diff --git a/howTo/openGeogAPI/local_auth_ghg_plots.R b/howTo/openGeogAPI/local_auth_ghg_plots.R
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+# rsconnect::deployApp('/Users/twr1m15/SotonGitLab/woRkflow/howTo/openGeogAPI', appName = "LAemissions",appTitle = "Hampshire local authority ghg emissions by sector")
+
+
+# Load libraries ----
+
+library(readxl)
+library(ggplot2)
+library(plotly)
+library(dplyr)
+library(reshape2)
+library(RColorBrewer)
+
+# Construct colour palette ----
+industry_pal <- brewer.pal(n = 8, name = "Greys")[4:8]    # industry greys, 5 categories
+domestic_pal <- brewer.pal(n = 4, name = "Blues")[2:4]    # domestic blues, 3 categories
+transport_pal <- brewer.pal(n = 6, name = "Oranges")[2:6] # transport oranges, 5 categories
+lulucf_pal <- brewer.pal(n = 9, name = "Greens")[4:9]     # lulucf greens, 6 categories
+
+# for details
+detailed_pal <- c(industry_pal, domestic_pal, transport_pal, lulucf_pal)
+
+# for totals/outlines
+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])
+
+# List local authority areas to load
+# These used to filter emissions data
+# and construct Open Geog API query (geo_query ... to do)
+las_to_load <- c("Southampton","Portsmouth","Winchester",
+                 "Eastleigh","Isle of Wight","Fareham",
+                 "Gosport","Test Valley","East Hampshire",
+                 "Havant","New Forest","Hart","Basingstoke and Deane")
+
+# Load GHG emissions data ----
+
+url_to_get <- "https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/894787/2005-18-uk-local-regional-co2-emissions.xlsx"
+
+tempf <- tempfile(fileext = ".xlsx")
+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 ----
+
+lvl_detail <-  "high"
+
+filter_detail <- function(lvl_detail =  lvl_detail){
+  
+  if(lvl_detail == "low"){  
+    dt <- dt %>%
+      filter(Name %in% las_to_load) %>%
+      select(`CTRY18NM/RGN18NM`,`Second Tier Authority`,Name,
+             Code,Year,`Industry and Commercial Total`,
+             `Domestic Total`,`Transport Total`,`LULUCF Net Emissions`)
+  }
+  
+  if(lvl_detail == "high"){
+    dt <- dt %>%
+      filter(Name %in% las_to_load) %>%
+      select(`CTRY18NM/RGN18NM`,`Second Tier Authority`,Name,Code,Year,
+             `A. Industry and Commercial Electricity`,`B. Industry and Commercial Gas`,
+             `C. Large Industrial Installations`,`D. Industrial and Commercial Other Fuels`,
+             `E. Agriculture`,
+             `F. Domestic Electricity`,`G. Domestic Gas`,`H. Domestic 'Other Fuels'`,
+             `I. Road Transport (A roads)`,`J. Road Transport (Motorways)`,`K. Road Transport (Minor roads)`,
+             `L. Diesel Railways`,`M. Transport Other`,
+             `LULUCF Net Emissions`)
+  }
+  
+  ghg_emissions <- melt(dt, id.vars = c("CTRY18NM/RGN18NM","Second Tier Authority","Name","Code","Year"))
+  return(ghg_emissions)
+}
+
+
+# Process data ----
+
+per_capita_dt <- dt %>%
+  filter(Name %in% las_to_load) %>%
+  rename(Population = `Population                                              ('000s, mid-year estimate)`) %>%
+  mutate(Population = Population*1000)
+
+
+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) )
+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
+
+pc_totals_plot <- data.frame(per_capita_totals[c(1:5,7:10)])
+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"))
+
+
+# Construct plots ----
+
+
+
+
+
+
+
+
+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)
+
+## 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
+
+