breaks_by(number_cifer)
plot <- ggplot(over_horizontal_chart, aes(x = fct_rev(asset_type), y = valid_value_usd, fill = Sector.All)) +
geom_bar(position = "stack", stat = "identity", width = 0.9) +
coord_flip() +
scale_fill_manual(
name = "", labels = c(
SectorsNotinScope,
# OtherClimateRelevantSectors,
SectorsinScenarioAnalysis
),
values = c(
"grey80",
# "#deebf7",
"#265b9b"
), drop = FALSE
) + #
# scale_x_discrete(name="Asset Type", labels = x_labels) +
scale_y_continuous(
name = MarketValue, labels = comprss, expand = c(0, 0, 0, 0),
breaks = seq(0, annotate.position$maxsumValueUSD, by = breaks_interval * 2)
) + # breaks = scales::pretty_breaks(n = 3)) + #
# limits = c(0, annotate.position$maxsumValueUSD+0.3*annotate.position$maxsumValueUSD)) + #
guides(fill = guide_legend(nrow = 1, byrow = TRUE, reverse = TRUE)) +
theme_barcharts() +
theme(
plot.title = element_text(colour = "#265b9b", size = 11, hjust = 1),
legend.position = "bottom",
legend.text = element_text(size = textsize),
legend.box.margin = unit(c(-15, 60, -10, 0), "pt"),
legend.spacing.y = unit(0, "pt"),
axis.line.x = element_blank(),
axis.line.y = element_line(size = 0.5),
axis.text.x = element_text(colour = textcolor, size = 11),
axis.text.y = element_text(colour = textcolor, size = 11),
axis.title.x = element_text(colour = textcolor, size = 11, margin = margin(5, 0, 0, 0, "pt"), hjust = 0.3),
axis.title.y = element_blank(),
panel.grid.major.x = element_line(colour = "#989898", size = 0.2),
plot.margin = unit(c(5, 10, 10, 2), "pt")
) +
annotate("text",
x = annotate.label$asset_type, y = annotate.position$maxsumValueUSD + 0.35 * annotate.position$maxsumValueUSD,
# label = wrap_labels(paste0(comma(round(annotate.label$valid_value_usd, digits = 0))), 10),
label = comprss(annotate.label$valid_value_usd),
family = textfont, colour = "#265b9b", hjust = 1
) +
ggtitle(Climaterelevantsectors)
ggsave(plot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 2.2, width = 4.5, dpi = ppi) # linewidth_in*.9
}
CarstenMetricChart <- function(plotnumber, chart_type, explicit_filename = "") {
PortName_IN <- portfolio_name_select
if (chart_type == "CB" & has_debt == TRUE) {
market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
port <- bind_rows(combin, market)
Type <- "Corporate bond"
port$portfolio_name <-
dplyr::case_when(
port$portfolio_name == portfolio_name_select ~ "Portfolio",
port$portfolio_name == cb_market_ref ~ cb_market_ref,
TRUE ~ port$portfolio_name
)
port$portfolio_name <- factor(port$portfolio_name, levels = c("Portfolio", cb_market_ref), ordered = TRUE)
lab <- wrap_labels(cb_market_ref, 12)
MarketRef <- cb_market_ref
} else if (chart_type == "CB" & has_debt == FALSE) {
PlotChart <- FALSE
}
if (chart_type == "EQ" & has_equity == TRUE) {
# Set as CB to ensure that the PortWeight values are used
market <- filter_by_parameters(eq_market, "CB", scenario.irrelevant = TRUE)
port <- filter_by_parameters(EQCombin, "CB", scenario.irrelevant = TRUE)
port <- bind_rows(port, market)
Type <- "equity"
port <- subset(port, portfolio_name %in% c(portfolio_name_select, eq_market_ref)) # "ListedEquity"
port$portfolio_name <-
dplyr::case_when(
port$portfolio_name == portfolio_name_select ~ "Portfolio",
port$portfolio_name == eq_market_ref ~ eq_market_ref,
TRUE ~ port$portfolio_name
)
port$portfolio_name <- factor(port$portfolio_name, levels = c("Portfolio", eq_market_ref), ordered = TRUE)
lab <- wrap_labels(eq_market_ref, 10)
MarketRef <- eq_market_ref
} else if (chart_type == "EQ" & has_equity == FALSE) {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
# print("Plotting preparation")
current.port <- subset(port, year == start_year & portfolio_name == "Portfolio") %>%
mutate(Metric = plan_carsten)
current.market <- subset(port, year == start_year & portfolio_name %in% c(eq_market_ref, cb_market_ref)) %>%
mutate(Metric = plan_carsten)
port <- bind_rows(current.port, current.market) %>% select(portfolio_name, ald_sector, technology, Metric)
sector_list <- sector_list[sector_list != "Fossil Fuels"]
tech_list <- tech_list[tech_list != "OilCap"]
port <- port %>%
complete(
portfolio_name = c("Portfolio", MarketRef),
technology = tech_list,
fill = list(Metric = 0)
)
port <- port %>% mutate( # portfolio_name = if_else(investor_name == investor_name_select, portfolio_name_select, MarketRef),
ald_sector = set_ald_sector(technology)
)
port$ald_sector2 <- paste0(port$ald_sector, " Production") #
port$ald_sector2 <- ifelse(port$ald_sector == "Power", C_Power, port$ald_sector2) #
port$ald_sector2 <- ifelse(port$ald_sector == "Automotive", S_Automotive, port$ald_sector2) #
port$ald_sector2 <- ifelse(port$ald_sector == "Oil&Gas", P_OilandGas, port$ald_sector2) #
port$ald_sector2 <- ifelse(port$ald_sector == "Coal", P_Coal, port$ald_sector2) #
port$ald_sector2 <- factor(port$ald_sector2, levels = c(P_OilandGas, P_Coal, C_Power, S_Automotive)) # levels OK
port <- subset(port, technology != "OilCap")
tech.levels <- technology_order_short
tech.labels <- c(
P_Gas, P_Oil, P_Coal, #
C_Renewables, C_Hydro, C_Nuclear, C_Gas, C_Coal, #
T_Electric, T_Hybrid, T_ICE
) #
if (max(port$Metric) <= 0.001) {
perc.labels.digits <- 0.01
} else if (max(port$Metric) <= 0.01) {
perc.labels.digits <- 0.1
} else {
perc.labels.digits <- 1
}
port$technology <- factor(port$technology, levels = tech.levels, ordered = TRUE)
port$portfolio_name <- dplyr::recode(port$portfolio_name,
"Portfolio" = Portf,
MarketRef = lab
)
port$portfolio_name <- factor(port$portfolio_name, levels = c(Portf, MarketRef), ordered = TRUE)
tech.colors <- c(GasProdColour, OilProdColour, CoalProdColour, RenewablesColour, HydroColour, NuclearColour, GasCapColour, CoalCapColour, ElectricColour, HybridColour, ICEColour)
tots <- port %>%
group_by(portfolio_name, ald_sector2) %>%
summarise(Metric = sum(Metric, na.rm = T))
outputplot <- ggplot(port, aes(x = portfolio_name, y = Metric, group = technology, fill = technology)) +
geom_bar(stat = "identity", position = "stack") +
# geom_text(data=tots, aes(x=PortName, y=Metric, label=percent(Metric))) +
scale_x_discrete(name = "", labels = c(Portf, lab)) + #
scale_y_continuous(
name = paste0(wrap_labels(Weightbymarketvalueofissuersexposedtothetechnology, 50), "\n"), labels = percent_format(accuracy = perc.labels.digits), expand = c(0, 0), #
limits = c(0, max(tots$Metric) + .01)
) +
scale_fill_manual(name = tech.levels, labels = tech.labels, values = tech.colors) +
guides(fill = guide_legend(ncol = 1)) +
theme_cdi() +
facet_wrap(~ald_sector2, nrow = 1, labeller = label_wrap_gen(width = 20)) +
theme(
legend.title = element_text(size = 14),
legend.text = element_text(size = 11),
axis.text.x = element_text(angle = 0, colour = textcolor, size = 11),
axis.text.y = element_text(size = 11),
axis.title.y = element_text(size = 13),
strip.text = element_text(size = 13),
# axis.ticks.y = element_line(colour=textcolor,size = 1),
axis.line.x = element_line()
)
} else {
Label <- paste(Nodataintheportfolio) #
outputplot <- no_chart(Label)
}
ggsave(outputplot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 4, width = 13, dpi = ppi * 0.8)
BV.asset_type <<- ""
}
RenewableAdditionsChart <- function(plotnumber, companiestoprint, chart_type) {
sector_to_plot <<- "Power"
RenewableAdds <- NA
RenewableAdditions <- readRDS(paste0(Location, "ReferenceData/RenewablesAdditionsData.rda"))
RenewableAdditions$ald_sector <- "Power"
# RenewableAdditions$scenario_geography <- ifelse(RenewableAdditions$ald_sector == "Power","Global",RenewableAdditions$scenario_geography)
if (chart_type == "EQ") {
RenewableAdditions <- filter_by_parameters(RenewableAdditions, "EQ") %>%
filter(FI %in% c("Equity", "EQUITY"))
CompProdSnapshot <- filter_by_parameters(EQCompProdSnapshot, "EQ", ActorSectorOnly = T)
RenewableAdds <- RenewableAdditions[RenewableAdditions$ID %in% CompProdSnapshot$bloomberg_id, ]
}
if (chart_type == "CB") {
RenewableAdditions <- filter_by_parameters(RenewableAdditions, "CB") %>%
filter(FI %in% c("Bonds", "BONDS"))
CompProdSnapshot <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F, ActorSectorOnly = T)
RenewableAdds <- RenewableAdditions[RenewableAdditions$ID %in% CompProdSnapshot$corp_bond_ticker, ]
}
if (data_check(RenewableAdds) == T) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
RenewablesBar <- RenewableAdds
maxcompanies <- min(companiestoprint, nrow(RenewablesBar))
RenewablesBar <- RenewablesBar[order(RenewablesBar$plan_tech_prod, decreasing = T), ]
# RenewablesBar$company_name <- factor(RenewablesBar$company_name, levels = RenewablesBar$company_name[order(RenewablesBar$Additions)])
RenewablesBar <- RenewablesBar[seq(1, maxcompanies), ]
Yaxislabel <- paste0(Renewablecapacityadditions, " (", start_year, " - ", start_year + 5, ")") #
stillreq <- " Remaining Required Additions" # not used?
remainlabel <- x_Progress #
progresslabel <- RemainingRequiredAdditions #
Target <- "Progress" # not used?
RenewablesBar <- RenewablesBar %>% select(company_name, Additions, RequiredAdditions)
RenewablesBarLong <- melt(RenewablesBar, variable.name = "variable", value.name = "value", id.vars = c("company_name"))
sumren <- RenewablesBarLong %>%
group_by(company_name) %>%
summarize(tot = sum(value))
RenewablesBarLong <- left_join(RenewablesBarLong, sumren, by = "company_name") %>%
mutate(perc = value / tot)
RenewablesBarLong$variable <- factor(RenewablesBarLong$variable, levels = c("RequiredAdditions", "Additions"))
RenewablesBarLong <- RenewablesBarLong %>%
filter(company_name %in% unique(RenewablesBarLong$company_name)[1:min(companiestoprint, length(unique(RenewablesBarLong$company_name)))])
comp <- subset(RenewablesBarLong, variable == "RequiredAdditions")
company_labels <- trimws(unique(RenewablesBarLong$company_name))
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
CompName <- RenewablesBarLong[order(RenewablesBarLong[which(RenewablesBarLong$variable == "Additions"), ]$perc, decreasing = T), ]$company_name
RenewablesBarLong$company_name <- factor(RenewablesBarLong$company_name, levels = rev(CompName))
RenAddBar <- ggplot(RenewablesBarLong, aes(x = company_name, y = perc, fill = variable)) +
geom_bar(stat = "identity", width = .8, position = "fill") +
# geom_segment(aes(x=0, xend = 0 , y=0, yend = 1), size=1, colour = AxisColour, arrow = arrow(length = unit(0.4,"cm")))+
geom_hline(yintercept = 1, colour = area_2, linetype = "longdash", size = 1) +
scale_fill_manual(values = c("RequiredAdditions" = badexpColour, "Additions" = YourportColour), labels = c("Additions" = remainlabel, "RequiredAdditions" = progresslabel)) +
scale_y_continuous(limits = c(0, 1), expand = c(-0.1, 0.1), label = percent) +
scale_x_discrete(expand = c(0, 0), label = company_labels) +
theme_barcharts() +
guides(fill = guide_legend(nrow = 1, reverse = TRUE)) +
coord_flip() +
theme(
axis.title.x = element_text(colour = AxisColour, size = 11),
axis.title.y = element_blank(),
axis.text.y = element_text(size = 11),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(size = 11, colour = AxisColour, margin = margin(l = 0.15, r = 0.35, unit = "cm")),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
legend.title = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(.4, 1.5, 0, 0), "lines")
) +
ylab(Yaxislabel) +
geom_text(
data = comp,
aes(x = company_name, y = 0.88, hjust = 1),
label = paste0(round(comp$value, digits = 0), " MW ", RemainingRequiredAdditions), color = textcolor, size = 3
) # 2.53 #
RenAddValues <- c(4.8, 8)
bar_size <- 8 / 15
height <- min(1 + companiestoprint, n_distinct(RenewablesBarLong$company_name) / 2) * bar_size + .86
# plotwidth <- .1*NoCompanies +7.1
} else {
Label <- YourPortfoliodoesnotincludeanycompanieswithrenewablepower #
RenAddBar <- no_chart(Label)
# plotwidth <- 7
height <- 3
}
ggsave(filename = graph_name(plotnumber, ParameterFile), height = height, width = 10, plot = RenAddBar, dpi = ppi)
sector_to_plot <<- ""
}
carboninoutdata <- function(chart_type) {
ccap <- NA
portfolio1 <- NA
if (chart_type == "EQ") {
EQCompProdSnapshot <- filter_by_parameters(EQCompProdSnapshot, "EQ")
if (data_check(EQCompProdSnapshot) == TRUE) {
CompProdSS <- EQCompProdSnapshot %>%
ungroup() %>%
filter(year == start_year + 5) %>%
select(company_name, port_weight, id) %>%
rename(Name = company_name) %>%
unique()
CompProdSS <- as.data.frame(CompProdSS)
CompProdSS$id <- as.integer(CompProdSS$id)
ccap <- CarbonData
} else {
PlotChart <- FALSE
}
} else if (chart_type == "CB") {
CBCompProdSnapshot <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = FALSE)
if (data_check(CBCompProdSnapshot) == TRUE) {
CompProdSS <- CBCompProdSnapshot %>%
filter(year == start_year + 5) %>%
select(port_weight, corp_bond_ticker) %>%
rename(EQY_FUND_TICKER = corp_bond_ticker) %>%
unique()
} else {
PlotChart <- FALSE
}
ccap <- CarbonData
ccap$EQY_FUND_TICKER <- gsub(" [A-z ]*", "", as.character(ccap$EQY_FUND_TICKER))
}
if (data_check(ccap) == TRUE) {
ccap$InsideCarbonBudget <- ccap$TotalCarbonBudget - ccap$OutsideCarbonBudget
ccap <- subset(ccap, select = c("id", "TotalCarbonBudget", "OutsideCarbonBudget", "InsideCarbonBudget"))
colnames(ccap) <- c("id", "TotalCarbonBudget", "Outside Carbon Budget", "Inside Carbon Budget")
ccap$Inside.Carbon.Budget <- ccap$`Inside Carbon Budget` / ccap$TotalCarbonBudget
ccap$Outside.Carbon.Budget <- ccap$`Outside Carbon Budget` / ccap$TotalCarbonBudget
portfolio <- left_join(CompProdSS, ccap, by = "id") %>%
select(Name, port_weight, Inside.Carbon.Budget, Outside.Carbon.Budget)
portfolio1 <- melt(portfolio, id.vars = c("Name", "port_weight"), variable.name = "CarbonBudget")
portfolio1 <- subset(portfolio1, !is.na(value))
if (data_check(portfolio1) == TRUE) {
HasCarbonBudget <<- TRUE
} else {
HasCarbonBudget <<- FALSE
}
} else {
HasCarbonBudget <<- FALSE
}
return(portfolio1)
}
CarbonBudget <- function(plotnumber, companiestoprint, chart_type) {
portfolio1 <- carboninoutdata(chart_type)
if (HasCarbonBudget == TRUE) {
carbonorder <- c("Inside Carbon Budget", "Outside Carbon Budget")
portfolio1$CarbonBudget <- gsub("\\.", " ", portfolio1$CarbonBudget)
colors <- c(OilProdColour, area_6)
# AllData <- filter(AllData, technology %in% techorder)
portfolio1$CarbonBudget <- factor(portfolio1$CarbonBudget, levels = carbonorder)
portfolio1 <- portfolio1 %>%
arrange(-port_weight)
portfolio1 <- portfolio1 %>%
filter(Name %in% unique(portfolio1$Name)[1:min(companiestoprint, length(unique(portfolio1$Name)))])
dummy <- data.frame(
c("Name", ""),
c("port_weight", NA),
c("CarbonBudget", NA),
c("value", 0)
)
colnames(dummy) <- as.character(unlist(dummy[1, ]))
dummy <- dummy[-1, ]
dummy$value <- as.numeric(dummy$value)
portfolio1 <- rbind(portfolio1, dummy)
# portfolio1$Name <- factor(portfolio1$Name, levels=rev(unique(c("",portfolio1$Name))))
portfolio1$port_weight <- as.numeric(portfolio1$port_weight)
portfolio1 <- portfolio1 %>%
arrange(port_weight)
company_labels <- trimws(unique(portfolio1[!is.na(portfolio1$CarbonBudget), ]$Name))
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
portfolio1$CarbonBudget <- recode(portfolio1$CarbonBudget,
"Outside Carbon Budget" = OutsideCarbonBudget,
"Inside Carbon Budget" = InsideCarbonBudget
)
portfolio1$CarbonBudget <- factor(portfolio1$CarbonBudget, levels = c(OutsideCarbonBudget, InsideCarbonBudget))
bar_labels <- c(company_labels, " ")
portfolio1$port_weight <- as.numeric(portfolio1$port_weight)
carb <- subset(portfolio1, !is.na(port_weight))
carbonorder <- recode(carbonorder,
"Outside Carbon Budget" = OutsideCarbonBudget,
"Inside Carbon Budget" = InsideCarbonBudget
)
names(colors) <- carbonorder
PortPlot <- ggplot(
data = portfolio1, aes(
x = reorder(Name, port_weight), y = value,
fill = factor(CarbonBudget, levels = c(OutsideCarbonBudget, InsideCarbonBudget))
),
show.guide = TRUE
) +
geom_bar(stat = "identity", position = "fill", width = .6) +
# geom_hline(yintercept = c(.25,.50,.75), color="white")+
scale_fill_manual(values = colors, labels = paste(carbonorder, " "), breaks = (carbonorder)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
scale_x_discrete(labels = bar_labels) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts() +
geom_text(
data = carb, aes(x = Name, y = 1),
label = perc(carb$port_weight),
hjust = -1, color = textcolor, size = textsize * (5 / 14)
) +
geom_text(aes(x = "", y = 1),
label = x_Weight, #
hjust = -0.5, color = textcolor, size = 10 * (5 / 14),
family = textfont
) +
xlab("") +
ylab("tech_share") +
coord_flip() +
theme(
legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(1, 6, 0, 0), "lines"), axis.line.x = element_line(colour = textcolor, size = 0.5)
) #+
# annotation_custom(
# grob = textGrob(label = x_Weight,
# gp=gpar(fontsize=8.5),
# hjust = 0),
# xmin = n_distinct(portfolio1$Name)+0.5, xmax = n_distinct(portfolio1$Name)+1, ymin = 1, ymax = 1.05)
#
# # gt <- PortPlot
gt <- ggplot_gtable(ggplot_build(PortPlot))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
dev.off()
# grid.draw(gt)
bar_size <- 4 / 15
h <- min(1 + companiestoprint, n_distinct(portfolio1$Name)) * bar_size + .66
HasCarbonBudget <<- TRUE
} else {
Label <- NoCarbonBudgetDataisavailablefortheOilCompaniesinyourportfolio #
# Label <- "No results are available for this combination of parameters"
gt <- no_chart(Label)
h <- 3
HasCarbonBudget <<- FALSE
}
ggsave(plot = gt, filename = graph_name(plotnumber, ParameterFile), bg = "white", height = h, width = 11, dpi = ppi)
}
OilGasBuildOut <- function(plotnumber, companiestoprint, chart_type, explicit_filename = "") {
sector_to_plot <<- "Oil&Gas"
# chart_type <<- chart_type
if (chart_type == "CB") {
comp <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F)
if (data_check(comp) == TRUE) {
comp <- subset(comp, portfolio_name == portfolio_name_select & ald_sector == "Oil&Gas")
comp$company_name <- comp$id
port <- filter_by_parameters(CBCombin, "CB", by_equity_market = F)
port.targets <- port %>%
filter(ald_sector == "Oil&Gas") %>%
select(investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography, ald_sector, technology, scen_tech_prod, year) %>%
arrange(year) %>%
group_by(investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography, ald_sector, technology) %>%
summarise(
Port.Scen.Diff = last(scen_tech_prod) - first(scen_tech_prod),
Port.Scen.Pct = Port.Scen.Diff / first(scen_tech_prod)
) %>%
ungroup() %>%
select(-scenario, -allocation, -equity_market, -scenario_geography, -ald_sector)
}
}
if (chart_type == "EQ") {
comp <- filter_by_parameters(EQCompProdSnapshot, "EQ")
if (data_check(comp) == TRUE) {
comp <- subset(comp, portfolio_name == portfolio_name_select & ald_sector == "Oil&Gas" & year %in% c(start_year, start_year + 5))
port <- filter_by_parameters(EQCombin, "EQ")
port <- subset(port, year %in% c(start_year, start_year + 5))
port.targets <- port %>%
filter(ald_sector == "Oil&Gas") %>%
select(
investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography,
ald_sector, technology, scen_tech_prod, year
) %>%
arrange(year) %>%
group_by(investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography, ald_sector, technology) %>%
summarise(
Port.Scen.Diff = last(scen_tech_prod) - first(scen_tech_prod),
Port.Scen.Pct = Port.Scen.Diff / first(scen_tech_prod)
) %>%
ungroup() %>%
select(-scenario, -allocation, -equity_market, -scenario_geography, -ald_sector)
}
}
if (data_check(comp) == TRUE) {
comp <- comp %>%
filter(ald_sector == "Oil&Gas") %>%
ungroup() %>%
select(scenario, company_name, ald_sector, technology, plan_tech_prod, scen_tech_prod, year) %>%
arrange(scenario, company_name, ald_sector, technology, year) %>%
group_by(scenario, company_name, ald_sector, technology) %>%
filter(plan_tech_prod > 0) %>%
summarise(first(plan_tech_prod), last(plan_tech_prod),
Plan.Diff = last(plan_tech_prod) - first(plan_tech_prod),
Scen.Diff = last(scen_tech_prod) - first(scen_tech_prod),
Plan.Pct = ifelse(Plan.Diff == 0, 0, (last(plan_tech_prod) - first(plan_tech_prod)) / first(plan_tech_prod))
)
}
## TODO: FIXME, THIS IS A HACK FOR THE SFC PROJECT
comp <- comp %>% mutate(Plan.Pct = ifelse(company_name == "PETRPE", (2347 - 1250) / 1250, Plan.Pct))
if (data_check(filter(comp, company_name == "PETRPE"))) {
tmp_row <- filter(comp, company_name == "PETRPE")
tmp_row <- tmp_row %>% mutate(technology = "Gas", Plan.Pct = 0)
comp <- rbind(comp, tmp_row)
}
## TODO: END FIXME, THIS IS A HACK FOR THE SFC PROJECT
if (data_check(comp) == TRUE) {
comp <- left_join(comp, port.targets %>% ungroup() %>% as.data.frame(), by = "technology")
comp$technology <- factor(comp$technology, levels = c("Gas", "Oil"), ordered = TRUE)
comp$technology <- dplyr::recode(comp$technology, Gas = T_GasProd, Oil = T_OilProd) #
comp <- comp %>%
group_by(scenario, technology) %>%
top_n(wt = `first(plan_tech_prod)`, n = companiestoprint)
# Seperate out the infinite values if necessary
comp_inf <- comp[is.infinite(comp$Plan.Pct), ]
comp <- comp[is.finite(comp$Plan.Pct), ]
MAX.VAL <- max(abs(comp$Plan.Pct), na.rm = TRUE)
temp.max <- ceiling(MAX.VAL * 10) / 10 + 0.2
temp.diff <- ceiling(temp.max * 2) / 10
breaks <- seq(0, temp.max + temp.diff, temp.diff)
breaks <- unlist(c(rev(-1 * breaks), breaks))
limits <- c(min(breaks), max(breaks))
comp$InfFlag <- 0
if (data_check(comp_inf) == TRUE) {
comp_inf$Plan.Pct <- MAX.VAL * 1
comp_inf$InfFlag <- 1
comp <- rbind(comp, comp_inf)
}
comp$Ord.Var <- paste0(comp$company_name, comp$technology)
comp <- comp %>%
ungroup() %>%
arrange(`first(plan_tech_prod)`)
comp$Ord.Var <- factor(comp$Ord.Var, levels = comp$Ord.Var, ordered = TRUE)
comp$Final.Name <- shortened_company_names(comp$company_name, 15)
port.targets$technology <- dplyr::recode(port.targets$technology, Gas = T_GasProd, Oil = T_OilProd) #
outputplot <- ggplot(comp, aes(x = Ord.Var, y = Plan.Pct, fill = technology)) +
geom_bar(stat = "identity") +
geom_hline(
data = port.targets, aes(
yintercept = Port.Scen.Pct,
linetype = paste0(ChangeinPortfolioProductionSpecifiedby, " ", Scenariochoose, " ", Scenariofrom, " ", start_year, "- ", start_year + 5)
