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# Script: filter different markets( just for cake oil market and sugar, cereals...)
#' function reads balance detailed, split it into "biofuels" and "non-biofuels"
#'
#' @param balance_detailed A data frame.
#' @param p_biofuels boolean.
#' @return links
#' @export
#'
prelinks <- function(balance_detailed, p_biofuels = TRUE){
# balance_detailed <- balance_detailed_Scenario
# add variable key, itemName and color from products
balance_detailed <- left_join(balance_detailed, products[,c(2,3,9)], by = c(".i4" = "key"))
# rename new variable
colnames(balance_detailed)[13] <- "Commodities"
colnames(balance_detailed)[1] <- "key"
balance_detailed$Commodities <- sub("\\[.*?\\]", "", balance_detailed$Commodities)
library(magrittr)
# prod_list_fullname <- balance_detailed %>% select(Commodities) %>% extract2(1)
# remove character begin with [ and end with ]
# prod_list_fullname <- gsub("\\[.*?\\]", "", prod_list_fullname)
# p_biofuels = T
if (p_biofuels){
selected_list = c("Rape seed",
"Soya seed",
"Sunflower seed",
"Rape seed oil",
"Soya oil",
"Soya cake",
"Sunflowe seed cake",
"Rape seed cake",
"Destilled dried grains from bio-ethanol processing",
"Pulses",
"Bio ethanol")
}else{
selected_list = c("Wheat","Grain maize","Barley", "Other cereals","Poultry meat",
"Sugar", "Fish and other acquatic products", "Rice milled")
}
#
balance_detailed_ <- balance_detailed %>% as.data.frame()
balance_detailed_selected <- filter(balance_detailed_, balance_detailed_[,13] %in% selected_list)
as_tibble(balance_detailed_selected)
#rename "Destilled dried grains from bio-ethanol processing" -> "DDGS"
# balance_detailed_selected <- balance_detailed_selected %>%
# mutate(Commodities = ifelse(Commodities!="Destilled dried grains from bio-ethanol processing",Commodities,"DDGS"))
# filter
balance_detailed_all <- balance_detailed_selected %>%
select(-.i1) %>% # remove region
select(-.i5) %>% # remove year: 2030
select(-interv_ch) %>%
group_by(Commodities) %>%
summarise_at(vars(supply:exports),sum,na.rm=TRUE)
library(magrittr)
prod_list_fullname <- balance_detailed_selected %>% select(Commodities) %>% extract2(1)
#print(prod_list_fullname)
vars_to_run <- prod_list_fullname
left_side <- c("supply","imports")
links_list <- list()
#nodes_list <- list()
vars_to_run <- vars_to_run[!vars_to_run %in%
c("Soya oil",
"Soya cake",
"Rape seed oil",
"Rape seed cake",
"Sunflower oil",
"Sunflowe seed cake",
"Bio ethanol",
"Destilled dried grains from bio-ethanol processing"
)]
# "Rape seed" "Sunflower seed" "Soya seed" "Pulses"
for (i in vars_to_run){
data_df <- reshape2::melt(balance_detailed_all)
data_df_1 <- data_df %>% filter(Commodities == i)
data_df_1_l <- data_df_1 %>% filter(variable %in% left_side )
data_df_1_r <- data_df_1 %>% filter(!variable %in% left_side)
data_df_1_l_agg <- data_df_1_l %>% group_by(Commodities) %>% summarise(value=sum(value))
data_df_1_l_agg$variable <- paste0(i)
first <- merge(data_df_1_l,data_df_1_l_agg,by="Commodities",all=TRUE)
first <- first[,c("variable.x","variable.y","value.x")]
colnames(first)[3] <- "value"
second <- merge(data_df_1_l_agg,data_df_1_r,by="Commodities",all=TRUE)
second <- second[,c("variable.x","variable.y","value.y")]
colnames(second)[3] <- "value"
if (p_biofuels){
if (i != "Pulses"){
if (i == "Soya seed") {produced = c("Soya oil","Soya cake")}
else if (i == "Rape seed") {produced = c("Rape seed oil","Rape seed cake") }
else if (i == "Sunflower seed") {produced = c("Sunflower oil","Sunflowe seed cake") }
else {print(i);break}
data_df_proc_help <- balance_detailed_all %>% filter(Commodities %in% produced)
data_df_proc_help <- reshape2::melt(data_df_proc_help)
data_df_proc_help <- data_df_proc_help %>% filter(variable== "supply" )
data_df_proc_help$variable <- paste(data_df_proc_help$Commodities)
data_df_proc_help$Commodities <- i
