(Disregard my name; I’m not even sure what it means myself)
I want to clarify that I'm not a newcomer to Wikipedia. I've been editing for years, although I hadn’t created an account back then.
Userboxes
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R-code
[edit]The following are some codes from the R (programming language) in which I use. Credits go to Wizmut, I felt like spreading the word.
World Electricity Data R-code
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# Attempt to set working directory
# setwd(getSrcDirectory()[1]) # if running entire file
setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # if running section
options(scipen=999) # don't use scientific notation
library(dplyr)
######################
#### import data #####
######################
# download from url - may be slow (30mb file)
# httr::GET("https://ember-climate.org/app/uploads/2022/07/yearly_full_release_long_format.csv",
# httr::write_disk(ember_world_full <- tempfile(fileext = ".csv")))
# ember_world_full = read.csv(ember_world_full)
# after manual download, read from local folder
ember_world_full = read.csv("yearly_full_release_long_format.csv")
#######################
#### clean up data ####
#######################
targets = c(" (the)", "Bosnia Herzegovina", "Brunei Darussalam", "Cabo Verde",
"Congo (the Democratic Republic of the)", "Cote d'Ivoire", "Falkland Islands [Malvinas]",
"Iran (Islamic Republic of)", "Korea (the Democratic People's Republic of)",
"Lao People's Democratic Republic", "Palestine, State of", "Russian Federation",
"Syrian Arab Republic", "Tanzania, the United Republic of", "Timor-Leste",
"United States of America", "Venezuela (Bolivarian Republic of)", "Viet Nam",
"Virgin Islands (British)", "Virgin Islands (U.S.)")
replacements = c("", "Bosnia and Herzegovina", "Brunei", "Cape Verde",
"DR Congo", "Ivory Coast", "Falkland Islands",
"Iran", "North Korea",
"Laos", "Palestine", "Russia",
"Syria", "Tanzania", "East Timor",
"United States", "Venezuela", "Vietnam",
"British Virgin Islands", "US Virgin Islands")
ember_world_trim = ember_world_full %>%
filter(Year >= max(Year)-1) %>% # last two years
filter(Area.type == "Country" | Area == "World") %>%
filter(Subcategory != "Aggregate fuel") %>%
select(Area, Area.type, Year, Category, Subcategory, Variable, Unit, Value, YoY...change) %>%
mutate(Area = stringi::stri_replace_all_fixed(Area, targets, replacements, vectorize_all = F))
rm(targets,replacements)
######################
#### world tables ####
######################
# names for world tables
ember_world = ember_world_trim %>%
filter(Year == max(Year)-1) %>% # most recent complete year
mutate(Area = paste0("{{flagg|uspef|pref=Electricity sector in|",Area,"}}")) %>%
mutate(Area = ifelse(stringr::str_detect(Area,"World"), "{{noflag|'''World'''}}", Area))
# overall table
world_overall = ember_world %>%
filter(Variable %in% c("Total Generation","Demand","Demand per capita","Net Imports","CO2 intensity")) %>%
select(Area, Variable, Value) %>%
tidyr::pivot_wider(names_from = Variable, values_from = Value) %>%
setNames(nm = c("Location","Consumption","Consumption_per_capita_MWh","Generation","Net_Imports","gCO2/kWh")) %>%
select(Location,Generation,Consumption,Consumption_per_capita_MWh,Net_Imports,`gCO2/kWh`) %>%
filter(Generation > 0) %>%
arrange(desc(Generation))
write.csv(world_overall, "world_electricity_2021.csv", row.names=F)
# production by source (TWh)
world_twh = ember_world %>%
filter(Unit == "TWh", Subcategory %in% c("Fuel","Total")) %>%
select(Area, Variable, Value) %>%
tidyr::pivot_wider(names_from = Variable, values_from = Value) %>%