), #
color = area_2, size = 1.5
) +
coord_flip() +
scale_x_discrete(name = "", labels = setNames(comp$Final.Name, as.character(comp$Ord.Var))) +
scale_y_continuous(
name = paste0(ChangeinPlannedProductionfrom, " ", start_year, "- ", start_year + 5), #
labels = percent_format(accuracy = 1), breaks = round(breaks, digits = 2), limits = limits
) +
scale_color_manual(values = area_2) +
scale_linetype_manual(name = "", values = c("solid")) +
scale_fill_manual(name = "", values = c(OilProdColour, GasProdColour)) + # guide_legend(reverse = TRUE)
facet_wrap(~technology, ncol = 1, scales = "free_y") +
theme_cdi() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 11)
) +
theme(axis.line = element_line()) +
theme(axis.ticks.x = element_line())
# Adds the infinite label
if (data_check(comp_inf) == TRUE) {
outputplot <- outputplot +
geom_text(data = comp[comp$InfFlag == 1, ], aes(x = Ord.Var, y = 0, label = "Future Production Only - Infinite Build Out", hjust = 0))
# annotate("text", x= 5, y=0, label = "Future Production Only - Infinite Build Out", hjust = 0)
}
bar_size <- 6 / 15
h <- min(1 + companiestoprint, n_distinct(comp$company_name)) * bar_size + 1.5
} else {
Label <- Nocompanieswithexposuretoeitheroilorgasproductioninyourportfolio #
outputplot <- no_chart(Label)
h <- 3.25
}
ggsave(outputplot,
filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename),
height = h, width = 10, dpi = ppi
)
sector_to_plot <<- ""
chart_type <<- ""
BV.asset_type <<- ""
}
CoalRetirementChart <- function(plotnumber, companiestoprint, chart_type) {
PlotChart <- F
chart_type <- chart_type
sector_to_plot <<- "Power"
PortCoalRetirements <- NA
CoalRetirements$equity_market <- gsub("Market", "", CoalRetirements$equity_market)
# CoalRetirements$scenario_geography <- ifelse(CoalRetirements$scenario_geography == "Global","GlobalAggregate",CoalRetirements$scenario_geography)
CoalRetirements$ald_sector <- "Power"
if (chart_type == "EQ" & has_equity) {
CoalRet <- filter_by_parameters(CoalRetirements, "EQ") %>%
filter(FI %in% c("Equity", "EQUITY"))
if ("ALD.ID" %in% colnames(CoalRet)) {
CoalRet <- CoalRet %>% rename(ID = ALD.ID)
}
PortProduction <- filter_by_parameters(EQCompProdSnapshot, "EQ") # %>%
# filter(technology == "CoalCap")
} else if (chart_type == "CB" & has_debt) {
CoalRet <- filter_by_parameters(CoalRetirements, "CB", by_equity_market = F) %>%
filter(FI %in% c("Bonds", "BONDS"))
if ("ALD.ID" %in% colnames(CoalRet)) {
CoalRet <- CoalRet %>% rename(ID = ALD.ID)
}
PortProduction <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F)
}
if (data_check(PortProduction)) {
PortCoalRetirements <- PortProduction %>% filter(technology == "CoalCap")
}
if (data_check(PortCoalRetirements)) {
if (chart_type == "EQ") {
PortCoalRetirements <- CoalRet %>%
filter(ID %in% PortProduction$bloomberg_id)
} else if (chart_type == "CB") {
PortCoalRetirements <- CoalRet %>%
filter(ID %in% PortProduction$corp_bond_ticker)
}
}
if (data_check(PortCoalRetirements)) {
PlotChart <- T
}
if (PlotChart == T) {
PortCoalRetirements <- as.data.frame(PortCoalRetirements)
PortCoalRet <- PortCoalRetirements %>%
select(company_name, ExcessiveAdditions, RequiredRetirements10yr, PctRetire) %>%
mutate(Total = ExcessiveAdditions + RequiredRetirements10yr)
PortCoalRet$company_nameShort <- shortened_company_names(PortCoalRet$company_name, 15)
PortCoalRet <- PortCoalRet[order(PortCoalRet$Total, decreasing = T), ]
PortCoalRet <- PortCoalRet[PortCoalRet$Total > 0, ]
maxcompanies <- min(companiestoprint, nrow(PortCoalRet))
PortCoalRet <- PortCoalRet[seq(1, maxcompanies), ]
PortCoalRet$company_name <- factor(PortCoalRet$company_name, levels = unique(PortCoalRet$company_name[order(PortCoalRet$Total)]))
CoalRetmaxxAxis <- max(PortCoalRet$Total)
PortCoalRet <- PortCoalRet[!is.na(PortCoalRet$ExcessiveAdditions), ]
PlotChart <- data_check(PortCoalRet)
}
if (PlotChart == T) {
PortCoalRet <- PortCoalRet %>% select(-Total, -PctRetire)
PortCoalRetLong <- melt(PortCoalRet, variable.name = "variable", value.name = "value", id.vars = c("company_name", "company_nameShort"))
RetUnit <- "MW"
if (CoalRetmaxxAxis > 1000) {
PortCoalRetLong$value <- PortCoalRetLong$value / 1000
CoalRetmaxxAxis <- ceiling(CoalRetmaxxAxis / 1000)
RetUnit <- "GW"
}
CoalRetmaxxAxis <- round(CoalRetmaxxAxis, digits = 1)
outputplot <- ggplot(PortCoalRetLong, aes(x = company_name, y = value, fill = variable)) +
geom_bar(stat = "identity", width = .8, colour = NA) +
scale_fill_manual(
values = c("ExcessiveAdditions" = "#170203", "RequiredRetirements10yr" = "#ed1c24"),
labels = c(
paste0(AdditionalCapacityby, " ", start_year + 10), #
paste0(RequiredRetirementsby, " ", start_year + 10)
), name = "year"
) + #
scale_y_continuous(
expand = c(0, 0), limits = c(0, CoalRetmaxxAxis * 1.05), breaks = seq(0, CoalRetmaxxAxis, CoalRetmaxxAxis * 0.1),
minor_breaks = seq(0, CoalRetmaxxAxis, CoalRetmaxxAxis * 0.05)
) +
scale_x_discrete(expand = c(0, 0), labels = rev(PortCoalRetLong$company_nameShort)) +
xlab("") +
ylab(paste0(Requiredcoalretirementspercompany, " (", RetUnit, ")")) + #
# annotate(hjust = 0.8,"text", x = c(1:(length(CoalRetLabels$Name))) , y = CoalRetLabels$RetTot+(CoalRetmaxxAxis/CoalRetValues[3]) , label = paste(round(CoalRetirementChartPerc$PctRetire*100,0), "% of total", start_year, "capacity",sep = " "), colour = AxisColour,size = 3)+
theme(
axis.title.x = element_text(colour = AxisColour, size = textsize),
axis.title.y = element_blank(),
axis.text.x = element_text(colour = AxisColour, size = textsize),
axis.text.y = element_text(colour = AxisColour, size = textsize),
axis.line.x = element_line(colour = AxisColour, size = 1),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.major.x = element_line(colour = "grey90", linetype = "solid"),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
# panel.grid.minor.x = element_line(colour = "grey80", linetype = "dotted"),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(size = textsize, colour = AxisColour, margin = margin(l = 0.15, r = 0.35, unit = "cm")),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
legend.title = element_blank(),
legend.key = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA),
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(.4, 1.5, 0, 0), "lines")
) +
coord_flip() +
guides(guide_legend(nrow = 1))
bar_size <- 4 / 15
height <- (1 + maxcompanies) * bar_size + .66
} else {
Label <- NoCoalcompaniesinyourportfolio #
outputplot <- no_chart(Label)
height <- 1
}
ggsave(outputplot, filename = graph_name(plotnumber, ParameterFile), bg = "white", height = height, width = 10, dpi = ppi)
sector_to_plot <- ""
}
CompareTechnologyExposure <- function(plotnumber, chart_type, sector_to_plot) {
Combin <- data.frame()
if (chart_type == "EQ" & has_equity == TRUE) {
Market <- filter_by_parameters(eq_market, "EQ")
Combin <- filter_by_parameters(EQCombin, "EQ")
} else if (chart_type == "CB" & has_debt == TRUE) {
Market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
Combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
}
if (data_check(Combin) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
### Portfolio Data Structure
Combin$Type <- "Portfolio"
Combin <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == start_year + 5) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>%
rename(WtProduction = plan_alloc_wt_tech_prod)
Combin <- Combin %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Portfolio")
)
Combin_A <- subset(Combin, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Combin_P <- subset(Combin, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Combin_F <- subset(Combin, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Combin <- bind_rows(Combin_F, Combin_A, Combin_P)
### Market Data Structure
Market$Type <- "Benchmark"
Market <- Market %>%
ungroup() %>%
filter(technology != "OilCap" & year == start_year + 5) %>% # year == PlotYr & Type =="Portfolio") %>%
select("portfolio_name", "ald_sector", "technology", "scen_alloc_wt_tech_prod", "Type") %>% # "Type"
rename(WtProduction = scen_alloc_wt_tech_prod)
Market <- Market %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Benchmark")
)
Market_A <- subset(Market, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Market_P <- subset(Market, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Market_F <- subset(Market, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Market <- bind_rows(Market_F, Market_A, Market_P)
### Combine Market and Portfolios to the same Data Structure
Production <- bind_rows(Combin, Market)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Oil&Gas", "Coal", "Power", "Automotive"))
levels(Production$ald_sector)[levels(Production$ald_sector) == "Coal"] <- "Fossil Fuels"
levels(Production$ald_sector)[levels(Production$ald_sector) == "Oil&Gas"] <- "Fossil Fuels"
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive"))
# Aggregate and rename CarstenMetric_Port
ID.COLS <- c("ald_sector", "technology", "Type")
Production <- Production %>% gather(key = Metric, value = Value, "WtProduction")
Production <- aggregate(Production["Value"], by = Production[c(ID.COLS)], FUN = sum)
# Created an average for the peers (or even just use fill!)
# Transfer coal to gigajoule for tech share charts only!!!
Production$Value[Production$technology == "Coal"] <- Production$Value[Production$technology == "Coal"] * 29307600 / 365.25 / 1e6
# , Production$Type != "Market"
if (sector_to_plot != "All") {
technologyorder <- technology_order
colours <- as.vector(ColourPalette[["Colours"]])
names(colours) <- technologyorder
labels <- technologyorder
names(labels) <- technologyorder
} else if (sector_to_plot == "All") {
technologyorder <- technology_order_short
colours <- as.vector(ColourPalette[["Colours"]])[1:11]
names(colours) <- technologyorder
labels <- technologyorder
names(labels) <- technologyorder
}
Production$technology <- factor(Production$technology, levels = technologyorder)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")) #
Production$Type <- wrap_labels(Production$Type, 20)
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Benchmark"))
xlabels <- c("Portfolio", "Aligned\nMarket")
titles <- c("Fossil Fuel Production", "Power Capacity", "Automotive Production", "Aviation Production", "Shipping Production")
names(titles) <- c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")
Production <- subset(Production, select = c("Type", "ald_sector", "technology", "Value"))
tot <- Production %>%
group_by(Type, ald_sector) %>%
summarise(Total = sum(Value))
Production <- left_join(Production, tot, by = c("Type", "ald_sector"))
Production$per <- Production$Value / Production$Total
levels(Production$ald_sector) <- c("Fossil Fuels Production", "Power Capacity", "Automotive Production", "Aviation", "Shipping")
Production$ald_sector <- dplyr::recode(Production$ald_sector,
"Fossil Fuels Production" = wrap_labels(FossilFuelsProduction, 30), "Power Capacity" = C_Power, #
"Automotive Production" = AutomotiveProduction, Aviation = Aviation, Shipping = Shipping
) #
# Production$technology<-gsub("Cap","",Production$technology)
Production$technology <- dplyr::recode(Production$technology,
Gas = T_GasProd, Oil = T_OilProd, Coal = T_CoalProd, #
RenewablesCap = paste(T_Renewables_1, "\n", T_Renewables_2), HydroCap = T_HydroCap, NuclearCap = T_NuclearCap, GasCap = T_GasCap, CoalCap = T_CoalCap, #
Electric = x_Electric, Hybrid = x_Hybrid, ICE = x_ICE, "Freight Passenger" = FreightPassenger
) #
Production$lab <- ifelse(is.nan(Production$per), "-%", paste0(round(100 * Production$per), "%"))
Production$per <- ifelse(is.nan(Production$per), 0, Production$per)
cmd <- ggplot(Production) +
geom_col(aes(x = technology, y = per, fill = Type), position = "dodge", width = 0.8) +
geom_text(aes(x = technology, y = per, label = lab, group = Type), vjust = -0.25, position = position_dodge(0.8), color = "black", size = 2) +
scale_y_continuous(expand = c(0, 0), labels = percent, limits = c(0, 1.1)) +
ylab(paste(technologySharein, start_year + 5)) + #
scale_fill_manual(values = c("Portfolio" = Port, "Benchmark" = Benchmark), labels = c(Portf, AlignedMarket)) +
theme_barcharts() +
theme(
plot.title = element_text(hjust = 0.5, colour = textcolor, size = 11, margin = unit(c(0, 0, 1, 0), "lines")),
axis.text.y = element_text(size = textsize - 1.5),
axis.text.x = element_text(size = textsize - 1.5),
legend.position = "bottom",
legend.title = element_blank()
) +
guides(fill = guide_legend(nrow = 1)) +
facet_grid(. ~ ald_sector, scales = "free", space = "free") +
theme(
strip.background = element_rect(fill = "white"),
strip.text = element_text(size = textsize)
)
# wid = unique(Production$technology)
} else {
Label <- YourPortfoliohasnoproductioninthissectorfortheselectedscenario_geographyandMarket #
cmd <- no_chart(Label)
# wid <- 4
}
ggsave(
filename = graph_name(plotnumber, ParameterFile), # bg = "transparent",
plot = cmd, height = 2.6, width = 7, dpi = ppi
)
}
##############
### Themes ###
##############
theme_barcharts <- function(base_size = textsize, base_family = "") {
theme(
axis.ticks = element_blank(),
axis.text.x = element_text(colour = textcolor, size = textsize),
axis.text.y = element_text(colour = textcolor, size = textsize),
axis.title.x = element_blank(),
axis.title.y = element_text(colour = textcolor, size = textsize),
axis.line.x = element_line(colour = textcolor, size = 0.5),
axis.line.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
# legend.position=c(0.5,0),#legend.position = "none",
legend.position = "none",
legend.direction = "horizontal",
legend.text = element_text(size = textsize, colour = textcolor),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
# legend.title=element_blank(),
legend.title = element_text(colour = textcolor, size = textsize),
legend.key = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(1, 1, 0, 0), "lines"),
plot.title = element_blank(),
text = element_text(family = textfont, size = textsize)
# plot.margin = unit(c(1,1, 5, 2), "lines")
)
}
theme_246 <- function() {
theme_minimal(base_size = 11, base_family = textfont) +
theme(
panel.background = element_rect(fill = "white", color = "white"),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank(),
plot.margin = unit(c(.5, .5, .5, .5), "cm"),
axis.line.x = element_blank(),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text = element_text(size = 11.5, color = "#3D3D3C"),
axis.title = element_text(size = 11, color = "#3D3D3C"),
plot.title = element_text(
size = 12,
hjust = 0
)
)
}
theme_linecharts <- function(base_size = textsize, base_family = "") {
theme(
axis.ticks = element_blank(),
axis.text.x = element_text(colour = textcolor, size = textsize),
axis.text.y = element_text(colour = textcolor, size = textsize),
axis.title.x = element_text(colour = textcolor, size = textsize),
axis.title.y = element_text(colour = textcolor, size = textsize),
axis.line = element_line(colour = textcolor, size = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# panel.background = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA),
# legend.position=c(0.5,0),#legend.position = "none",
legend.position = "none",
legend.direction = "horizontal",
legend.text = element_text(size = textsize, colour = textcolor),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
# legend.title=element_blank(),
legend.title = element_text(colour = textcolor, size = textsize),
legend.key = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(1, 1, 0, 0), "lines")
# plot.margin = unit(c(1,1, 5, 2), "lines")
)
}
theme_cdi <- function() {
theme_minimal(base_size = 11, base_family = textfont) +
theme(
panel.background = element_rect(fill = "white", color = "white"),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank(),
axis.line.x = element_line(),
axis.line.y = element_blank(),
axis.ticks.y = element_line(),
plot.title = element_text(
family = textfont,
face = "bold",
size = 12,
hjust = 0
)
)
}
theme_distribution <- function(base_size = textsize, base_family = "") {
theme(
axis.ticks = element_blank(),
axis.text.x = element_text(colour = textcolor, size = textsize),
axis.text.y = element_text(colour = textcolor, size = textsize),
axis.title.x = element_text(colour = textcolor, size = textsize),
axis.title.y = element_text(colour = textcolor, size = textsize),
# axis.line.x = element_line(colour = textcolor,size=1),
axis.line.y = element_line(colour = textcolor, size = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.position = "bottom",
legend.text = element_text(color = textcolor, size = textsize),
legend.title = element_blank(),
plot.margin = unit(c(0.6, 1.0, 2.5, 0), "lines"),
plot.background = element_blank(),
plot.title = element_blank(),
text = element_text(family = textfont)
)
}
stacked_bar_chart <- function(dat, colors, legend_labels) {
# "item", "family", "score", "value"
colnames <- colnames(dat)
plottheme <- ggplot(data = dat, show.guide = TRUE) +
geom_bar(aes_string(x = colnames[1], y = colnames[4], fill = colnames[3]),
stat = "identity", position = "fill", width = .6
) +
scale_fill_manual(values = colors, labels = legend_labels, breaks = names(legend_labels)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts()
return(plottheme)
}
no_chart <- function(Label) {
wrap_it <- function(x, len) {
sapply(x, function(y) paste(strwrap(y, len), collapse = "\n"), USE.NAMES = FALSE)
}
wrap_labels <- function(x, len) {
if (is.list(x)) {
lapply(x, wrap_it, len)
} else {
wrap_it(x, len)
}
}
outputplot <- ggplot() +
annotate(geom = "text", x = 0, y = 0, label = wrap_labels(Label, 25), size = 5) +
geom_blank() +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA)
)
return(outputplot)
}
2DegreesInvesting/PACTA_analysis documentation built on April 19, 2023, 6:42 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
wrap_it <- function(x, len) {
sapply(x, function(y) paste(strwrap(y, len), collapse = "\n"), USE.NAMES = FALSE)
}
wrap_labels <- function(x, len) {
if (is.list(x)) {
lapply(x, wrap_it, len)
} else {
wrap_it(x, len)
}
}
comprss <- function(tx) {
tx[is.na(tx)] <- 0
div <- findInterval(tx, c(1, 1e3, 1e6, 1e9, 1e12))
div[div == 0] <- 1
labels <- paste(
"$", round(tx / 10^(3 * (div - 1)), 2),
c("", "K", "Mn", "Bn", "Tn")[div]
)
return(labels)
}
comprss_long <- function(tx) {
tx[is.na(tx)] <- 0
div <- findInterval(tx, c(1, 1e3, 1e6, 1e9, 1e12))
div[div == 0] <- 1
labels <- paste(
round(tx / 10^(3 * (div - 1)), 1),
c("", thousand, million, billion, trillion)[div]
)
return(labels)
}
perc <- function(x, format = "f", ...) {
x <- ifelse(x * 100 >= 10,
paste0(" ", formatC(100 * x, format = format, digits = 1, ...), "%"),
paste0(formatC(100 * x, format = format, digits = 2, ...), "%")
)
return(x)
}
shortened_company_names <- function(company_labels, length) {
for (i in 1:length(company_labels)) {
if (str_length(company_labels[[i]]) > length) {
new_name <- strtrim(company_labels[[i]], length)
company_labels[[i]] <- paste0(new_name, "...")
} else if (str_length(company_labels[[i]]) < length) {
for (j in 1:((length + 3) - str_length(company_labels[[i]]))) {
company_labels[[i]] <- paste0(" ", company_labels[[i]])
}
}
}
company_labels
}
set_initial_variables <- function() {
EQCombin <<- NA
CBCombin <<- NA
EQCompProdSnapshot <<- NA
CBCompProdSnapshot <<- NA
EQportmap <<- NA
CBportmap <<- NA
eq_market <<- NA
cb_market <<- NA
has_equity <<- FALSE
has_debt <<- FALSE
HasCarbonBudget <<- FALSE
RiskToPlot <<- NA
PlotChart <<- TRUE
chart_type <<- ""
tech_to_plot <<- ""
sector_to_plot <<- ""
BV.asset_type <<- ""
HasRenCapCB <<- F
HasRenCapEQ <<- F
HasCoalCapCB <<- F
HasCoalCapEQ <<- F
HasPower <<- FALSE
HasAuto <<- FALSE
HasOG <<- FALSE
HasCoal <<- FALSE
HasPowerCB <<- FALSE
HasAutoCB <<- FALSE
HasOGCB <<- FALSE
HasCoalCB <<- FALSE
HasPowerEQ <<- FALSE
HasAutoEQ <<- FALSE
HasOGEQ <<- FALSE
HasCoalEQ <<- FALSE
SovBondCov <<- NA
}
define_peers <- function() {
if (twodii_internal) {
if (file.exists(paste0(results_path, "/Equity_results_portfolio.rda"))) {
eq_peers <<- read_rds(paste0(results_path, "/Equity_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
} else {
warning("No Equity Peers File available. Setting inc_meta_portfolio to true MAY solve this. Alternatively there is no equity in any portfolio.")
}
if (file.exists(paste0(results_path, "/Bonds_results_portfolio.rda"))) {
cb_peers <<- read_rds(paste0(results_path, "/Bonds_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
} else {
warning("No bonds peers file available. Setting inc_meta_portfolio to true MAY solve this. Alternatively there are no bonds in any portfolio.")
}
} else {
if (project_name == "web_tool") {
eq_peers <<- NA
cb_peers <<- NA
} else {
eq_peers <<- read_rds(paste0(project_location_ext, "/", project_name, "/40_Results/Equity_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
cb_peers <<- read_rds(paste0(project_location_ext, "/", project_name, "/40_Results/Bonds_results_portfolio.rda")) %>%
filter(investor_name == meta_investor_name)
}
}
}
define_benchmarks <- function() {
if (twodii_internal) {
eq_market <<- read_rds(paste0(portcheck_v2_path, "/10_Projects/INDEX_2019/40_Results/Equity_results_portfolio.rda")) %>%
filter(portfolio_name == eq_market_ref)
cb_market <<- read_rds(paste0(portcheck_v2_path, "/10_Projects/INDEX_2019/40_Results/Bonds_results_portfolio.rda")) %>%
filter(portfolio_name == cb_market_ref)
} else {
# Set per project
eq_market <<- read_rds(paste0(data_location_ext, "/Fake_Index/Equity_results_portfolio.rda"))
cb_market <<- read_rds(paste0(data_location_ext, "/Fake_Index/Bonds_results_portfolio.rda"))
}
if (!start_year %in% eq_market$year) {
stop(paste0("Check that start years align. EQ Market Start year = ", min(eq_market$year), ". Report start year = ", start_year))
}
if (!start_year %in% cb_market$year) {
stop(paste0("Check that start years align. CB Market Start year = ", min(cb_market$year), ". Report start year = ", start_year))
