In paul4forest/GFPMoutput: This program reads output simulation of the Global Forest Product Model (GFPM).

Load Sawnwood data

# Set same path for knitr evaluation as for interactive use
library(knitr)
opts_knit$set(root.dir = '../..')
opts_chunk$set(fig.width=10)

library(ggplot2)
library(dplyr)
library(reshape2)
# opts_chunk$set(cache=TRUE) # Set global chunk options
options(width=80)

Load raw FAOSTAT data

FAOSTAT is the source of forest products data. We have loaded this data for another project, reuse it here. May need to update this data?

load("/home/paul/hubic/work/EFI/Y/forestproductsdemand/rawdata/sawnwood.RData")
# EU = read.csv("rawdata/EUCountries.csv")
swd = sawnwood$entity # Give a shorter name to the data frame

Load GFPM data

load("enddata/GFPM_training_scenarios.RDATA")
country = "United Kingdom"
product = "Sawnwood"
gfpm = subset(trainingScenarios$entity,
              Country == country & 
                  Product == product & 
                  Scenario=="Base" )

Calculate apparent consumption

Could change this to import forestproductsdemand package

calculateConsumptionNetTrade = function(dtf){
    # Change NA values to 0 - Not recommended 
    # But makes sence at least that import into Finland and Sweden are 0
    dtf[is.na(dtf)] = 0

    # Calculate apparent consumption and net trade
    dtf = mutate(dtf, 
                 Consumption = Production + Import_Quantity - Export_Quantity, 
                 Net_Trade =  Export_Quantity - Import_Quantity)
    return(dtf)
}

swd <- calculateConsumptionNetTrade(swd)
# names tolower?
# names(airquality) <- tolower(names(airquality))
swd <- melt(swd, id=c("FAOST_CODE", "Country", "Year", "Item"),
            value.name = "Volume", variable.name = "Element")

Plot

UK historical sawnwood data

swduk <- filter(swd, Item=="Sawnwood" &
                Element %in% c("Production", "Import_Quantity", 
                              "Export_Quantity", "Consumption", "Net_Trade")&
                Country=="the United Kingdom of Great Britain and Northern Ireland")
ggplot(data=swduk) + 
    geom_line(aes(x=Year, y=Volume, colour=Element)) +
    ylab("M3") + theme_bw()

UK GFPM sawnwood scenario

gfpm_product <- subset(gfpm, Product == "Sawnwood" & Element!="DPrice")
p = ggplot(data=gfpm_product) +
    aes(x=Period, y=Volume, colour=Element, label = Element) +
    ggtitle(paste(country)) +
    geom_line() + 
    # Subset last period to print country labels on the curves
    geom_text(data=subset(gfpm_product, Period==max(gfpm_product$Period))) +
    xlim(1,5.5)
print(p)

Merge GFPM and historical data

Naming Issues

Naming issues are by-passed because we use only one country, one product and one variable. To merge tables containing many countries and products, we will need correspondance tables between the 2 encoding schemes.

# elements --> we will need a matching table
unique(swduk$Element)
unique(gfpm$Element)
# Items or product --> We will need a matching table
unique(swduk$Item)
unique(gfpm$Product)
# Countries or geographical area 
unique(swduk$Country)
unique(gfpm$Country)
# Add gfpm  years
years <- data.frame(Period=seq(1,5), Year=seq(2010,2030,5))
gfpmdemand <- gfpm %>% 
    merge(years) %>%
    filter(Element=="Demand") %>%
    mutate(Volume = 1000 * Volume) %>%
    select(Country, Year, Volume)


# Merge by keys: 
# swd <- melt(swd, id=c("FAOST_CODE", "Country", "Year", "Item"))
swdukdemand <- swduk %>% 
    filter(Element=="Consumption") %>%
    select(Country, Year, Volume) %>%
    mutate(Country = "United Kingdom") %>%
    rbind(gfpmdemand)

Show sawnwood demand for the different years

ggplot(data=swdukdemand) +
    aes(x=Year, y=Volume) +
    geom_line() + ylim(0,NA)

paul4forest/GFPMoutput documentation built on May 24, 2019, 8:25 p.m.