Finish Hampshire average and tidying up.

howTo/openGeogAPI/local_auth_ghg_plots.R

@@ -42,11 +42,7 @@ download.file(url_to_get, tempf, method = "curl")
 
 dt <- readxl::read_xlsx(tempf, sheet = "Full dataset",skip = 1)
 
-x_min <- min(dt$Year)
-x_max <- max(dt$Year)
-
-
-# Functions ----
+# Functions (REDUNDANT) ----
 
 lvl_detail <-  "high"
 
@@ -79,19 +75,26 @@ filter_detail <- function(lvl_detail =  lvl_detail){
 
 
 # Process data ----
-
+# Filter local authorities and correct population
 per_capita_dt <- dt %>%
   filter(Name %in% las_to_load) %>%
   rename(Population = `Population                                              ('000s, mid-year estimate)`) %>%
   mutate(Population = Population*1000)
 
+# Add Hampshire totals (sum of all las in las_to_load) to calculate averages across las
 total_hampshire_dt <- dt  %>%
   filter(Name %in% las_to_load) %>%
+  rename(Population = `Population                                              ('000s, mid-year estimate)`) %>%
+  mutate(Population = Population*1000) %>%
   group_by(Year) %>%
   summarise_if(is.numeric, sum, na.rm = TRUE) %>%
-  mutate(Name = "Hampshire")
-
+  mutate(Name = " Hampshire (Average)",
+         `CTRY18NM/RGN18NM` = "South East",
+         `Second Tier Authority` = "Hampshire",
+         Code = "E00000000") %>%
+  select(`CTRY18NM/RGN18NM`,`Second Tier Authority`,Name,Code,Year,everything())
 
+per_capita_dt <- rbind(per_capita_dt,total_hampshire_dt)
 
 per_cap_fun <- function(x) x*1000/per_capita_dt$Population
 per_capita_totals <- data.frame(per_capita_dt[c(1:5,30)], lapply(per_capita_dt[c(11,15,21,28,29,32,33)], per_cap_fun) )
@@ -119,8 +122,10 @@ 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"))
 
 
-## Subset data - Southampton by default
-auth_area  <- "New Forest"
+## Subset data - Hampshire by default
+las_to_load <- c(las_to_load, " Hampshire (Average)") # add Hampshire average to list to loop over
+
+auth_area  <- " Hampshire (Average)" # Use to set auth_area manually (outside of loop)
 
 ghg_subset <- function(dt, auth_area = "Southampton"){
   dt <- dt %>%
@@ -171,9 +176,6 @@ i <- i+1
 
 print(plotNames)
 
-plotly::ggplotly(plot)
-
-
 # By local authority
 years_to_plot <- c(2005,2018)
 
@@ -204,7 +206,7 @@ for(plot_year in years_to_plot) {
     theme(legend.position = "none") +
     theme_classic()
   
-  ggsave(paste0(here::here(),"/howTo/openGeogAPI/plots/",plotName,".png"), dpi = 150, width = 12, height = 6, units = "in")
+  ggsave(paste0(here::here(),"/howTo/openGeogAPI/plots/",plotName,"_hants.png"), dpi = 150, width = 12, height = 6, units = "in")
   
 }
 
@@ -227,51 +229,3 @@ ggplot() +
   theme_classic()
 
 
-
-
-
-
-dt2 <- dt %>%
-  
-  filter(Name %in% las_to_load) %>%
-  rename(Population = `Population                                              ('000s, mid-year estimate)`) %>%
-  mutate(
-    Population = Population*1000,
-    `Industry per capita` = `Industry and Commercial Total`*1000/Population,
-    `Domestic per capita` = `Domestic Total`*1000/Population,
-    `Transport per capita` = `Transport Total`*1000/Population,
-    `LULUCF per capita` = `LULUCF Net Emissions`*1000/Population,
-    `Total per capita chk` = `Industry per capita` + `Domestic per capita` +
-      `Transport per capita` + `LULUCF per capita`,
-    `Total per capita` = `Grand Total`*1000/Population
-  )
-
-dt3 <- dt %>%
-  
-  filter(Name %in% las_to_load) %>%
-  rename(Population = `Population                                              ('000s, mid-year estimate)`) %>%
-  mutate(
-    Population = Population*1000,
-    `Industry (elec) per capita` = `A. Industry and Commercial Electricity`*1000/Population,
-    `Industry (gas) per capita` = `B. Industry and Commercial Gas`*1000/Population,
-    `Industry (lge) per capita` = `C. Large Industrial Installations`*1000/Population,
-    `Industry (other fuels) per capita` = `D. Industrial and Commercial Other Fuels`*1000/Population,
-    `Domestic (elec) per capita` = `F. Domestic Electricity`*1000/Population,
-    `Domestic (gas) per capita` = `G. Domestic Gas`*1000/Population,
-    `Domestic (other fuels) per capita` = `H. Domestic 'Other Fuels'`*1000/Population,
-    `Transport (A roads) per capita` = `I. Road Transport (A roads)`*1000/Population,
-    `Transport (Motorways) per capita` = `J. Road Transport (Motorways)`*1000/Population,
-    `Transport (Minor roads) per capita` = `K. Road Transport (Minor roads)`*1000/Population,
-    `Transport (Diesel rail) per capita` = `L. Diesel Railways`*1000/Population,
-    `Transport (other) per capita` = `M. Transport Other`*1000/Population
-  )
-
-# ghg_emissions <- filter_detail(lvl_detail = "low")
-ghg_emissions <- filter_detail(lvl_detail = "high")
-rm(dt)
-
-
-
-# Plots
-
-