}
}
results_call <- function() {
# Filtering Market should already happen here.
subgroup_overview <<- portfolio_overview[portfolio_overview$investor_name == investor_name_select & portfolio_overview$portfolio_name == portfolio_name_select & portfolio_overview$valid_input == TRUE, ]
define_benchmarks()
define_peers()
# RenewableAdditions <<- readRDS(paste0(analysis_inputs_path, "/RenewablesAdditionsData.rda"))
# CoalRetirements <<- readRDS(paste0(analysis_inputs_path, "/CoalRetirementsData.rda"))
# CarbonData <<- readRDS(paste0(analysis_inputs_path, "/CarbonCapexUpstream.rda"))
if (has_equity) {
EQCompProdSnapshot <<- read_rds(paste0(results_path, "/", investor_name_select, "/Equity_results_company.rda"))
EQCompProdSnapshot <<- EQCompProdSnapshot[EQCompProdSnapshot$portfolio_name == portfolio_name_select, ]
if (has_map) {
if (file.exists(paste0(results_path, "/", investor_name_select, "/Equity_results_map.rda"))) {
EQportmap <- readRDS(paste0(results_path, "/", investor_name_select, "/Equity_results_map.rda"))
EQportmap <- EQportmap %>% filter(portfolio_name == portfolio_name_select)
}
}
if (data_check(EQportmap)) {
EQportmap$scenario_geography <- ifelse(EQportmap$ald_sector == "Power", "GlobalAggregate", "Global")
}
EQportmap <<- EQportmap
EQTechData <<- read_rds(paste0(analysis_inputs_path, "/masterdata_ownership_datastore_technology_type_view.rda"))
EQOilShareData <<- EQTechData %>%
select(id, ald_sector, technology, technology_type, year, ald_production, ald_production_unit) %>%
filter(
year %in% seq(start_year, start_year + 5, 1),
ald_sector == "Oil&Gas"
) %>%
mutate(technology = if_else(technology == "Oil and Condensate", "Oil", technology))
# EQOilShareData <<- readRDS(paste0(analysis_inputs_path, "oil_and_gas_resource_type_rollup_ownership.rda"))
# EQOilShareData <<- readRDS(paste0(Location, "ReferenceData/oil_and_gas_resource_type_rollup_ownership.rda"))
}
if (has_debt == TRUE) {
CBCompProdSnapshot <<- readRDS(paste0(results_path, "/", investor_name_select, "/Bonds_results_company.rda"))
CBCompProdSnapshot <<- CBCompProdSnapshot[CBCompProdSnapshot$portfolio_name == portfolio_name_select, ]
if (has_map) {
if (file.exists(paste0(results_path, "/", investor_name_select, "/Bonds_results_map.rda"))) {
CBportmap <<- readRDS(paste0(results_path, "/", investor_name_select, "/Bonds_results_map.rda"))
CBportmap <<- CBportmap %>% filter(portfolio_name == portfolio_name_select)
}
if (data_check(CBportmap)) {
CBportmap$scenario_geography <- ifelse(CBportmap$ald_sector == "Power", "GlobalAggregate", "Global")
}
CBportmap <<- CBportmap
}
CBTechData <<- read_rds(paste0(analysis_inputs_path, "/masterdata_debt_datastore_technology_type_view.rda"))
CBOilShareData <<- CBTechData %>%
select(id, ald_sector, technology, technology_type, year, ald_production, ald_production_unit) %>%
filter(
year %in% seq(start_year, start_year + 5, 1),
ald_sector == "Oil&Gas"
) %>%
mutate(technology = if_else(technology == "Oil and Condensate", "Oil", technology))
}
if (has_sb) {
SB.Summary <<- read.csv(paste0(PROC.INPUT.PATH, "SovereignBondSummary.csv"), strip.white = T, stringsAsFactors = F)
}
if (inc_sda_approach) {
if (data_check(EQCombin)) {
max_market <- max(eq_market$year, na.rm = T)
max_year_data <- max(EQCombin$year, na.rm = T)
if (max_year_data == max_market) {
EQCombin <- sda_portfolio_target(eq_market, EQCombin,
start_year = start_year,
target_year = max_year_data
)
}
}
if (data_check(CBCombin)) {
max_market <- max(cb_market$year, na.rm = T)
max_year_data <- max(CBCombin$year, na.rm = T)
if (max_year_data == max_market) {
CBCombin <- sda_portfolio_target(cb_market, CBCombin,
start_year = start_year,
target_year = max_year_data
)
}
}
}
}
filter_by_parameters <- function(df, dfType, byscenario = TRUE, scenario.irrelevant = FALSE, by_equity_market = TRUE, ActorSectorOnly = FALSE, BV.asset_type = "") {
# This Df type should allow us to clear a few things up
if (data_check(df) == FALSE) {
print("No Results")
PlotChart <<- FALSE
} else {
PlotChart <<- TRUE
if ("asset_type" %in% colnames(df) & BV.asset_type != "") {
df <- df %>% filter(asset_type %in% BV.asset_type)
if (data_check(df) == FALSE) {
print("BV.asset_type Filter removed All Data")
}
}
if (tech_to_plot != "" & "technology" %in% colnames(df)) {
if (GraphType %in% c("CurrentTechnologyExposure", "FutureTechnologyExposure") & sector_to_plot %in% c("Oil&Gas", "Coal")) {
df <- df %>% filter(technology %in% c("Oil", "Gas", "Coal"))
if (data_check(df) == FALSE) {
print("Techology Filter removed All Data")
}
} else {
df <- df %>% filter(technology == tech_to_plot)
}
if (data_check(df) == FALSE) {
print("Techology Filter removed All Data")
}
}
if (RiskToPlot != "" & "RiskType" %in% colnames(df)) {
df <- df %>% filter(RiskType == RiskToPlot)
if (data_check(df) == FALSE) {
print("Risk Filter removed All Data")
}
}
# if (EquityMarketSelection != "" & "equity_market" %in% colnames(df) & by_equity_market == TRUE & data_check(df) == TRUE){df <- df %>% filter(equity_market == EquityMarketSelection)
if (EquityMarketSelection != "" & "equity_market" %in% colnames(df) & by_equity_market == TRUE & data_check(df) == TRUE) {
df <- df %>% filter(equity_market == EquityMarketSelection)
if (data_check(df) == FALSE) {
print("Equity Market Filter removed All Data")
}
}
if (!AccountingPrinciple %in% c("", "Mixed") & "allocation" %in% colnames(df) & data_check(df) == TRUE) { # AccountingPrinciple %in% df$allocation &
df <- df %>% filter(allocation == AccountingPrinciple)
if (data_check(df) == FALSE) {
print("Accounting Principle Filter removed All Data")
}
}
if (AccountingPrinciple == "Mixed" & "allocation" %in% colnames(df) & data_check(df) == TRUE) {
if (dfType == "EQ") {
df <- df %>% filter(allocation == "ownership_weight")
} else if (dfType == "CB") {
df <- df %>% filter(allocation == "portfolio_weight")
}
if (data_check(df) == FALSE) {
print("Accounting Principle Filter removed All Data")
}
}
if (ScenarioGeographyChoose != "" & "scenario_geography" %in% colnames(df) & data_check(df) == TRUE) {
if (ScenarioGeographyChoose == "GlobalAggregate") {
df1 <- df %>% filter(ald_sector == "Power", scenario_geography == "GlobalAggregate")
if (!data_check(df1)) {
df1 <- df %>% filter(ald_sector == "Power", scenario_geography == "Global")
}
df2 <- df %>% filter(ald_sector != "Power", scenario_geography == "Global")
df <- rbind(df1, df2)
} else {
df <- df %>% filter(scenario_geography == ScenarioGeographyChoose)
}
if (data_check(df) == FALSE) {
print("scenario_geography Filter removed All Data")
}
}
if (!sector_to_plot %in% c("", "All") & data_check(df) == TRUE) {
if (GraphType %in% c("CurrentTechnologyExposure", "FutureTechnologyExposure") & sector_to_plot %in% c("Oil&Gas", "Coal")) {
df <- df %>% filter(ald_sector %in% c("Oil&Gas", "Coal"))
} else {
df <- df %>% filter(ald_sector == sector_to_plot)
}
if (ActorSectorOnly == TRUE & !sector_to_plot %in% c("", "All") & data_check(df) == TRUE) {
df <- df %>% filter(financial_sector == sector_to_plot)
}
if (data_check(df) == FALSE) {
print("ald_sector Filter removed All Data")
}
}
# if (AccountingPrinciple != "" & "allocation" %in% colnames(df) & AccountingPrinciple %in% df$allocation & data_check(df) == TRUE){df <- df %>% filter(allocation == AccountingPrinciple)
# if (data_check(df) == FALSE){print("Accounting Principle Filter removed All Data")}}
if (byscenario == TRUE & Scenariochoose != "" & "scenario" %in% colnames(df) & Scenariochoose %in% df$scenario & data_check(df) == TRUE) {
df <- df %>% filter(scenario == Scenariochoose)
if (data_check(df) == FALSE) {
print("scenario Filter removed All Data")
}
}
if (byscenario == TRUE & scenario.irrelevant == TRUE & "scenario" %in% colnames(df) & data_check(df) == TRUE) {
# df$scenario <- NULL
df <- df[df$scenario == unique(df$scenario)[1], ]
df$scenario <- "NA"
if (data_check(df) == FALSE) {
print("scenario Filter removed All Data")
}
}
if (PeerGroupSelection != "" & "investor_name" %in% colnames(df) & PeerGroupSelection %in% df$investor_name & data_check(df) == TRUE) {
df <- df %>% filter(investor_name == PeerGroupSelection)
if (data_check(df) == FALSE) {
print("Peer Group Filter removed All Data")
}
}
if (GraphType %in% c("FutureTechnologyExposure", "Map")) {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year == start_year + 5)
}
} else if (GraphType %in% c("CurrentTechnologyExposure")) {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year == start_year)
}
}
if (GraphType %in% "CompanyInformation") {
if (sector_to_plot %in% c("Oil&Gas", "CoalMining")) {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year %in% c(start_year, start_year + 5))
}
} else {
if ("year" %in% colnames(df)) {
df <- df %>% filter(year == start_year + 5)
}
}
}
if (GraphType %in% c("PeerComparison")) {
if (PeerGroupSelection != "" & "portfolio_name" %in% colnames(df) & "PeerGroup" %in% colnames(df) & data_check(df) == TRUE) {
df <- df %>% filter(portfolio_name != "MetaPort")
if (data_check(df) == FALSE) {
print("Peer Group Sub-Filter removed All Data")
}
}
} else {
if (PeerGroupSelection != "" & "portfolio_name" %in% colnames(df) & "PeerGroup" %in% colnames(df) & data_check(df) == TRUE) {
df <- df %>% filter(portfolio_name == "MetaPort")
if (data_check(df) == FALSE) {
print("Peer Group Sub-Filter removed All Data")
}
}
}
if (data_check(df) == FALSE) {
PlotChart <<- FALSE
}
}
return(df)
} # finalize and adjust for future & current exposure
create_test_list <- function() {
# ID.COLS <- c("PortName","investor_name_select","Type") # removed Type as this is not existing
ID.COLS <- c("investor_name", "portfolio_name")
ID.COLS.LONG <- c("investor_name", "portfolio_name", "year", "allocation", "ald_sector", "technology", "scenario_geography")
# test_list <- NA
ald_sectors <- c("Automotive", "Coal", "OilGas", "Power")
if (file.exists(paste0(results_path, "/", investor_name_select, "/Equity_results_portfolio.rda"))) {
EQCombin <<- read_rds(paste0(results_path, "/", investor_name_select, "/Equity_results_portfolio.rda"))
EQCombin <<- EQCombin %>% filter(portfolio_name == portfolio_name_select)
# EQCombin
}
if (file.exists(paste0(results_path, "/", investor_name_select, "/Bonds_results_portfolio.rda"))) {
CBCombin <<- read_rds(paste0(results_path, "/", investor_name_select, "/Bonds_results_portfolio.rda"))
CBCombin <<- CBCombin %>% filter(portfolio_name == portfolio_name_select)
}
if (data_check(EQCombin) == T) {
# if (sector_to_plot %in% c("Shipping"))
Equity <- filter_by_parameters(EQCombin, "EQ", ActorSectorOnly = T) %>%
select(investor_name, portfolio_name, year, scenario, allocation, ald_sector, technology, scenario_geography, equity_market, plan_tech_prod) %>%
group_by(investor_name, portfolio_name, scenario_geography, allocation, equity_market, year, ald_sector, technology) %>%
summarise(Production = sum(plan_tech_prod, na.rm = T))
EquityTech <- Equity %>%
ungroup() %>%
select(investor_name, portfolio_name, technology, Production) %>%
group_by(investor_name, portfolio_name, technology) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
Equity <- Equity %>%
ungroup() %>%
select(investor_name, portfolio_name, ald_sector, Production) %>%
group_by(investor_name, portfolio_name, ald_sector) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
if (nrow(Equity) > 0) {
Equity$ald_sector <- ifelse(Equity$ald_sector == "Oil&Gas", "OilGas", Equity$ald_sector)
Equity$Indicator <- ifelse(Equity$ald_sectorProd > 0, TRUE, FALSE)
Equity$ald_sectorProd <- NULL
Equity$ald_sector <- paste0(Equity$ald_sector, ".EQ")
Equity <- Equity %>% spread(value = Indicator, key = ald_sector)
Equity <<- as.data.frame(Equity)
HasCoalCapEQ <<- "CoalCap" %in% EquityTech$technology
HasRenCapEQ <<- "RenewableCap" %in% EquityTech$technology
has_equity <<- TRUE
} else {
has_equity <<- FALSE
}
}
if (all(data_check(CBCombin) == T, chart_type %in% c("CB", "") | GraphType == "PercentageOfPortfolioAssessed")) {
Debt <- filter_by_parameters(CBCombin, "CB", by_equity_market = F, ActorSectorOnly = T) %>%
select(investor_name, portfolio_name, year, scenario, allocation, ald_sector, technology, scenario_geography, equity_market, plan_tech_prod) %>%
# filter(year == start_year) %>%
group_by(investor_name, portfolio_name, scenario_geography, allocation, equity_market, year, ald_sector, technology) %>%
summarise(Production = sum(plan_tech_prod, na.rm = T))
DebtTech <- Debt %>%
ungroup() %>%
select(investor_name, portfolio_name, technology, Production) %>%
group_by(investor_name, portfolio_name, technology) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
Debt <- Debt %>%
ungroup() %>%
select(investor_name, portfolio_name, ald_sector, Production) %>%
group_by(investor_name, portfolio_name, ald_sector) %>%
summarise(ald_sectorProd = sum(Production, na.rm = T))
Debt$ald_sector <- ifelse(Debt$ald_sector == "Oil&Gas", "OilGas", Debt$ald_sector)
if (nrow(Debt) > 0) {
Debt$Indicator <- ifelse(Debt$ald_sectorProd > 0, TRUE, FALSE)
Debt$ald_sectorProd <- NULL
Debt$ald_sector <- paste0(Debt$ald_sector, ".CB")
Debt <- Debt %>% spread(value = Indicator, key = ald_sector)
Debt <<- as.data.frame(Debt)
HasCoalCapCB <<- "CoalCap" %in% DebtTech$technology
HasRenCapCB <<- "RenewableCap" %in% DebtTech$technology
# Debt$Indicator <<- ifelse(Debt$ > 0,1,0)
has_debt <<- TRUE
} else {
has_debt <<- FALSE
}
}
# missingcols <- rbind(paste0(ald_sectors,".EQ"),paste0(ald_sectors,".CB"))
if (has_debt == TRUE & has_equity == TRUE) {
test_list <- base::merge(Equity, Debt, by = ID.COLS)
test_list[is.na(test_list)] <- FALSE
colnames(test_list) <- gsub("\\.x", ".EQ", colnames(test_list))
colnames(test_list) <- gsub("\\.y", ".CB", colnames(test_list))
} else if (has_debt == FALSE & has_equity == TRUE) {
test_list <- Equity
# test_list$has_debt <- FALSE
} else if (has_debt == TRUE & has_equity == FALSE) {
test_list <- Debt
# test_list$has_equity <- FALSE
} else {
test_list <- NA
}
eqcols <- paste0(sector_list, ".EQ")
cbcols <- paste0(sector_list, ".CB")
missingeqcols <- setdiff(eqcols, colnames(test_list))
missingcbcols <- setdiff(cbcols, colnames(test_list))
test_list[missingeqcols] <- FALSE
test_list[missingcbcols] <- FALSE
if (is.data.frame(test_list)) {
test_list <- test_list %>%
filter(portfolio_name == portfolio_name_select)
}
return(test_list)
}
define_test_variables <- function(test_list) {
PortSummary <<- test_list[1, ]
# TestType <<- test_list[1,"Type"]
portfolio_name_selectLong <<- test_list[1, "portfolio_name_select"]
investor_name_selectLong <<- test_list[1, "investor_name_select"]
investor_name_select <<- gsub(" ", "", test_list[1, "investor_name_select"])
portfolio_name_select <<- gsub("[[:punct:]]", "", test_list[1, "portfolio_name"])
# portfolio_name_select <<- gsub(" ", "", test_list[1,"PortName"]) # KH: Adjusted this as i assume it wasn?t planned to overwrite portfolio_name_select
portfolio_name_select <<- gsub(" ", "", portfolio_name_select)
has_equity <<- test_list[1, "has_equity"]
has_debt <<- test_list[1, "has_debt"]
}
graph_values <- function() {
textfont <<- "Calibri"
windowsFonts(Calibri = windowsFont(textfont))
# orangeish
RenewablesColour <<- "#feedde"
HydroColour <<- "#fdbe85"
NuclearColour <<- "#fd8d3c"
GasCapColour <<- "#e6550d"
CoalCapColour <<- "#a63603"
# purpleish
ElectricColour <<- "#efedf5"
HybridColour <<- "#bcbddc"
ICEColour <<- "#756bb1"
# goldish
GasProdColour <<- "#D9DDD4" # "#F5F5F5" #D9DDD4
OilProdColour <<- "#BEBCAE" # "#BEA07B" #BEBCAE
CoalProdColour <<- "#8B7E66" # "#8C510A" #8B7E66
# blueish
FreightColor <<- "#6ba7e3"
PassengerColor <<- "#d6e7f7"
#
GColor <<- "#D73027"
FColor <<- "#FC8D59"
EColor <<- "#FEE08B"
DColor <<- "#FFFFBF"
CColor <<- "#D9EF8B"
Bcolor <<- "#91CF60"
AColor <<- "#1A9850"
# map
MAXRenewablesCap <<- "#a75105"
MAXHydroCap <<- "#9b4b03"
MAXNuclearCap <<- "#6b2e01"
MAXGasCap <<- "#260e02"
MAXCoalCap <<- "#290e01"
MAXElectric <<- "#6b5aa3"
MAXHybrid <<- "#464987"
MAXICE <<- "#3d3667"
MAXGas <<- "#737f65"
MAXOil <<- "#595647"
MAXCoal <<- "#423b30"
MINRenewablesCap <<- "#fde0c5"
MINHydroCap <<- "#fdcb9e"
MINNuclearCap <<- "#feb998"
MINGasCap <<- "#f5834b"
MINCoalCap <<- "#d84604"
MINElectric <<- "#e0ddec"
MINHybrid <<- "#bcbddc"
MINICE <<- "#a49dcc"
MINGas <<- "#D9DDD4"
MINOil <<- "#d5d3ca"
MINCoal <<- "#b9af9e"
# sector
energy <<- "#8B7E66"
pow <<- "#a63603"
trans <<- "#756bb1"
othr <<- "#9793c6"
notrelevant <<- "grey"
shipping <<- "#0F1e82" #
material <<- "#e46c00" #
# trajectory
area_6 <<- "#e07b73"
area_4_6 <<- "#fde291"
area_2_4 <<- "#FFFFCC"
area_2 <<- "#c3d69b"
area_1 <<- "#9cab7c"
eq_port <<- "#1056ff"
peer_group <<- "black"
eq_line <<- "#a63603" # same as CoalCapColor
cb_line <<- "#756bb1" # same as ICEColour
bv_line <<- "#265B9B"
# side-by-side techshare
Port <<- "#265B9B"
Benchmark <<- "#D9D9D9"
# moodys
HighRisk <<- area_6
MedRisk <<- area_4_6
LowRisk <<- area_2_4
# physical risk
pal_stacked_riskWS <<- c("#D73027", area_6, area_2, area_2_4) # water stress charts
pal_stacked_risks <<- c(area_6, area_2_4, area_2) # flood and wildfire charts
# text size
textsize <<- 8.5
textcolor <<- "#3D3D3C"
AxisColour <<- "#3D3D3C"
YourportColour <<- "#265b9b" # "#2e4b6e" #"#17224D"
IndexColour <<- "grey85"
Tar2DColourBar <<- "#b3de69"
Tar2DColour <<- "#a1c75e"
goodexpColour <<- "#1F891F"
badexpColour <<- "#ed1c24" # "#fb8072"
ReqCapColour <<- "grey55"
CurrCapColour <<- "grey75"
technology_order <<- c(
"Gas", "Oil", "Coal",
"RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap",
"Electric", "Hybrid", "ICE",
"Freight", "Passenger",
"A", "B", "C", "D", "E", "F", "G"
)
sector_order_short <<- c("FossilFuels", "Power", "Automotive")
technology_order_short <<- c(
"Gas", "Oil", "Coal",
"RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap",
"Electric", "Hybrid", "ICE"
)
goodtechs <<- c("RenewablesCap", "HydroCap", "NuclearCap", "Electric", "Hybrid")
badtechs <<- c("GasCap", "CoalCap", "ICE", "Gas", "Oil", "Coal")
ColourPalette <<- data.frame(
ald_sector = c(
"Fossil Fuels", "Fossil Fuels", "Fossil Fuels",
"Power", "Power", "Power", "Power", "Power",
"Automotive", "Automotive", "Automotive",
"Aviation", "Aviation",
"Shipping", "Shipping", "Shipping", "Shipping", "Shipping", "Shipping", "Shipping"
),
technology = technology_order,
Colours = c(
GasProdColour, OilProdColour, CoalProdColour,
RenewablesColour, HydroColour, NuclearColour, GasCapColour, CoalCapColour,
ElectricColour, HybridColour, ICEColour,
FreightColor, PassengerColor,
AColor, Bcolor, CColor, DColor, EColor, FColor, GColor
)
)
ColourPalette$technology <- factor(ColourPalette$technology, levels = technology_order)
ColourPalette <- arrange(ColourPalette, technology)
linesize <<- 1
ppi <<- 600
}
graph_name <- function(plotnumber, ParameterFile, explicit_filename = "") {
namelist <- ParameterFile[, !names(ParameterFile) %in% c("portfolio_name_select", "investor_name_select")]
namelist[is.na(namelist)] <- ""
namelist <- lapply(1:length(namelist), function(x) paste0(toupper(substr(as.character(namelist[[x]]), 1, 3)), "_"))
namelist <- namelist[1:6]
namelist <- c(namelist, start_year)
graphname <- do.call(paste0, namelist)
graphname <- paste0(graphname, ".png")
if (plotnumber == "00") {
graphname <- gsub(".png", "", graphname)
} else if (plotnumber != "99") {
graphname <- paste0(report_path, plotnumber, "_", explicit_filename, graphname)
} else {
graphname <- paste0(GRAPH.PATH, graphname)
}
return(graphname)
