index.Rmd

---
title: "GHG Emissions of the UK"
author: "Kaydee S. Barker"
date: "Mar. 2024"
output:
  html_document:
    toc: true
    toc_float: true
---

```{r setup, include=FALSE}

rm(list=ls()) #clear global environment/workspace
knitr::opts_chunk$set(echo = FALSE, message = FALSE) #set echo and message to false universally
#knitr::knit(input = "GHG_UK.Rmd", output = "index.html")
#rmarkdown::render(input = "GHG_UK.Rmd", output_file = "index.html") #set name of knit file

```

```{r load libraries, message=FALSE}

library(readODS) #read ods files
library(readxl) #reads excel files
library(tidyverse) #Tidy packages
library(dplyr) #lots of functions - data manipulation
library(janitor) #helps with data cleaning
library(usethis) #look at and apply settings to Github actions
#library(data.tree) #transform df or file to data tree
library(ggplot2) #pretty plots
library(ggthemes) #themes for ggplot
library(RColorBrewer) #color palettes
library(wesanderson) #color palettes from Wes Anderson films
library(scales) #axis formatting options
library(plotly) #interactive web graphs
library(networkD3) #interactive web network graphs

#usethis::edit_r_environ() #set environment with Github token for API


```

## The National Atmospheric Emissions Inventory of the UK

Introduction to the inventory, with links megatonnes of CO2 equivalents Territorial Emissions Statistics (TES) sectors and subsectors used, as they are replacing National Communications (NC) sectors and subsectors in all reports (<https://www.gov.uk/government/statistics/final-uk-greenhouse-gas-emissions-national-statistics-1990-to-2022>)

```{r load data, warning=FALSE}

#Map loop to download UK 2022 data from the UK Department for Energy Security and Net Zero
downloaded <- file.exists("UKGHG_2022.ods") #checks if file is downloaded in working directory
if(downloaded != T){ #if downloaded is not true
  map2("https://assets.publishing.service.gov.uk/media/65c0d54663a23d000dc821ca/final-greenhouse-gas-emissions-2022-by-source-dataset.ods", #update this link when new data available
                         "UKGHG_2022.ods", download.file)} #else{print('data downloaded')} #name and download or print

#Read in ods file
GHG_UK22 <- read_ods(
  path = "UKGHG_2022.ods",
  sheet = 1, #define tab/sheet to read
  col_names = TRUE, #use header row for column names
  col_types = NULL, #guess data types
  na = "", #treat blank cells as NA
  skip = 0, #don't skip rows
  formula_as_formula = FALSE, #values only
  range = NULL, 
  row_names = FALSE, #no row names
  strings_as_factors = TRUE) %>% #use factors
  clean_names() %>% #clean column names to lowercase, with underscores
  separate_wider_delim(source, delim = " - ", #separate source, define delimiter as " - "
                       names = c("source_1", "source_2", "source_3"), #name new columns
                       too_few = "align_start", too_many = "merge", #what to do with fewer or more than 3
                       cols_remove = FALSE) %>% #keep original column
  separate_wider_delim(tes_category, delim = " - ", #separate category, define delimiter as " - "
                       names = c("category_1", "category_2"), #name new columns
                       too_few = "align_start", too_many = "merge", #what to do with fewer or more than 2
                       cols_remove = FALSE) %>% #keep original column
  separate_wider_delim(activity, delim = " - ", #separate activity, define delimiter as " - "
                       names = c("activity_1", "activity_2"), #name new columns
                       too_few = "align_start", too_many = "merge", #what to do with fewer or more than 2
                       cols_remove = FALSE) %>% #keep original column
  replace(is.na(.), "") #get rid of NA values, input blank


#Map loop to download UK sector mapping data
downloaded2 <- file.exists("UKsectors.xlsx") #checks if file is downloaded in working directory
if(downloaded2 != T){ #if downloaded is not true
  map2("https://assets.publishing.service.gov.uk/media/65c0d392c4319100141a455d/ghg-statistics-tes-to-nc-sector-mapping-2022.xlsx", #update this link when new data available
                         "UKsectors.xlsx", download.file)} #else{print('data downloaded')} #name and download or print

#Read in excel file
UKsectors <- read_xlsx(
  "UKsectors.xlsx", #excel file from current working directory
  sheet = "Table 1", #define tab/sheet to read
  col_names = FALSE, #the format of this excel file doesn't allow headers to be column names
  col_types = NULL, #guess data types
  na = "", #treat blank cells as NA
  trim_ws = TRUE, #trim any whitespace/unused columns and rows
  skip = 6, #skip 6 rows to get to pivot table data
  n_max = Inf, #set maximum number of rows to include
  guess_max = min(1000, Inf), #how many rows to use to guess data types
  progress = readxl_progress(), #display progress in reading in data
  .name_repair = "unique" #makes sure all column names not empty or duplicated
) 

#Assign headers/column names for UK sectors dataframe (doesn't import with headers)
colnames(UKsectors) <- c("ipcc_code", "source", "activity", "nc_sector", "nc_subsector", "nc_category", 
                         "tes_sector", "tes_subsector", "tes_category") 

#Assign more specific codes than IPCC codes
UKsectors <- UKsectors %>%
  mutate(subcode = case_when(tes_subsector == "Bioenergy crops" ~ "a", #create a subcode for LULUCF subsectors
                             tes_subsector == "Cropland mineral soils change" ~ "b",
                             tes_subsector == "Forestry" ~ "c",
