rm(list=ls())

# install devtools
install.packages("devtools")
library(devtools)

# install tidykml
devtools::install_github("briatte/tidykml")
library(tidykml)

# install gdxrrw
# 1: download from website: https://support.gams.com/gdxrrw:interfacing_gams_and_r
# 2: install package
install.packages("D:/public/yang/CAPRIR_Project/gdxrrw_1.0.5.zip", repos = NULL, type="source")
# or install from github
devtools::install_github("GAMS-dev/gdxrrw/gdxrrw")
library(gdxrrw)

# install capriR package
# remotes::install_github("trialsolution/caprir", force = TRUE)
devtools::install_git("https://git-dmz.thuenen.de/mindstep/caprir.git")
library(caprir)


devtools::install_git("https://git-dmz.thuenen.de/mindstep/capriv.git")
library(capriv)

#
# requiredPackages_Github <- c("briatte/tidykml", "GAMS-dev/gdxrrw/gdxrrw")
#
#
# requiredPackages_Gitlab <- "https://git-dmz.thuenen.de/mindstep/caprir.git"
#                              # "https://git-dmz.thuenen.de/mindstep/capriv.git")

# load packages

requiredPackages = c('caprir','capriv','gdxrrw', 'usethis','hablar', 'tibble',

                     'networkD3','readxl','tidyr','dplyr','reshape2',

                     'data.table','plotly', 'gt', 'ggtext', 'rlang', 'Hmisc','webshot')

for(p in requiredPackages){

  if(!require(p,character.only = TRUE))install.packages(p)

  library(p,character.only = TRUE)}

# set gams path, if necessary
gamsPath <- "D://gams//win64//24.9"
igdx(gamsPath)

# set currently working directory
#setwd("D:/public/yang/CAPRIR_Project/caprir_yang")
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")

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

# get links and nodes for different selected market -------------------
# biofuels, baseline

biofuels_ln_baseline <- links_nodes(baseline_biofuels, scenario_biofuels, p_baseline =TRUE, fixedNodePosition = TRUE)

# biofuels, scenario

biofuels_ln <- links_nodes(baseline_biofuels, scenario_biofuels, p_baseline = FALSE,fixedNodePosition = TRUE)

# others, baseline

ln_baseline <- links_nodes(baseline, scenario, TRUE,fixedNodePosition = TRUE)

# others, scenario

ln <- links_nodes(baseline, scenario, FALSE,fixedNodePosition = TRUE)

# sankey plots ----------------------

# one sankey diagram in one page ----

# biofuels, baseline,

capriv::plot_sankey(biofuels_ln_baseline,

                  p_baseline = TRUE,
                  png = FALSE)

# biofuels and scenario

capriv::plot_sankey(biofuels_ln,

                  p_baseline = FALSE,
                  png = FALSE)

# cereals, sugar .. and baseline

capriv::plot_sankey(ln_baseline,
                    p_baseline =TRUE,
                    png = FALSE) ###

# cereals, sugar .. and scenario

capriv::plot_sankey(ln,
                    p_baseline = FALSE,
                    png = FALSE)

# two sankey diagrams in one page ----

# combination two data frames

cake_oil_m <- combine_dfs(biofuels_ln_baseline, biofuels_ln)
capriv::plot_sankey(cake_oil_m,

                    p_baseline = TRUE,
                    png = FALSE)

cereals <- combine_dfs(ln_baseline, ln)

capriv::plot_sankey(cereals,
            p_baseline = TRUE,
            png = TRUE)

# make beautiful tables -----

# oil cake balance market ----
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")

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

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

# calculate the absolute and percentage changes between baseline and scenario.

oil_cake <- cal_percentage_change(b_oil_cake,s_oil_cake)

# make a nice table

oil_cake_tb <-  nicetable(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_percentage_change(b_cereals,s_cereals)


cereals_tb <- nicetable(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"),