}
# FIXME: There is another green_brown() in the repo create_interactive_report and
# they are not identical. Which one should win?
green_brown <- function(Tech) {
GreenTechs <- c("Electric", "Hybrid", "RenewablesCap", "HydroCap", "NuclearCap")
stopifnot(length(Tech) == 1L)
if (Tech %in% GreenTechs) {
TechIs <- "green"
} else {
TechIs <- "brown"
}
return(TechIs)
}
set_ald_sector <- function(Tech) {
ald_sector <- NA
ald_sector <- ifelse(grepl("Cap", Tech), "Power", ald_sector)
ald_sector <- ifelse(Tech %in% c("Electric", "ICE", "Hybrid", "FuelCell"), "Automotive", ald_sector)
ald_sector <- ifelse(Tech %in% c("Oil", "Gas"), "Oil&Gas", ald_sector)
ald_sector <- ifelse(Tech == "Coal", "Coal", ald_sector)
ald_sector
}
translate_labels <- function(Language) {
Translations <- read_excel(paste0("data/TranslationFileUTF8.xlsx"))
Translations <- Translations %>%
select(TextLabel, all_of(Language)) %>%
as.data.frame()
for (t in 1:nrow(Translations)) {
assign(x = Translations[t, "TextLabel"], value = Translations[t, Language], envir = .GlobalEnv)
}
}
ReportFigures <- function(explicit_filenames = F) {
explicit_filename <- ""
# Introduction
if (!explicit_filenames == F) {
explicit_filename <- "_scope_of_analysis_"
}
ScopeOfAnalysis("01", explicit_filename = explicit_filename) #
if (!explicit_filenames == F) {
explicit_filename <- "_climate_relevant_sectors_"
}
PercentageOfPortfolioAssessed("02", explicit_filename = explicit_filename) #
ScenarioGeographyChoose <- "Global"
if (!explicit_filenames == F) {
explicit_filename <- "_current_exposure_bonds_"
}
CarstenMetricChart("03", "CB", explicit_filename = explicit_filename) #
if (!explicit_filenames == F) {
explicit_filename <- "_current_expousre_equity_"
}
CarstenMetricChart("04", "EQ", explicit_filename = explicit_filename) #
ScenarioGeographyChoose <- "GlobalAggregate"
if (!explicit_filenames == F) {
explicit_filename <- "_future_tech_mix_bonds_"
}
TechnologyExposure("05", "CB", "All", start_year + 5, explicit_filename = explicit_filename) #
if (has_debt) {
# 5 year Trajectory
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_coal_bonds_"
}
FiveYearGrowthTrend("07", "CB", "CoalCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_renewables_bonds_"
}
FiveYearGrowthTrend("08", "CB", "RenewablesCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_coal_bonds_"
}
FiveYearGrowthTrend("09", "CB", "GasCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_hydro_bonds_"
}
FiveYearGrowthTrend("10", "CB", "HydroCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_oil_bonds_"
}
FiveYearGrowthTrend("11", "CB", "Oil", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_gas_bonds_"
}
FiveYearGrowthTrend("12", "CB", "Gas", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_coal_bonds_"
}
FiveYearGrowthTrend("13", "CB", "Coal", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_ice_bonds_"
}
FiveYearGrowthTrend("14", "CB", "ICE", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_electric_bonds_"
}
FiveYearGrowthTrend("15", "CB", "Electric", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_hybrid_bonds_"
}
FiveYearGrowthTrend("16", "CB", "Hybrid", LegendOn = F, explicit_filename = explicit_filename)
}
if (!explicit_filenames == F) {
explicit_filename <- "_future_tech_mix_equity_"
}
TechnologyExposure("06", "EQ", "All", start_year + 5, explicit_filename = explicit_filename) #
if (has_equity) {
# 5 year Trajectory
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_coal_equity_"
}
FiveYearGrowthTrend("17", "EQ", "CoalCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_renewables_equity_"
}
FiveYearGrowthTrend("18", "EQ", "RenewablesCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_hydro_equity_"
}
FiveYearGrowthTrend("19", "EQ", "GasCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_power_hydro_equity_"
}
FiveYearGrowthTrend("20", "EQ", "HydroCap", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_oil_equity_"
}
FiveYearGrowthTrend("21", "EQ", "Oil", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_gas_equity_"
}
FiveYearGrowthTrend("22", "EQ", "Gas", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_fossil_coal_equity_"
}
FiveYearGrowthTrend("23", "EQ", "Coal", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_ice_equity_"
}
FiveYearGrowthTrend("24", "EQ", "ICE", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_electric_equity_"
}
FiveYearGrowthTrend("25", "EQ", "Electric", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_alignment_auto_hybrid_equity_"
}
FiveYearGrowthTrend("26", "EQ", "Hybrid", LegendOn = F, explicit_filename = explicit_filename)
}
if (IncOtherSectors == T) {
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_cement_"
}
CO2IntensityTrend("30", "Cement", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_steel_"
}
CO2IntensityTrend("31", "Steel", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_aviation_"
}
CO2IntensityTrend("32", "Aviation", LegendOn = F, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_emissions_shipping_"
}
ShippingChart("33", "All", start_year + 5, explicit_filename = explicit_filename) # All
}
if (has_map) {
if (!explicit_filenames == F) {
explicit_filename <- "_map_coal_bonds_"
}
MapChart("49", "CB", "Coal", start_year + 5, explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_map_coal_equity_"
}
MapChart("50", "EQ", "Coal", start_year + 5, explicit_filename = explicit_filename)
}
if (has_debt) {
if (!explicit_filenames == F) {
explicit_filename <- "_companies_power_bonds_"
}
CompanyInformation("40", no_companies, "CB", "Power", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_auto_bonds_"
}
CompanyInformation("41", no_companies, "CB", "Automotive", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_built_out_bonds_"
}
OilGasBuildOut("42", no_companies, "CB", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_share_bonds_"
}
OilShare("43", no_companies, "CB", explicit_filename = explicit_filename)
# if (IncCoalRetirement){
# CoalRetirementChart("62",no_companies,"CB")
# RenewableAdditionsChart("64",no_companies,"CB")
# }
}
if (has_equity) {
if (IncPeersChart) {
PeerComparison(81, chart_type = "EQ")
}
if (!explicit_filenames == F) {
explicit_filename <- "_companies_power_equity_"
}
CompanyInformation("44", no_companies, "EQ", "Power", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_auto_equity_"
}
CompanyInformation("45", no_companies, "EQ", "Automotive", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_built_out_equity_"
}
OilGasBuildOut("46", no_companies, "EQ", explicit_filename = explicit_filename)
if (!explicit_filenames == F) {
explicit_filename <- "_companies_oil_share_equity_"
}
OilShare("47", no_companies, "EQ", explicit_filename = explicit_filename)
# CarbonBudget("48", no_companies, "EQ")
# if (IncCoalRetirement){
# CoalRetirementChart("63",no_companies,"EQ")
# RenewableAdditionsChart("65",no_companies,"EQ")
# }
}
}
##############
### Charts ###
##############
PercentageOfPortfolioAssessed <- function(plotnumber, explicit_filename = "") {
over <- SectorDataAnalysis()
over <- over[over$financial_sector != "Not Included", ]
# over$financial_sector <- factor(over$financial_sector, levels = c("Fossil Fuels","Power", "Automotive",
# "Cement & Steel","Aviation & Shipping"), ordered=TRUE)
over <- as.data.frame(over)
if (data_check(over)) {
over$financial_sector <- if_else(over$financial_sector == "Cement&Steel", "Cement & Steel", over$financial_sector)
over <- over %>% filter(!is.na(financial_sector))
## "steelblue" color below should be changed to whatever our Portfolio color is
over1 <- subset(over, portfolio_name == portfolio_name_select & asset_type %in% c("Equity", "Bonds"))
over1 <- over1 %>%
group_by(investor_name, portfolio_name, asset_type, financial_sector) %>%
summarise(ValueUSD = sum(valid_value_usd))
over1$asset_type <- gsub("Bonds", BondReference, over1$asset_type)
over1$asset_type <- gsub("Equity", "Equity", over1$asset_type)
over1$asset_type <- factor(over1$asset_type, levels = c("Equity", BondReference))
# if(BondReference == "Corporate bond") {
over1$asset_type <- dplyr::recode(over1$asset_type, "Bond" = wrap_labels(BondsTitle, 10), Equity = EquityTitle) #
# } else {
# over1$asset_type <- dplyr::recode(over1$asset_type, "Other Title" = " ", Equity = EquityTitle) #
# }
over1$financial_sector <- factor(over1$financial_sector, levels = c(
"Aviation & Shipping", "Cement & Steel",
"Automotive", "Power", "Fossil Fuels"
), ordered = TRUE)
over1$financial_sector <- dplyr::recode(over1$financial_sector,
"Fossil Fuels" = wrap_labels(S_FossilFuels, 15),
"Power" = wrap_labels(S_Power, 15),
"Automotive" = wrap_labels(S_Automotive, 15), #
"Cement & Steel" = wrap_labels(S_CementSteel, 15),
"Aviation & Shipping" = wrap_labels(S_AviationShipping, 15)
) #
over_horizontal_perc <- over1 %>%
group_by(asset_type) %>%
mutate(percfinancial_sector = ValueUSD / sum(ValueUSD))
# ymax<-max(aggregate(over1["ValueUSD"],by=over1["asset_type"],FUN=sum)$ValueUSD)
## version horizontal percentage
plot <- ggplot(over_horizontal_perc, aes(x = asset_type, y = percfinancial_sector, fill = financial_sector)) +
geom_bar(position = "stack", stat = "identity", width = 0.9) +
coord_flip() +
scale_fill_manual(name = "", values = c(shipping, pow, trans, material, energy), drop = FALSE) + #
# scale_x_discrete(name="Asset Type",drop=F, labels=c(BondsTitle, EquityTitle)) + #
scale_y_continuous(name = SectorShare, labels = percent, expand = c(0, 0)) +
guides(fill = guide_legend(nrow = 2, reverse = TRUE)) +
theme_barcharts() +
theme(
plot.title = element_text(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(size = textsize),
legend.spacing.y = unit(-22, "pt"),
legend.box.margin = unit(c(-5, 40, -10, 10), "pt"),
axis.line.x = element_blank(),
axis.line.y = element_line(size = 0.5),
axis.text.x = element_text(colour = textcolor, size = 11),
axis.text.y = element_text(colour = textcolor, size = 11),
axis.title.x = element_text(size = 11, margin = margin(3, 0, 0, 0, "pt")),
axis.title.y = element_blank(),
panel.grid.major.x = element_line(colour = "#989898", size = 0.2),
plot.margin = unit(c(-2, 20, 10, 2), "pt")
) +
ggtitle("\n")
} else {
label <- Therearenoclimaterelevantequityorbondholdingsintheportfolios #
plot <- no_chart(label)
}
ggsave(plot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 2.3, width = 4.5, dpi = ppi) # linewidth_in*.9
}
TechnologyExposure <- function(plotnumber, chart_type, sector_to_plot, plot_year, explicit_filename = "") { # PlotYr
Combin <- data.frame()
if (chart_type == "EQ" & has_equity == TRUE) {
Peers <- filter_by_parameters(eq_peers, "EQ")
Market <- filter_by_parameters(eq_market, "EQ")
Combin <- filter_by_parameters(EQCombin, "EQ")
} else if (chart_type == "CB" & has_debt == TRUE) {
Peers <- filter_by_parameters(cb_peers, "CB", by_equity_market = FALSE)
Market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
Combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
}
if (data_check(Combin) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
# print("format and bind data")
# Remove all portfolios other than Market, Average
Peers$Type <- "Peers"
Peers <- unique(Peers)
Peers <- Peers %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "Type", "plan_alloc_wt_tech_prod") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
Peers <- Peers %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Peers")
)
Peer_A <- subset(Peers, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Peer_P <- subset(Peers, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Peer_F <- subset(Peers, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Peers <- bind_rows(Peer_F, Peer_A, Peer_P)
if (plot_year == start_year + 5) {
TARGET <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>%
select("portfolio_name", "ald_sector", "technology", "scen_alloc_wt_tech_prod") %>% # "Type"
rename(WtProduction = scen_alloc_wt_tech_prod)
TARGET$Type <- "Target"
TARGET <- TARGET %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Target")
)
TARGET_A <- subset(TARGET, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
TARGET_P <- subset(TARGET, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
TARGET_F <- subset(TARGET, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
TARGET <- bind_rows(TARGET_F, TARGET_A, TARGET_P)
}
Combin$Type <- "Portfolio"
Production <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
Market$Type <- "Benchmark"
Market <- Market %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr & Type =="Portfolio") %>%
select("portfolio_name", "ald_sector", "technology", "scen_alloc_wt_tech_prod", "Type") %>% # "Type"
rename(WtProduction = scen_alloc_wt_tech_prod)
Market <- Market %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Benchmark")
)
Market_A <- subset(Market, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Market_P <- subset(Market, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Market_F <- subset(Market, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Market <- bind_rows(Market_F, Market_A, Market_P)
# Filter and select
# print("Filter and select data")
if (plot_year == start_year + 5) {
Production <- bind_rows(Production, Peers, TARGET, Market)
} else if (plot_year == start_year) {
Production <- bind_rows(Production, Peers, Market)
}
Production$ald_sector <- factor(Production$ald_sector, levels = c("Oil&Gas", "Coal", "Power", "Automotive"))
levels(Production$ald_sector)[levels(Production$ald_sector) == "Coal"] <- "Fossil Fuels"
levels(Production$ald_sector)[levels(Production$ald_sector) == "Oil&Gas"] <- "Fossil Fuels"
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive"))
# Aggregate and rename CarstenMetric_Port
ID.COLS <- c("ald_sector", "technology", "Type")
Production <- Production %>% gather(key = Metric, value = Value, "WtProduction")
Production <- aggregate(Production["Value"], by = Production[c(ID.COLS)], FUN = sum)
# Created an average for the peers (or even just use fill!)
# Transfer coal to gigajoule for tech share charts only!!!
Production$Value[Production$technology == "Coal"] <- Production$Value[Production$technology == "Coal"] * 29307600 / 365.25 / 1e6
# , Production$Type != "Market"
if (sector_to_plot != "All") {
technologyorder <- technology_order
colours <- as.vector(ColourPalette[["Colours"]])
names(colours) <- technologyorder
# labels <- gsub("Cap","",technologyorder)
labels <- dplyr::recode(technologyorder,
Gas = T_GasProd, Oil = T_OilProd, Coal = T_CoalProd, #
RenewablesCap = T_Renewables, HydroCap = T_HydroCap, NuclearCap = T_NuclearCap, GasCap = T_GasCap, CoalCap = T_CoalCap, #
Electric = x_Electric, Hybrid = x_Hybrid, ICE = x_ICE, "Freight Passenger" = FreightPassenger
)
names(labels) <- technologyorder
} else if (sector_to_plot == "All") {
technologyorder <- technology_order_short
colours <- as.vector(ColourPalette[["Colours"]])[1:11]
names(colours) <- technologyorder
# labels <- gsub("Cap","",technologyorder)
labels <- dplyr::recode(technologyorder,
Gas = T_GasProd, Oil = T_OilProd, Coal = wrap_labels(paste0(T_CoalProd, " "), 10), #
RenewablesCap = wrap_labels(T_Renewables, 10), HydroCap = wrap_labels(T_HydroCap, 15), NuclearCap = wrap_labels(T_NuclearCap, 15), GasCap = wrap_labels(T_GasCap, 15), CoalCap = wrap_labels(T_CoalCap, 15), #
Electric = wrap_labels(x_Electric, 10), Hybrid = wrap_labels(x_Hybrid, 10), ICE = wrap_labels(x_ICE, 10), "Freight Passenger" = FreightPassenger
)
names(labels) <- technologyorder
}
Production$technology <- factor(Production$technology, levels = technologyorder)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping"))
Production$Type <- wrap_labels(Production$Type, 20)
if (plot_year == start_year + 5) {
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Peers", "Target", "Benchmark"))
xlabels <- c(
Portf, wrap_labels(x_Peers, 10), ifelse(Language %in% c("FR", "ES"), paste(Portf, "\n", AlignedFEM), paste(Aligned, "\n", Portf)), #
ifelse(Language %in% c("FR", "ES"), paste(AlignMarket, "\n", AlignedMAS), paste(Aligned, "\n", AlignMarket))
) #
} else if (plot_year == start_year) {
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Peers", "Benchmark"))
xlabels <- c(Portf, wrap_labels(x_Peers, 9), ifelse(Language %in% c("FR", "ES"), paste(AlignMarket, "\n", Aligned), paste(Aligned, "\n", AlignMarket))) #
}
titles <- c("Fossil Fuel Production", "Power Capacity", "Automotive Production", "Aviation Production", "Shipping Production")
names(titles) <- c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")
Production <- subset(Production, select = c("Type", "ald_sector", "technology", "Value"))
plottheme <- stacked_bar_chart(Production, colours, labels) +
ylab(ShareofSectorProduction) +
scale_x_discrete(labels = xlabels) +
guides(fill = guide_legend(
keywidth = 0.1,
keyheight = 0.1,
default.unit = "inch"
)) +
theme(
plot.title = element_text(hjust = 0.5, colour = textcolor, size = 11, margin = unit(c(0, 0, 1, 0), "lines")),
legend.position = "bottom",
legend.box.margin = margin(-15, 0, 0, 0, unit = "pt"),
legend.title = element_blank(),
legend.text = element_text(size = 6.5), # , margin = margin(l = -0.5, r = -.5, unit = "pt")
axis.text.x = element_text(size = 6.5),
axis.text.y = element_text(size = 6.5)
)
if (sector_to_plot %in% c("Automotive", "Power", "Fossil Fuels", "Aviation", "Shipping")) {
# print("plotting")
dat <- subset(Production, ald_sector == sector_to_plot)
cmd <- plottheme %+% dat +
xlab("")
# print(p1)
# if (sector_to_plot == "Fossil Fuels"){
# sector_to_plot <- "FossilFuels"
# }
n <- 4
} else if (sector_to_plot %in% c("All", "")) {
dat <- subset(Production, ald_sector == "Automotive")
if (nrow(subset(dat, Type == "Portfolio")) > 0) {
p1 <- plottheme %+% dat +
ggtitle(AutomotiveProduction) #
} else {
dat <- rbind(dat, c(
"Type" = "Portfolio",
"ald_sector" = "Automotive",
"technology" = "ICE",
"Value" = 0
))
dat$Value <- as.numeric(dat$Value)
p1 <- plottheme %+% dat +
ggtitle(AutomotiveProduction) + #
geom_text(
data = subset(dat, Type == "Portfolio"),
aes(Type, y = .5, angle = 90, label = NoAutomotiveProduction), size = 2.5
) #
}
dat <- subset(Production, ald_sector == "Fossil Fuels")
if (nrow(subset(dat, Type == "Portfolio")) > 0) {
p2 <- plottheme %+% dat +
ggtitle(FossilFuelsProduction) #
} else {
dat <- rbind(dat, c(
"Type" = "Portfolio",
"ald_sector" = "Fossil Fuels",
"technology" = "OilProd",
"Value" = 0
))
dat$Value <- as.numeric(dat$Value)
p2 <- plottheme %+% dat +
ggtitle(FossilFuelsProduction) + #
geom_text(
data = subset(dat, Type == "Portfolio"),
aes(Type, y = 0.5, angle = 90, label = NoFossilFuelsProduction), size = 2.5
) #
}
dat <- subset(Production, ald_sector == "Power")
if (nrow(subset(dat, Type == "Portfolio")) > 0) {
p3 <- plottheme %+% dat +
ggtitle(C_Power) #
} else {
dat <- rbind(dat, c(
"Type" = "Portfolio",
"ald_sector" = "Power",
"technology" = "CoalCap",
"Value" = 0
))
dat$Value <- as.numeric(dat$Value)
p3 <- plottheme %+% dat +
ggtitle(C_Power) + #
geom_text(
data = subset(dat, Type == "Portfolio"),
aes(Type, y = .5, angle = 90, label = NoC_Power), size = 2.5
) #
}
# print("plotting")
n <- 8
cmd <- arrangeGrob(p2,
p3,
p1,
nrow = 1
)
# p3+theme(axis.text.y = element_blank(), axis.title.y = element_blank()),
# p1+theme(axis.text.y = element_blank(), axis.title.y = element_blank()), nrow=1)
# p3+theme(axis.text.y = element_text(color="white"), axis.title.y = element_text(color="white")),
# p1+theme(axis.text.y = element_text(color="white"), axis.title.y = element_text(color="white")), nrow=1)
dev.off()
}
} else {
Label <- YourPortfoliohasnoproductioninthissectorfortheselectedscenario_geographyandMarket #
cmd <- no_chart(Label)
n <- 4
}
ggsave(graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), # bg = "transparent",
plot = cmd, height = 3, width = n, dpi = ppi
)
}
FiveYearGrowthTrend <- function(plotnumber, chart_type, tech_to_plot, LegendOn = FALSE, explicit_filename = "") {
tech_to_plot <<- tech_to_plot
GoodBad <- green_brown(tech_to_plot)
if (GraphType %in% c("", "Report")) {
LegendOn <- FALSE
} else {
LegendOn <- TRUE
}
filternames <- c("Listed Market", "Bond Universe", "ListedEquity", "MetaPort", meta_investor_name)
PortNames <- portfolio_name_select
### Equity PRODUCTION
if (chart_type == "EQ") {
ALD_Market <- filter_by_parameters(eq_market, "EQ", byscenario = TRUE) %>% distinct()
ALD <- filter_by_parameters(EQCombin, "EQ", byscenario = FALSE) %>% filter(ald_sector != "Other")
if (data_check(ALD) == TRUE) {
ALD$asset_type <- "Equity"
ALD_Market <- ALD_Market %>%
filter(portfolio_name == eq_market_ref)
if (sum(ALD$plan_alloc_wt_tech_prod) > 0) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
}
}
if (chart_type == "CB") {
# ScenarioGeographyChoose <- "Global"
ALD_Market <- filter_by_parameters(cb_market, "CB", byscenario = TRUE, by_equity_market = FALSE)
# ScenarioGeographyChoose <- "GlobalAggregate"
ALD <- filter_by_parameters(CBCombin, "CB", byscenario = FALSE, by_equity_market = FALSE)
if (data_check(ALD) == TRUE) {
ALD$asset_type <- "Bonds"
if (sum(ALD$plan_alloc_wt_tech_prod, na.rm = T) > 0) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
}
}
if (PlotChart == TRUE) {
ALD$plan_alloc_wt_tech_prod <- ifelse(ALD$technology == "Oil", ALD$plan_alloc_wt_tech_prod * (1 / 6.12), ALD$plan_alloc_wt_tech_prod)
ALD$plan_alloc_wt_tech_prod <- ifelse(ALD$technology == "Gas", ALD$plan_alloc_wt_tech_prod * (1 / 0.0372), ALD$plan_alloc_wt_tech_prod)
ALD$scen_alloc_wt_tech_prod <- ifelse(ALD$technology == "Oil", ALD$scen_alloc_wt_tech_prod * (1 / 6.12), ALD$scen_alloc_wt_tech_prod)
ALD$scen_alloc_wt_tech_prod <- ifelse(ALD$technology == "Gas", ALD$scen_alloc_wt_tech_prod * (1 / 0.0372), ALD$scen_alloc_wt_tech_prod)
if (data_check(ALD_Market)) {
ALD_Market$investor_name <- "Market"
ALD_Market$plan_alloc_wt_tech_prod <- ifelse(ALD_Market$technology == "Oil", ALD_Market$plan_alloc_wt_tech_prod * (1 / 6.12), ALD_Market$plan_alloc_wt_tech_prod)
ALD_Market$plan_alloc_wt_tech_prod <- ifelse(ALD_Market$technology == "Gas", ALD_Market$plan_alloc_wt_tech_prod * (1 / 0.0372), ALD_Market$plan_alloc_wt_tech_prod)
}
TotalProduction <- sum(ALD$plan_tech_prod, na.rm = T)
ALD <- bind_rows(ALD, ALD_Market)
ALD <- subset(ALD, year >= start_year & year <= start_year + 5)
ALD <- ALD[ALD$technology == tech_to_plot, ]
table(ALD$asset_type, useNA = "always")
### Separate into CurrentPlans and scenario, get into same column headers
ALD <- subset(ALD, select = c(investor_name, portfolio_name, ald_sector, year, technology, scenario, plan_alloc_wt_tech_prod, scen_alloc_wt_tech_prod))
ALD.cp <- ALD %>%
select(investor_name, portfolio_name, ald_sector, scenario, year, technology, plan_alloc_wt_tech_prod) %>% # Tech.Type,
distinct() %>%
rename(Production = plan_alloc_wt_tech_prod) %>%
filter(
year %in% seq(start_year, start_year + 5),
scenario == Scenariochoose
) %>%
mutate(Line.Type = "CurrentPlan")
ALD.sc <- ALD %>%
select(investor_name, portfolio_name, scenario, ald_sector, year, technology, scen_alloc_wt_tech_prod) %>% # Tech.Type,
rename(Production = scen_alloc_wt_tech_prod) %>%
filter(
portfolio_name == portfolio_name_select,
year %in% seq(start_year, start_year + 5),
scenario %in% iea_scenario_list
) %>%
mutate(Line.Type = "scenario")
### Rename the scenarios from Green to Red - so the names are then independent
### Set the number of scenarios that are available
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPS", "NPSRTS", as.character(ALD.sc$scenario)) # New policy scenarios
ALD.sc$scenario <- ifelse(ALD.sc$scenario %in% c("450S", "SDS"), "SDS", as.character(ALD.sc$scenario)) # New policy scenarios
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPSProxy", "CPS", as.character(ALD.sc$scenario))
no.scenarios <- 0
if (n_distinct(ALD.sc$scenario) == 3) {
no.scenarios <- 3
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "SDS","Scen2",as.character(ALD.sc$scenario)) # 450S
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPSRTS","Scen3",as.character(ALD.sc$scenario)) # New policy scenarios
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPS","Scen4",as.character(ALD.sc$scenario)) # Current policy scenarios
}
if (n_distinct(ALD.sc$scenario) == 4) {
no.scenarios <- 4
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "B2DS","Scen1",as.character(ALD.sc$scenario)) # Below 2 degrees
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "SDS","Scen2",as.character(ALD.sc$scenario)) # 450S
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPSRTS","Scen3",as.character(ALD.sc$scenario)) # New policy scenarios
# ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPS","Scen4",as.character(ALD.sc$scenario)) # Current policy scenarios
}
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "B2DS", "Scen1", as.character(ALD.sc$scenario)) # Below 2 degrees
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "SDS", "Scen2", as.character(ALD.sc$scenario)) # 450S