                             tes_subsector == "Grassland mineral soils change" ~ "d",
                             tes_subsector == "Peatland" ~ "e",
                             tes_subsector == "Settlement" ~ "f",
                             tes_subsector == "Other LULUCF" ~ "g",
                             .default = "")) %>%
  unite('ipcc_code2', c(ipcc_code, subcode), remove = FALSE, sep = "_") #combine IPCC code with subcode


IPCC_TES <- UKsectors[, c(1,2,8,9,10)] #dataframe with just IPCC codes and TES sectors, subsectors, and categories




#Map loop to download UK Regional data 1990-2022 from the UK National Atmospheric Emissions Inventory
downloaded3 <- file.exists("GHG_regions.xlsx") #checks if file is downloaded in working directory
if(downloaded3 != T){ #if downloaded is not true
  map2("https://uk-air.defra.gov.uk/reports/cat09/2306200930_DA_GHGI_1990-2022_Final_v3.1.xlsx", #update this link when new data available
                        "GHG_regions.xlsx", download.file)} #else{print('data downloaded')} #name and download or print

#Read in excel file, by source, with region
GHG_regions <- read_xlsx(
  "GHG_regions.xlsx", #excel file from current working directory
  sheet = "BySource_data", #define tab/sheet to read
  col_names = TRUE, #use header row for column names
  col_types = NULL, #guess data types
  na = "", #treat blank cells as NA
  trim_ws = TRUE, #trim any whitespace/unused columns and rows
  skip = 6, #skip 6 rows to get to pivot table data
  n_max = Inf, #set maximum number of rows to include
  guess_max = min(1000, Inf), #how many rows to use to guess data types
  progress = readxl_progress(), #display progress in reading in data
  .name_repair = "unique" #makes sure all column names not empty or duplicated
) %>%
  clean_names() %>% #clean column names to lowercase, with underscores
  separate_wider_delim(source_name, delim = " - ", #separate source, define delimiter as " - "
                       names = c("source_1", "source_2", "source_3"), #name new columns
                       too_few = "align_start", too_many = "merge", #what to do with fewer or more than 3
                       cols_remove = FALSE) #keep original column

GHG_regions$ipcc_name <- gsub('_', ' ', GHG_regions$ipcc_name) #replace string underscores with spaces

#Look at distinct instances of codes and categories in regional dataset
GHG_codes <- GHG_regions[, c(5:9)] %>%
  distinct()

#Assign more specific codes than IPCC codes
#LULUCF TES Subsectors search strings
Bioenergy <- c("Bioenergy crops", "Miscanthus", "coppice")
Cropland <- c("Remaining Cropland", "converted to Cropland")
Forestry <- c("remaining Forest Land", "converted to Forest Land", "converted from forest land", "Harvested Wood")
Grassland <- c("Remaining Grassland", "converted to Grassland")
Peatland <- c("drain", "wet", "peat")
Settlement <- c("converted to settlement")
O_LULUCF <- c("Biomass Burning","carbon stock change")

#Assign codes from strings
GHG_regions <- GHG_regions %>%
  mutate(subcode = case_when(grepl(paste(Bioenergy, collapse='|'), source_name, ignore.case = TRUE) ~ "a", #for LULUCF
                             grepl(paste(Cropland, collapse='|'), source_name, ignore.case = TRUE) ~ "b",
                             grepl(paste(Forestry, collapse='|'), source_name, ignore.case = TRUE) ~ "c",
                             grepl(paste(Grassland, collapse='|'), source_name, ignore.case = TRUE) ~ "d",
                             grepl(paste(Peatland, collapse='|'), source_name, ignore.case = TRUE) ~ "e",
                             grepl(paste(Settlement, collapse='|'), source_name, ignore.case = TRUE) ~ "f",
                             grepl(paste(O_LULUCF, collapse='|'), source_name, ignore.case = TRUE) ~ "g", 
                              .default = "")) %>%
  unite('ipcc_code2', c(ipcc_code, subcode), remove = FALSE, sep = "_") #combine IPCC code with subcode



#Add subsector into GHG_regions, match format, add more specific IPCC codes to join by
GHG_regions2 <- inner_join(IPCC_TES, GHG_regions,
	by=c("ipcc_code2", "ipcc_code")) %>% #use inner join to combine dataframes by shared variables
  rename("ghg_grouped" = "pollutant") %>% #rename variable to match UK only csv
  rename("nc_sector" = "nc_format") #rename variable




#Newest years only 
#UK-wide dataset
GHG_UK2022 <- GHG_UK22 %>%
  filter(year == "2022") #%>%
  #filter(tes_sector != "LULUCF") #filter out LULUCF to see if negative values impact things

GHG_UK2022pos <- GHG_UK2022 %>%
  filter(emissions_mt_co2e >= "0") #subset filter only positive values
  
#Regional dataset
GHG_regions2021 <- GHG_regions2 %>%
  filter(emission_year == "2021")

#Simplify
GHG_simp <- GHG_UK2022 %>%
  group_by(tes_sector, tes_subsector, category_2, category_1, activity) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  replace(is.na(.), "") %>%
  filter(Temissions >= "0") #subset filter only positive values

#Pivot - rows are greenhouse gas groups
GHG_wide <- GHG_UK22 %>%
  pivot_wider(names_from = ghg_grouped, values_from=emissions_mt_co2e) %>%
  replace(is.na(.), 0)

```

## Visualization at High Level

```{r vis set up, warning=FALSE}

#Define custom color palette
colors <- c(
        "#3CBD9C", #transport
        "#F0A7CA", #buildings
        "#F0C954", #industry
        "#C24841", #electricity
        "#96BC61", #ag green
        "#5D7ED1", #lulucf blue
        "#D38744", #fuel
        "#A780BB"  #waste purple
      )


colors_3lvls <- c(
  "#C24841", #CH4
  "#3CBD9C", #CO2
  "#F0C954"  #N2O
)


