sankey_chart_tables.R

# 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)

# load packages
requiredPackages = c('caprir','capriv','gdxrrw', 'usethis','hablar', 'tibble',
                     'networkD3','readxl','tidyr','dplyr','reshape2',
                     'data.table','plotly', 'gt', 'ggtext', 'rlang', 'Hmisc')
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)
# biofuels, scenario
biofuels_ln <- links_nodes(baseline_biofuels, scenario_biofuels, p_baseline = FALSE)

# others, baseline
ln_baseline <- links_nodes(baseline, scenario, TRUE)
# others, scenario
ln <- links_nodes(baseline, scenario, FALSE)

# sankey plots ----------------------
# biofuels, baseline,
p1 <- plot_sankey(biofuels_ln_baseline[[1]],
                  nodes_position(biofuels_ln_baseline[[2]]),
                  p_baseline = TRUE,
                  png = FALSE)
# biofuels and scenario
p2 <- plot_sankey(biofuels_ln[[1]],
                  nodes_position(biofuels_ln[[2]]),
                  p_baseline = FALSE,
                  png = FALSE)


# cereals, sugar .. and baseline
plot_sankey(ln_baseline[[1]],nodes_position(ln_baseline[[2]]),TRUE, png = FALSE) ###
# cereals, sugar .. and scenario
plot_sankey(ln[[1]],nodes_position(ln[[2]]), FALSE, png = TRUE)

# tables -----

# basline: load balance market and split into cake oil market and others.
res_b <- market_balance(balance_detailed_Baseline)

# Scenario: load balance market and split into cake oil market and others.
res_S <- market_balance(balance_detailed_Scenario)

oil_cake_market_baseline <- res_b[[1]]
other_baseline <- res_b[[2]]

oil_cake_market_Scenario <- res_S[[1]]
other_Scenario <- res_S[[2]]

# calculate the absolute and percentage changes between baseline and scenario.
oil_cake <- output_df(oil_cake_market_baseline,oil_cake_market_Scenario)
cereals <- output_df(other_baseline,other_Scenario)

# make a nice table
oil <- 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.html", inline_css = FALSE,
    path = outdata.dir)

cereals <- nicetable(cereals,"baseline for cereals, sugar, and meat markets")%>%
  gtsave(
    "cereals.html", inline_css = FALSE,
    path = outdata.dir)
#
webshot(paste0(outdata.dir,"/oil_cake.html"),paste0(outdata.dir,"/png/","oil_cake.png"),
        vwidth = 1100)

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