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "NPSRTS", "Scen3", as.character(ALD.sc$scenario)) # New policy scenarios
ALD.sc$scenario <- ifelse(ALD.sc$scenario == "CPS", "Scen4", as.character(ALD.sc$scenario)) # Current policy scenarios
### Remove additional scenarios
ALD.sc <- as.data.frame(ALD.sc)
ALD.sc <- ALD.sc[grepl("Scen", ALD.sc[, "scenario"]), ]
### Calculate GROWTH
ALD2 <- bind_rows(ALD.cp, ALD.sc)
### Normalisation of market to Portfolio
ALD.cp <- ALD2 %>% filter(Line.Type == "CurrentPlan")
var <- ifelse(ALD.cp[which(ALD.cp$portfolio_name == PortNames & ALD.cp$year == start_year & ALD.cp$technology == tech_to_plot), ]$Production == 0, 0,
ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$year == start_year & ALD.cp$technology == tech_to_plot), ]$Production / ALD.cp[which(ALD.cp$portfolio_name == PortNames & ALD.cp$year == start_year & ALD.cp$technology == tech_to_plot), ]$Production
)
# var <- 1
# ALD.cp[which(ALD.cp$investor_name_select=="Market" & ALD.cp$technology ==tech_to_plot),]$Production<- ifelse(var ==0,0,ALD.cp[which(ALD.cp$investor_name_select=="Market" & ALD.cp$technology ==tech_to_plot),]$Production/var)
if (data_check(ALD_Market)) {
if (var == 0) {
ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production <- 0
} else {
ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production <- ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production / var
}
}
if (sum(ALD.cp[which(ALD.cp$investor_name == "Market" & ALD.cp$technology == tech_to_plot), ]$Production) == 0) {
ALD.cp <- ALD.cp[ALD.cp$investor_name != "Market", ]
}
ALD.sc <- ALD2 %>% filter(Line.Type == "scenario")
ALD2 <- bind_rows(ALD.cp, ALD.sc)
ylims <- ALD2 %>%
filter(investor_name != "Market" | Line.Type != "scenario") %>%
summarise(min = min(Production), max = max(Production))
### Units
unitscaleval <- max(ylims$max, ALD.cp$Production)
unitscalefactor <- 1
green.unit <- c(
"Electric" = Vehicles,
"RenewablesCap" = "MW",
"NuclearCap" = "MW",
"HydroCap" = "MW",
"Hybrid" = Vehicles
)
brown.unit <- c(
"CoalCap" = "MW",
"GasCap" = "MW",
"OilCap" = "MW",
"Oil" = "bbl",
"Gas" = "m3",
"ICE" = Vehicles,
"Coal" = "t"
)
if (unitscaleval < 1e-3) {
unitscalefactor <- 1e-3
green.unit <- c(
"Electric" = paste0("1/1000 x ", Vehicles),
"RenewablesCap" = "W",
"NuclearCap" = "W",
"HydroCap" = "W",
"Hybrid" = paste0("1/1000 x ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "W",
"GasCap" = "W",
"OilCap" = "W",
"Oil" = paste0("1/1000 x ", "bbl"),
"Gas" = "L",
"ICE" = paste0("1/1000 x ", Vehicles),
"Coal" = "g"
)
}
if (unitscaleval > 1e3) {
unitscalefactor <- 1e3
green.unit <- c(
"Electric" = paste0(thousand, " ", Vehicles),
"RenewablesCap" = "GW",
"NuclearCap" = "GW",
"HydroCap" = "GW",
"Hybrid" = paste0(thousand, " ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "GW",
"GasCap" = "GW",
"OilCap" = "GW",
"Oil" = "Mbbl",
"Gas" = paste0(thousand, " m3"),
"ICE" = paste0(thousand, " ", Vehicles),
"Coal" = paste0(thousand, " t")
)
}
if (unitscaleval > 1e6) {
unitscalefactor <- 1e6
green.unit <- c(
"Electric" = paste0(million, " ", Vehicles),
"RenewablesCap" = "TW",
"NuclearCap" = "TW",
"HydroCap" = "TW",
"Hybrid" = paste0(million, " ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "TW",
"GasCap" = "TW",
"OilCap" = "TW",
"Oil" = "MMbbl",
"Gas" = paste0(million, " m3"),
"ICE" = paste0(million, " ", Vehicles),
"Coal" = paste0(million, " t")
)
}
if (unitscaleval > 1e9) {
unitscalefactor <- 1e9
green.unit <- c(
"Electric" = paste0(billion, " ", Vehicles),
"RenewablesCap" = "PW",
"NuclearCap" = "PW",
"HydroCap" = "PW",
"Hybrid" = paste0(billion, " ", Vehicles)
)
brown.unit <- c(
"CoalCap" = "PW",
"GasCap" = "PW",
"Oil" = "Gbbl",
"Gas" = paste0(billion, " m3"),
"ICE" = paste0(billion, " ", Vehicles),
"Coal" = paste0(billion, " t")
)
}
ALD2$Production <- ALD2$Production / unitscalefactor
ylims <- ALD2 %>%
filter(investor_name != "Market" | Line.Type != "scenario") %>%
summarise(min = min(Production), max = max(Production))
### GET SCENARIOS INTO RIBBON FORMAT
ALD.sc <- ALD2 %>% filter(Line.Type == "scenario")
ALD.cp <- ALD2 %>% filter(Line.Type == "CurrentPlan")
ALD.sc.wide <- ALD.sc %>%
ungroup() %>%
select(investor_name, portfolio_name, scenario, ald_sector, technology, Line.Type, year, Production) %>% # Tech.Type,
spread(key = scenario, value = Production)
if (no.scenarios == 3) {
ALD.sc.wide <- ALD.sc.wide %>%
arrange(investor_name, portfolio_name, ald_sector, technology, Line.Type, year) %>% # Tech.Type,
group_by(investor_name, portfolio_name, ald_sector, technology, Line.Type) %>% # Tech.Type,
mutate(Green = ifelse(last(Scen2) > last(Scen3), 1, 0))
if ("Scen4" %in% colnames(ALD.sc.wide)) {
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line2 = ifelse(Green == 1, Scen4, Scen2),
Line3 = Scen3,
Line4 = ifelse(Green == 1, Scen2, Scen4)
)
}
if ("Scen1" %in% colnames(ALD.sc.wide) & green_brown(tech_to_plot) == "brown") {
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line1 = ifelse(Green == 1, Scen3, Scen1),
Line2 = Scen2,
Line3 = ifelse(Green == 1, Scen1, Scen3)
)
} else if ("Scen1" %in% colnames(ALD.sc.wide) & green_brown(tech_to_plot) == "green") {
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line2 = Scen3,
Line3 = Scen2,
Line4 = Scen1
)
}
} else if (no.scenarios == 4) {
ALD.sc.wide <- ALD.sc.wide %>%
arrange(investor_name, portfolio_name, ald_sector, technology, Line.Type, year) %>% # Tech.Type,
group_by(investor_name, portfolio_name, ald_sector, technology, Line.Type) %>% # Tech.Type,
mutate(Green = ifelse(last(Scen2) > last(Scen4), 1, 0))
if (green_brown(tech_to_plot) == "brown") {
if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 < ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen4 <- ALD.sc.wide$Scen4
} else if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 > ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
temp <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen4
ALD.sc.wide$Scen4 <- temp
}
} else if (green_brown(tech_to_plot) == "green") {
if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 > ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen4 <- ALD.sc.wide$Scen4
} else if (all(ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen3 < ALD.sc.wide[which(ALD.sc.wide$year != 2018), ]$Scen4)) {
temp <- ALD.sc.wide$Scen3
ALD.sc.wide$Scen3 <- ALD.sc.wide$Scen4
ALD.sc.wide$Scen4 <- temp
}
}
ALD.sc.wide <- ALD.sc.wide %>% mutate(
Line1 = ifelse(Green == 1, Scen4, Scen1),
Line2 = ifelse(Green == 1, Scen3, Scen2),
Line3 = ifelse(Green == 1, Scen2, Scen3),
Line4 = ifelse(Green == 1, Scen1, Scen4)
)
}
### IDENTIFY LIMITS of the Y axis
ymin <- min(ylims$min)
ymax <- max(ylims$max)
round.digits <- 1
if ((ymax - ymin) < 0.01) {
unit <- 0.001
round.digits <- 3
} else if ((ymax - ymin) >= 0.01 & (ymax - ymin) < 0.1) {
unit <- .01
round.digits <- 2
} else if ((ymax - ymin) >= 0.1 & (ymax - ymin) < 1) {
unit <- .1
} else if ((ymax - ymin) >= 1 & (ymax - ymin) < 10) {
unit <- 1
} else if ((ymax - ymin) >= 10 & (ymax - ymin) < 150) {
unit <- 10
} else if ((ymax - ymin) >= 150 & (ymax - ymin) < 1000) {
unit <- 100
} else if ((ymax - ymin) >= 1000 & (ymax - ymin) < 10000) {
unit <- 1000
} else if ((ymax - ymin) >= 10000 & (ymax - ymin) < 100000) {
unit <- 10000
}
MAX.Y <- ceiling(ymax / unit) * unit
MIN.Y <- floor(ymin / unit) * unit
NEXT.LINE <- paste0("Line", as.numeric(max(gsub("Line", "", colnames(ALD.sc.wide)[grep("Line", colnames(ALD.sc.wide))]))) + 1)
ALD.sc.wide[, NEXT.LINE] <- MAX.Y
ALD.sc.tall <- ALD.sc.wide %>%
select(-grep("Scen", colnames(ALD.sc.wide))) %>%
gather(key = "Target", value = "Value", -investor_name, -portfolio_name, -ald_sector, -technology, -Green, -Line.Type, -year)
ALD.sc.tall <- ALD.sc.tall %>%
group_by(investor_name, portfolio_name, ald_sector, technology, Line.Type, Green, year) %>% # Tech.Type
mutate(
lower = dplyr::lag(Value),
lower = ifelse(is.na(lower), MIN.Y, lower)
)
ALD.sc.tall <- as.data.frame(ALD.sc.tall)
ALD.cp <- as.data.frame(ALD.cp)
# if (no.scenarios == 4){ # WITH 1.75°
green.fill <- c(
"Line5" = area_1,
"Line4" = area_2,
"Line3" = area_2_4,
"Line2" = area_4_6,
"Line1" = area_6
)
brown.fill <- c(
"Line5" = area_6,
"Line4" = area_4_6,
"Line3" = area_2_4,
"Line2" = area_2,
"Line1" = area_1
)
green.labels <- c(
"Line5" = "1D",
"Line4" = "2D",
"Line3" = "2D-4D",
"Line2" = "4D-6D",
"Line1" = "6D"
)
brown.labels <- c(
"Line5" = "6D",
"Line4" = "4D-6D",
"Line3" = "2D-4D",
"Line2" = "2D",
"Line1" = "1D"
)
outputplot <- ggplot(data = subset(ALD.sc.tall, technology == tech_to_plot & ALD.sc.tall$portfolio_name == PortNames)) +
geom_ribbon(aes(ymin = lower, ymax = Value, x = year, fill = Target), alpha = 0.75) +
scale_fill_manual(labels = eval(parse(text = paste(GoodBad, ".labels", sep = ""))), values = eval(parse(text = paste(GoodBad, ".fill", sep = "")))) +
scale_x_continuous(name = Year, expand = c(0, 0), limits = c(start_year, start_year + 5.6)) +
scale_y_continuous(
name = paste0(WeightedProduction, " (", eval(parse(text = paste(GoodBad, ".unit", sep = "")))[tech_to_plot], ")"),
expand = c(0, 0),
breaks = round(seq(round(MIN.Y, digits = round.digits), round(MAX.Y, digits = round.digits), length.out = 5), digits = round.digits)
) +
theme_246() +
theme(legend.position = "none") +
coord_cartesian(ylim = c(round(MIN.Y, digits = round.digits), round(MAX.Y, digits = round.digits)))
if (chart_type == "CB") {
outputplot <- outputplot +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & portfolio_name == portfolio_name_select),
aes(x = year, y = Production), color = cb_line, size = .75
) +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & investor_name == "Market"),
aes(x = year, y = Production), color = cb_line, size = .75, linetype = "dashed"
)
}
if (chart_type == "EQ") {
outputplot <- outputplot +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & portfolio_name == portfolio_name_select),
aes(x = year, y = Production), color = eq_line, size = .75
) +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & investor_name == "Market"),
aes(x = year, y = Production), color = eq_line, size = .75, linetype = "dashed"
)
}
if (chart_type == "BV") {
outputplot <- outputplot +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & portfolio_name == portfolio_name_select),
aes(x = year, y = Production), color = bv_line, size = .75
) +
geom_line(
data = subset(ALD.cp, technology == tech_to_plot & investor_name == "Market"),
aes(x = year, y = Production), color = bv_line, size = .75, linetype = "dashed"
)
}
if (LegendOn == TRUE) {
rect_246 <- gList(
# <1?C
rectGrob(
x = 0.09, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_1)
),
# 1-2?C
rectGrob(
x = 0.21, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_2)
),
# 2-4?C
rectGrob(
x = 0.33, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_2_4)
),
# 4-6?C
rectGrob(
x = 0.45, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_4_6)
),
# >6?C
rectGrob(
x = 0.57, y = 0.4, width = 0.12, height = 0.3,
gp = gpar(col = NA, fill = area_6)
)
)
arrows_246 <- gList(
linesGrob(
x = unit(0.15, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
),
linesGrob(
x = unit(0.27, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
),
linesGrob(
x = unit(0.39, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
),
linesGrob(
x = unit(0.51, "npc"), y = unit(c(0.25, 0.71), "npc"),
gp = gpar(fill = textcolor),
arrow = arrow(length = unit(0.15, "cm"), ends = "last", type = "closed")
)
)
if (chart_type == "CB") {
linecolour <- cb_line
Line_labels <- c(paste0(BondReference, " portfolio"), paste0(BondReference, " market"))
} else {
linecolour <- eq_line
Line_labels <- c("Equity portfolio", "Listed equity market")
}
portfolio_lines <- gList(
linesGrob(
x = unit(c(0.65, 0.735), "npc"), y = unit(0.68, "npc"),
gp = gpar(col = linecolour, lty = 1, lwd = 3)
),
linesGrob(
x = unit(c(0.65, 0.735), "npc"), y = unit(0.32, "npc"),
gp = gpar(col = linecolour, lty = 2, lwd = 3)
)
)
### Legend labels
# Labels
scenario_labels <- c("1.75C scenario", "2C scenario", "4C scenario", "6C scenario")
Legend_labels <- c("<1.75C", "<2C", "1.75 - 2C", "2 - 4C", "4 - 6C", "6C")
# Positions
scenario_labels_position <- c(0.15, 0.27, 0.39, 0.51)
Legend_labels_position <- c(0.09, 0.21, 0.33, 0.45, 0.57)
Line_labels_position <- c(0.68, 0.32)
# Label scenario and 2-4-6
label_text_246 <- gList(
textGrob(scenario_labels,
x = scenario_labels_position, y = 0.88,
just = "center", gp = gpar(fontsize = 5.8, col = textcolor)
),
textGrob(Legend_labels[c(1, 3:6)],
x = Legend_labels_position, y = 0.4,
just = "center", gp = gpar(fontsize = 7, col = textcolor)
),
textGrob(scenario_labels[2:4],
x = scenario_labels_position[2:4], y = 0.8,
just = "center", gp = gpar(fontsize = 5.8, col = textcolor)
),
textGrob(Legend_labels[c(2, 4:6)],
x = Legend_labels_position[2:5], y = 0.4,
just = "center", gp = gpar(fontsize = 7, col = textcolor)
)
)
# Label line
label_text_lines <- gList(
textGrob(Line_labels,
x = 0.745, y = Line_labels_position,
just = "left", gp = gpar(fontsize = 7, col = textcolor)
)
)
if (no.scenarios == 3) {
footer_246 <- grobTree(
rectGrob(gp = gpar(fill = "white", lwd = 0, col = "white")),
rect_246[2:5], arrows_246[2:4], label_text_246[3:4], portfolio_lines[1:2], label_text_lines[1]
)
# outputplot <- arrangeGrob(outputplot, footer_246, heights = unit(c(0.85, 0.15), c('npc', 'npc'))) #
outputplot <- arrangeGrob(outputplot, footer_246, heights = unit(c(0.85, 0.15), c("npc", "npc"))) #
} else if (no.scenarios == 4) {
footer_1246 <- grobTree(
rectGrob(gp = gpar(fill = "white", lwd = 0, col = "white")),
rect_246, arrows_246, label_text_246[1:2], portfolio_lines[1:2], label_text_lines[1]
)
# outputplot <- arrangeGrob(outputplot, footer_1246, heights = unit(c(0.85, 0.15), c('npc', 'npc'))) #
outputplot <- arrangeGrob(outputplot, footer_1246, heights = unit(c(0.85, 0.15), c("npc", "npc"))) #
}
}
} else {
Label <- YourportfoliohasnoproductionfortheselectedTechnologyScenarioGeographyandMarket
outputplot <- no_chart(Label)
}
ggsave(outputplot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 3.6, width = 4.6, dpi = ppi * 2) # ,bg="white"
tech_to_plot <<- ""
BV.asset_type <<- ""
}
CO2IntensityTrend <- function(plotnumber, sector_to_plot, LegendOn = TRUE, explicit_filename = "") {
PlotChart <- FALSE
Data <- NA
Data_debt <- NA
sector_to_plot <<- sector_to_plot
if (GraphType == "Report") {
LegendOn <- FALSE
} else {
LegendOn <- TRUE
}
if (has_equity == TRUE) {
Data <- filter_by_parameters(EQCombin, "EQ", byscenario = F)
if (data_check(Data) == TRUE) {
Data$Type <- "equity"
} else {
print("no EQ")
}
}
if (has_debt == TRUE) {
Data_debt <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE, byscenario = F)
if (data_check(Data_debt) == TRUE) {
Data_debt$Type <- BondReference
} else {
print("no CB")
}
}
if (data_check(Data) == TRUE & data_check(Data_debt) == TRUE) {
Data <- subset(Data, select = colnames(Data_debt))
Data <- rbind(Data, Data_debt)
Data <- subset(Data, year >= start_year & year <= start_year + 5)
} else if (data_check(Data) == FALSE & data_check(Data_debt) == TRUE) {
Data <- Data_debt
} else if (data_check(Data) == TRUE & data_check(Data_debt) == FALSE) {
Data <- Data
}
if (data_check(Data) == TRUE) {
Data <- Data[!is.na(Data$plan_sec_emissions_factor), ]
}
if (data_check(Data) == TRUE) {
Data <- Data[Data$ald_sector == sector_to_plot, ]
if (data_check(Data) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
}
if (PlotChart == TRUE) {
# print(PlotChart)
if (sector_to_plot == "Aviation") {
PlotData <- Data %>%
ungroup() %>%
select(portfolio_name, year, technology, plan_sec_emissions_factor, scen_sec_emissions_factor, Type) %>%
group_by(portfolio_name, year, Type) %>%
filter(year <= start_year + 5) %>%
summarise(
plan_emission_factor = mean(plan_sec_emissions_factor, na.rm = T) * 1000,
scen_emission_factor = mean(scen_sec_emissions_factor, na.rm = T) * 1000
)
Ref.Data <- PlotData %>%
ungroup() %>%
filter(year == start_year) %>%
rename(Ref.Scen.EF = scen_emission_factor) %>%
select(Type, Ref.Scen.EF)
PlotData <- PlotData %>% left_join(Ref.Data, by = "Type")
PlotData <- PlotData %>%
group_by(year, Type) %>%
mutate(scen_emission_factor = scen_emission_factor * plan_emission_factor / Ref.Scen.EF) %>%
select(-Ref.Scen.EF)
} else {
PlotData <- Data %>%
ungroup() %>%
select(portfolio_name, year, technology, plan_sec_emissions_factor, scen_sec_emissions_factor, Type) %>%
group_by(portfolio_name, year, Type) %>%
filter(year <= start_year + 5) %>%
summarise(
plan_emission_factor = mean(plan_sec_emissions_factor, na.rm = T),
scen_emission_factor = mean(scen_sec_emissions_factor, na.rm = T)
)
Ref.Data <- PlotData %>%
ungroup() %>%
filter(year == start_year) %>%
rename(Ref.Scen.EF = scen_emission_factor) %>%
select(Type, Ref.Scen.EF)
PlotData <- PlotData %>% left_join(Ref.Data, by = "Type")
PlotData <- PlotData %>%
group_by(year, Type) %>%
mutate(scen_emission_factor = scen_emission_factor * plan_emission_factor / Ref.Scen.EF) %>%
select(-Ref.Scen.EF)
}
# print(nrow(PlotData))
OtherSectorUnits <- data.frame("ald_sector" = c("Cement", "Steel", "Aviation"), "Units" = c(
paste0("t CO2 / t ", L_Cement), #
paste0("t CO2 / t ", L_Steel), #
paste0("CO2/", x_passenger, "km")
)) #
year_lab <- Year # "year" #GT["year"][[1]]
ylabel <- paste0(EmissionsIntensity, OtherSectorUnits$Units[OtherSectorUnits$ald_sector == sector_to_plot], ")") #
df <- PlotData
colourdf <- data.frame("Colour" = c(eq_line, cb_line), "TargetCol" = c(area_2, area_2), "Type" = c("equity", BondReference))
df <- base::merge(df, colourdf, by = "Type")
outputplot <- ggplot() +
geom_line(data = df, aes(x = year, y = scen_emission_factor, colour = Type, group = Type), size = 1.5, linetype = 1, colour = area_2) +
annotate(geom = "point", y = df$plan_emission_factor[df$year == start_year], x = df$year[df$year == start_year], size = 5, colour = unique(df$Colour), fill = unique(df$Colour), shape = 22)
outputplot <- outputplot +
# scale_fill_identity(name = "", guide = 'legend',labels = c("Exposure gap","Current capacity + planned additions")) +
scale_colour_manual(name = "", guide = "legend", values = unique(df$Colour), labels = unique(df$Type)) +
xlab(year_lab) + ylab(ylabel) + # Set axis labels
# legend(values=legelabels)+
scale_x_continuous(breaks = seq(start_year, max(df$year), 1), expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
expand_limits(x = c(start_year - 0.5, start_year + 5.5), y = c(.7 * min(df[, c("plan_emission_factor", "scen_emission_factor")], na.rm = TRUE), 1.3 * max(df[, c("scen_emission_factor")], na.rm = TRUE))) +
theme_linecharts() #+
# theme(axis.line.x = element_line(colour = "red",size=0.5))
if (LegendOn == TRUE) {
# Count number of sectors present
nosectors <- length(unique(df$Type))
if (nosectors == 1) {
chartsectors <- unique(df$Type)
if (chartsectors == "equity") {
boxcol <- eq_line
} else if (chartsectors == "Corporate bond") {
boxcol <- cb_line
}
}
if (nosectors == 2) {
y1 <- 0.8
y2 <- 0.5
y3 <- 0.2
} else {
y3 <- 0.4
y2 <- 0.8
}
boxdim <- c(0.03, 0.2)
### Target emissions intensity lines
Emission_lines <- gList(
linesGrob(
x = unit(c(0.03, 0.13), "npc"), y = unit(y3, "npc"),
gp = gpar(col = area_2, lty = 1, lwd = 3)
)
)
### Current Portfolio Emission Intensity
if (nosectors == 2) {
Emission_rectangle <- gList(
rectGrob(
x = 0.07, y = unit(y2, "npc"), width = boxdim[1], height = boxdim[2],
gp = gpar(col = NA, fill = eq_line)
),
rectGrob(
x = 0.07, y = unit(y1, "npc"), width = boxdim[1], height = boxdim[2],
gp = gpar(col = NA, fill = cb_line)
)
)
} else {
Emission_rectangle <- gList(
rectGrob(
x = 0.07, y = unit(y2, "npc"), width = boxdim[1], height = boxdim[2],
gp = gpar(col = NA, fill = boxcol)
)
)
}
### legend labels
# labels
if (nosectors == 2) {
Emission_labels <- c(
paste0("Current emissions intensity for your", tolower(BondReference), "portfolio"),
"Current emissions intensity for your equity portfolio",
"Target emissions intensity for the relevant portfolio"
)
Emission_labels_position <- c(y1, y2, y3)
} else {
Emission_labels <- c(
paste0("Current emissions intensity for your ", chartsectors, " portfolio"),
"Target emissions intensity for the portfolio"
)
Emission_labels_position <- c(y2, y3)
}
# positions
# all labels together
Emission_label_text <-
textGrob(Emission_labels,
x = 0.15, y = Emission_labels_position,
just = "left", gp = gpar(fontsize = 8, col = textcolor, family = textfont)
)
# all legend objects togeter
# footer_Emission = grobTree(rectGrob(gp = gpar(fill = 'transparent', lwd = 0, colour = "white")),
# Emission_lines, Emission_rectangle, Emission_label_text) # one footer too much
footer_Emission <- grobTree(Emission_lines, Emission_rectangle, Emission_label_text)
# outputplot <- arrangeGrob(footer_Emission, heights = unit(c(.15), c('npc')))
# legend with "a" - a ggplot object
outputplot <- arrangeGrob(outputplot, footer_Emission, heights = unit(c(0.85, 0.15), c("npc", "npc")))
}
} else {
if (sector_to_plot == "Cement") {
Label <- paste0(TheportfoliohasnocompaniesintheCementsector) #
} else if (sector_to_plot == "Steel") {
Label <- paste0(TheportfoliohasnocompaniesintheSteelsector) #
} else if (sector_to_plot == "Aviation") {
Label <- paste0(TheportfoliohasnocompaniesintheAviationsector) #
} else { # (sector_to_plot == "Shipping")
Label <- paste0(TheportfoliohasnocompaniesintheShippingsector) #
}
outputplot <- no_chart(Label)
}
ggsave(filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", outputplot, height = 3, width = 4, dpi = ppi) # ,bg="transparent"
sector_to_plot <<- ""
# return()
}
CompanyInformation <- function(plotnumber, companiestoprint, chart_type, sector_to_plot, explicit_filename = "") {
sector_to_plot <<- sector_to_plot
PlotChart <- F
if (chart_type == "EQ" & has_equity == T) {
market <- filter_by_parameters(eq_market, "EQ")
combin <- filter_by_parameters(EQCombin, "EQ")
CompProdSS <- filter_by_parameters(EQCompProdSnapshot, "EQ", ActorSectorOnly = T)
}
if (chart_type == "CB" & has_debt == T) {
market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
CompProdSS <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = FALSE, ActorSectorOnly = T)
}
if (data_check(CompProdSS) == T) {
PlotChart <- T
}
if (PlotChart == TRUE) {
if (!"Entity.Name" %in% colnames(CompProdSS) & chart_type == "CB") {
CompProdSS$Entity.Name <- CompProdSS$id
}
if (!"Entity.Name" %in% colnames(CompProdSS) & chart_type == "EQ") {
CompProdSS$Entity.Name <- CompProdSS$company_name
}
# Portfolio (Weighted by the AUM)
Portfoliomix <- subset(combin, year == start_year + 5, select = c("technology", "plan_alloc_wt_tech_prod"))
Portfoliomix <- unique(Portfoliomix) # KLAUS: Added this line to avoid duplicate lines!
Portfoliomix$Classification <- "Portfolio"
Portfoliomix$Name <- "Portfolio"
Portfoliomix <- subset(Portfoliomix, select = c("Name", "Classification", "technology", "plan_alloc_wt_tech_prod"))
Portfoliomix$plan_alloc_wt_tech_prod <- Portfoliomix$plan_alloc_wt_tech_prod
colnames(Portfoliomix) <- c("Name", "Classification", "technology", "tech_share")
# Add 2D Target (Global Market under 2D scenario)
Targetmix <- subset(combin, year == start_year + 5, select = c("technology", "scen_alloc_wt_tech_prod")) # Scen.WtProduction needs to be adjusted to BenchmarkPortfolio (Trajectory vs Market approach) - will address this in FilterByParameters
Targetmix <- unique(Targetmix) # KLAUS: Added this line to avoid duplicate lines!
Targetmix$Classification <- "Portfolio"
Targetmix$Name <- "Aligned Portfolio"
Targetmix <- subset(Targetmix, select = c("Name", "Classification", "technology", "scen_alloc_wt_tech_prod"))
Targetmix$scen_alloc_wt_tech_prod <- Targetmix$scen_alloc_wt_tech_prod
colnames(Targetmix) <- c("Name", "Classification", "technology", "tech_share")
# Add Benchmark / Global Market
Marketmix <- NA
if (data_check(market)) {
Marketmix <- subset(market, year == start_year + 5, select = c("technology", "scen_alloc_wt_tech_prod"))
Marketmix <- unique(Marketmix)
Marketmix$Classification <- "Portfolio"
Marketmix$Name <- "Aligned Market"
Marketmix <- subset(Marketmix, select = c("Name", "Classification", "technology", "scen_alloc_wt_tech_prod"))
Marketmix$scen_alloc_wt_tech_prod <- Marketmix$scen_alloc_wt_tech_prod
colnames(Marketmix) <- c("Name", "Classification", "technology", "tech_share")
}
PortfolioData <- rbind(Marketmix, Targetmix, Portfoliomix)
# PortfolioData <- rbind(Targetmix, Portfoliomix)
# Percentage share of each technology for each company in the portfolio
Companies <- subset(CompProdSS, year == start_year + 5, select = c("Entity.Name", "technology", "plan_tech_prod", "plan_sec_prod", "port_weight"))
Companies <- Companies %>%
filter(plan_sec_prod > 0) %>%
select(-plan_sec_prod)
Companies <- unique(Companies)
Companies$tech_share <- Companies$plan_tech_prod
Companies$Classification <- "Companies"