mutate(across(where(is.numeric), ~replace(., is.na(.), 0))) %>%
setNames(nm = c("Location","Bio","Coal","Gas","Hydro","Nuclear","Oil","Geo","Solar","Wind","Total")) %>%
select(Location,Total,Coal,Gas,Hydro,Nuclear,Wind,Solar,Oil,Bio,Geo) %>%
filter(Total > 0) %>%
arrange(desc(Total))
write.csv(world_twh, "world_twh_by_source_2021.csv", row.names=F)
# production by source (pct)
world_pct = ember_world %>%
filter(Unit == "%" | Variable == "Total Generation") %>%
select(Area, Variable, Value) %>%
tidyr::pivot_wider(names_from = Variable, values_from = Value) %>%
mutate(across(where(is.numeric), ~replace(., is.na(.), 0))) %>%
setNames(nm = c("Location","Bio","Coal","Gas","Hydro","Nuclear","Oil","Geo","Solar","Wind","Total")) %>%
select(Location,Total,Coal,Gas,Hydro,Nuclear,Wind,Solar,Oil,Bio,Geo) %>%
filter(Total > 0) %>%
arrange(desc(Total)) %>%
mutate(Total = Total / max(Total))
write.csv(world_pct, "world_pct_by_source_2021.csv", row.names=F)
# renewable (TWh)
renew_twh = world_twh %>%
mutate(Renew = Hydro + Wind + Solar + Bio + Geo) %>%
select(Location,Renew,Hydro,Wind,Solar,Bio,Geo) %>%
arrange(desc(Renew))
write.csv(renew_twh, "renew_twh_by_source_2021.csv", row.names=F)
# renewable (pct)
renew_pct = world_pct %>%
mutate(Renew = renew_twh$Renew / world_twh$Total) %>%
select(Location,Renew,Hydro,Wind,Solar,Bio,Geo) %>%
arrange(desc(Renew))
write.csv(renew_pct, "renew_pct_by_source_2021.csv", row.names=F)
# rm(ember_world,world_overall,world_twh,world_pct,renew_twh,renew_pct)
#######################
#### source tables ####
#######################
get_source_table = function(source,source_name,link_name) {
if(missing(link_name)) { link_name = "missing" }
source_table = ember_world_trim %>%
filter(Variable == source, Unit != "mtCO2") %>%
select(Area, Year, Unit, Value, YoY...change) %>%
tidyr::pivot_wider(names_from = Unit, values_from = c(Value, YoY...change)) %>%
select(c(1:6)) %>%
mutate(across(where(is.numeric), ~replace(., is.na(.), 0))) %>%
setNames(nm = c("Area","Year","Cap_GW","Pct_gen","Gen_TWh","Pct_cap_growth")) %>%
select(Area,Year,Gen_TWh,Pct_gen,Cap_GW,Pct_cap_growth) %>%
mutate(Cap_fac = Gen_TWh/Cap_GW*1000/365/24) %>%
group_by(Area) %>% slice_max(order_by = Year) %>% ungroup() %>%
filter(Gen_TWh > 0.1) %>%
arrange(desc(Gen_TWh)) %>%
mutate(Location = link_name) %>%
mutate(Location = ifelse(Location == "missing",
paste0("{{flag|",Area,"}}"),
paste0("{{flagg|uspef|pref=",link_name,"|",Area,"}}")
)) %>%
mutate(Location = ifelse(stringr::str_detect(Location,"World"), "{{noflag|'''World'''}}", Location)) %>%
mutate(Location = ifelse(Year < max(Year), paste0(Location," (",Year,")"), Location)) %>%
select(Location,Gen_TWh,Pct_gen,Cap_GW,Pct_cap_growth)
write.csv(source_table, paste0("source_",source_name,"_2021.csv"), row.names=F)
return (source_table)
}
ember_bio = get_source_table("Bioenergy","biomass")
ember_coal = get_source_table("Coal","coal")
ember_gas = get_source_table("Gas","gas")
ember_hydro = get_source_table("Hydro","hydro","Hydroelectricity in")
ember_nuclear = get_source_table("Nuclear","nuclear","Nuclear power in")
ember_oil = get_source_table("Other Fossil","oil") # mostly oil
ember_geo = get_source_table("Other Renewables","geo") # mostly geo
ember_solar = get_source_table("Solar","solar","Solar power in")
ember_wind = get_source_table("Wind","wind","Wind power in")
# rm(get_source_table_ember_bio,ember_coal,ember_gas,ember_hydro,
# ember_nuclear,ember_oil,ember_geo,ember_solar,ember_wind)
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The code below generates treemaps and maps that visually represent the data from the tables mentioned above.