```

### Sunburst Diagram of Emissions

```{r emissions sunburst, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Sunburst diagram demonstrating proportional breakdown of greenhouse gas emissions in the UK in 2022."}

#Format data for sunburst
#Separate out variables summaries - all GHGs combined
sectors <- GHG_UK2022pos %>%
  group_by(tes_sector) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE))

subsectors <- GHG_UK2022pos %>%
  group_by(tes_sector, tes_subsector) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE))

cat_2 <- GHG_UK2022pos %>%
  filter(category_2 != "") %>%
  group_by(tes_sector, tes_subsector, category_2) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  unite('parents', c(tes_sector, tes_subsector), remove = TRUE, sep = " - ") #combine higher categories into parents

cat_1 <- GHG_UK2022pos %>%
  group_by(tes_sector, tes_subsector, category_2, category_1) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  mutate(parents = case_when(
    category_2 != "" ~str_c(tes_sector,tes_subsector,category_2, sep = " - "),
    category_2 == "" ~str_c(tes_sector,tes_subsector, sep = " - ")))

act_1 <- GHG_UK2022pos %>%
  group_by(tes_sector, tes_subsector, category_2, category_1, activity_1) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  mutate(parents = case_when(
    category_2 != "" ~str_c(tes_sector,tes_subsector,category_2, category_1, sep = " - "),
    category_2 == "" ~str_c(tes_sector,tes_subsector, category_1, sep = " - ")
  ))

act_2 <- GHG_UK2022pos %>%
  filter(activity_2 != "") %>%
  group_by(tes_sector, tes_subsector, category_2, category_1, activity_1, activity_2) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  mutate(parents = case_when(
    category_2 != "" ~str_c(tes_sector,tes_subsector,category_2, category_1, activity_1, sep = " - "),
    category_2 == "" ~str_c(tes_sector,tes_subsector, category_1, activity_1, sep = " - ")
  ))


#Now paste together distinct instances into dataframe with hierarchical structure - start simple, set root
GHGsb1 <- data.frame(
  parents =c(rep("", 8)),
  labels =c(paste0(sectors$tes_sector)),
  values =as.numeric(c(paste0(sectors$Temissions)))
)

#Add id column
GHGsb1 <- GHGsb1 %>%
          mutate(ids = labels) 

#Now paste together distinct instances into dataframe with hierarchical structure - add on
GHGsb2 <- data.frame(
  parents =c(paste0(subsectors$tes_sector), paste0(cat_2$parents), paste0(cat_1$parents), 
             paste0(act_1$parents), paste0(act_2$parents)),
  labels =c(paste0(subsectors$tes_subsector), paste0(cat_2$category_2), paste0(cat_1$category_1), 
            paste0(act_1$activity_1), paste0(act_2$activity_2)),
  values =as.numeric(c(paste0(subsectors$Temissions), paste0(cat_2$Temissions), paste0(cat_1$Temissions), paste0(act_1$Temissions), paste0(act_2$Temissions)))
)

#Add id column                    
GHGsb2 <- GHGsb2 %>%
          unite('ids', c(parents, labels), remove = FALSE, sep = " - ") #combine columns for id

#Combine into one dataframe
GHGsb <- rbind(GHGsb1, GHGsb2) 


#Interactive sunburst with Plotly
sb <- plot_ly(GHGsb,
  type = 'sunburst',
  ids = ~ids,
  labels = ~labels,
  parents = ~parents,
  values = ~values,
  branchvalues = 'total', #parents are totals of children
  insidetextorientation='radial',
  maxdepth = 2, #how many levels to show
  level = "",
  sort = TRUE #sort by size
  #width = 800, height = 800 #plot dimensions
) %>%
    layout(
      margin = list(l = 10, r = 10, b = 10, t = 10),
      sunburstcolorway = colors,
      extendsunburstcolors = TRUE
      )

style(sb, hovertemplate="%{label} 
      %{value:,.3r} Mt CO2 equivalents
      %{percentParent:.1%} of %{parent} emissions
      <extra></extra>")



```

### Sunburst Diagram of Removals

```{r removals sunburst, warning=FALSE, fig.align = "center", out.width = '65%', fig.cap="Sunburst diagram demonstrating proportional breakdown of greenhouse gas removals in the UK (Land Use, Land Use Change, and Forestry Sector) in 2022."}

#Format data for sunburst
GHG_UK2022neg <- GHG_UK2022 %>%
  filter(emissions_mt_co2e <= "0") #subset filter only negative values

GHG_UK2022neg$source_2[c(6:8,13,14)] <- GHG_UK2022neg$source_1[c(6:8,13,14)] #replace empty source 2 values with source 1

#Separate out variables summaries - all GHGs combined
subsectors_neg <- GHG_UK2022neg %>%
  group_by(tes_subsector) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE))

cat_neg <- GHG_UK2022neg %>%
  group_by(tes_subsector, tes_category) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) 

src_neg <- GHG_UK2022neg %>%
  group_by(tes_subsector, tes_category, source_2) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  unite('parents', c(tes_subsector, tes_category), remove = TRUE, sep = " - ") #combine higher categories into parents


#Now paste together distinct instances into dataframe with hierarchical structure - start simple, set root
GHGsb1_neg <- data.frame(
  parents =c(rep("", 6)),
  labels =c(paste0(subsectors_neg$tes_subsector)),
  values =as.numeric(c(paste0(subsectors_neg$Temissions)))
)

#Add id column
GHGsb1_neg <- GHGsb1_neg %>%
          mutate(ids = labels) 

#Now paste together distinct instances into dataframe with hierarchical structure - add on
GHGsb2_neg <- data.frame(
  parents =c(paste0(cat_neg$tes_subsector), paste0(src_neg$parents)),
  labels =c(paste0(cat_neg$tes_category), paste0(src_neg$source_2)),
  values =as.numeric(c(paste0(cat_neg$Temissions), paste0(src_neg$Temissions)))
)

#Add id column                    
GHGsb2_neg <- GHGsb2_neg %>%
          unite('ids', c(parents, labels), remove = FALSE, sep = " - ") #combine columns for id

#Combine into one dataframe
GHGsb_neg <- rbind(GHGsb1_neg, GHGsb2_neg) 

#Convert negative values to absolute (positive) values
GHGsb_neg$values <- abs(GHGsb_neg$values)

#Interactive sunburst with Plotly
sb_neg <- plot_ly(GHGsb_neg,
  type = 'sunburst',
  ids = ~ids,
  labels = ~labels,
  parents = ~parents,
  values = ~values,
  branchvalues = 'total', #parents are totals of children
  insidetextorientation='radial',
  maxdepth = 2, #how many levels to show
  level = "",
  sort = TRUE #sort by size
  #width = 800, height = 800 #plot dimensions
) %>%
    layout(
      margin = list(l = 10, r = 10, b = 10, t = 10),
      sunburstcolorway = c(
        "#3CBD9C", #forestry
        "#F0C954", #grassland
        "#5D7ED1", #peatland
        "#C24841", #cropland
        "#96BC61",
        "#F0A7CA", 
        "#D38744", 
        "#A780BB"
      ),
      extendsunburstcolors = TRUE
      )

style(sb_neg, hovertemplate="%{label} 
      %{value:,.3r} Mt CO2 equivalents
      %{percentParent:.1%} of %{parent} removals
      <extra></extra>")



