Australian Energy Consumption

Overview

• Data from the Department of the Environment and Energy
• Energy intensity - amount of energy used to produce a unit of economic output (energy consumption/GDP)
• Energy productivity - economic output produced per unit of energy input (GDP/energy consumption)

# Bug
knitr::include_app("https://yihui.shinyapps.io/miniUI/", 
  height = "600px")

animate_plot <- tbl_2_4_energy_consumption_sector_level %>%
  ggplot(aes(x = rank, group = Sectors, fill = as.factor(Sectors), colour = as.factor(Sectors))) +
  geom_bar(aes(y = as.integer(round(Consumption, 0))), stat = "identity", position = "identity") +
  geom_text(aes(y = 0, label = paste(Sectors, " ")), vjust = 0.2, hjust = 1) +
  theme_bw() +
  theme(legend.position = "none",
        axis.ticks.y = element_blank(),
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        plot.margin = margin(1,1,1,5, "cm")) +
  coord_flip(clip = "off", expand = FALSE) +
  scale_y_continuous(labels = scales::comma) +
  scale_x_reverse() +
  geom_text(aes(y = Consumption, label = as.character(round(Consumption, 0))), colour = "black", vjust = 0.2, hjust = -0.5) +
  labs(title = "Australian energy consumption by sector, {closest_state}") +
  transition_states(Year, transition_length = 30, 
                    state_length = 20, 
                    wrap = FALSE) +
  ease_aes('linear') +
  enter_fade() +
  exit_fade()

animate(animate_plot, fps = 10, duration = 20, width = 800, height = 450)

# Load networkD3
library(networkD3)

# Create edge list with energy flow (US)
energy_to_elecheatplant_2017_US <- IEA_data %>%
  filter(Country == "United States", Flow == "Electricity, CHP and heat plants (ktoe)", !Product %in% c("Total", "Electricity", "Heat")) %>%
  mutate(`2017` = abs(`2017`)) %>%
  select(Product, Flow, `2017`) 

# Create edge list with electricity & heat plant flow (US)
elecheatplant_to_elecheat_2017_US <- IEA_data %>%
  filter(Country == "United States", Flow == "Electricity, CHP and heat plants (ktoe)", Product %in% c("Electricity", "Heat")) %>%
  rename(Product = Flow, Flow = Product) %>%
  select(Product, Flow, `2017`) 

# Create edge list with electricity & heat flow (US)
elecheat_to_sector_2017_US <- IEA_data %>%
  filter(Country == "United States", Product %in% c("Electricity", "Heat"), Flow %in% c("Industry (ktoe)", "Transport (ktoe)", "Residential (ktoe)", "Commercial and public services (ktoe)", "Other final consumption (ktoe)")) %>%
  select(Product, Flow, `2017`)

# Create total energy input
country_energy_input_2017 <- IEA_data %>%
  filter(Flow %in% c("Electricity, CHP and heat plants (ktoe)", "Total final consumption (ktoe)"),
         !Product %in% c("Electricity", "Heat", "Total"),
         Flow != "Total final consumption (ktoe)") %>%
  select(Country, Product, Flow, `2017`) %>%
  group_by(Country) %>%
  # energy input into electricity and heat plants
  mutate(energy_input = abs(sum(`2017`))) %>%
  ungroup() %>%
  select(-`2017`) %>%
  rename(`2017` = energy_input) %>%
  mutate(Product = "Total Input", Flow = "Electricity, CHP and heat plants (ktoe)") %>%
  distinct()

# Create edge list with electricity & heat plant to rejected energy
elecheatplant_to_rejected_2017 <- IEA_data %>%
  filter(Flow == "Total final consumption (ktoe)" & Product %in% c("Electricity", "Heat")) %>%
  select(Country, Product, Flow, `2017`) %>%
  bind_rows(country_energy_input_2017) %>%
  arrange(Country) %>%
  group_by(Country) %>%
  mutate(rejected_energy = nth(`2017`, n = 3) - nth(`2017`, n = 2) - first(`2017`)) %>%
  ungroup() %>%
  select(-`2017`) %>%
  rename(`2017` = rejected_energy) %>%
  mutate(Product = "Electricity, CHP and heat plants (ktoe)",
         Flow = "Rejected Energy") %>%
  distinct()

elecheatplant_to_rejected_2017_US <- elecheatplant_to_rejected_2017 %>%
filter(Country == "United States") %>%
  select(-Country)

# Row bind above edge list tables
sankey_data <- energy_to_elecheatplant_2017_US %>% 
  bind_rows(elecheatplant_to_elecheat_2017_US, elecheat_to_sector_2017_US, elecheatplant_to_rejected_2017_US) %>%
  # Electricity and heat plants are the source that flows to the target: elec & heat
  rename(source = Product, target = Flow, value = `2017`)

# Create a node data frame (contains all entities in the edge list)
node <- data.frame(
  name=c(as.character(sankey_data$source), 
  as.character(sankey_data$target)) %>% unique()
)
 
# With networkD3, connection must be provided using id, not using real name like in the links dataframe, so we need to reformat it
sankey_data$IDsource <- match(sankey_data$source, node$name)-1 
sankey_data$IDtarget <- match(sankey_data$target, node$name)-1
 
# Make the Network
sankey_plot <- sankeyNetwork(Links = sankey_data, Nodes = node,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name", 
              sinksRight=FALSE)

sankey_plot