In MollieCarroll/NonCO2-Figs: What the Package Does (One Line, Title Case)

This file provides some high-level glances at the composite data results (including the variables present, etc.)

knitr::opts_knit$set(root.dir = normalizePath("../"))
knitr::opts_chunk$set(echo = FALSE)

source(here::here("scripts/setup.R"))

loadd(proj, mac)

proj <- factorize_proj(proj, gas_lvl = "gas_grps_2")

# non-co2 breakout within co2/non-co2 context

# bar of pies ideas:
# https://community.rstudio.com/t/bar-of-pie-chart-in-r-ggplot/48098/4

# for now, stacked bars
# stacked bar by gas
proj %>%
  filter(year == 2015) %>%
  factorize_proj(gas_lvl = "gas_grps_2") %>%

  ggplot(aes(x = "by-gas-grp", y = value, fill = gas)) +
  geom_col(position = "stack") +
  GER_theme() +
  scale_fill_ger("gases")

# stacked bar by sector
proj %>%
  filter(year == 2015) %>%
  group_by(sector) %>% 
  summarize(along = "sector", value = sum(value, na.rm = TRUE)) %>%

  ggplot(aes(x = "by-sector", y = value, fill = sector)) +
  geom_col(position = "stack") +
  GER_theme() +
  scale_fill_ger("sectors")

The following values for country, sector, source, subsource, gas, unit, and year are present in the GER composite results:

srcgas_combos <- proj %>%
  filter(! str_detect(source, "^Other")) %>%
  distinct(sector, source, gas) %>%
  group_by(sector, source) %>%
  summarize(gases = glue::glue_collapse(gas, sep = ", ")) %>%
  ungroup()

kable(srcgas_combos) %>%
  kable_styling(bootstrap_options = c("condensed"), 
                full_width = FALSE) %>%
  collapse_rows(columns = c(1, 2))

proj_gas_grps_2 <- proj %>%
  mutate(gas  = if_else(gas %in% c('HFCs', 'PFCs', 'SF6', 'NF3'), 'F-GHGs', as.character(gas))) %>%
  mutate(
    sector = factor(sector, levels = sectors),
    gas = factor(gas, levels = gas_grps_2))

Global Emissions by Gas

plot_bar_by_var_time_series(proj_gas_grps_2, byvar = gas)

Total Emissions by Sector

plot_bar_by_var_time_series(proj_gas_grps_2, byvar = sector)

Mitigation Potential and Residual Emissions by Sector, 2030

mac_sector <- mac %>%
  filter(year == 2030) %>%
  group_by(sector) %>%
  summarize(
    no_cost = sum(q[p <= 0], na.rm = TRUE),
    inc_cost = sum(q[p > 0], na.rm = TRUE))

proj_sector <- proj %>%
  filter(year == 2030) %>%
  group_by(sector) %>%
  summarize(baseline = sum(value, na.rm = TRUE))

mac_all <- full_join(mac_sector, proj_sector, by = "sector") %>%
  mutate(no_cost_pct = no_cost / baseline,
         inc_cost_pct = inc_cost / baseline,
         resid_pct = (baseline - no_cost - inc_cost) / baseline)

mac_bars <- mac_all %>%
  select(sector, no_cost_pct, inc_cost_pct, resid_pct) %>%
  pivot_longer(
    cols = c("no_cost_pct", "inc_cost_pct", "resid_pct"),
    names_to = "reduction_type",
    values_to = "reduction_pct") %>%
  mutate(reduction_type = factor(reduction_type, levels = 
                                   c("resid_pct", "inc_cost_pct", "no_cost_pct")))

mac_bars %>%
  mutate(sector = fct_rev(factor(sector, levels = sectors))) %>%
  ggplot(aes(x = sector, y = reduction_pct, fill = reduction_type)) +
  geom_col() +
  coord_flip() +
  scale_fill_ger(guide = guide_legend(
    direction = "horizontal", reverse = TRUE)) +
  GER_theme() +
  theme(legend.position = "bottom")

Mitigation Potential and Residual Emissions by Gas, 2030

# TODO: transform mac data to add gas info

BAU Emission Projections and Residual Emissions by Sector

# Need:
# Projection total by year
# Total miti at $0 by year, subtract from baseline to get resid at $0
# Total miti by sector, joined to sector, to get resid by sector

Top 5 countries (in 2030) and ROW Map

# TODO: make it a map

top2030 <- proj %>%

  filter(year == 2030) %>%
  group_by(country) %>%
  summarize(value = sum(value, na.rm = TRUE)) %>%
  ungroup() %>%
  arrange(desc(value)) %>%
  slice(1:5)

proj %>%
  filter(year == 2030) %>%
  mutate(topcountry = case_when(
    country %in% top2030$country ~ country,
    TRUE ~ "Rest of World"
  )) %>%
  mutate(topcountry = factor(topcountry, 
                             levels = c(top2030$country, "Rest of World"))) %>%
  arrange(topcountry) %>%
  group_by(year, topcountry) %>%
  summarize(value = sum(value, na.rm = TRUE)) %>%
  ungroup() %>%
  ggplot(aes(x="top countries", y=value, fill=topcountry)) +
  geom_col(position = "stack", linetype = 1, size = 0.5, colour = "white") + 
  GER_theme() +
  scale_fill_ger("countries")