```


### Time Series Graph of Net Emissions by Sector

```{r time series, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Greenhouse gas emissions in megatonnes of CO2 equivalents in the UK over time, by sector."}

#Plot time series
Sect_time <- GHG_UK22 %>%
  group_by(year, tes_sector) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  rename("Sector" = "tes_sector") %>% #rename variable
  pivot_wider(names_from = Sector, values_from=Temissions) %>%
  clean_names() %>%
  droplevels() %>%
  ungroup() %>%
plot_ly(x = ~year,
                 y = ~agriculture, name = "Agriculture", line = list(color = "#96BC61", width = 2), 
                 type = 'scatter', mode = 'lines') %>%
  add_trace(y = ~buildings_and_product_uses, name = 'Buildings and product uses', line = list(color ="#F0A7CA", width = 2)) %>%
  add_trace(y = ~domestic_transport, name = 'Domestic transport', line = list(color ="#3CBD9C", width = 2)) %>%
  add_trace(y = ~electricity_supply, name = 'Electricity supply', line = list(color ="#C24841", width = 2)) %>%
  add_trace(y = ~fuel_supply, name = 'Fuel supply', line = list(color ="#D38744", width = 2)) %>%
  add_trace(y = ~industry, name = 'Industry', line = list(color ="#F0C954", width = 2)) %>%
  add_trace(y = ~lulucf, name = 'Land Use, Land Use Change, and Forestry', line = list(color ="#5D7ED1", width = 2)) %>%
  add_trace(y = ~waste, name = 'Waste', line = list(color ="#A780BB", width = 2)) %>%
  layout(title = "Greenhouse Gas Emissions of the UK per Sector Over Time",
                      xaxis = list(title = ""),
                      yaxis = list(title = "Greenhouse Gas Emissions (Megatonnes CO2 eq.)"),
                      legend = list(orientation = "h"))



#Plotly customization
style(Sect_time, hovertemplate="Year: %{x:,.r}
CO2eq: %{y:,.4r} kt") #define what hover label says, round x values, round y values to 4 sig. figs


```

## Sector and Subsector Summaries

### Agriculture

```{r ag sankey1, eval=FALSE, include=FALSE, fig.align = "center", out.width = '95%', fig.cap="Sankey diagram demonstrating breakdown of greenhouse gas emissions in the UK's agricultural sector in 2022."}

ag_df <- GHG_UK2022 %>%
  filter(tes_sector == "Agriculture") %>% #filter sector
  replace(is.na(.), "")
  #group_by(tes_subsector, tes_category, source, ghg, ghg_grouped, ipcc_code) %>%
  #summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE))

#Set up Sankey links dataframe
aglinks <- data.frame(source=c(paste0(ag_df$ghg_grouped), paste0(ag_df$tes_subsector), paste0(ag_df$category_2)), 
                    target=c(paste0(ag_df$tes_subsector), paste0(ag_df$category_2), paste0(ag_df$category_1)),
                    value=as.numeric(paste0(ag_df$emissions_mt_co2e)))

#Create nodes df from names in links df
agnodes <- data.frame(
  name=unique(c(as.character(aglinks$source), 
  as.character(aglinks$target))))

#Add ID numbers
aglinks$IDsource <- as.numeric(match(aglinks$source, agnodes$name)-1)
aglinks$IDtarget <- as.numeric(match(aglinks$target, agnodes$name)-1)


#Plot with networkD3
agsankey <- sankeyNetwork(Links = aglinks, Nodes = agnodes,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name",
              units = "Megatonnes CO2 eq.",
              sinksRight=FALSE, nodePadding = 8,
              iterations = 5)

agsankey

```

```{r ag sankey2, eval=FALSE, include=FALSE}


#Plot with Plotly
agsankey2 <- plot_ly(type = "sankey",
                   domain = list(x =  c(0,1),y =  c(0,1)),
                   orientation = "h",
                   valueformat = ".0f",
                   valuesuffix = "Mt CO2 eq.",
      node = list(
        label = agnodes$name,
        pad = 15,
        thickness = 20,
        line = list(color = "black", width = 0.5),
      link = list(
        source = aglinks$IDsource,
        target = aglinks$IDtarget,
        value = aglinks$value
        #label = linklist$label
        ))) %>% 
  layout(
    title = "Greenhouse Gas Emissions Per Agriculture Subsector in the UK",
    font = list(size = 10),
    xaxis = list(showgrid = F, zeroline = F),
    yaxis = list(showgrid = F, zeroline = F))

agsankey2


```

#### Agriculture Breakdown Graphs

```{r ag pies, eval=FALSE, include=FALSE, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Doughnut charts of the UK's agriculture greenhouse gas emissions by greenhouse gas identity and subsector in 2022."}

agpie <- ag_df %>% plot_ly(textinfo = 'label', insidetextorientation='horizontal') %>%
  add_pie(labels = ~ghg, values = ~emissions_mt_co2e, hole = 0.6, domain = list(row = 0, column = 0)) %>% 
  add_pie(labels = ~tes_subsector, values = ~emissions_mt_co2e, hole = 0.6, domain = list(row = 0, column = 2)) %>% 
  layout(title = "Emissions by Greenhouse Gas Identity and Subsector",  showlegend = F,
         #annotations = list(list(x = 0.20 , y = 1, text = "Emissions by Greenhouse Gas Identity"), 
          #                  list(x = 0.80 , y = 1, text = "Emissions by Subsector")),
         colorscale = colors,
         grid=list(rows=1, columns=3),
                      xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                      yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE)) 

style(agpie, hovertemplate="%{label} 
      Emissions: %{value:,.4r} Mt CO2 eq.
      %{percent}
      <extra></extra>")

```

```{r ag facet grid, eval=FALSE, include=FALSE, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's agricultural sector in  by category, colored by subsector and grouped by greenhouse gas identity."}

#Plot GHGs into side-by-side plots with Plotly
ag_CO2 <- agwideGHG %>% 
  plot_ly(x = ~tes_category, y = ~CO2, color = ~tes_subsector, type = 'bar', 
          legendgroup = ~tes_subsector) %>%
  layout(xaxis = list(showticklabels=FALSE, showgrid = FALSE))

ag_CH4 <- agwideGHG %>% 
  plot_ly(x = ~tes_category, y = ~CH4, color = ~tes_subsector, type = 'bar', 
          legendgroup = ~tes_subsector, showlegend = F) %>%
  layout(xaxis = list(showticklabels=FALSE, showgrid = FALSE))

ag_N2O <- agwideGHG %>% 
  plot_ly(x = ~tes_category, y = ~N2O, color = ~tes_subsector, type = 'bar', 
          legendgroup = ~tes_subsector, showlegend = F) %>%
  layout(xaxis = list(showticklabels=FALSE, showgrid = FALSE))

ag_GHGs <- subplot(ag_CO2, ag_CH4, ag_N2O, shareY = FALSE, margin = 0.05) %>%
  layout(title = list(text = "UK's Agriculture Emissions in 2022", pad = list(t=50, b = 50)),
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         plot_bgcolor='#e5ecf6',
         legend = list(orientation = "h", title=list(text='<b>Subsector</b>')),
         annotations = list(list(
           x = 0.125,  y = 1.0,
           text = "CO2",  
           xref = "paper",
           yref = "paper",  
           showarrow = FALSE),  
           list(
           x = 0.5,  y = 1.0,
           text = "CH4",  
           xref = "paper",
           yref = "paper",  
           showarrow = FALSE),  
           list(
           x = 0.875,  y = 1.0,
           text = "N2O",  
           xref = "paper",
           yref = "paper", 
           showarrow = FALSE)))

#Plotly customization
style(ag_GHGs, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs


```

```{r ag subsectors, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's agriculture sector in 2022 by subsector, stacked by greenhouse gas identity."}

#Bar plot by subsector, stacked by GHG with Plotly
agbar_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  group_by(tes_subsector, ghg, ghg_grouped) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg_grouped, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_subsector, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         barmode = 'stack'
         )

#Plotly customization
style(agbar_int, hovertemplate="Subsector: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r ag categories 1, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's agriculture sector in 2022 by category, filled by subsector."}

#Bar plot by category, filled by subsector with Plotly
agcat_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  group_by(tes_subsector, tes_category) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, color = ~tes_subsector, colors = colors,
          marker = list(
            width = 1.5)) %>%
  layout(title = 'Agriculture Emissions by TES Subsector and Category',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'),
         xaxis = list(title = '', tickangle = -90),
         legend = list(x = 0.8, y = 1, orientation = 'v'),
         autosize = F, width = 1000, height = 600,
         autoexpand = T,
         barmode = 'group'
         )


#Plotly customization
style(agcat_int, 
hovertemplate="Category: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

##### Subsector GHGs

```{r ag c, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the agricultural combustion subsector in 2022 by category, stacked by fuel type."}

#Bar plot combustion, stacked by fuel type with Plotly
agcom_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  filter(tes_subsector == "Agricultural combustion") %>%
  group_by(category_1, activity) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = activity, values_from=Temissions) %>%
  clean_names() %>%
  droplevels() %>%
  plot_ly(x = ~category_1, type = 'bar', y = ~gas_oil, name = 'Gas oil', marker = list(color ="#C24841")) %>%
  add_trace(y = ~petrol, name = 'Petrol', marker = list(color ="#A780BB")) %>%
  add_trace(y = ~biogas, name = 'Biogas', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~burning_oil, name = 'Burning oil', marker = list(color ="#F0A7CA")) %>%
  add_trace(y = ~fuel_oil, name = 'Fuel oil', marker = list(color ="#D38744")) %>%
  add_trace(y = ~natural_gas, name = 'Natural gas', marker = list(color ="#5D7ED1")) %>%
  add_trace(y = ~straw, name = 'Straw', marker = list(color ="#F0C954")) %>%
  layout(title = 'Agricultural Combustion Emissions by Fuel Type',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''),
         barmode = 'stack'
         )