colnames(data_df_proc_help) <- c("variable.x","variable.y","value")
second <- rbind(second,data_df_proc_help)
second <- second %>% filter(variable.y!="processing")
data_df_proc_help <- balance_detailed_all %>% filter(Commodities %in% produced)
data_df_proc_help <- reshape2::melt(data_df_proc_help)
data_df_proc_help <- data_df_proc_help %>% filter(variable!="supply")
colnames(data_df_proc_help) <- c("variable.x","variable.y","value")
second <- rbind(second,data_df_proc_help)
second_imports <- second %>% filter(variable.y=="imports")
second <- second %>% filter(!variable.y=="imports")
colnames(second_imports) <- c("variable.y","variable.x","value")
second <- rbind(second,second_imports)
}
}
combined <- rbind(first,second)
links_list[[i]] <- combined
links <- rbindlist(links_list)
links <- unique(links)
}
if(p_biofuels){
# links[grepl("Bio",variable.x) | grepl("Bio",variable.y)]
setnames(links,old=c("variable.x","variable.y"),new=c("source","target"))
# links[grepl("Bio",source) | grepl("Bio",target)]
i <- c("Bio ethanol","Destilled dried grains from bio-ethanol processing")
data_df_proc_help <- balance_detailed_all %>% filter(Commodities %in% i)
data_df_proc_help <- reshape2::melt(data_df_proc_help)
data_df_proc_help <- data_df_proc_help %>% filter(variable=="supply")
data_df_proc_help$variable <- paste(data_df_proc_help$Commodities)
data_df_proc_help$Commodities <- "biofuels"
colnames(data_df_proc_help) <- c("variable.x","variable.y","value")
data_df <- reshape2::melt(balance_detailed_all)
data_df_1 <- data_df[data_df$Commodities %in% i,]
data_df_1_l <- data_df_1[data_df_1$variable %in% left_side,]
data_df_1_l$variable <- as.character(data_df_1_l$variable)
data_df_1_l <- data_df_1_l %>% filter(variable!="supply")
data_df_1_r <- data_df_1[!data_df_1$variable %in% left_side,]
data_df_1_r$variable <- as.character(data_df_1_r$variable)
data_df_1_l_agg <- data_df_1_l%>%group_by(Commodities) %>% summarise(value=sum(value))
data_df_1_l_agg$variable <- paste0(i)
first <- merge(data_df_1_l,data_df_1_l_agg,by="Commodities",all=TRUE)
first <- first[,c("variable.x","variable.y","value.x")]
colnames(first)[3] <- "value"
second <- merge(data_df_1_l_agg,data_df_1_r,by="Commodities",all=TRUE)
second <- second[,c("variable.x","variable.y","value.y")]
colnames(second)[3] <- "value"
second <- rbind(second,data_df_proc_help)
second <- second %>% filter(!variable.y %in% c("processing","biofuels"))
#nodes <- rbind(nodes,data.frame("name"=i))
combined <- rbind(first,second)
setnames(combined,old=c("variable.x","variable.y"),new=c("source","target"))
links <- rbind(links,combined)
}
else{
setnames(links,old=c("variable.x","variable.y"),new=c("source","target"))
}
# remove 0 value and <=900
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# links[,value:=ifelse(value<=50,NA,value)]
links<-na.omit(links)
return(links)
}
#' function calulates sum of links
#'
#' @param balance_detailed_links links.
#' @return sum
#'
linksum <- function(balance_detailed_links){
# the total value of different products from source
linkcopysum<- aggregate(balance_detailed_links$value,
by=list(Category=balance_detailed_links$source),
FUN=sum)
# add a column: the total value of different prodcuts from target
linkcopysum <- merge(linkcopysum,
aggregate(balance_detailed_links$value,
by=list(Category=balance_detailed_links$target),
FUN=sum),
by="Category")
return(linkcopysum)
}
#' function Replaces specific characters within strings
#' @param old old column names.
#' @param new new column names.
#' @param df replace data frame
#' @return df
#'
#'
#'
replacestr <- function(old, new, df){
df$label <- gsub(old, new, df$label)
return(df)
}
#' function get links and nodes for drawing a sankey diagram
#' @param baseline Baseline balance market.
#' @param scenario Scenario balance market.
#' @param p_baseline boolean.
#'
#' @return links and nodes
#' @export
#'
#'
links_nodes <- function(baseline, scenario, p_baseline){
linksum_baseline = linksum(baseline)
linksum_scenario = linksum(scenario)
# percentage diferences between scenario and baseline
linksum_scenario$percent_source = (linksum_scenario$x.x*100/linksum_baseline$x.x)-100
linksum_scenario$percent_target = (linksum_scenario$x.y*100/linksum_baseline$x.y)-100
#links = baseline
if (p_baseline){
links = baseline
}else{links= scenario}
nodes <- data.frame("name" = c(unique(c(as.character(links$source),links$target)) ))
# product: names -> number
links$source <- sapply(links$source,function(x)which(nodes$name %in% x))
links$source <- links$source -1
links$target <- sapply(links$target,function(x)which(nodes$name %in% x))
links$target <- links$target-1
if (p_baseline){
links$group <- paste0(gsub(" ", "_", baseline$source),"->", gsub(" ", "_", baseline$target))
}else{
links$group <- paste0(gsub(" ", "_", scenario$source),"->", gsub(" ", "_", scenario$target))
}
source_baseline <- baseline %>% as.data.frame() %>%
filter(source %in% c("imports", "supply")) %>%
group_by(source) %>%
summarise(sum(value))%>%
rename(name=source, sum = `sum(value)`)
source_scenario <- scenario %>% as.data.frame() %>%
filter(source %in% c("imports", "supply")) %>%
group_by(source) %>%
summarise(sum(value))%>%
rename(name=source, sum = `sum(value)`)
# calculate percent
source_scenario$percent<- (source_scenario$sum/source_baseline$sum)*100-100
target_baseline <- baseline %>% as.data.frame() %>%
filter(!(target %in% c("imports", "supply"))) %>%
group_by(target) %>%
summarise(sum(value)) %>%
rename(name=target, sum = `sum(value)`)
target_scenario <- scenario %>% as.data.frame() %>%
filter(!(target %in% c("imports", "supply"))) %>%
group_by(target) %>%
summarise(sum(value)) %>%
rename(name=target, sum = `sum(value)`)
# calculate percent
target_scenario$percent<- (target_scenario$sum/target_baseline$sum)*100-100
all_baseline <- rbind(source_baseline, target_baseline)
all_scenario <- rbind(source_scenario, target_scenario)
# if p_basline != TRUE -> add percent for nodes
if(!p_baseline){
# nodes$label <- ifelse(nodes$percent_target=="",
# as.character(nodes$label),
# paste0(nodes$label, " (", nodes$percent_target,"%", ")"))
all_scenario$label <- paste0(all_scenario$name, ": ",
format(round((all_scenario$sum)/1000,1), decimal.mark = ","),
"K (",round(all_scenario$percent,1), "%)" )
all <- all_scenario
#nodes$levels <- 1:3
}else{
all_baseline$label <- paste0(all_baseline$name, ": ",
format(round((all_baseline$sum)/1000,1), decimal.mark = "," ), "K" )
all <- all_baseline }
nodes_ <- left_join(nodes, all)
rm(nodes)
nodes <- nodes_
nodes$rank=c(1:nrow(nodes))
nodes = merge(nodes, linksum_scenario[,c("Category","percent_target")],
by.x="name",
by.y="Category",
all.x=TRUE)
nodes = nodes[order(nodes$rank,decreasing = FALSE),]
nodes$percent_target = round(nodes$percent_target,0)
nodes$percent_target <- ifelse(nodes$name=="biofuels","",nodes$percent_target)
nodes[is.na(nodes)]<-""
selectedcolor <- bind_rows(products %>% select(c(itemName,color)),
dim5s %>% select(c(itemName,color)))
selectedcolor$itemName <- sub("\\[.*?\\]", "", selectedcolor$itemName)
nodes <- left_join(nodes, selectedcolor, by = c("name" = "itemName"))
#print(nodes)
nodes <- nodes %>% select(c(rank,name,label,color))
# add links color
links_temp <- links
links_temp$color <- ""
colorlink <- c("Rape", "Sunflowe", "Soya", "Pulses",
"Wheat","Grain","Barley",
"Other","Poultry",
"Sugar", "Fish",
"Rice")
# get colors from nodes
color_nodes <- c("Wheat|Grain|Barley|Other|Poultry|Sugar|Fish|Rice|Rape seed$|Sunflowe seed|Soya seed$|Pulses")
getcolor <- nodes[grep(color_nodes, nodes$name), ] %>% select(c(name, color))
library(colorspace)
for (colorcol in colorlink){
temp <- getcolor %>%
filter(grepl(colorcol, getcolor$name))
# message(colorcol)
# print(links_temp[grepl(colorcol, group), ])
# print(links_temp[grep(colorcol, group), ])
links_temp[grep(colorcol, group), ]$color <- paste0(colorspace::lighten(temp$color, 0.05),"80")
}
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links <- links_temp %>%
mutate(color = ifelse(color=="","rgba(123,123,123,0.1)",color))
# Replace specific characters within strings
old=c("supply",
"human_cons",
"processing",
"biofuels",
"feed",
"imports",
"exports",
"Destilled dried grains from bio-ethanol Processing")
new=c("Production",
"Human Consumption",
"Processing",
"Biofuels",
"Feed",
"Imports",
"Exports",
"DDG")
for (i in 1:length(old)){
nodes <- replacestr(old[i],new[i], nodes)
}
return(list(links,nodes))
}
#' sets the position of nodes
#' @param nodes nodes for drawing a sankey diagram.
