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#' Generic function which filters the data cube
#' @param datacube R object with full CAPRI resutls
#' @param region_list List of Regions to narrowed down on
#' @param dim5_list List of the fifth dimension elements
#' @param cols_list List of the elements in the column (COLS)
#' @param rows_list List of elements in the rows (ROWs)
#' @param scenario_name Name of the scenario you wish
#' @return tibble with filtered results
#'
filter_results_cube <- function(datacube, region_list, dim5_list, cols_list, rows_list, scenario_name){
datacube <- datacube %>%
filter(i1 %in% region_list, i2 %in% dim5_list, i3 %in% cols_list, i4 %in% rows_list, i5 != "BAS") %>%
select(i1, i2, i3, i4, i5, value)
datacube$scenario <- scenario_name
return(datacube)
}
#' Extracts pre-defined thematic tables from the results data cube
#'
#' @param datacube A dplyr table with the raw capmod results
#' @param region_list A character list of regions
#' @param product_list List of commodities
#' @param scenario Scenario for which you want to retrieve results
#'
#' @return A dplyr table (tibble) containing the market balance
#'
#' @export
#'
extract_gui_table <- function(region_list, dim5_list, cols_list, rows_list, scenario_list, folder = "mydata"){
# load all scenario results into memory
for(i in 1:length(scenario_list)) {load(file=paste(folder, "/", scenario_list[i], ".RData", sep=""))}
# get selected results (defined by the region, dim5, cols and rows lists) for the first scenario
GUI_table <- filter_results_cube(get(scenario_list[1]), region_list, dim5_list, cols_list, rows_list, scenario_list[1])
# continue with the other scenarios
if (length(scenario_list) > 1) {
for(i in 2:length(scenario_list)){
GUI_table <- rbind(GUI_table, filter_results_cube(get(scenario_list[i]), region_list, dim5_list, cols_list, rows_list, scenario_list[i]))
}
}
return(GUI_table)
}
#' GEts pre-defined thematic tables direclty from a result folder
#'
#' @param datacube A dplyr table with the raw capmod results
#' @param region_list A character list of regions
#' @param product_list List of commodities
#' @param scenario Scenario for which you want to retrieve results
#'
#' @return A dplyr table (tibble) containing the market balance
#'
#' @export
#'
#' @examples
#'
#' my_scenarios <- c("res_2_0830ghg_refpol_endotech_set12")
#' supply_table <- get_GUI_table(table = "supply details", my_scenarios, folder = "mydata")
#'
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get_GUI_table <- function(table = "supply details", scenario_list, folder = "mydata"){
# extract 'supply tables'
if(table == "supply details"){
selection_regions <- regions %>% filter(grepl("Regions and C", sel, ignore.case = TRUE)) %>% select(key)
selection_dim5s <- tbl_df(data.frame(key = c("")))
selection_cols <- activities %>% select(key)
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 <- tbl_df(data.frame())
# 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(get(paste("item.key.", i, sep = "")),
get(paste("item.itemName.", i, sep = "")),
get(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$unit
supply_details <- extract_gui_table(region_list = selection_regions, dim5_list = selection_dim5s, cols_list = selection_cols,
rows_list = selection_rows,
scenario_list = scenario_list, folder = "mydata")
supply_details <- left_join(supply_details, x, c("i4" = "key"))
supply_details <- supply_details %>% select(i1, i3, i4, name, unit, i5, scenario, value)
products <- products %>% select(key, itemName)
supply_details <- left_join(supply_details, products, c("i3" = "key"))
supply_details <- supply_details %>% select(i1, i3,itemName, i4, name, unit, i5, scenario, value)
colnames(supply_details) <- c("region", "activity_code", "activity_name", "variable_code", "variable_name", "unit", "year", "scenario", "value")
rm(x)
# calculate additional columns
# 1 a. crop shares UAAR.LEVL:
levels <- supply_details %>% group_by(region, activity_code) %>% filter(variable_code == "LEVL")
uaars <- supply_details %>% filter(variable_code == "LEVL") %>% filter(activity_code == "UAAR")
uaars <- uaars %>% select(region, activity_code, year, scenario, value)
x <- left_join(levels, uaars, c("region","year","scenario"))
rm(levels, uaars)
colnames(x) <- c("region","activity_code","activity_name","variable_code","variable_name","unit","year","scenario","valuex","todelete","valuey")
x <- x %>% mutate(value = valuex/valuey)
x <- x %>% select(-valuex, -valuey, -todelete)
x$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$unit))
x <- x %>% filter(activity_code != "UAAR")
supply_details <- rbind(supply_details, x)
rm(x)
# rename UAAR to CropShare to avoid misunderstandings
setDT(supply_details)[variable_name == "Crop Share", variable_code := "CropShare"]
supply_details <- tbl_df(supply_details)
# 1 b. crop shares per arable land ARAB.LEVL:
levels <- supply_details %>% group_by(region, activity_code) %>% filter(variable_code == "LEVL")
arabs <- supply_details %>% filter(variable_code == "LEVL") %>% filter(activity_code == "ARAB")
arabs <- arabs %>% select(region, activity_code, year, scenario, value)
x <- left_join(levels, arabs, c("region","year","scenario"))
rm(levels, arabs)
colnames(x) <- c("region","activity_code","activity_name","variable_code","variable_name","unit","year","scenario","valuex","todelete","valuey")
x <- x %>% mutate(value = valuex/valuey)
x <- x %>% select(-valuex, -valuey, -todelete)
x$variable_code <- gsub('LEVL', 'ARAB', x$variable_code, ignore.case = TRUE)
x$variable_name <- rep("Crop Share per Arable land", length(x$variable_name))
x$unit <- rep("% or 0.01 animals/ha", length(x$unit))
x <- x %>% filter(activity_code != "ARAB")
supply_details <- rbind(supply_details, x)
rm(x)
# rename ARAB to CropShareArab to avoid misunderstandings
setDT(supply_details)[variable_name == "Crop Share per Arable land", variable_code := "CropShareArab"]
supply_details <- tbl_df(supply_details)
# 2. Supply = yield * levl
levels <- supply_details %>% group_by(region, activity_code) %>% filter(variable_code == "LEVL")
yields <- supply_details %>% group_by(region, activity_code) %>% filter(variable_code == "YILD")
x <- inner_join(levels, yields, c("region","year","scenario","activity_code","activity_name"))
x <- x %>% mutate(value = value.x * value.y)
x <- x %>% select(-value.x, -value.y, -variable_name.x, -variable_name.y, -unit.x, -unit.y)
x <- x %>% select(-variable_code.x, -variable_code.y)
x$variable_name <- rep("Supply", length(x$value))
x$variable_code <- rep("Supply", length(x$value))
x$unit <- rep("1000 t", length(x$value))
supply_details <- rbind(supply_details, x)
rm(x)
return(supply_details)
}
}