#Plotly customization
style(agcom_int, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r ag soil, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the agricultural soils subsector in 2022 by category, stacked by greenhouse gas identity."}

#Bar plot soil, stacked by GHG with Plotly
agsoil_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  filter(tes_subsector == "Agricultural soils") %>%
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Agricultural Soil Emissions by Category',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(agsoil_int, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs


```

```{r livestock1, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the livestock subsector in 2022 by category, stacked by greenhouse gas identity."}


#Bar plot livestock animals, stacked by GHG with Plotly
agls_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  filter(tes_subsector == "Livestock") %>%
  group_by(category_1, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~category_1, type = 'bar', y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Agricultural Livestock Emissions by Category',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(agls_int, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r livestock2, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the livestock subsector in 2022 by category, stacked by animal kind."}

#Bar plot livestock category, stacked by animal with Plotly
agls2_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  filter(tes_subsector == "Livestock") %>%
  group_by(category_1, category_2) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = category_2, values_from=Temissions) %>%
  clean_names() %>%
  droplevels() %>%
  plot_ly(x = ~category_1, type = 'bar', y = ~beef_cattle, name = 'Beef cattle', marker = list(color ="#C24841")) %>%
  add_trace(y = ~dairy_cattle, name = 'Dairy cattle', marker = list(color ="#D38744")) %>%
  add_trace(y = ~sheep, name = 'Sheep', marker = list(color ="#96BC61")) %>%
  add_trace(y = ~pigs, name = 'Pigs', marker = list(color ="#F0A7CA")) %>%
  add_trace(y = ~poultry, name = 'Poultry', marker = list(color ="#A780BB")) %>%
  add_trace(y = ~other_livestock, name = 'Other livestock', marker = list(color ="#3CBD9C")) %>%
  layout(title = 'Agricultural Livestock Emissions by Category and Animal',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(agls2_int, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r livestock3, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the livestock subsector in 2022 by animal, stacked by greenhouse gas identity."}


#Bar plot livestock animals, stacked by GHG with Plotly
agls3_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  filter(tes_subsector == "Livestock") %>%
  group_by(category_2, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~category_2, type = 'bar', y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Agricultural Livestock Emissions by Animal',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(agls3_int, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the agricultural 'other' subsector in 2022 by category, stacked by greenhouse gas identity."}

#Bar plot other ag, stacked by GHG
agother_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Agriculture") %>%
  filter(tes_subsector == "Other agriculture") %>%
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  layout(title = 'Other Agricultural Emissions',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(agother_int, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs
  
```

### LULUCF

```{r, eval=FALSE, include=FALSE, fig.align = "center", out.width = '95%', fig.cap="Sankey diagram demonstrating breakdown of greenhouse gas emissions in the UK's LULUCF sector in 2022."}

LULUCF <- GHG_UK2022 %>%
  filter(tes_sector == "LULUCF") %>% #filter sector
  replace(is.na(.), "")
  #group_by(tes_subsector, tes_category, source, ghg, ghg_grouped, ipcc_code) %>%
  #summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE))

#Set up Sankey links dataframe
LULUCFlinks <- data.frame(source=c(paste0(LULUCF$ghg)), 
                    target=c(paste0(LULUCF$tes_subsector)),
                    value=as.numeric(paste0(LULUCF$emissions_mt_co2e)))

#Create nodes df from names in links df
LULUCFnodes <- data.frame(
  name=unique(c(as.character(LULUCFlinks$source), 
  as.character(LULUCFlinks$target))))

#Add ID numbers
LULUCFlinks$IDsource <- as.numeric(match(LULUCFlinks$source, LULUCFnodes$name)-1)
LULUCFlinks$IDtarget <- as.numeric(match(LULUCFlinks$target, LULUCFnodes$name)-1)


#Plot with networkD3
LULUCFsankey <- sankeyNetwork(Links = LULUCFlinks, Nodes = LULUCFnodes,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name",
              units = "Mt CO2 eq.",
              sinksRight=TRUE, nodePadding = 30,
              iterations = 100)

LULUCFsankey

```

#### LULUCF Breakdown Graphs

```{r LULUCF subsectors, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's LULUCF sector in 2022 by subsector, stacked by greenhouse gas identity."}


#Bar plot by subsector, stacked by GHG with Plotly
LULUCF_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  group_by(tes_subsector, ghg, ghg_grouped) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg_grouped, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_subsector, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = 'Subsector'), 
         barmode = 'stack'
         )

#Plotly customization
style(LULUCF_int, hovertemplate="Subsector: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r ag categories, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's agriculture sector in 2022 by category, filled by subsector."}

#Bar plot by category, filled by subsector with Plotly
LULUCFcat_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  group_by(tes_subsector, tes_category) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, color = ~tes_subsector, 
          colors = c("#C24841","#D38744","#96BC61", "#F0C954","#5D7ED1","#3CBD9C", "#F0A7CA"),
          marker = list(
            width = 1.5
                        )) %>%
  layout(title = 'LULUCF Sources and Sinks by TES Subsector and Category',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'),
         xaxis = list(title = '', tickangle = -90),
         legend = list(x = 0.75, y = 0.4, orientation = 'v'),
         autosize = F, width = 1000, height = 600,
         autoexpand = T,
         barmode = 'group'
         )


#Plotly customization
style(LULUCFcat_int, 
hovertemplate="Category: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```


```{r LULUCF cat, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's LULUCF sector in 2022 by source, stacked by greenhouse gas identity."}

colors <- c(
        "#3CBD9C", #transport
        "#F0A7CA", #buildings
        "#F0C954", #industry
        "#C24841", #electricity
        "#96BC61", #ag green
        "#5D7ED1", #lulucf blue
        "#D38744", #fuel
        "#A780BB"  #waste purple
      )

#Bar plot by source with Plotly
LULUCFcat2_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>% 
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(barmode = 'stack', 
         title = list(text = "LULUCF Sources and Sinks by TES Category and Greenhouse Gas", pad = list(t=50, b=50)),
         yaxis = list(title = 'Emissions/Removals (Mt CO2 equivalents)'),
         xaxis = list(title = '', tickangle = -90),
         autosize = F, width = 1000, height = 600,
         autoexpand = T
         #legend = list(orientation = "h", title=list(text='<b>Subsector</b>'))
  )

#Plotly customization
style(LULUCFcat2_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") 

