# get_GUI_table___<- function(table = "supply details", scenario_list, folder = "mydata"){
# 
# 
# 
# # extract 'supply tables'
#  if(table == "supply details"){
#   
#  print("table == supply details")
# 
#  selection_regions <- regions %>% filter(grepl("Regions and C", sel, ignore.case = TRUE)) %>% select(key)
#  selection_dim5s   <- as_tibble(data.frame(key = c("")))
#  selection_cols    <- activities %>% select(key)
# 
# 
#  print("selection_cols              1")
#  selection_regions <- selection_regions$key
#  selection_dim5s   <- selection_dim5s$key
#  selection_cols    <- selection_cols$key
# 
# 
# # load("data/table_coco.RData")
# 
#  # initialize code list
#  code_list <- as_tibble(data.frame())
#  print("selection_cols              2")
#  # add the columns without numbering
#   x           <- coco %>% filter(name == "Supply details") %>% select(item.key,
#                                                                       item.itemName,
#                                                                       item.unit)
#   colnames(x) <- c("key","name","unit")
#   code_list   <- rbind(code_list, x)
#   
#  for(i in 1:17){
#                 x           <- coco %>% filter(name == "Supply details") %>% 
#                   select(paste("item.key.", i, sep = ""),
#                          paste("item.itemName.", i, sep = ""),
#                          paste("item.unit.", i, sep = ""))
#                 colnames(x) <- c("key","name","unit")
#                 code_list   <- rbind(code_list, x)
# 
#  }
# 
# 
# # remove NA's and duplicates
#  x <- code_list %>% filter(!is.na(key)) %>% filter(!duplicated(key))
# 
#  selection_rows <- x$key
#  name_rows      <- x$name
#  unit_rows      <- x$unit
# 
# # call extract_gui_table() 
#  supply_details <- extract_gui_table(region_list = selection_regions, dim5_list = selection_dim5s, cols_list = selection_cols,
#                   rows_list = selection_rows,
#                   scenario_list = scenario_list, folder )
#  
#  # xinxin did 
#  # names of supply_deteils: .i1 .i2 .i3 .i4 .i5 dataOut scenario name
#  # i1- i5 -> .i1-i5
#  # dataOut <- value
# 
#  supply_details <- left_join(supply_details, x, c(".i4" = "key"))
# 
#  supply_details <- supply_details %>% select(.i1, .i3, .i4, name, unit, .i5, scenario, dataOut)
# 
#  products <- products %>% select(key, itemName)
#  supply_details <- left_join(supply_details, products, c(".i3" = "key"))
#  supply_details <- supply_details %>% select(.i1, .i3,itemName, .i4, name, unit, .i5, scenario, dataOut)
# 
#  colnames(supply_details) <- c("region", "activity_code", "activity_name", "variable_code", "variable_name", "unit", "year", "scenario", "value")
#  rm(x)
# 
#  # calculate additional columns
# 
#  # 1 a. crop shares UAAR.LEVL:
#  levels <- supply_details %>% group_by(region, activity_code) %>% filter(variable_code == "LEVL")
#  uaars  <- supply_details %>% filter(variable_code == "LEVL") %>% filter(activity_code == "UAAR")
#  uaars  <- uaars %>% select(region, activity_code, year, scenario, value)
# 
# 
#  x <- left_join(levels, uaars, c("region","year","scenario"))
#  rm(levels, uaars)
# 
#  colnames(x) <- c("region","activity_code","activity_name","variable_code","variable_name","unit","year","scenario","valuex","todelete","valuey")
#  x <- x %>% mutate(value = valuex/valuey)
#  x <- x %>% select(-valuex, -valuey, -todelete)
# 
#  x$variable_code <- gsub('LEVL', 'UAAR', x$variable_code, ignore.case = TRUE)
#  x$variable_name <- rep("Crop Share", length(x$variable_name))
#  x$unit <- rep("% or 0.01 animals/ha", length(x$unit))
#  x <- x %>% filter(activity_code != "UAAR")
# 
#  #
#  supply_details <- bind_rows(supply_details, x)
#  rm(x)
#  library(data.table)
# # 
# #  
# #  
# #  
# #  # xinxin did: bind_rows(one, two) 
# #  supply_details <- bind_rows(supply_details, x)
#  
#  
#  # convert matrix to df
#  supply_details <- as_tibble(supply_details)
# 
#  
# # rename UAAR to CropShare to avoid misunderstandings
#  setDT(supply_details)[variable_name == "Crop Share", variable_code := "CropShare"]
#  
#  supply_details <- as_tibble(supply_details)
# 
# 
#   # 1 b. crop shares per arable land ARAB.LEVL:
#  levels <- supply_details %>% group_by(region, activity_code) %>% filter(variable_code == "LEVL")
#  arabs  <- supply_details %>% filter(variable_code == "LEVL") %>% filter(activity_code == "ARAB")
#  arabs  <- arabs          %>% select(region, activity_code, year, scenario, value)
# 
#