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

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

Load functions and scenario data

library(dplyr)
library(reshape2)
source("code/func.R")
# A list of data frames
load("enddata/GFPM_training_scenarios.RDATA")
# Only one data frame, sorry that the structure is different
load("enddata/GFPM_training_scenarios_with_historical.RDATA")

Base scenario + other 2 training scenarios, calculated by changing the demand elasticities by plus or minus 1 standard error, corresponding to a confidence interval of 70%.

Introduction

Explore country specific data. Unfortunately GFPM doesn't simulate bilateral trade.

Todo

add all elements on one graph grid to after individual plots to help visualiwe differences in scale.

country = "United Kingdom"
dtf = subset(trainingScenarios$entity, Country == country & 
                 Scenario=="Base" )

Percentage increase of demand in the UK

change <- function(country, element="Demand"){
    if (country %in% unique(gfpm$Country)){
        gfpm %>%
            filter(Country == country &
                       Element == element & Scenario =="Base2011" &
                       Year %in% c(2011, 2030)) %>%
            # Add a V to year so that the future column name
            # of the reshaped table is not a number
            mutate(Year = paste0("V",Year)) %>% 
            select(Country, Product, Element, Year, Volume) %>%
            dcast(Country + Product + Element ~  Year, value.var = "Volume") %>%
            mutate(changepercent = round((V2030 - V2011) / V2011 *100)) %>%
            kable()
        }
    else {
        warning("Country is not in gfpm$Country")
    }

}
change("United Kingdom")
change("United Kingdom", "Production")
change("United Kingdom", "Supply")
change("United Kingdom", "Import")
change("United Kingdom", "Export")

change("China")
change("Brazil")
change("Germany")

Issues by product

Industrial roundwood decreasing and increasing in the base2011 scenario.

ggplot(data=filter(gfpm, Country==country & 
                       Product %in% c("IndRound")& Scenario == "Base2011")) +
    aes(x=Year, y=Volume, color=Element, linetype=Scenario) +
    geom_line() + ylim(0,NA) + 
    ylab(expression(paste("Volume ",m^3))) +
    facet_grid(Element ~ Product, scales="free_y") 

Plot by element

# Import
for (element in unique(dtf$Element)){
#     print(head(subset(dtf, