Tweaking plot and add by local auth.

howTo/openGeogAPI/local_auth_ghg_plots.R

@@ -23,7 +23,7 @@ detailed_pal <- c(industry_pal, domestic_pal, transport_pal, lulucf_pal)
 totals_pal <- c(brewer.pal(n = 9, name = "Greys")[8],
                 brewer.pal(n = 9, name = "Blues")[8],
                 brewer.pal(n = 9, name = "Oranges")[8],
-                brewer.pal(n = 9, name = "Greens")[8])
+                brewer.pal(n = 9, name = "Greens")[7:8])
 
 # List local authority areas to load
 # These used to filter emissions data
@@ -91,18 +91,110 @@ per_capita_totals <- data.frame(per_capita_dt[c(1:5,30)], lapply(per_capita_dt[c
 per_capita_detail <- data.frame(per_capita_dt[c(1:5,30)], lapply(per_capita_dt[c(6:10,12:14,16:20,22:27,29,32,33)], per_cap_fun) )
 # Note that for hampshire LAs there are no emissions in categories Q or S
 
-# Reshape data
+# Calculate emissions and removals for LULUCF separately
+per_capita_lulucf <- pc_detail_plot %>%
+  filter(grepl("LULUCF", variable)) %>%
+  group_by(Name,Year) %>%
+  summarise(
+    LULUCF.emissions = sum(value[value > 0]),
+    LULUCF.removals = sum(value[value < 0]))
 
-pc_totals_plot <- data.frame(per_capita_totals[c(1:5,7:10)])
+per_capita_totals <- left_join(per_capita_totals,per_capita_lulucf, by = c("Name","Year"))
+
+# Create plot tables ----
+
+pc_totals_plot <- data.frame(per_capita_totals[c(1:5,7:9,14,15)])
 pc_totals_plot <- melt(pc_totals_plot, id.vars = c("CTRY18NM.RGN18NM","Second.Tier.Authority","Name","Code","Year"))
 
-pc_details_plot <- data.frame(per_capita_totals[c(1:5,7:10)])
-pc_details_plot <- melt(pc_totals_plot, id.vars = c("CTRY18NM.RGN18NM","Second.Tier.Authority","Name","Code","Year"))
+pc_detail_plot <- data.frame(per_capita_detail[c(1:5,7:25)])
+pc_detail_plot <- melt(pc_detail_plot, id.vars = c("CTRY18NM.RGN18NM","Second.Tier.Authority","Name","Code","Year"))
 
+pc_general_plot <- data.frame(per_capita_totals[c(3,5,11:13)])
 
-# Construct plots ----
+## Subset data - Southampton by default
+auth_area  <- "New Forest"
+
+ghg_subset <- function(dt, auth_area = "Southampton"){
+  dt <- dt %>%
+    filter(dt$Name %in% auth_area)
+  # add filter for categories with input$
+}
 
+# Construct plots ----
 
+plotCaption = paste0("Emissions data: Department for Business, Energy & Industrial Strategy",
+                     "\nUK local authority and regional carbon dioxide emissions national statistics: 2005 to 2018",
+                     "\nVisualisation: t.w.rushby@soton.ac.uk | energy.soton.ac.uk")
+
+plotTitle = paste0("Greenhouse gas emissions by source: ",auth_area)
+
+plot_data1 <-  ghg_subset(pc_detail_plot, auth_area = auth_area)
+plot_data2 <- ghg_subset(pc_totals_plot, auth_area = auth_area)
+
+ggplot() +
+  geom_col(data = plot_data1, aes(x = Year, y = value, fill = variable), position = "stack") +
+  geom_col(data = plot_data2, aes(x = Year, y = value, colour = variable), fill = "none", position = "stack") +
+  geom_hline(yintercept=0, lwd=0.4, colour="black", linetype = "dashed") +
+  coord_cartesian(ylim = c(-1,15)) +
+  scale_y_continuous(labels=abs) +
+  scale_x_continuous(breaks = 2005:2018) +
+  scale_color_manual(values = totals_pal, guide = FALSE) +
+  scale_fill_manual(values = detailed_pal) +
+  labs(x = "Year",
+       y = "Emissions per capita, tCO2",
+       fill = "Source",
+       title = plotTitle,
+       caption = plotCaption) +
+  theme(legend.position = "right") +
+  theme_classic()
+
+plotly::ggplotly(plot)
+
+
+# By local authority
+
+plot_year <- 2005
+
+plot_data1 <-  pc_detail_plot %>% filter(Year == plot_year)
+plot_data2 <- pc_totals_plot %>% filter(Year == plot_year)
+
+plotTitle = paste0("Greenhouse gas emissions by sector for Hampshire local authorities: ",plot_year)
+
+ggplot() +
+  geom_col(data = plot_data1, aes(x = Name, y = value, fill = variable), position = "stack") +
+  geom_col(data = plot_data2, aes(x = Name, y = value, colour = variable), fill = "none", position = "stack") +
+  geom_hline(yintercept=0, lwd=0.4, colour="black", linetype = "dashed") +
+  coord_cartesian(xlim = c(-1,8)) +
+  #scale_y_continuous(labels=abs) +
+  #scale_x_continuous(breaks = 2005:2018) +
+  scale_color_manual(values = totals_pal, guide = FALSE) +
+  scale_fill_manual(values = detailed_pal) +
+  coord_flip() +
+  labs(x = "Local authority",
+       y = "Emissions per capita, tCO2",
+       fill = "Source",
+       title = plotTitle,
+       caption = plotCaption) +
+  theme(legend.position = "none") +
+  theme_classic()
+
+ggplot() +
+  geom_col(data = plot_data1, aes(x = Name, y = value, fill = variable), position = "stack") +
+  geom_col(data = plot_data2, aes(x = Name, y = value, colour = variable), fill = "none", position = "stack") +
+  geom_hline(yintercept=0, lwd=0.4, colour="black", linetype = "dashed") +
+  coord_cartesian(xlim = c(-1,8)) +
+  #scale_y_continuous(labels=abs) +
+  #scale_x_continuous(breaks = 2005:2018) +
+  scale_color_manual(values = totals_pal, guide = FALSE) +
+  scale_fill_manual(values = detailed_pal) +
+  coord_flip() +
+  labs(x = "Local authority",
+       y = "Emissions per capita, tCO2",
+       fill = "Source",
+       title = plotTitle,
+       caption = plotCaption) +
+  theme(legend.position = "none") +
+  theme_classic()
 
 
 
@@ -148,14 +240,7 @@ dt3 <- dt %>%
 ghg_emissions <- filter_detail(lvl_detail = "high")
 rm(dt)
 
-## Subset data - Southampton by default
-ghg_subset <- function(auth_area = "Southampton"){
-  ghg_emissions_sub <- ghg_emissions %>%
-    filter(ghg_emissions$Name %in% auth_area)
-  # add filter for categories with input$
-}
 
-ghg_emissions_sub <- ghg_subset()
 
 # Plots