Treemaps of the World R-code
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##################
#### treemaps ####
##################
library(ggplot2)
library(treemapify)
type.palette = c("#804000","#303030","#0fafff",
"#a82000","#009080","#d0b090",
"#403020","#d8d800","#d0d0d0")
names(type.palette) = c("Biomass","Coal","Nuclear",
"Geothermal","Hydro","Gas",
"Oil","Solar","Wind")
treemap.theme = theme(legend.position="none",
panel.border = element_rect(color="#404040", fill=NA, linewidth=1),
plot.title = element_text(size=40, family="serif",color='#e6e6e6',hjust=.5),
plot.background = element_rect(fill = '#181818', color = '#404040'),
plot.margin = unit(c(0,0,0,0), "cm"))
targets = c("Bioenergy","Coal","Nuclear",
"Other Renewables","Hydro","Gas",
"Other Fossil","Solar","Wind")
replacements = c("Biomass","Coal","Nuclear",
"Geothermal","Hydro","Gas",
"Oil","Solar","Wind")
treemap_elec = ember_world_trim %>%
filter(Area.type == "Country", Year == 2021) %>%
filter(Subcategory == "Fuel", Unit == "TWh") %>%
select(Area, Variable, Value) %>%
mutate(Variable = stringi::stri_replace_all_fixed(Variable, targets, replacements, vectorize_all = F)) %>%
group_by(Area) %>%
mutate(Area.total = sum(Value)) %>%
group_by(Variable) %>%
# mutate(Label = if_else(Value == max(Value), Variable, "")) %>%
mutate(Label = if_else(Area == "China", Variable, "")) %>%
ungroup()
# by country and source
ggplot(treemap_elec, aes(area = Value, fill = Variable, subgroup = Area)) +
scale_fill_manual("Energy source",values = type.palette) +
geom_treemap(start="topleft") +
geom_treemap_subgroup_border(start="topleft", color="#404040", size=2) +
ggfx::with_outer_glow(geom_treemap_text(start="topleft", place="bottom",
reflow=T, color="#c49000",
label=treemap_elec$Label,
size=1*treemap_elec$Area.total^.3, min.size = 1),
sigma=1, expand=1) +
ggfx::with_outer_glow(geom_treemap_subgroup_text(start="topleft", place="topleft",
reflow=F, color="#d0d0d0",
size = 5*treemap_elec$Area.total^.25),
sigma=1, expand=2) +
treemap.theme
ggsave("World_generation_treemap_2021_country_source.png",width=8,height=5)
# by source and country
ggplot(treemap_elec, aes(area = Value, fill = Variable, subgroup = Variable, subgroup2 = Area)) +
scale_fill_manual("Energy source",values = type.palette) +
geom_treemap(start="topleft") +
geom_treemap_subgroup_border(start="topleft", color="#404040", size=2) +
ggfx::with_outer_glow(geom_treemap_text(start="topleft", place="bottom",
reflow=T, color="#c49000",
label=treemap_elec$Area,
size=1*treemap_elec$Area.total^.3, min.size = 1),
sigma=1, expand=1) +
ggfx::with_outer_glow(geom_treemap_subgroup_text(start="topleft", place="topleft",
reflow=F, color="#d0d0d0",
size = 32, min.size = 1),
sigma=1, expand=2) +
treemap.theme
ggsave("World_generation_treemap_2021_source_country.png",width=8,height=5)
##############
#### maps ####
##############
# library(maps)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
map.theme = theme_void() +
theme(plot.background = element_rect(fill = '#202020', color = '#404040'),