```

```{r, eval=FALSE, include=FALSE, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's LULUCF sector in 2022 by source, colored by subsector and grouped by greenhouse gas identity."}

#Plot GHGs into side-by-side plots with Plotly
LULUCF_CO2 <- LULUCFwideGHG %>% 
  plot_ly(x = ~tes_category, y = ~CO2, color = ~tes_subsector, type = 'bar', legendgroup = ~tes_subsector) %>%
  layout(xaxis = list(showticklabels=FALSE, showgrid = FALSE))

LULUCF_CH4 <- LULUCFwideGHG %>% 
  plot_ly(x = ~tes_category, y = ~CH4, color = ~tes_subsector, type = 'bar', legendgroup = ~tes_subsector, showlegend = F) %>%
  layout(xaxis = list(showticklabels=FALSE, showgrid = FALSE))

LULUCF_N2O <- LULUCFwideGHG %>% 
  plot_ly(x = ~tes_category, y = ~N2O, color = ~tes_subsector, type = 'bar', legendgroup = ~tes_subsector, showlegend = F) %>%
  layout(xaxis = list(showticklabels=FALSE, showgrid = FALSE))

LULUCF_GHGs <- subplot(LULUCF_CO2, LULUCF_CH4, LULUCF_N2O, shareY = FALSE, margin = 0.05) %>%
  layout(title = list(text = "UK's LULUCF Emissions in 2022", pad = list(t=50, b = 50)),
         yaxis = list(title = 'Emissions (Mt CO2 equivalents)'), 
         plot_bgcolor='#e5ecf6',
         legend = list(orientation = "h", title=list(text='<b>Subsector</b>')),
         annotations = list(list(
           x = 0.125,  y = 1.0,
           text = "CO2",  
           xref = "paper",
           yref = "paper",  
           showarrow = FALSE),  
           list(
           x = 0.5,  y = 1.0,
           text = "CH4",  
           xref = "paper",
           yref = "paper",  
           showarrow = FALSE),  
           list(
           x = 0.875,  y = 1.0,
           text = "N2O",  
           xref = "paper",
           yref = "paper", 
           showarrow = FALSE)))

#Plotly customization
style(LULUCF_GHGs, 
hovertemplate="Source: %{x}
Emissions: %{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

##### Subsector GHGs

```{r biocrops, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the bioenergy crops subsector in 2022 by category."}

#Bar plot biocrops, stacked by GHG with Plotly
biocrops_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Bioenergy crops") %>%
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  layout(title = 'LULUCF Bioenergy crops',
         yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )

#Plotly customization
style(biocrops_int, 
hovertemplate="Source: %{x}
Removals: %{y:,.4r} Mt CO2 eq.
<extra></extra>") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r crop soil1, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the cropland mineral soils change subsector in 2022 by category, stacked by greenhouse gas identity."}

#Bar plot subsector, stacked by GHG with Plotly
cropsoilchg_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Cropland mineral soils change") %>%
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Cropland mineral soils change by Category',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
  
#Plotly customization
style(cropsoilchg_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq."
) #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r crop soil, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the cropland mineral soils change subsector in 2022 by category, stacked by source."}


#Bar plot subsector, filled by source_3 with Plotly
cropsoilchg2 <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Cropland mineral soils change") %>%
  group_by(tes_category, source_3) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) 
cropsoilchg2$source_3[cropsoilchg2$source_3 == ''] <- 'Cropland'

cropsoilchg2_int <- cropsoilchg2 %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, 
                           color = ~source_3, colors = colors) %>%
  layout(title = 'Cropland mineral soils change by Category and Source',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
  
#Plotly customization
style(cropsoilchg2_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r grass soil, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the grassland mineral soils change subsector in 2022 by category, stacked by source."}

#Bar plot subsector, filled by source_3 with Plotly
grsoilchg <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Grassland mineral soils change") %>%
  group_by(tes_category, source_3, activity) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) 
grsoilchg$source_3[grsoilchg$source_3 == ''] <- "Converted to grassland"

grsoilchg_int <- grsoilchg %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, 
                           color = ~source_3, colors = colors) %>%
  layout(title = 'Grassland mineral soils change by Category and Source',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(grsoilchg_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r forestry, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the forestry subsector in 2022 by category, stacked by source."}

#Bar plot subsector, filled by source_3 with Plotly
forestry <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Forestry") %>%
  group_by(tes_category, source_3, source_2) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) 
forestry$source_3[forestry$source_3 == ''] <- forestry$source_2

forestry_int <- forestry %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, 
                           color = ~source_3, colors = colors) %>%
  layout(title = 'Forestry by Category and Source',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(forestry_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r peat, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the peatland subsector in 2022 by category, stacked by greenhouse gas identity."}

#Bar plot subsector, stacked by GHG with Plotly
peat_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Peatland") %>%
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Peatland by Category and GHG',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(peat_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r set1, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the settlement subsector in 2022 by category, stacked by source."}

#Bar plot subsector, filled by source_2 with Plotly
settle_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Settlement") %>%
  group_by(tes_category, source_2) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, 
                           color = ~source_2, colors = colors) %>%
  layout(title = 'Settlement by Category and Source',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(settle_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r set2, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the settlement subsector in 2022 by source, stacked by greenhouse gas identity."}