# Companies$Name <- paste0(substr(Companies$Name, 1, 15),"...")
Companies <- subset(Companies, select = c("Entity.Name", "Classification", "technology", "tech_share", "port_weight"))
colnames(Companies) <- c("Name", "Classification", "technology", "tech_share", "PortWeight")
Companies$Name <- as.character(Companies$Name)
Companies$Name <- ifelse(is.na(Companies$Name), "No Name", Companies$Name)
colors <- as.vector(ColourPalette$Colours[ColourPalette$ald_sector %in% sector_to_plot])
if (sector_to_plot == "Power") {
techorder <- c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap")
tech_labels <- c(T_Renewables, T_HydroCap, T_Nuclear, T_GasCap, T_CoalCap) #
# colors <- as.vector(ColourPalette$Colours[ColourPalette$ald_sector %in% sector_to_plot])
} else if (sector_to_plot == "Automotive") {
techorder <- c("Electric", "Hybrid", "ICE")
tech_labels <- c(x_Electric, x_Hybrid, x_ICE) #
} else if (sector_to_plot == "CoalMining") {
techorder <- c("Coal")
tech_labels <- c(T_CoalProd) #
colors <- as.vector(ColourPalette$Colours[ColourPalette$technology %in% "Coal"])
} else if (sector_to_plot == "OilGas") {
techorder <- c("Gas", "Oil")
tech_labels <- c(T_GasProd, T_OilProd) #
colors <- as.vector(ColourPalette$Colours[ColourPalette$technology %in% tech_labels])
} else if (sector_to_plot == "Shipping") {
tech_labels <- c("GHG_A", "GHG_B", "GHG_C", "GHG_D", "GHG_E", "GHG_F", "GHG_G")
# colors <- as.vector(ColourPalette$Colours[12:13])
} else if (sector_to_plot == "Steel") {
tech_labels <- c(ElectricArc, BlastFurnace) #
# colors <- as.vector(ColourPalette$Colours[14:20])
}
PortfolioData <- filter(PortfolioData, technology %in% techorder)
Companies <- Companies %>%
filter(technology %in% techorder) %>%
arrange(-PortWeight)
Companies <- Companies %>%
filter(Name %in% unique(Companies$Name)[1:min(companiestoprint, length(unique(Companies$Name)))])
dummy <- data.frame(
c("Name", ""),
c("Classification", NA),
c("technology", NA),
c("tech_share", 0)
)
colnames(dummy) <- as.character(unlist(dummy[1, ]))
dummy <- dummy[-1, ]
dummy$tech_share <- as.numeric(dummy$tech_share)
AllData <- rbind(
PortfolioData,
dummy,
select(Companies, -PortWeight)
)
AllData <- unique(AllData)
AllData$Name <- factor(AllData$Name, levels = rev(unique(c(PortfolioData$Name, "", Companies$Name))))
AllData$Name <- dplyr::recode(AllData$Name,
"Aligned Market" = AlignedMarket, #
"Aligned Portfolio" = AlignedPortfolio, #
Portfolio = Portf
) #
AllData$technology <- factor(AllData$technology, levels = rev(techorder))
names(colors) <- techorder
names(tech_labels) <- techorder
Companies <- unique(select(Companies, Name, PortWeight, technology))
PortfolioData <- unique(select(PortfolioData, Name))
PortfolioData$Name <- dplyr::recode(PortfolioData$Name,
"Aligned Market" = AlignedMarket,
"Aligned Portfolio" = AlignedPortfolio,
Portfolio = Portf
)
bar_labels <- c(PortfolioData$Name, "", Companies$Name)
# company_labels <- trim(Companies$Name)
company_labels <- Companies$Name
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
bar_labels <- c(PortfolioData$Name, "", company_labels)
Companies_PortWeight <- Companies %>%
group_by(Name) %>%
summarise(PortWeight = mean(PortWeight))
PortPlot <- stacked_bar_chart(AllData, colors, tech_labels) +
geom_text(
data = Companies_PortWeight,
aes(x = Name, y = 1),
label = perc(Companies_PortWeight$PortWeight),
hjust = -1., color = textcolor, size = 10 * (5 / 14),
family = textfont
) +
geom_text(
data = Companies,
aes(x = "", y = 1),
label = x_Weight, #
hjust = -0.9, color = textcolor, size = 10 * (5 / 14),
family = textfont
) +
xlab("") +
coord_flip() +
theme(
legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(margin = margin(l = 0.15, r = 0.35, unit = "cm")),
plot.margin = unit(c(1, 8, 0, 0), "lines")
)
gt <- ggplot_gtable(ggplot_build(PortPlot))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
if (sector_to_plot == "Fossil Fuels") {
sector_to_plot <- "FossilFuels"
}
bar_size <- 8 / 15
# height <- min(4+companiestoprint,n_distinct(AllData$Name))*bar_size + 2
if (sector_to_plot == "Fossil Fuels") {
sector_to_plot <- "FossilFuels"
}
height <- max(2.6, length(unique(Companies$Name)) * .3)
} else {
if (sector_to_plot == "Automotive") {
Label <- paste0(YourPortfoliohasnocompaniesintheAutomotivesector) #
} else if (sector_to_plot == "CoalMining") {
Label <- paste0(YourPortfoliohasnocompaniesintheCoalMiningsector) #
} else if (sector_to_plot == "OliGas") {
Label <- paste0(YourPortfoliohasnocompaniesintheOilGassector) #
} else if (sector_to_plot == "Power") {
Label <- paste0(YourPortfoliohasnocompaniesinthePowersector) #
} else if (sector_to_plot == "Shipping") {
Label <- paste0(YourPortfoliohasnocompaniesintheShippingsector) #
} else if (sector_to_plot == "Steel") {
Label <- paste0(YourPortfoliohasnocompaniesintheSteelsector) #
}
gt <- no_chart(Label)
height <- 3
}
ggsave(gt,
filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), # bg = "transparent",
height = height, width = 10, dpi = ppi
)
sector_to_plot <<- ""
BV.asset_type <<- ""
}
OilShare <- function(plotnumber, companiestoprint, chart_type, explicit_filename = "") {
tech_to_plot <<- "Oil"
chart_type <<- chart_type
OilData <- NA
if (chart_type == "EQ") {
comps <- filter_by_parameters(EQCompProdSnapshot, "EQ") %>% ungroup()
# comps$id<-comps$bloomberg_id
# comps$bloomberg_id <-NULL
# comps$id <-as.integer(comps$id)
if (data_check(comps)) {
OilData <- EQOilShareData
OilData <- OilData[OilData$id %in% comps$id, ]
OilData <- OilData[OilData$technology == "Oil", ]
OilData <- OilData[OilData$year == start_year + 5, ]
OilData <- OilData %>%
group_by(id, year) %>%
mutate(SecOilProd = sum(ald_production)) %>%
filter(SecOilProd > 0) %>%
select(-SecOilProd) %>%
ungroup()
if (data_check(OilData)) {
OilData$Type <- "equity"
OilData$ID <- OilData$id
OilData$bloomberg_id <- NULL
}
OilData <- left_join(comps %>% select(technology, company_name, id, port_weight), OilData, by = c("technology" = "technology", "id" = "id"))
}
}
if (chart_type == "CB") {
comps <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F)
# comps$id <- comps$corp_bond_ticker
if (data_check(comps)) {
OilData <- CBOilShareData %>%
ungroup() %>%
filter(
technology == "Oil",
year == start_year + 5,
id %in% comps$id
)
OilData <- OilData %>%
group_by(id, year) %>%
mutate(SecOilProd = sum(ald_production)) %>%
filter(SecOilProd > 0) %>%
select(-SecOilProd) %>%
ungroup()
if (data_check(OilData)) {
OilData <- OilData %>% mutate(
Type = "corporate bond",
ID = id,
company_name = ID,
company_lvl_production = ald_production
)
OilData <- left_join(comps %>% select(technology, id, port_weight), OilData, by = c("technology" = "technology", "id" = "id"))
}
}
}
if (data_check(OilData)) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart) {
OilCompanies <- subset(OilData, select = c("ald_production", "port_weight", "technology_type", "company_name"))
OilCompanies1 <- OilCompanies %>%
group_by(company_name, technology_type, port_weight) %>%
summarise("Oilsum" = sum(ald_production)) %>%
ungroup()
OilCompanies2 <- OilCompanies1 %>%
group_by(company_name) %>%
summarise("Oiltotal" = sum(Oilsum))
OilCompanies <- left_join(OilCompanies1, OilCompanies2, by = c("company_name"))
OilCompanies$OilShare <- (OilCompanies$Oilsum / OilCompanies$Oiltotal)
OilCompanies$Classification <- "Companies"
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Conventional Gas", "Other & Unknown", OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Unconventional Gas", "Other & Unknown", OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "-", "Other & Unknown", OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Oil Sands", OilSands, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Heavy Oil", HeavyOil, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Conventional Oil", ConventionalOil, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Unconventional Oil", UnconventionalOil, OilCompanies$technology_type)
OilCompanies$technology_type <- ifelse(OilCompanies$technology_type == "Other & Unknown", OtherandUnknown, OilCompanies$technology_type)
OilCompanies$technology_type <- as.factor(OilCompanies$technology_type)
# levels(OilCompanies$technology_type)[levels(OilCompanies$technology_type)=="Conventional Gas"] <- "Other & Unknown"
# levels(OilCompanies$technology_type)[levels(OilCompanies$technology_type)=="Unconventional Gas"] <- "Other & Unknown"
# levels(OilCompanies$technology_type)[levels(OilCompanies$technology_type)=="-"] <- "Other & Unknown"
# techorder <- c("Oil Sands","Heavy Oil","Conventional Oil","Unconventional Oil","Other & Unknown")
techorder <- c(OilSands, HeavyOil, ConventionalOil, UnconventionalOil, OtherandUnknown)
tech_labels <- techorder
colors <- c("#72755e", "#8d9176", "#a5a792", "#bcbeae", "#d3d5ca")
OilCompanies$technology_type <- factor(OilCompanies$technology_type, levels = techorder)
OilCompanies <- OilCompanies %>%
filter(technology_type %in% techorder) %>%
arrange(-port_weight)
# LIne to aggregate to portfolio level
# aggregated all companies by oil type to get the portfolio oilshare
# OilCompanies_P<-OilCompanies %>%
# group_by(technology_type) %>%
# summarise(OilShare=sum(OilShare)) %>%
# ungroup()%>%
# mutate(company_name="Portfolio",
# Classification="Portfolio")
# colnames(OilCompanies_P)[which(names(OilCompanies_P) == "technology_type")] <- "Oil.Type"
OilCompanies <- OilCompanies %>%
filter(company_name %in% unique(OilCompanies$company_name)[1:min(companiestoprint, length(unique(OilCompanies$company_name)))])
colnames(OilCompanies)[which(names(OilCompanies) == "technology_type")] <- "Oil.Type"
OilCompanies <- subset(OilCompanies, select = c("Oil.Type", "company_name", "OilShare", "Classification", "port_weight"))
dummy <- data.frame(
c("Oil.Type", NA),
c("company_name", ""),
c("OilShare", 0),
c("Classification", NA),
c("port_weight", NA)
)
colnames(dummy) <- as.character(unlist(dummy[1, ]))
dummy <- dummy[-1, ]
dummy$OilShare <- as.numeric(dummy$OilShare)
OilCompany <- rbind(OilCompanies, dummy)
OilCompany$Oil.Type <- factor(OilCompany$Oil.Type, levels = techorder)
OilCompany$port_weight <- as.numeric(OilCompany$port_weight)
OilCompany <- as.data.frame(OilCompany)
# OilCompany$company_name <- factor(OilCompany$company_name, levels=(unique(c("",OilCompany$company_name))))
names(colors) <- techorder
names(tech_labels) <- techorder
# company_labels <- c(trimws(unique(OilCompanies[!is.na(OilCompanies$Oil.Type),]$company_name))," ","Portfolio")
Oil <- na.omit(OilCompany[, c("company_name", "port_weight")])
OilCompany <- OilCompany %>% arrange(port_weight)
company_labels <- trimws(unique(OilCompany[!is.na(OilCompany$Oil.Type), ]$company_name))
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
bar_labels <- c(company_labels, " ") # company_labels#
OilCompany <- OilCompany %>% filter(!is.na(port_weight))
PortPlot <- ggplot(data = OilCompany, aes(x = company_name, y = OilShare, fill = Oil.Type)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colors, labels = rev(paste(tech_labels, " ")), breaks = rev(techorder)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
scale_x_discrete(labels = bar_labels) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts() +
geom_text(
data = OilCompany,
aes(x = company_name, y = 1),
label = perc(OilCompany$port_weight),
hjust = -1, color = textcolor, size = 8 * (5 / 14),
family = textfont
) +
# geom_text(aes(x="",y=1),
# label = x_Weight,
# hjust = -0.5, color = textcolor, size=8*(5/14),
# family = textfont)+
xlab("") +
ylab("tech_share") +
coord_flip() +
theme(
legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(1, 6, 0, 0), "lines"), axis.line.x = element_line(colour = textcolor, size = 0.5)
) +
guides(fill = guide_legend(ncol = 5, keywidth = 1))
gt <- ggplot_gtable(ggplot_build(PortPlot))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
bar_size <- 4 / 15
height <- min(1 + companiestoprint, n_distinct(OilCompanies$company_name)) * bar_size + .86
} else {
if (chart_type == "EQ") {
Type <- oil_equity
} else if (chart_type == "CB") {
Type <- oil_bond
} #
Label <- paste0(Nooilproductionbreakdowninyour, " ", Type, portfo) #
gt <- no_chart(Label) #+
height <- 3
}
ggsave(gt, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = height, width = 10, dpi = ppi)
tech_to_plot <<- ""
chart_type <<- ""
BV.asset_type <<- ""
}
MapChart <- function(plotnumber, chart_type, tech_to_plot, plot_year, explicit_filename = "") {
Power <- data.frame()
tech_to_plot <<- tech_to_plot
# ScenarioGeographyChoose <<- "Global"
mapfilename <- ""
if (chart_type == "EQ" & has_equity == TRUE) {
Power <- filter_by_parameters(EQportmap, "EQ", byscenario = F)
mapfilename <- paste0(results_path, "/", investor_name_select, "/", "Equiry_results_map.rda")
}
if (chart_type == "CB" & has_debt == TRUE) {
Power <- filter_by_parameters(CBportmap, "CB", byscenario = F, by_equity_market = FALSE)
mapfilename <- paste0(results_path, "/", investor_name_select, "/", "Bonds_results_map.rda")
}
if (data_check(Power)) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (!file.exists(mapfilename)) {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
# tech_to_plot <- gsub("Cap","",tech_to_plot)
Power <- as.data.frame(Power)
# Power$technology<-gsub("RenewablesCap","Renewables",Power$technology)
# Power$technology<-gsub("NuclearCap","Nuclear",Power$technology)
# Power$technology<-gsub("HydroCap","Hydro",Power$technology)
Power$technology <- as.factor(Power$technology)
Power <- subset(Power, year == plot_year)
Power <- Power %>%
group_by(ald_location, technology) %>%
summarise(Production = sum(plan_alloc_wt_tech_prod)) %>%
ungroup() %>%
filter(technology == tech_to_plot) %>%
arrange(desc(Production))
Power <- as.data.frame(Power)
UNIT <- c("MW", "MW", "MW", "MW", "MW", Vehicles, Vehicles, Vehicles, "m3", "bbl", MetricTons) #
names(UNIT) <- c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap", "Electric", "Hybrid", "ICE", "Gas", "Oil", "Coal")
if (sector_to_plot %in% c("Power")) {
capprod <- Power_Unit
} else {
capprod <- xProduction
} #
# if (nrow(Power)>0){
tech_map <- joinCountryData2Map(Power, joinCode = "ISO2", nameJoinColumn = "ald_location")
tech_map_poly <- fortify(tech_map) # extract polygons
tech_map_poly <- base::merge(tech_map_poly, tech_map@data, by.x = "id", by.y = "ADMIN", all.x = T)
tech_map_poly <- tech_map_poly %>% arrange(id, order)
outputplot <- ggplot() +
coord_map(xlim = c(-180, 180), ylim = c(-60, 75)) +
geom_polygon(data = tech_map_poly, aes(long, lat,
group = group,
fill = Production
), size = 0.3) +
scale_fill_gradient(
name = paste0(capprod, " (", UNIT[tech_to_plot], ")"),
# breaks=floor(seq(0,max(Power$Production),length.out = 3)),
low = eval(parse(text = paste("MIN", tech_to_plot, sep = ""))),
high = eval(parse(text = paste("MAX", tech_to_plot, sep = ""))),
labels = comma
) +
theme_bw() +
xlab(NULL) +
ylab(NULL) +
guides(fill = guide_colorbar(
keywidth = 0.7, keyheight = 0.7,
reverse = F, title.position = "top"
)) +
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
legend.position = "right",
legend.title.align = 0.5,
legend.direction = "vertical",
legend.key.width = unit(1, "cm"),
legend.key.height = unit(0.8, "cm")
)
#
# mapWorld <- borders("world", colour="#E7E7E7", fill="#1a0000", alpha = .5)
# outputplot<- ggplot()+mapWorld+
# geom_point(aes(x=Power$Longitude, y=Power$Latitude, size=Power$Production), alpha = .9, color="#000033")+
# labs(size = "Production")+
# scale_size_continuous(breaks = round(unname(quantile(Power$Production)),digits = 0))+
# theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
# axis.ticks.x=element_blank(),
# axis.title.y=element_blank(),
# axis.text.y=element_blank(),
# axis.ticks.y=element_blank())
} else {
Label <- YourportfoliohasnoproductionfortheselectedTechnologyScenarioGeographyandMarket #
outputplot <- no_chart(Label)
}
ggsave(plot = outputplot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 5, width = 10, dpi = ppi) # linewidth_in*.9
tech_to_plot <<- ""
BV.asset_type <<- ""
}
PeerComparison <- function(plotnumber, VariableToPlot = "plan_carsten", Grouping.Level = "portfolio_name", chart_type, BV.asset_type = "") {
BV.asset_type <<- BV.asset_type
# Title <- paste0("Percent of ", ifelse(chart_type=="CB",BondReference,"Equity")," Portfolio Value")
browntechs <- data.frame("ald_sector" = c("Automotive", "Power", "Power", "OilGas", "OilGas", "CoalMining"), "technology" = c("ICE", "CoalCap", "GasCap", "Oil", "Gas", "Coal"))
if (chart_type == "EQ") {
Title <- PercentofEquityPortfolioValue #
Batch <- filter_by_parameters(eq_peersSep, "EQ", scenario.irrelevant = T)
combin <- filter_by_parameters(EQCombin, "EQ", scenario.irrelevant = T)
} else if (chart_type == "CB") {
Title <- PercentofCorporateBondsPortfolioValue #
Batch <- filter_by_parameters(cb_peersSep, "CB", by_equity_market = FALSE, scenario.irrelevant = T)
combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE, scenario.irrelevant = T)
} else if (chart_type == "BV") {
if (BV.asset_type == "BV_Equity") {
Title <- PercentofEquityPortfolioValue #
} else if (BV.asset_type == "BV_Bonds") {
Title <- PercentofCorporateBondsPortfolioValue #
}
Batch <- filter_by_parameters(BVPeersSep, "BV", by_equity_market = FALSE, scenario.irrelevant = T, BV.asset_type = BV.asset_type)
combin <- filter_by_parameters(BVCombin, "BV", by_equity_market = FALSE, scenario.irrelevant = T, BV.asset_type = BV.asset_type)
}
combin <- as.data.frame(combin)
Batch <- as.data.frame(Batch)
# Batch <- Batch %>% filter(investor_name != investor_name_select)
MetricCol <- VariableToPlot
df <- bind_rows(combin, Batch)
df <- as.data.frame(df)
df <- subset(df, year %in% c(start_year, 2018) & ald_sector %in% c("Oil&Gas", "Coal"), select = c("investor_name", "portfolio_name", "ald_sector", "technology", MetricCol))
df <- df[!is.na(df[, MetricCol]), ]
if (investor_name_select %in% df$investor_name) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
ID.COLS <- Grouping.Level
BarColors <- energy
Labels <- FossilFuelsSector #
port.value <- sum(filter(df, portfolio_name == portfolio_name_select & investor_name == investor_name_select)[MetricCol])
portfolio_label <- paste0(
ThisPortfolio, " ", #
unique(round(100 * (port.value), 2)), "%"
)
dfagg <- aggregate(eval(parse(text = MetricCol)) ~ eval(parse(text = ID.COLS)) + ald_sector, data = df, sum)
colnames(dfagg) <- c("ID.COL", "ald_sector", "Metric")
dfagg$Metric <- as.numeric(dfagg$Metric)
dfagg <- as.data.frame(dfagg)
dfagg <- dfagg %>%
group_by(ID.COL) %>%
summarise(Metricsum = sum(Metric))
x_length <- length(unique(dfagg$ID.COL))
xlabel <- ""
yaxislabel <- paste0(wrap_labels(ExposureoftheportfoliotoFossilFuels, 30)) #
if (range(dfagg$Metricsum)[1] >= 0 & range(dfagg$Metricsum)[2] >= 0) {
lim <- c(0, range(dfagg$Metricsum)[2] + 0.005)
} else if (range(dfagg$Metricsum)[1] < 0 & range(dfagg$Metricsum)[2] >= 0) {
lim <- c(range(dfagg$Metricsum)[1] - 0.005, range(dfagg$Metricsum)[2] + 0.005)
} else if (range(dfagg$Metricsum)[1] < 0 & range(dfagg$Metricsum)[2] < 0) {
lim <- c(range(dfagg$Metricsum)[1] - 0.005, 0)
}
dfagg <- dfagg %>% arrange(-Metricsum)
dfagg$ID.COL <- factor(dfagg$ID.COL, levels = dfagg$ID.COL[rev(order(dfagg$Metricsum))])
if (ID.COLS == "portfolio_name") {
x_coord <- which(dfagg$ID.COL == investor_name_select)
}
if (ID.COLS == "portfolio_name") {
x_coord <- which(dfagg$ID.COL == portfolio_name_select)
}
is_left <- x_coord / x_length < .50
is_first <- if_else(x_coord == 1, .005, 0)
distribution_plot <- ggplot(dfagg) +
geom_bar(aes(x = ID.COL, y = Metricsum),
stat = "identity", width = 1
) +
annotate("text",
x = x_coord, y = port.value,
label = portfolio_label,
hjust = ifelse(is_left, -.05, -0.05), # 1.05
vjust = -0.3,
size = textsize * (5 / 14)
) +
scale_fill_manual(values = BarColors, labels = Labels) + # , breaks=c(MetricC) #
scale_y_continuous(
limits = lim,
expand = expand_scale(add = c(0, 0)),
name = yaxislabel,
labels = percent
) +
scale_x_discrete(labels = NULL, expand = c(0, 0)) +
coord_cartesian(ylim = c(0, lim[2]), clip = "off") +
theme_distribution() +
geom_hline(yintercept = 0, size = 1) +
xlab(xlabel) +
geom_vline(xintercept = x_coord, linetype = 2) +
theme(
legend.position = "none",
plot.margin = unit(c(1, 0, 0, 0), "cm")
)
# gt <- ggplot_gtable(ggplot_build(distribution_plot))
# gt$layout$clip[gt$layout$name == "panel"] <- "off"
# grid.draw(gt)
} else {
if (sector_to_plot == "Power") {
SectorToInsert <- PowerSector #
} else if (sector_to_plot == "Fossil Fuels") {
SectorToInsert <- FossilFuelsSector #
} else {
SectorToInsert <- " "
}
Label <- paste0(Yourportfoliocontainsnoholdingsinthe, " ", SectorToInsert, " ", sectorintheselectedMarket) #
distribution_plot <- no_chart(Label)
}
ggsave(plot = distribution_plot, filename = graph_name(plotnumber, ParameterFile), bg = "white", height = 2.8, width = 10, dpi = ppi)
BV.asset_type <<- ""
}
ShippingChart <- function(plotnumber, chart_type, plot_year, sector_to_plot = "Shipping", explicit_filename = "") {
# print(chart_type)
# print(sector_to_plot)
# chart_type <-"All"
Combin <- data.frame()
if (GraphType == "Report") {
LegendOn <- FALSE
} else {
LegendOn <- TRUE
}
if (chart_type == "EQ" & has_equity == TRUE) {
Peers <- filter_by_parameters(eq_peers, "EQ")
Market <- filter_by_parameters(eq_market, "EQ")
Combin <- filter_by_parameters(EQCombin, "EQ")
} else if (chart_type == "CB" & has_debt == TRUE) {
Peers <- filter_by_parameters(cb_peers, "CB", by_equity_market = FALSE)
Market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
Combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
} else if (chart_type %in% c("All", "")) {
sector_to_plot <<- "Shipping"
Market_e <- filter_by_parameters(eq_market, "EQ")
if (data_check(Market_e) == TRUE) {
Market_e$Type <- "Equity Market"
}
Market_b <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
if (data_check(Market_b) == TRUE) {
Market_b$Type <- "Bond Market"
}
Combin_e <- filter_by_parameters(EQCombin, "EQ")
if (data_check(Combin_e) == TRUE) {
Combin_e$Type <- "Equity Portfolio"
}
Combin_b <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
if (data_check(Combin_b) == TRUE) {
Combin_b$Type <- "Bond Portfolio"
}
if (has_equity & has_debt) {
Market <- bind_rows(Market_e, Market_b)
} else if (has_debt & (has_equity == FALSE)) {
Market <- Market_b
} else if (has_equity & (has_debt == FALSE)) {
Market <- Market_e
}
if (has_equity & has_debt) {
Combin <- bind_rows(Combin_e, Combin_b)
} else if (has_debt & (has_equity == FALSE)) {
Combin <- Combin_b
} else if (has_equity & (has_debt == FALSE)) {
Combin <- Combin_e
}
}
# print(unique(Combin$ald_sector))
if (data_check(Combin) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
if (!chart_type %in% c("All", "")) {
if (data_check(Peers)) {
Peers$Type <- "Peers"
} else {
Type <- c()
Type <- as.character(Type)
Peers[, "Type"] <- Type
}
Peers <- unique(Peers)
Peers <- Peers %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "Type", "plan_alloc_wt_tech_prod") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
Peers <- Peers %>%
complete(
Type = c("Peers"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
Combin$Type <- "Portfolio"
if (data_check(Market)) {
Market$Type <- "Market"
} else {
Type <- c()
Type <- as.character(Type)
Market[, "Type"] <- Type
}
}
Combin <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>% # "Type
rename(WtProduction = plan_alloc_wt_tech_prod)
if (chart_type %in% c("All", "")) {
Combin <- Combin %>%
complete(
Type = c("Equity Portfolio", "Bond Portfolio"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
}
if (chart_type %in% c("All", "") && (data_check(Market) == FALSE)) {
Type <- c()
Type <- as.character(Type)
Market[, "Type"] <- Type
}
Market <- Market %>%
ungroup() %>%
filter(technology != "OilCap" & year == plot_year) %>% # year == PlotYr & Type =="Portfolio") %>%
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>% # "Type"
rename(WtProduction = plan_alloc_wt_tech_prod)
if (chart_type %in% c("All", "")) {
Market <- Market %>%
complete(
Type = c("Equity Market", "Bond Market"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
} else {
Market <- Market %>%
complete(
Type = c("Market"), technology = c("A", "B", "C", "D", "E", "F", "G"),
fill = list(WtProduction = 0, ald_sector = "Shipping")
)
}
if (!chart_type %in% c("All", "")) {
Production <- bind_rows(Combin, Peers, Market)
} else if (chart_type %in% c("All", "")) {
Production <- bind_rows(Combin, Market)
}
# print(unique(Production$Type))
Production$ald_sector <- as.factor(Production$ald_sector)
levels(Production$ald_sector)[levels(Production$ald_sector) == "Coal"] <- "Fossil Fuels"
levels(Production$ald_sector)[levels(Production$ald_sector) == "Oil&Gas"] <- "Fossil Fuels"
# Aggregate and rename CarstenMetric_Port
ID.COLS <- c("ald_sector", "technology", "Type")
Production <- Production %>% gather(key = Metric, value = Value, "WtProduction")
Production <- aggregate(Production["Value"], by = Production[c(ID.COLS)], FUN = sum)
colours <- as.vector(ColourPalette[["Colours"]])
names(colours) <- technology_order
labels <- c(
"Renewables", "Hydro", "Nuclear", "Gas", "Coal", "Electric", "Hybrid", "ICE", "Gas", "Oil", "Coal",
"Freight", "Passenger", "A", "B", "C", "D", "E", "F", "G"
)
names(labels) <- technology_order
Production$technology <- factor(Production$technology, levels = technology_order)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")) #
Production$Type <- wrap_labels(Production$Type, 20)
if (!chart_type %in% c("All", "")) {
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Peers", "Market"))
xlabels <- c(Portf, wrap_labels(x_Peers, 10), x_Benchmark) #
} else if (chart_type %in% c("All", "")) {
Production$Type <- factor(Production$Type, levels = c("Bond Portfolio", "Equity Portfolio", "Bond Market", "Equity Market"))
xlabels <- c(
paste0(wrap_labels(BondPortfolio, 10)), #
paste0(wrap_labels(EquityPortfollio, 10)), #
paste0(wrap_labels(BondMarket, 10)), #
paste0(wrap_labels(EquityMarket, 10))
) #
}
Production <- subset(Production, select = c("Type", "ald_sector", "technology", "Value"))
shippingchart <- stacked_bar_chart(Production, colours, labels) +
ylab(ShareofSectorProduction) + #
scale_x_discrete(labels = xlabels, expand = c(0, 0)) +
theme(plot.margin = unit(c(0.2, 0.2, 0.2, 0), "cm"))
if (LegendOn == TRUE) {
shippingchart <- shippingchart +
theme(
plot.title = element_text(hjust = 0.5, colour = textcolor, size = 11, margin = unit(c(0, 0, 1, 0), "lines")),
legend.position = "bottom",
legend.title = element_blank()
)
}
} else {
Label <- Theportfoliohasnocompaniesintheshippingsector #
shippingchart <- no_chart(Label)
}
ggsave(shippingchart, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 3, width = 4, dpi = ppi)
sector_to_plot <<- ""
}
steel_chart <- function(plotnumber) {
if (chart_type == "EQ") {
ProdSnapshot <- filter_by_parameters(EQCompProdSnapshot, "EQ")
}
if (chart_type == "CB") {
ProdSnapshot <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = FALSE)
}
ProdSnapshot <- subset(ProdSnapshot, year = start_year + 5)
steel <- base::merge(ProdSnapshot, SteelData, by.x = "Name", by.y = "Company") # import steel data
nosteel <- nrow(steel)
if (nosteel > 0) {
# categorize high c vs. low c
# Classification<- ifelse()
steel <- steel %>%
group_by(Classification) %>%
summarize(tot = sum(tech_share)) %>%
ungroup() %>%
mutate(per = tech_share / tot)
plottheme <- ggplot(
data = steel, aes_string(x = asset_type, y = per, fill = Classification),
show.guide = TRUE
) +
geom_bar(stat = "identity", position = "fill", width = .6) +
# geom_hline(yintercept = c(.25,.50,.75), color="white")+
scale_fill_manual(values = colors, labels = legend_labels, breaks = names(legend_labels)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts()
} else {
Label <- YourPortfolioincludesnocompanieswithsteelproductionintheselectedscenario_geographyandMarket #
outputplot <- no_chart(Label)
}
ggsave(filename = graph_name(plotnumber, ParameterFile), bg = "white", height = 3.6, width = 3.6, plot = outputplot, dpi = ppi)
}
SectorDataAnalysis <- function() {
over <- subgroup_overview
over <- subset(over, over$portfolio_name == portfolio_name_select & valid_input == 1)
over$financial_sector <- ifelse(over$financial_sector %in% c("Coal", "Oil&Gas"), "Fossil Fuels", over$financial_sector)
over$financial_sector <- ifelse(over$financial_sector %in% c("Aviation", "Shipping"), "Aviation & Shipping", over$financial_sector)
over$financial_sector <- ifelse(over$financial_sector %in% c("Steel", "Cement", "Cement & Steel"), "Cement & Steel", over$financial_sector)
over$financial_sector <- ifelse(over$financial_sector %in% c("Other"), "Not Included", over$financial_sector)
return(over)
}
ScopeOfAnalysis <- function(plotnumber, explicit_filename = "") {
over <- SectorDataAnalysis()
Bond_translation <- if (BondReference == "Corporate bond") {
paste0(wrap_labels(BondsTitle, 10)) #
} else {
wrap_labels(Corporate_Bond_1, 10) ## place for other category!!!