legend.position = c(.8,.04), legend.direction = "horizontal", legend.justification = c(1,0),
legend.key.size = unit(1, "cm"), legend.key.height = unit(.7, "cm"),
text = element_text(color="#e6e6e6", face="bold", size = 15))
# map.guide = guides(fill = guide_colorbar(title.hjust = .5, title.position = "top"))
# map.colors = c("#c0f0f0","#004800","#c0c030","#801010","#100010")
# remake the TWh table
country_twh = ember_world_full %>%
filter(Area.type == "Country", Unit == "TWh", Subcategory %in% c("Fuel","Total")) %>%
filter(Year == max(Year)-1) %>%
select(Country.code, Variable, Value) %>%
tidyr::pivot_wider(names_from = Variable, values_from = Value) %>%
mutate(across(where(is.numeric), ~replace(., is.na(.), 0))) %>%
setNames(nm = c("ISO","Bio","Coal","Gas","Hydro","Nuclear","Oil","Geo","Solar","Wind","Total")) %>%
select(ISO,Total,Coal,Gas,Hydro,Nuclear,Wind,Solar,Oil,Bio,Geo) %>%
filter(Total > 0) %>%
arrange(desc(Total))
# find the largest source in each state
country_majors = country_twh %>%
mutate(Major = colnames(.)[-c(1,2)][max.col(.[,-c(1,2)], ties.method = "first")]) %>%
mutate(Renew = colnames(.)[c(5,7,8,10,11)][max.col(.[,c(5,7,8,10,11)], ties.method = "first")]) %>%
select(ISO,Major,Renew)
country_majors %>% count(Major)
country_majors %>% count(Renew)
# import world map
sf_use_s2(FALSE) # let st_area work
world_map = rnaturalearth::ne_countries(scale = "medium", returnclass = "sf") %>%
mutate(Area.km2 = units::set_units(st_area(geometry), NULL)/1000^2) %>%
filter(Area.km2 > 5000)
# fix somalia
somalia_combined = world_map[world_map$sov_a3 %in% c("SOM","SOL"),]
world_map[world_map$sov_a3 == "SOM",]$geometry = sf::st_union(somalia_combined)
# merge map and table
country_majors_map = merge(world_map, country_majors, by.x="iso_a3", by.y="ISO")
# map of major overall source
ggplot() +
geom_sf(data = country_majors_map, aes(fill = Major), color = "white") +
coord_sf(crs = "+proj=robin") +
scale_fill_manual(values = type.palette) +
guides(fill="none") + map.theme
ggsave("World_electric_major_overall_2021.png",width=8,height=3.4)
# map of major renewable source
ggplot() +
geom_sf(data = country_majors_map, aes(fill = Renew), color = "white") +
coord_sf(crs = "+proj=robin") +
scale_fill_manual(values = type.palette) +
guides(fill="none") + map.theme
ggsave("World_electric_major_renew_2021.png",width=8,height=3.4)
# map of most salient source
country_salient = country_twh %>%
mutate(across(3:11, ~./Total * 100, .names = "{.col}")) %>%
select(-Total) %>%
mutate_if(is.numeric, funs(c(first(.), (. - first(.))[-1])) ) %>%
mutate(Salient = colnames(.)[-c(1)][max.col(.[,-c(1)], ties.method = "first")]) %>%
inner_join(world_map,c("ISO"="iso_a3")) %>% st_as_sf
ggplot() +
geom_sf(data = country_salient, aes(fill = Salient), color = "white") +
coord_sf(crs = "+proj=robin") +
scale_fill_manual(values = type.palette) +
guides(fill="none") + map.theme
ggsave("World_electric_salient_2021.png",width=8,height=3.4)
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