#Bar plot subsector, stacked by GHG with Plotly
peat_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Settlement") %>%
  group_by(source_2, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~source_2, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Settlement by Source and GHG',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(peat_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

```{r LULUCF other1, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the 'other LULUCF' subsector in 2022 by category, stacked by greenhouse gas identity."}

#Bar plot subsector, stacked by GHG with Plotly
Lother_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Other LULUCF") %>%
  group_by(tes_category, ghg) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(title = 'Other LULUCF by Category and GHG',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(Lother_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs
  
```

```{r LULUCF other2, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the 'other LULUCF' subsector in 2022 by TES category, stacked by National Communication category."}

#Bar plot subsector, filled by source_2 with Plotly
Lother2_int <- GHG_UK2022 %>% 
  filter(tes_sector == "LULUCF") %>%
  filter(tes_subsector == "Other LULUCF") %>%
  group_by(tes_category, national_communication_category) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~Temissions, 
                           color = ~national_communication_category, colors = colors) %>%
  layout(title = 'Other LULUCF by TES and National Communication Categories',
         yaxis = list(title = 'Emissions/Removals (Mt CO2 eq.)'), 
         xaxis = list(title = ''), 
         barmode = 'stack'
         )
  
#Plotly customization
style(Lother2_int, 
hovertemplate="Source: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```


### Waste

#### Waste Breakdown Graphs

```{r waste categories, warning=FALSE, fig.align = "center", out.width = '95%', fig.cap="Bar graph showing greenhouse gas emissions in megatonnes of CO2 equivalents in the UK's Waste sector in 2022 by category, stacked by greenhouse gas identity."}


#Bar plot by subsector, stacked by GHG with Plotly
Waste_int <- GHG_UK2022 %>% 
  filter(tes_sector == "Waste") %>%
  group_by(tes_category, ghg, ghg_grouped) %>%
  summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) %>%
  pivot_wider(names_from = ghg_grouped, values_from=Temissions) %>%
  droplevels() %>%
  plot_ly(x = ~tes_category, type = 'bar', y = ~CO2, name = 'CO2', marker = list(color ="#3CBD9C")) %>%
  add_trace(y = ~CH4, name = 'CH4', marker = list(color ="#C24841")) %>%
  add_trace(y = ~N2O, name = 'N2O', marker = list(color ="#D38744")) %>%
  layout(yaxis = list(title = 'Emissions (Mt CO2 eq.)'), 
         xaxis = list(title = 'Category'), 
         barmode = 'stack'
         )

#Plotly customization
style(Waste_int, hovertemplate="Category: %{x}
%{y:,.4r} Mt CO2 eq.") #define what hover label says, round x values, round y values to 4 sig. figs

```

### Other Sectors

Buildings and product uses

```{r, eval=FALSE, include=FALSE, fig.align = "center", out.width = '95%', fig.cap="Sankey diagram demonstrating breakdown of greenhouse gas emissions in the UK's buildings and product uses sector in 2022."}

build_df <- GHG_UK22 %>%
  filter(tes_sector == "Buildings and product uses") %>% #filter sector
  replace(is.na(.), "")
  #group_by(tes_subsector, tes_category, source, ghg, ghg_grouped, ipcc_code) %>%
  #summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) 

#Set up Sankey links dataframe
buildlinks <- data.frame(source=c(paste0(build_df$ghg), paste0(build_df$tes_subsector)), 
                    target=c(paste0(build_df$tes_subsector), paste0(build_df$source_1)),
                    value=as.numeric(paste0(build_df$emissions_mt_co2e)))

#Create nodes df from names in links df
buildnodes <- data.frame(
  name=unique(c(as.character(buildlinks$source), 
  as.character(buildlinks$target))))

#Add ID numbers
buildlinks$IDsource <- as.numeric(match(buildlinks$source, buildnodes$name)-1)
buildlinks$IDtarget <- as.numeric(match(buildlinks$target, buildnodes$name)-1)


#Plot with networkD3
buildsankey <- sankeyNetwork(Links = buildlinks, Nodes = buildnodes,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name",
              units = "Mt CO2 eq.",
              sinksRight=FALSE, nodePadding = 15,
              iterations = 15)

buildsankey

```

Domestic transport

```{r, eval=FALSE, include=FALSE, fig.align = "center", out.width = '95%', fig.cap="Sankey diagram demonstrating breakdown of greenhouse gas emissions in the UK's transport sector in 2022."}

transport <- GHG_UK22 %>%
  filter(tes_sector == "Domestic transport") %>% #filter sector
  replace(is.na(.), "")
  #group_by(tes_subsector, tes_category, source, ghg, ghg_grouped, ipcc_code) %>%
  #summarize(Temissions = sum(emissions_mt_co2e, na.rm = TRUE)) 

#Set up Sankey links dataframe
transportlinks <- data.frame(source=c(paste0(transport$ghg), paste0(transport$tes_subsector)), 
                    target=c(paste0(transport$tes_subsector), paste0(transport$tes_category)),
                    value=as.numeric(paste0(transport$emissions_mt_co2e)))

#Create nodes df from names in links df
transportnodes <- data.frame(
  name=unique(c(as.character(transportlinks$source), 
  as.character(transportlinks$target))))

#Add ID numbers
transportlinks$IDsource <- as.numeric(match(transportlinks$source, transportnodes$name)-1)
transportlinks$IDtarget <- as.numeric(match(transportlinks$target, transportnodes$name)-1)


#Plot with networkD3
transportsankey <- sankeyNetwork(Links = transportlinks, Nodes = transportnodes,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name",
              units = "Mt CO2 eq.",
              sinksRight=FALSE, nodePadding = 15,
              iterations = 15)

transportsankey

```

Electricity supply

Fuel supply

Industry