#' @return nodes with fixed position
#' @export
#'
nodes_position <- function(nodes){
nodes$level_x = ""
nodes$level_y = ""
if( "Pulses" %in% nodes$name){
# for oil cake
nodes <- nodes %>%
mutate(level_x = ifelse(name %in% c("supply", "imports"), 0.01,
ifelse(name %in% c("human_cons", "feed", "exports", "processing"),0.8,
ifelse(name %in% c("Rape seed", "Soya seed", "Sunflower seed","Pulses"), 0.2,
ifelse(name %in% c("Rape seed oil", "Soya oil"),0.35,
ifelse(name %in% c("Rape seed cake", "Soya cake", "Sunflowe seed cake", "biofuels"), 0.55, 0.7))) ))) %>%
mutate(level_y = ifelse(name %in% c("supply", "imports"), c(0.1,0.2),
ifelse(name %in% c("human_cons","Bio ethanol", "feed", "exports", "processing"),c(0.01,0.1,0.25,0.6),
ifelse(name %in% c("Rape seed", "Soya seed","Sunflower seed","Pulses"), c(0.01,0.15,0.3,0.6),
ifelse(name %in% c("Rape seed oil", "Soya oil"),c(0.3,0.6),
ifelse(name %in% c("Rape seed cake", "Soya cake", "Sunflowe seed cake", "biofuels"), c(0.2,0.3,0.5,0.7), 0.5))) )))
}else{
nodes <- nodes %>%
# for rest
mutate(level_x = ifelse(name %in% c("supply", "imports"),0.01, ifelse(name %in% c("human_cons","biofuels", "feed", "exports", "processing"),0.8,0.4)))%>%
mutate(level_y = ifelse(name %in% c("supply", "imports"),c(0.1,0.2), ifelse(name %in% c("human_cons","biofuels", "feed", "exports", "processing"),c(0.1,0.3,0.5,0.7,0.9),c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8))))
}
return(nodes)
}
#' function draws sankey diagram and saves html pages for sankey
#'
#'
#' @param links a data frame object with the links between the nodes. It should have include the Source and Target for each link. An optional Value variable can be included to specify how close the nodes are to one another.
#' @param nodes a data frame containing the node id and properties of the nodes. If no ID is specified then the nodes must be in the same order as the Source variable column in the Links data frame. Currently only grouping variable is allowed.
#' @param p_baseline boolean.
#' @param png boolean. if TRUE, the sankey chart will be saved.
#'
#' @return sankey diagram.
#' @export
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if(p_baseline){if("Bio ethanol" %in% nodes$name){ NAME <- "baseline"; print("Biofuels")} else{ NAME <- "baseline_Rest"}}
else{if("Bio ethanol" %in% nodes$name){ NAME <- "scenario"; print("Biofuels")} else{ NAME <- "scenario_Rest"}}
fig <- plot_ly(
width = 800,
height = 800,
type = "sankey",
domain = list(
x = c(0,1),
y = c(0,1)
),
orientation = "h",
arrangement = 'snap',
valueformat = ".0f",
valuesuffix = " tonnes",
node = list(
label = nodes$label,
color = nodes$color,
x = nodes$level_x,
y= nodes$level_y,
pad = 20,
thickness = 15,
line = list(
color = "blau",
width = 1
)
),
link = list(
source = links$source,
target = links$target,
value = links$value,
color = links$color
#label = links$group
)
) %>% layout(
autosize = F,
margin = list(l=10, r=10, b=50, t=50, pad=40),
title = "Market Balance in Tonnens",
font = list(size = 13, family ="Arial"),
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F)
)
#
library(htmlwidgets)
print(paste0("Export sankey diagram: ",paste0(outdata.dir,"/",NAME,"_fig.html")))
saveWidget(fig, paste0(outdata.dir,"/",NAME,"_fig.html"), selfcontained = F, libdir = "lib")
library(webshot)
#you convert it as png
if(png){
webshot(paste0(outdata.dir,"/",NAME,"_fig.html"),paste0(outdata.dir,"/png/",NAME,"_fig.png"),
vwidth = 1000, vheight = 600)}
fig
}