market_balances_sankey_tables.R

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###                                                                      ###
###                              SECTION 1:                              ###
###                       SANKEY CHARTS AND TABLES                       ###
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#################################################################
##                            Setup                            ##
#################################################################

# set currently working directory
gdx.dir <- paste0(getwd(), "/inst/extdata/gdx")

# define gdx file name for reading
sanky_file1 <- "res_2_1230cap_after_2014_refdefaultA" # refdefautA
sanky_file2 <- "res_2_1230fta_import_bandefaultA" # bandefaultA

# loaded gdx files and saved in dataout
outdata.dir <- paste0(getwd(), "/inst/outdata")

prod_list = c("WHEA","MAIZ","BARL","RAPE","SUNF","PULS",
              "DDGS","SUGR","ACQU", "SOYC", "OCER","SOYA",
              "RAPO","POUM","RAPC","SUNC","BIOE","SOYO",
              "RICE", "PULS", "SUGA", "SUNO")


###################################################################################################
##              Get detailed balance tables (demand broken down to its comoponents)              ##
###################################################################################################


# load market balance
balance_detailed_Baseline <- convert_balance_detailed("EU",
                                                      prod_list,
                                                      sanky_file1,
                                                      folder = gdx.dir)
balance_detailed_Scenario <- convert_balance_detailed("EU",
                                                      prod_list,
                                                      sanky_file2,
                                                      folder = gdx.dir)


#################################################################
##                        Sankey charts                        ##
#################################################################
# biofuels = T
baseline_biofuels <- prelinks(balance_detailed = balance_detailed_Baseline, p_biofuels = T, products = dimdef_product)
scenario_biofuels <- prelinks(balance_detailed = balance_detailed_Scenario, p_biofuels = T, products = dimdef_product)
# biofuels = F
baseline <- prelinks(balance_detailed = balance_detailed_Baseline, p_biofuels = FALSE, products = dimdef_product)
scenario <- prelinks(balance_detailed = balance_detailed_Scenario, p_biofuels = FALSE, products = dimdef_product)

# get links and nodes for different selected market
# biofuels, baseline
biofuels_ln_baseline <- links_nodes(baseline_biofuels, scenario_biofuels, p_baseline =TRUE, fixedNodePosition = FALSE,
                                    products = dimdef_product, dim5s = dimdef_dim5)
# biofuels, scenario
biofuels_ln <- links_nodes(baseline_biofuels, scenario_biofuels, p_baseline = FALSE, fixedNodePosition = FALSE,
                           products = dimdef_product, dim5s = dimdef_dim5)

# others, baseline
ln_baseline <- links_nodes(baseline, scenario, TRUE, fixedNodePosition = FALSE,
                           products = dimdef_product, dim5s = dimdef_dim5)
# others, scenario
ln <- links_nodes(baseline, scenario, FALSE,fixedNodePosition = FALSE,
                  products = dimdef_product, dim5s = dimdef_dim5)



# sankey plots

# one sankey diagram in one page
# biofuels, baseline,
plot_sankey(biofuels_ln_baseline,p_baseline = TRUE,png = FALSE,outdata.dir = outdata.dir)
# biofuels and scenario
plot_sankey(biofuels_ln,p_baseline = FALSE,png = FALSE,outdata.dir = outdata.dir)
# cereals, sugar .. and baseline
plot_sankey(ln_baseline,p_baseline =TRUE,png = FALSE,outdata.dir = outdata.dir)
# cereals, sugar .. and scenario
plot_sankey(ln, p_baseline = FALSE,png = FALSE,outdata.dir= outdata.dir)



# two sankey diagrams in one page
# combination two data frames
cake_oil_m <- combine_dfs(biofuels_ln_baseline, biofuels_ln,SidebySide =  FALSE)
plot_sankey(cake_oil_m,p_baseline = TRUE,png = TRUE,  outdata.dir= outdata.dir)

cereals_m <- combine_dfs(ln_baseline, ln, SidebySide =  FALSE)
plot_sankey(cereals_m,p_baseline = TRUE,png = TRUE, outdata.dir= outdata.dir)

##################################################################
##                            Tables                            ##
##################################################################
# make beautiful tables -----

# List of commodities for the market balances table
# oil cake market balance
# A list if long commodities name
oil_cake_market_list= c("Rape seed",
                        "Soya seed",
                        "Sunflower seed",
                        "Rape seed oil",
                        "Soya oil",
                        "Sunflower seed oil",
                        "Soya cake",
                        "Sunflowe seed cake",
                        "Rape seed cake",
                        "Destilled dried grains from bio-ethanol processing",
                        "Pulses",
                        "Bio ethanol")
# or A List of commodities' key
oil_cake_key_list  = c("RAPE","SUNF","SOYA","PULS","RAPO","SUNO","SOYO", "RAPC", "SUNC","SOYC","BIOE","DDGS")


# basline: load balance market.
b_oil_cake <- filter_market_balance(balance_detailed_Baseline, oil_cake_market_list, dimdef_product)
b_oil_cake <- filter_market_balance(balance_detailed_Baseline, oil_cake_key_list, dimdef_product)

# Scenario: load balance market.
s_oil_cake <- filter_market_balance(balance_detailed_Scenario, oil_cake_market_list, dimdef_product)
s_oil_cake <- filter_market_balance(balance_detailed_Scenario, oil_cake_key_list,  dimdef_product)

# calculate the absolute and percentage changes between baseline and scenario.
oil_cake <- cal_diff_percentage_change(b_oil_cake,s_oil_cake,supply_details = FALSE)

# make a nice table
oil_cake_tb <-  nicetable_market_balances(oil_cake,"baseline for oil and cake markets") %>%
  tab_footnote(
    locations = cells_stub(rows = c(2)),
    footnote = md("Destilled dried grains from bio-ethanol processing")
  )%>%
  gtsave(
    "oil_cake_tb.html", inline_css = FALSE,
    path = outdata.dir)

webshot(paste0(outdata.dir,"/oil_cake_tb.html"),paste0(outdata.dir,"/png/","oil_cake_tb.png"),
        vwidth = 1100)

# cereals, meate.. balance market ----
cereals_list <- c("Barley",
                  "Grain maize",
                  "Other cereals",
                  "Wheat",
                  "Poultry meat",
                  "Rice milled",
                  "Sugar","Fish and other acquatic products")

# basline: load balance market.
b_cereals <- filter_market_balance(balance_detailed_Baseline, cereals_list)

# Scenario: load balance market.
s_cereals <- filter_market_balance(balance_detailed_Scenario, cereals_list)

# calculate the absolute and percentage changes between baseline and scenario.
cereals <- cal_diff_percentage_change(b_cereals,s_cereals,supply_details = FALSE)


cereals_tb <- nicetable_market_balances(cereals,"baseline for cereals, sugar, and meat markets") %>%
  gtsave(
    "cereals_tb.html", inline_css = FALSE,
    path = outdata.dir)


webshot(paste0(outdata.dir,"/cereals_tb.html"),paste0(outdata.dir,"/png/","cereals_tb.png"),
        vwidth = 1100)