}
over$asset_type <- gsub("Funds", "Others", over$asset_type) #
over$asset_type <- gsub("Bonds", Bond_translation, over$asset_type) #
over$asset_type <- gsub("Equity", EquityTitle, over$asset_type) #
over$asset_type <- gsub("Others", OthersTitle, over$asset_type) #
over$Sector.All <- ifelse(over$financial_sector == "Not Included", "Sectors Not in Scope", "Scope of the Analysis")
over <- over %>%
ungroup() %>%
select(Sector.All, asset_type, valid_value_usd) %>%
group_by(Sector.All, asset_type) %>%
summarise(valid_value_usd = sum(valid_value_usd))
if (data_check(over) == F) {
over <- over %>%
ungroup() %>%
add_row(Sector.All = "Power", asset_type = Bond_translation, valid_value_usd = 0)
}
over <- over %>%
complete(
asset_type = c(Bond_translation, EquityTitle, OthersTitle), #
Sector.All = c(
"Sectors Not in Scope",
# "Other Climate Sectors",
"Scope of the Analysis"
),
fill = list(valid_value_usd = 0)
) %>%
unique()
over <- as.data.frame(over)
orderofchart <- c(Bond_translation, EquityTitle, OthersTitle) #
over$asset_type <- factor(over$asset_type, levels = orderofchart)
over$Sector.All <- factor(over$Sector.All, levels = c(
"Sectors Not in Scope",
# "Other Climate Sectors",
"Scope of the Analysis"
), ordered = TRUE)
tot <- over %>%
group_by(asset_type) %>%
summarise(s = sum(valid_value_usd))
# for NEW VERSION horizontal chart
over_horizontal_chart <- over %>% filter(asset_type != OthersTitle)
annotate.position <- over_horizontal_chart %>%
group_by(asset_type) %>%
summarize(sumValueUSD = sum(valid_value_usd)) %>%
summarize(maxsumValueUSD = max(sumValueUSD))
annotate.label <- over_horizontal_chart %>% filter(Sector.All == "Scope of the Analysis")
number_cifer <- nchar(round(annotate.position$maxsumValueUSD, digits = 0))
breaks_by <- function(number_cifer) {
10^(number_cifer - 1)
}
breaks_interval <- breaks_by(number_cifer)
plot <- ggplot(over_horizontal_chart, aes(x = fct_rev(asset_type), y = valid_value_usd, fill = Sector.All)) +
geom_bar(position = "stack", stat = "identity", width = 0.9) +
coord_flip() +
scale_fill_manual(
name = "", labels = c(
SectorsNotinScope,
# OtherClimateRelevantSectors,
SectorsinScenarioAnalysis
),
values = c(
"grey80",
# "#deebf7",
"#265b9b"
), drop = FALSE
) + #
# scale_x_discrete(name="Asset Type", labels = x_labels) +
scale_y_continuous(
name = MarketValue, labels = comprss, expand = c(0, 0, 0, 0),
breaks = seq(0, annotate.position$maxsumValueUSD, by = breaks_interval * 2)
) + # breaks = scales::pretty_breaks(n = 3)) + #
# limits = c(0, annotate.position$maxsumValueUSD+0.3*annotate.position$maxsumValueUSD)) + #
guides(fill = guide_legend(nrow = 1, byrow = TRUE, reverse = TRUE)) +
theme_barcharts() +
theme(
plot.title = element_text(colour = "#265b9b", size = 11, hjust = 1),
legend.position = "bottom",
legend.text = element_text(size = textsize),
legend.box.margin = unit(c(-15, 60, -10, 0), "pt"),
legend.spacing.y = unit(0, "pt"),
axis.line.x = element_blank(),
axis.line.y = element_line(size = 0.5),
axis.text.x = element_text(colour = textcolor, size = 11),
axis.text.y = element_text(colour = textcolor, size = 11),
axis.title.x = element_text(colour = textcolor, size = 11, margin = margin(5, 0, 0, 0, "pt"), hjust = 0.3),
axis.title.y = element_blank(),
panel.grid.major.x = element_line(colour = "#989898", size = 0.2),
plot.margin = unit(c(5, 10, 10, 2), "pt")
) +
annotate("text",
x = annotate.label$asset_type, y = annotate.position$maxsumValueUSD + 0.35 * annotate.position$maxsumValueUSD,
# label = wrap_labels(paste0(comma(round(annotate.label$valid_value_usd, digits = 0))), 10),
label = comprss(annotate.label$valid_value_usd),
family = textfont, colour = "#265b9b", hjust = 1
) +
ggtitle(Climaterelevantsectors)
ggsave(plot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 2.2, width = 4.5, dpi = ppi) # linewidth_in*.9
}
CarstenMetricChart <- function(plotnumber, chart_type, explicit_filename = "") {
PortName_IN <- portfolio_name_select
if (chart_type == "CB" & has_debt == TRUE) {
market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
port <- bind_rows(combin, market)
Type <- "Corporate bond"
port$portfolio_name <-
dplyr::case_when(
port$portfolio_name == portfolio_name_select ~ "Portfolio",
port$portfolio_name == cb_market_ref ~ cb_market_ref,
TRUE ~ port$portfolio_name
)
port$portfolio_name <- factor(port$portfolio_name, levels = c("Portfolio", cb_market_ref), ordered = TRUE)
lab <- wrap_labels(cb_market_ref, 12)
MarketRef <- cb_market_ref
} else if (chart_type == "CB" & has_debt == FALSE) {
PlotChart <- FALSE
}
if (chart_type == "EQ" & has_equity == TRUE) {
# Set as CB to ensure that the PortWeight values are used
market <- filter_by_parameters(eq_market, "CB", scenario.irrelevant = TRUE)
port <- filter_by_parameters(EQCombin, "CB", scenario.irrelevant = TRUE)
port <- bind_rows(port, market)
Type <- "equity"
port <- subset(port, portfolio_name %in% c(portfolio_name_select, eq_market_ref)) # "ListedEquity"
port$portfolio_name <-
dplyr::case_when(
port$portfolio_name == portfolio_name_select ~ "Portfolio",
port$portfolio_name == eq_market_ref ~ eq_market_ref,
TRUE ~ port$portfolio_name
)
port$portfolio_name <- factor(port$portfolio_name, levels = c("Portfolio", eq_market_ref), ordered = TRUE)
lab <- wrap_labels(eq_market_ref, 10)
MarketRef <- eq_market_ref
} else if (chart_type == "EQ" & has_equity == FALSE) {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
# print("Plotting preparation")
current.port <- subset(port, year == start_year & portfolio_name == "Portfolio") %>%
mutate(Metric = plan_carsten)
current.market <- subset(port, year == start_year & portfolio_name %in% c(eq_market_ref, cb_market_ref)) %>%
mutate(Metric = plan_carsten)
port <- bind_rows(current.port, current.market) %>% select(portfolio_name, ald_sector, technology, Metric)
sector_list <- sector_list[sector_list != "Fossil Fuels"]
tech_list <- tech_list[tech_list != "OilCap"]
port <- port %>%
complete(
portfolio_name = c("Portfolio", MarketRef),
technology = tech_list,
fill = list(Metric = 0)
)
port <- port %>% mutate( # portfolio_name = if_else(investor_name == investor_name_select, portfolio_name_select, MarketRef),
ald_sector = set_ald_sector(technology)
)
port$ald_sector2 <- paste0(port$ald_sector, " Production") #
port$ald_sector2 <- ifelse(port$ald_sector == "Power", C_Power, port$ald_sector2) #
port$ald_sector2 <- ifelse(port$ald_sector == "Automotive", S_Automotive, port$ald_sector2) #
port$ald_sector2 <- ifelse(port$ald_sector == "Oil&Gas", P_OilandGas, port$ald_sector2) #
port$ald_sector2 <- ifelse(port$ald_sector == "Coal", P_Coal, port$ald_sector2) #
port$ald_sector2 <- factor(port$ald_sector2, levels = c(P_OilandGas, P_Coal, C_Power, S_Automotive)) # levels OK
port <- subset(port, technology != "OilCap")
tech.levels <- technology_order_short
tech.labels <- c(
P_Gas, P_Oil, P_Coal, #
C_Renewables, C_Hydro, C_Nuclear, C_Gas, C_Coal, #
T_Electric, T_Hybrid, T_ICE
) #
if (max(port$Metric) <= 0.001) {
perc.labels.digits <- 0.01
} else if (max(port$Metric) <= 0.01) {
perc.labels.digits <- 0.1
} else {
perc.labels.digits <- 1
}
port$technology <- factor(port$technology, levels = tech.levels, ordered = TRUE)
port$portfolio_name <- dplyr::recode(port$portfolio_name,
"Portfolio" = Portf,
MarketRef = lab
)
port$portfolio_name <- factor(port$portfolio_name, levels = c(Portf, MarketRef), ordered = TRUE)
tech.colors <- c(GasProdColour, OilProdColour, CoalProdColour, RenewablesColour, HydroColour, NuclearColour, GasCapColour, CoalCapColour, ElectricColour, HybridColour, ICEColour)
tots <- port %>%
group_by(portfolio_name, ald_sector2) %>%
summarise(Metric = sum(Metric, na.rm = T))
outputplot <- ggplot(port, aes(x = portfolio_name, y = Metric, group = technology, fill = technology)) +
geom_bar(stat = "identity", position = "stack") +
# geom_text(data=tots, aes(x=PortName, y=Metric, label=percent(Metric))) +
scale_x_discrete(name = "", labels = c(Portf, lab)) + #
scale_y_continuous(
name = paste0(wrap_labels(Weightbymarketvalueofissuersexposedtothetechnology, 50), "\n"), labels = percent_format(accuracy = perc.labels.digits), expand = c(0, 0), #
limits = c(0, max(tots$Metric) + .01)
) +
scale_fill_manual(name = tech.levels, labels = tech.labels, values = tech.colors) +
guides(fill = guide_legend(ncol = 1)) +
theme_cdi() +
facet_wrap(~ald_sector2, nrow = 1, labeller = label_wrap_gen(width = 20)) +
theme(
legend.title = element_text(size = 14),
legend.text = element_text(size = 11),
axis.text.x = element_text(angle = 0, colour = textcolor, size = 11),
axis.text.y = element_text(size = 11),
axis.title.y = element_text(size = 13),
strip.text = element_text(size = 13),
# axis.ticks.y = element_line(colour=textcolor,size = 1),
axis.line.x = element_line()
)
} else {
Label <- paste(Nodataintheportfolio) #
outputplot <- no_chart(Label)
}
ggsave(outputplot, filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename), bg = "white", height = 4, width = 13, dpi = ppi * 0.8)
BV.asset_type <<- ""
}
RenewableAdditionsChart <- function(plotnumber, companiestoprint, chart_type) {
sector_to_plot <<- "Power"
RenewableAdds <- NA
RenewableAdditions <- readRDS(paste0(Location, "ReferenceData/RenewablesAdditionsData.rda"))
RenewableAdditions$ald_sector <- "Power"
# RenewableAdditions$scenario_geography <- ifelse(RenewableAdditions$ald_sector == "Power","Global",RenewableAdditions$scenario_geography)
if (chart_type == "EQ") {
RenewableAdditions <- filter_by_parameters(RenewableAdditions, "EQ") %>%
filter(FI %in% c("Equity", "EQUITY"))
CompProdSnapshot <- filter_by_parameters(EQCompProdSnapshot, "EQ", ActorSectorOnly = T)
RenewableAdds <- RenewableAdditions[RenewableAdditions$ID %in% CompProdSnapshot$bloomberg_id, ]
}
if (chart_type == "CB") {
RenewableAdditions <- filter_by_parameters(RenewableAdditions, "CB") %>%
filter(FI %in% c("Bonds", "BONDS"))
CompProdSnapshot <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F, ActorSectorOnly = T)
RenewableAdds <- RenewableAdditions[RenewableAdditions$ID %in% CompProdSnapshot$corp_bond_ticker, ]
}
if (data_check(RenewableAdds) == T) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
RenewablesBar <- RenewableAdds
maxcompanies <- min(companiestoprint, nrow(RenewablesBar))
RenewablesBar <- RenewablesBar[order(RenewablesBar$plan_tech_prod, decreasing = T), ]
# RenewablesBar$company_name <- factor(RenewablesBar$company_name, levels = RenewablesBar$company_name[order(RenewablesBar$Additions)])
RenewablesBar <- RenewablesBar[seq(1, maxcompanies), ]
Yaxislabel <- paste0(Renewablecapacityadditions, " (", start_year, " - ", start_year + 5, ")") #
stillreq <- " Remaining Required Additions" # not used?
remainlabel <- x_Progress #
progresslabel <- RemainingRequiredAdditions #
Target <- "Progress" # not used?
RenewablesBar <- RenewablesBar %>% select(company_name, Additions, RequiredAdditions)
RenewablesBarLong <- melt(RenewablesBar, variable.name = "variable", value.name = "value", id.vars = c("company_name"))
sumren <- RenewablesBarLong %>%
group_by(company_name) %>%
summarize(tot = sum(value))
RenewablesBarLong <- left_join(RenewablesBarLong, sumren, by = "company_name") %>%
mutate(perc = value / tot)
RenewablesBarLong$variable <- factor(RenewablesBarLong$variable, levels = c("RequiredAdditions", "Additions"))
RenewablesBarLong <- RenewablesBarLong %>%
filter(company_name %in% unique(RenewablesBarLong$company_name)[1:min(companiestoprint, length(unique(RenewablesBarLong$company_name)))])
comp <- subset(RenewablesBarLong, variable == "RequiredAdditions")
company_labels <- trimws(unique(RenewablesBarLong$company_name))
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
CompName <- RenewablesBarLong[order(RenewablesBarLong[which(RenewablesBarLong$variable == "Additions"), ]$perc, decreasing = T), ]$company_name
RenewablesBarLong$company_name <- factor(RenewablesBarLong$company_name, levels = rev(CompName))
RenAddBar <- ggplot(RenewablesBarLong, aes(x = company_name, y = perc, fill = variable)) +
geom_bar(stat = "identity", width = .8, position = "fill") +
# geom_segment(aes(x=0, xend = 0 , y=0, yend = 1), size=1, colour = AxisColour, arrow = arrow(length = unit(0.4,"cm")))+
geom_hline(yintercept = 1, colour = area_2, linetype = "longdash", size = 1) +
scale_fill_manual(values = c("RequiredAdditions" = badexpColour, "Additions" = YourportColour), labels = c("Additions" = remainlabel, "RequiredAdditions" = progresslabel)) +
scale_y_continuous(limits = c(0, 1), expand = c(-0.1, 0.1), label = percent) +
scale_x_discrete(expand = c(0, 0), label = company_labels) +
theme_barcharts() +
guides(fill = guide_legend(nrow = 1, reverse = TRUE)) +
coord_flip() +
theme(
axis.title.x = element_text(colour = AxisColour, size = 11),
axis.title.y = element_blank(),
axis.text.y = element_text(size = 11),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(size = 11, colour = AxisColour, margin = margin(l = 0.15, r = 0.35, unit = "cm")),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
legend.title = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(.4, 1.5, 0, 0), "lines")
) +
ylab(Yaxislabel) +
geom_text(
data = comp,
aes(x = company_name, y = 0.88, hjust = 1),
label = paste0(round(comp$value, digits = 0), " MW ", RemainingRequiredAdditions), color = textcolor, size = 3
) # 2.53 #
RenAddValues <- c(4.8, 8)
bar_size <- 8 / 15
height <- min(1 + companiestoprint, n_distinct(RenewablesBarLong$company_name) / 2) * bar_size + .86
# plotwidth <- .1*NoCompanies +7.1
} else {
Label <- YourPortfoliodoesnotincludeanycompanieswithrenewablepower #
RenAddBar <- no_chart(Label)
# plotwidth <- 7
height <- 3
}
ggsave(filename = graph_name(plotnumber, ParameterFile), height = height, width = 10, plot = RenAddBar, dpi = ppi)
sector_to_plot <<- ""
}
carboninoutdata <- function(chart_type) {
ccap <- NA
portfolio1 <- NA
if (chart_type == "EQ") {
EQCompProdSnapshot <- filter_by_parameters(EQCompProdSnapshot, "EQ")
if (data_check(EQCompProdSnapshot) == TRUE) {
CompProdSS <- EQCompProdSnapshot %>%
ungroup() %>%
filter(year == start_year + 5) %>%
select(company_name, port_weight, id) %>%
rename(Name = company_name) %>%
unique()
CompProdSS <- as.data.frame(CompProdSS)
CompProdSS$id <- as.integer(CompProdSS$id)
ccap <- CarbonData
} else {
PlotChart <- FALSE
}
} else if (chart_type == "CB") {
CBCompProdSnapshot <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = FALSE)
if (data_check(CBCompProdSnapshot) == TRUE) {
CompProdSS <- CBCompProdSnapshot %>%
filter(year == start_year + 5) %>%
select(port_weight, corp_bond_ticker) %>%
rename(EQY_FUND_TICKER = corp_bond_ticker) %>%
unique()
} else {
PlotChart <- FALSE
}
ccap <- CarbonData
ccap$EQY_FUND_TICKER <- gsub(" [A-z ]*", "", as.character(ccap$EQY_FUND_TICKER))
}
if (data_check(ccap) == TRUE) {
ccap$InsideCarbonBudget <- ccap$TotalCarbonBudget - ccap$OutsideCarbonBudget
ccap <- subset(ccap, select = c("id", "TotalCarbonBudget", "OutsideCarbonBudget", "InsideCarbonBudget"))
colnames(ccap) <- c("id", "TotalCarbonBudget", "Outside Carbon Budget", "Inside Carbon Budget")
ccap$Inside.Carbon.Budget <- ccap$`Inside Carbon Budget` / ccap$TotalCarbonBudget
ccap$Outside.Carbon.Budget <- ccap$`Outside Carbon Budget` / ccap$TotalCarbonBudget
portfolio <- left_join(CompProdSS, ccap, by = "id") %>%
select(Name, port_weight, Inside.Carbon.Budget, Outside.Carbon.Budget)
portfolio1 <- melt(portfolio, id.vars = c("Name", "port_weight"), variable.name = "CarbonBudget")
portfolio1 <- subset(portfolio1, !is.na(value))
if (data_check(portfolio1) == TRUE) {
HasCarbonBudget <<- TRUE
} else {
HasCarbonBudget <<- FALSE
}
} else {
HasCarbonBudget <<- FALSE
}
return(portfolio1)
}
CarbonBudget <- function(plotnumber, companiestoprint, chart_type) {
portfolio1 <- carboninoutdata(chart_type)
if (HasCarbonBudget == TRUE) {
carbonorder <- c("Inside Carbon Budget", "Outside Carbon Budget")
portfolio1$CarbonBudget <- gsub("\\.", " ", portfolio1$CarbonBudget)
colors <- c(OilProdColour, area_6)
# AllData <- filter(AllData, technology %in% techorder)
portfolio1$CarbonBudget <- factor(portfolio1$CarbonBudget, levels = carbonorder)
portfolio1 <- portfolio1 %>%
arrange(-port_weight)
portfolio1 <- portfolio1 %>%
filter(Name %in% unique(portfolio1$Name)[1:min(companiestoprint, length(unique(portfolio1$Name)))])
dummy <- data.frame(
c("Name", ""),
c("port_weight", NA),
c("CarbonBudget", NA),
c("value", 0)
)
colnames(dummy) <- as.character(unlist(dummy[1, ]))
dummy <- dummy[-1, ]
dummy$value <- as.numeric(dummy$value)
portfolio1 <- rbind(portfolio1, dummy)
# portfolio1$Name <- factor(portfolio1$Name, levels=rev(unique(c("",portfolio1$Name))))
portfolio1$port_weight <- as.numeric(portfolio1$port_weight)
portfolio1 <- portfolio1 %>%
arrange(port_weight)
company_labels <- trimws(unique(portfolio1[!is.na(portfolio1$CarbonBudget), ]$Name))
for (i in 1:length(company_labels)) {
if (str_length(company_labels[i]) > 15) {
new_name <- strtrim(company_labels[i], 15)
company_labels[i] <- paste0(new_name, "...")
}
}
portfolio1$CarbonBudget <- recode(portfolio1$CarbonBudget,
"Outside Carbon Budget" = OutsideCarbonBudget,
"Inside Carbon Budget" = InsideCarbonBudget
)
portfolio1$CarbonBudget <- factor(portfolio1$CarbonBudget, levels = c(OutsideCarbonBudget, InsideCarbonBudget))
bar_labels <- c(company_labels, " ")
portfolio1$port_weight <- as.numeric(portfolio1$port_weight)
carb <- subset(portfolio1, !is.na(port_weight))
carbonorder <- recode(carbonorder,
"Outside Carbon Budget" = OutsideCarbonBudget,
"Inside Carbon Budget" = InsideCarbonBudget
)
names(colors) <- carbonorder
PortPlot <- ggplot(
data = portfolio1, aes(
x = reorder(Name, port_weight), y = value,
fill = factor(CarbonBudget, levels = c(OutsideCarbonBudget, InsideCarbonBudget))
),
show.guide = TRUE
) +
geom_bar(stat = "identity", position = "fill", width = .6) +
# geom_hline(yintercept = c(.25,.50,.75), color="white")+
scale_fill_manual(values = colors, labels = paste(carbonorder, " "), breaks = (carbonorder)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
scale_x_discrete(labels = bar_labels) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts() +
geom_text(
data = carb, aes(x = Name, y = 1),
label = perc(carb$port_weight),
hjust = -1, color = textcolor, size = textsize * (5 / 14)
) +
geom_text(aes(x = "", y = 1),
label = x_Weight, #
hjust = -0.5, color = textcolor, size = 10 * (5 / 14),
family = textfont
) +
xlab("") +
ylab("tech_share") +
coord_flip() +
theme(
legend.position = "bottom", legend.title = element_blank(),
plot.margin = unit(c(1, 6, 0, 0), "lines"), axis.line.x = element_line(colour = textcolor, size = 0.5)
) #+
# annotation_custom(
# grob = textGrob(label = x_Weight,
# gp=gpar(fontsize=8.5),
# hjust = 0),
# xmin = n_distinct(portfolio1$Name)+0.5, xmax = n_distinct(portfolio1$Name)+1, ymin = 1, ymax = 1.05)
#
# # gt <- PortPlot
gt <- ggplot_gtable(ggplot_build(PortPlot))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
dev.off()
# grid.draw(gt)
bar_size <- 4 / 15
h <- min(1 + companiestoprint, n_distinct(portfolio1$Name)) * bar_size + .66
HasCarbonBudget <<- TRUE
} else {
Label <- NoCarbonBudgetDataisavailablefortheOilCompaniesinyourportfolio #
# Label <- "No results are available for this combination of parameters"
gt <- no_chart(Label)
h <- 3
HasCarbonBudget <<- FALSE
}
ggsave(plot = gt, filename = graph_name(plotnumber, ParameterFile), bg = "white", height = h, width = 11, dpi = ppi)
}
OilGasBuildOut <- function(plotnumber, companiestoprint, chart_type, explicit_filename = "") {
sector_to_plot <<- "Oil&Gas"
# chart_type <<- chart_type
if (chart_type == "CB") {
comp <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F)
if (data_check(comp) == TRUE) {
comp <- subset(comp, portfolio_name == portfolio_name_select & ald_sector == "Oil&Gas")
comp$company_name <- comp$id
port <- filter_by_parameters(CBCombin, "CB", by_equity_market = F)
port.targets <- port %>%
filter(ald_sector == "Oil&Gas") %>%
select(investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography, ald_sector, technology, scen_tech_prod, year) %>%
arrange(year) %>%
group_by(investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography, ald_sector, technology) %>%
summarise(
Port.Scen.Diff = last(scen_tech_prod) - first(scen_tech_prod),
Port.Scen.Pct = Port.Scen.Diff / first(scen_tech_prod)
) %>%
ungroup() %>%
select(-scenario, -allocation, -equity_market, -scenario_geography, -ald_sector)
}
}
if (chart_type == "EQ") {
comp <- filter_by_parameters(EQCompProdSnapshot, "EQ")
if (data_check(comp) == TRUE) {
comp <- subset(comp, portfolio_name == portfolio_name_select & ald_sector == "Oil&Gas" & year %in% c(start_year, start_year + 5))
port <- filter_by_parameters(EQCombin, "EQ")
port <- subset(port, year %in% c(start_year, start_year + 5))
port.targets <- port %>%
filter(ald_sector == "Oil&Gas") %>%
select(
investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography,
ald_sector, technology, scen_tech_prod, year
) %>%
arrange(year) %>%
group_by(investor_name, portfolio_name, scenario, allocation, equity_market, scenario_geography, ald_sector, technology) %>%
summarise(
Port.Scen.Diff = last(scen_tech_prod) - first(scen_tech_prod),
Port.Scen.Pct = Port.Scen.Diff / first(scen_tech_prod)
) %>%
ungroup() %>%
select(-scenario, -allocation, -equity_market, -scenario_geography, -ald_sector)
}
}
if (data_check(comp) == TRUE) {
comp <- comp %>%
filter(ald_sector == "Oil&Gas") %>%
ungroup() %>%
select(scenario, company_name, ald_sector, technology, plan_tech_prod, scen_tech_prod, year) %>%
arrange(scenario, company_name, ald_sector, technology, year) %>%
group_by(scenario, company_name, ald_sector, technology) %>%
filter(plan_tech_prod > 0) %>%
summarise(first(plan_tech_prod), last(plan_tech_prod),
Plan.Diff = last(plan_tech_prod) - first(plan_tech_prod),
Scen.Diff = last(scen_tech_prod) - first(scen_tech_prod),
Plan.Pct = ifelse(Plan.Diff == 0, 0, (last(plan_tech_prod) - first(plan_tech_prod)) / first(plan_tech_prod))
)
}
## TODO: FIXME, THIS IS A HACK FOR THE SFC PROJECT
comp <- comp %>% mutate(Plan.Pct = ifelse(company_name == "PETRPE", (2347 - 1250) / 1250, Plan.Pct))
if (data_check(filter(comp, company_name == "PETRPE"))) {
tmp_row <- filter(comp, company_name == "PETRPE")
tmp_row <- tmp_row %>% mutate(technology = "Gas", Plan.Pct = 0)
comp <- rbind(comp, tmp_row)
}
## TODO: END FIXME, THIS IS A HACK FOR THE SFC PROJECT
if (data_check(comp) == TRUE) {
comp <- left_join(comp, port.targets %>% ungroup() %>% as.data.frame(), by = "technology")
comp$technology <- factor(comp$technology, levels = c("Gas", "Oil"), ordered = TRUE)
comp$technology <- dplyr::recode(comp$technology, Gas = T_GasProd, Oil = T_OilProd) #
comp <- comp %>%
group_by(scenario, technology) %>%
top_n(wt = `first(plan_tech_prod)`, n = companiestoprint)
# Seperate out the infinite values if necessary
comp_inf <- comp[is.infinite(comp$Plan.Pct), ]
comp <- comp[is.finite(comp$Plan.Pct), ]
MAX.VAL <- max(abs(comp$Plan.Pct), na.rm = TRUE)
temp.max <- ceiling(MAX.VAL * 10) / 10 + 0.2
temp.diff <- ceiling(temp.max * 2) / 10
breaks <- seq(0, temp.max + temp.diff, temp.diff)
breaks <- unlist(c(rev(-1 * breaks), breaks))
limits <- c(min(breaks), max(breaks))
comp$InfFlag <- 0
if (data_check(comp_inf) == TRUE) {
comp_inf$Plan.Pct <- MAX.VAL * 1
comp_inf$InfFlag <- 1
comp <- rbind(comp, comp_inf)
}
comp$Ord.Var <- paste0(comp$company_name, comp$technology)
comp <- comp %>%
ungroup() %>%
arrange(`first(plan_tech_prod)`)
comp$Ord.Var <- factor(comp$Ord.Var, levels = comp$Ord.Var, ordered = TRUE)
comp$Final.Name <- shortened_company_names(comp$company_name, 15)
port.targets$technology <- dplyr::recode(port.targets$technology, Gas = T_GasProd, Oil = T_OilProd) #
outputplot <- ggplot(comp, aes(x = Ord.Var, y = Plan.Pct, fill = technology)) +
geom_bar(stat = "identity") +
geom_hline(
data = port.targets, aes(
yintercept = Port.Scen.Pct,
linetype = paste0(ChangeinPortfolioProductionSpecifiedby, " ", Scenariochoose, " ", Scenariofrom, " ", start_year, "- ", start_year + 5)
), #
color = area_2, size = 1.5
) +
coord_flip() +
scale_x_discrete(name = "", labels = setNames(comp$Final.Name, as.character(comp$Ord.Var))) +
scale_y_continuous(
name = paste0(ChangeinPlannedProductionfrom, " ", start_year, "- ", start_year + 5), #
labels = percent_format(accuracy = 1), breaks = round(breaks, digits = 2), limits = limits
) +
scale_color_manual(values = area_2) +
scale_linetype_manual(name = "", values = c("solid")) +
scale_fill_manual(name = "", values = c(OilProdColour, GasProdColour)) + # guide_legend(reverse = TRUE)
facet_wrap(~technology, ncol = 1, scales = "free_y") +
theme_cdi() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 11)
) +
theme(axis.line = element_line()) +
theme(axis.ticks.x = element_line())
# Adds the infinite label
if (data_check(comp_inf) == TRUE) {
outputplot <- outputplot +
geom_text(data = comp[comp$InfFlag == 1, ], aes(x = Ord.Var, y = 0, label = "Future Production Only - Infinite Build Out", hjust = 0))
# annotate("text", x= 5, y=0, label = "Future Production Only - Infinite Build Out", hjust = 0)
}
bar_size <- 6 / 15
h <- min(1 + companiestoprint, n_distinct(comp$company_name)) * bar_size + 1.5
} else {
Label <- Nocompanieswithexposuretoeitheroilorgasproductioninyourportfolio #
outputplot <- no_chart(Label)
h <- 3.25
}
ggsave(outputplot,
filename = graph_name(plotnumber, ParameterFile, explicit_filename = explicit_filename),
height = h, width = 10, dpi = ppi
)
sector_to_plot <<- ""
chart_type <<- ""
BV.asset_type <<- ""
}
CoalRetirementChart <- function(plotnumber, companiestoprint, chart_type) {
PlotChart <- F
chart_type <- chart_type
sector_to_plot <<- "Power"
PortCoalRetirements <- NA
CoalRetirements$equity_market <- gsub("Market", "", CoalRetirements$equity_market)
# CoalRetirements$scenario_geography <- ifelse(CoalRetirements$scenario_geography == "Global","GlobalAggregate",CoalRetirements$scenario_geography)
CoalRetirements$ald_sector <- "Power"
if (chart_type == "EQ" & has_equity) {
CoalRet <- filter_by_parameters(CoalRetirements, "EQ") %>%
filter(FI %in% c("Equity", "EQUITY"))
if ("ALD.ID" %in% colnames(CoalRet)) {
CoalRet <- CoalRet %>% rename(ID = ALD.ID)
}
PortProduction <- filter_by_parameters(EQCompProdSnapshot, "EQ") # %>%
# filter(technology == "CoalCap")
} else if (chart_type == "CB" & has_debt) {
CoalRet <- filter_by_parameters(CoalRetirements, "CB", by_equity_market = F) %>%
filter(FI %in% c("Bonds", "BONDS"))
if ("ALD.ID" %in% colnames(CoalRet)) {
CoalRet <- CoalRet %>% rename(ID = ALD.ID)
}
PortProduction <- filter_by_parameters(CBCompProdSnapshot, "CB", by_equity_market = F)
}
if (data_check(PortProduction)) {
PortCoalRetirements <- PortProduction %>% filter(technology == "CoalCap")
}
if (data_check(PortCoalRetirements)) {
if (chart_type == "EQ") {
PortCoalRetirements <- CoalRet %>%
filter(ID %in% PortProduction$bloomberg_id)
} else if (chart_type == "CB") {
PortCoalRetirements <- CoalRet %>%
filter(ID %in% PortProduction$corp_bond_ticker)
}
}
if (data_check(PortCoalRetirements)) {
PlotChart <- T
}
if (PlotChart == T) {
PortCoalRetirements <- as.data.frame(PortCoalRetirements)
PortCoalRet <- PortCoalRetirements %>%
select(company_name, ExcessiveAdditions, RequiredRetirements10yr, PctRetire) %>%
mutate(Total = ExcessiveAdditions + RequiredRetirements10yr)
PortCoalRet$company_nameShort <- shortened_company_names(PortCoalRet$company_name, 15)
PortCoalRet <- PortCoalRet[order(PortCoalRet$Total, decreasing = T), ]
PortCoalRet <- PortCoalRet[PortCoalRet$Total > 0, ]
maxcompanies <- min(companiestoprint, nrow(PortCoalRet))
PortCoalRet <- PortCoalRet[seq(1, maxcompanies), ]
PortCoalRet$company_name <- factor(PortCoalRet$company_name, levels = unique(PortCoalRet$company_name[order(PortCoalRet$Total)]))
CoalRetmaxxAxis <- max(PortCoalRet$Total)
PortCoalRet <- PortCoalRet[!is.na(PortCoalRet$ExcessiveAdditions), ]
PlotChart <- data_check(PortCoalRet)
}
if (PlotChart == T) {
PortCoalRet <- PortCoalRet %>% select(-Total, -PctRetire)
PortCoalRetLong <- melt(PortCoalRet, variable.name = "variable", value.name = "value", id.vars = c("company_name", "company_nameShort"))
RetUnit <- "MW"
if (CoalRetmaxxAxis > 1000) {
PortCoalRetLong$value <- PortCoalRetLong$value / 1000
CoalRetmaxxAxis <- ceiling(CoalRetmaxxAxis / 1000)
RetUnit <- "GW"
}
CoalRetmaxxAxis <- round(CoalRetmaxxAxis, digits = 1)
outputplot <- ggplot(PortCoalRetLong, aes(x = company_name, y = value, fill = variable)) +
geom_bar(stat = "identity", width = .8, colour = NA) +
scale_fill_manual(
values = c("ExcessiveAdditions" = "#170203", "RequiredRetirements10yr" = "#ed1c24"),
labels = c(
paste0(AdditionalCapacityby, " ", start_year + 10), #
paste0(RequiredRetirementsby, " ", start_year + 10)
), name = "year"
) + #
scale_y_continuous(
expand = c(0, 0), limits = c(0, CoalRetmaxxAxis * 1.05), breaks = seq(0, CoalRetmaxxAxis, CoalRetmaxxAxis * 0.1),
minor_breaks = seq(0, CoalRetmaxxAxis, CoalRetmaxxAxis * 0.05)
) +
scale_x_discrete(expand = c(0, 0), labels = rev(PortCoalRetLong$company_nameShort)) +
xlab("") +
ylab(paste0(Requiredcoalretirementspercompany, " (", RetUnit, ")")) + #
# annotate(hjust = 0.8,"text", x = c(1:(length(CoalRetLabels$Name))) , y = CoalRetLabels$RetTot+(CoalRetmaxxAxis/CoalRetValues[3]) , label = paste(round(CoalRetirementChartPerc$PctRetire*100,0), "% of total", start_year, "capacity",sep = " "), colour = AxisColour,size = 3)+
theme(
axis.title.x = element_text(colour = AxisColour, size = textsize),
axis.title.y = element_blank(),
axis.text.x = element_text(colour = AxisColour, size = textsize),
axis.text.y = element_text(colour = AxisColour, size = textsize),
axis.line.x = element_line(colour = AxisColour, size = 1),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.major.x = element_line(colour = "grey90", linetype = "solid"),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
# panel.grid.minor.x = element_line(colour = "grey80", linetype = "dotted"),
legend.position = "bottom",
legend.direction = "horizontal",
legend.text = element_text(size = textsize, colour = AxisColour, margin = margin(l = 0.15, r = 0.35, unit = "cm")),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
legend.title = element_blank(),
legend.key = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA),
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(.4, 1.5, 0, 0), "lines")
) +
coord_flip() +
guides(guide_legend(nrow = 1))
bar_size <- 4 / 15
height <- (1 + maxcompanies) * bar_size + .66
} else {
Label <- NoCoalcompaniesinyourportfolio #
outputplot <- no_chart(Label)
height <- 1
}
ggsave(outputplot, filename = graph_name(plotnumber, ParameterFile), bg = "white", height = height, width = 10, dpi = ppi)
sector_to_plot <- ""
}
CompareTechnologyExposure <- function(plotnumber, chart_type, sector_to_plot) {
Combin <- data.frame()
if (chart_type == "EQ" & has_equity == TRUE) {
Market <- filter_by_parameters(eq_market, "EQ")
Combin <- filter_by_parameters(EQCombin, "EQ")
} else if (chart_type == "CB" & has_debt == TRUE) {
Market <- filter_by_parameters(cb_market, "CB", by_equity_market = FALSE)
Combin <- filter_by_parameters(CBCombin, "CB", by_equity_market = FALSE)
}
if (data_check(Combin) == TRUE) {
PlotChart <- TRUE
} else {
PlotChart <- FALSE
}
if (PlotChart == TRUE) {
### Portfolio Data Structure
Combin$Type <- "Portfolio"
Combin <- Combin %>%
ungroup() %>%
filter(technology != "OilCap" & year == start_year + 5) %>% # year == PlotYr &
select("portfolio_name", "ald_sector", "technology", "plan_alloc_wt_tech_prod", "Type") %>%
rename(WtProduction = plan_alloc_wt_tech_prod)
Combin <- Combin %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Portfolio")
)
Combin_A <- subset(Combin, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Combin_P <- subset(Combin, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Combin_F <- subset(Combin, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Combin <- bind_rows(Combin_F, Combin_A, Combin_P)
### Market Data Structure
Market$Type <- "Benchmark"
Market <- Market %>%
ungroup() %>%
filter(technology != "OilCap" & year == start_year + 5) %>% # year == PlotYr & Type =="Portfolio") %>%
select("portfolio_name", "ald_sector", "technology", "scen_alloc_wt_tech_prod", "Type") %>% # "Type"
rename(WtProduction = scen_alloc_wt_tech_prod)
Market <- Market %>%
complete(
ald_sector = c("Oil&Gas", "Coal", "Power", "Automotive"), technology = c(technology_order),
fill = list(WtProduction = 0, Type = "Benchmark")
)
Market_A <- subset(Market, ald_sector == "Automotive" & technology %in% c("ICE", "Electric", "Hybrid"))
Market_P <- subset(Market, ald_sector == "Power" & technology %in% c("RenewablesCap", "HydroCap", "NuclearCap", "GasCap", "CoalCap"))
Market_F <- subset(Market, (ald_sector == "Coal" & technology %in% c("Coal")) | (ald_sector == "Oil&Gas" & technology %in% c("Oil", "Gas")))
Market <- bind_rows(Market_F, Market_A, Market_P)
### Combine Market and Portfolios to the same Data Structure
Production <- bind_rows(Combin, Market)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Oil&Gas", "Coal", "Power", "Automotive"))
levels(Production$ald_sector)[levels(Production$ald_sector) == "Coal"] <- "Fossil Fuels"
levels(Production$ald_sector)[levels(Production$ald_sector) == "Oil&Gas"] <- "Fossil Fuels"
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive"))
# Aggregate and rename CarstenMetric_Port
ID.COLS <- c("ald_sector", "technology", "Type")
Production <- Production %>% gather(key = Metric, value = Value, "WtProduction")
Production <- aggregate(Production["Value"], by = Production[c(ID.COLS)], FUN = sum)
# Created an average for the peers (or even just use fill!)
# Transfer coal to gigajoule for tech share charts only!!!
Production$Value[Production$technology == "Coal"] <- Production$Value[Production$technology == "Coal"] * 29307600 / 365.25 / 1e6
# , Production$Type != "Market"
if (sector_to_plot != "All") {
technologyorder <- technology_order
colours <- as.vector(ColourPalette[["Colours"]])
names(colours) <- technologyorder
labels <- technologyorder
names(labels) <- technologyorder
} else if (sector_to_plot == "All") {
technologyorder <- technology_order_short
colours <- as.vector(ColourPalette[["Colours"]])[1:11]
names(colours) <- technologyorder
labels <- technologyorder
names(labels) <- technologyorder
}
Production$technology <- factor(Production$technology, levels = technologyorder)
Production$ald_sector <- factor(Production$ald_sector, levels = c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")) #
Production$Type <- wrap_labels(Production$Type, 20)
Production$Type <- factor(Production$Type, levels = c("Portfolio", "Benchmark"))
xlabels <- c("Portfolio", "Aligned\nMarket")
titles <- c("Fossil Fuel Production", "Power Capacity", "Automotive Production", "Aviation Production", "Shipping Production")
names(titles) <- c("Fossil Fuels", "Power", "Automotive", "Aviation", "Shipping")
Production <- subset(Production, select = c("Type", "ald_sector", "technology", "Value"))
tot <- Production %>%
group_by(Type, ald_sector) %>%
summarise(Total = sum(Value))
Production <- left_join(Production, tot, by = c("Type", "ald_sector"))
Production$per <- Production$Value / Production$Total
levels(Production$ald_sector) <- c("Fossil Fuels Production", "Power Capacity", "Automotive Production", "Aviation", "Shipping")
Production$ald_sector <- dplyr::recode(Production$ald_sector,
"Fossil Fuels Production" = wrap_labels(FossilFuelsProduction, 30), "Power Capacity" = C_Power, #
"Automotive Production" = AutomotiveProduction, Aviation = Aviation, Shipping = Shipping
) #
# Production$technology<-gsub("Cap","",Production$technology)
Production$technology <- dplyr::recode(Production$technology,
Gas = T_GasProd, Oil = T_OilProd, Coal = T_CoalProd, #
RenewablesCap = paste(T_Renewables_1, "\n", T_Renewables_2), HydroCap = T_HydroCap, NuclearCap = T_NuclearCap, GasCap = T_GasCap, CoalCap = T_CoalCap, #
Electric = x_Electric, Hybrid = x_Hybrid, ICE = x_ICE, "Freight Passenger" = FreightPassenger
) #
Production$lab <- ifelse(is.nan(Production$per), "-%", paste0(round(100 * Production$per), "%"))
Production$per <- ifelse(is.nan(Production$per), 0, Production$per)
cmd <- ggplot(Production) +
geom_col(aes(x = technology, y = per, fill = Type), position = "dodge", width = 0.8) +
geom_text(aes(x = technology, y = per, label = lab, group = Type), vjust = -0.25, position = position_dodge(0.8), color = "black", size = 2) +
scale_y_continuous(expand = c(0, 0), labels = percent, limits = c(0, 1.1)) +
ylab(paste(technologySharein, start_year + 5)) + #
scale_fill_manual(values = c("Portfolio" = Port, "Benchmark" = Benchmark), labels = c(Portf, AlignedMarket)) +
theme_barcharts() +
theme(
plot.title = element_text(hjust = 0.5, colour = textcolor, size = 11, margin = unit(c(0, 0, 1, 0), "lines")),
axis.text.y = element_text(size = textsize - 1.5),
axis.text.x = element_text(size = textsize - 1.5),
legend.position = "bottom",
legend.title = element_blank()
) +
guides(fill = guide_legend(nrow = 1)) +
facet_grid(. ~ ald_sector, scales = "free", space = "free") +
theme(
strip.background = element_rect(fill = "white"),
strip.text = element_text(size = textsize)
)
# wid = unique(Production$technology)
} else {
Label <- YourPortfoliohasnoproductioninthissectorfortheselectedscenario_geographyandMarket #
cmd <- no_chart(Label)
# wid <- 4
}
ggsave(
filename = graph_name(plotnumber, ParameterFile), # bg = "transparent",
plot = cmd, height = 2.6, width = 7, dpi = ppi
)
}
##############
### Themes ###
##############
theme_barcharts <- function(base_size = textsize, base_family = "") {
theme(
axis.ticks = element_blank(),
axis.text.x = element_text(colour = textcolor, size = textsize),
axis.text.y = element_text(colour = textcolor, size = textsize),
axis.title.x = element_blank(),
axis.title.y = element_text(colour = textcolor, size = textsize),
axis.line.x = element_line(colour = textcolor, size = 0.5),
axis.line.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
# legend.position=c(0.5,0),#legend.position = "none",
legend.position = "none",
legend.direction = "horizontal",
legend.text = element_text(size = textsize, colour = textcolor),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
# legend.title=element_blank(),
legend.title = element_text(colour = textcolor, size = textsize),
legend.key = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(1, 1, 0, 0), "lines"),
plot.title = element_blank(),
text = element_text(family = textfont, size = textsize)
# plot.margin = unit(c(1,1, 5, 2), "lines")
)
}
theme_246 <- function() {
theme_minimal(base_size = 11, base_family = textfont) +
theme(
panel.background = element_rect(fill = "white", color = "white"),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank(),
plot.margin = unit(c(.5, .5, .5, .5), "cm"),
axis.line.x = element_blank(),
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text = element_text(size = 11.5, color = "#3D3D3C"),
axis.title = element_text(size = 11, color = "#3D3D3C"),
plot.title = element_text(
size = 12,
hjust = 0
)
)
}
theme_linecharts <- function(base_size = textsize, base_family = "") {
theme(
axis.ticks = element_blank(),
axis.text.x = element_text(colour = textcolor, size = textsize),
axis.text.y = element_text(colour = textcolor, size = textsize),
axis.title.x = element_text(colour = textcolor, size = textsize),
axis.title.y = element_text(colour = textcolor, size = textsize),
axis.line = element_line(colour = textcolor, size = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# panel.background = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA),
# legend.position=c(0.5,0),#legend.position = "none",
legend.position = "none",
legend.direction = "horizontal",
legend.text = element_text(size = textsize, colour = textcolor),
legend.background = element_rect(fill = "transparent", colour = NA),
legend.key.size = unit(0.4, "cm"),
# legend.title=element_blank(),
legend.title = element_text(colour = textcolor, size = textsize),
legend.key = element_blank(),
plot.background = element_rect(fill = "transparent", colour = NA),
plot.margin = unit(c(1, 1, 0, 0), "lines")
# plot.margin = unit(c(1,1, 5, 2), "lines")
)
}
theme_cdi <- function() {
theme_minimal(base_size = 11, base_family = textfont) +
theme(
panel.background = element_rect(fill = "white", color = "white"),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank(),
axis.line.x = element_line(),
axis.line.y = element_blank(),
axis.ticks.y = element_line(),
plot.title = element_text(
family = textfont,
face = "bold",
size = 12,
hjust = 0
)
)
}
theme_distribution <- function(base_size = textsize, base_family = "") {
theme(
axis.ticks = element_blank(),
axis.text.x = element_text(colour = textcolor, size = textsize),
axis.text.y = element_text(colour = textcolor, size = textsize),
axis.title.x = element_text(colour = textcolor, size = textsize),
axis.title.y = element_text(colour = textcolor, size = textsize),
# axis.line.x = element_line(colour = textcolor,size=1),
axis.line.y = element_line(colour = textcolor, size = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.position = "bottom",
legend.text = element_text(color = textcolor, size = textsize),
legend.title = element_blank(),
plot.margin = unit(c(0.6, 1.0, 2.5, 0), "lines"),
plot.background = element_blank(),
plot.title = element_blank(),
text = element_text(family = textfont)
)
}
stacked_bar_chart <- function(dat, colors, legend_labels) {
# "item", "family", "score", "value"
colnames <- colnames(dat)
plottheme <- ggplot(data = dat, show.guide = TRUE) +
geom_bar(aes_string(x = colnames[1], y = colnames[4], fill = colnames[3]),
stat = "identity", position = "fill", width = .6
) +
scale_fill_manual(values = colors, labels = legend_labels, breaks = names(legend_labels)) +
scale_y_continuous(expand = c(0, 0), labels = percent) +
guides(fill = guide_legend(nrow = 1)) +
theme_barcharts()
return(plottheme)
}
no_chart <- function(Label) {
wrap_it <- function(x, len) {
sapply(x, function(y) paste(strwrap(y, len), collapse = "\n"), USE.NAMES = FALSE)
}
wrap_labels <- function(x, len) {
if (is.list(x)) {
lapply(x, wrap_it, len)
} else {
wrap_it(x, len)
}
}
outputplot <- ggplot() +
annotate(geom = "text", x = 0, y = 0, label = wrap_labels(Label, 25), size = 5) +
geom_blank() +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_rect(fill = "transparent", colour = NA)
)
return(outputplot)
}
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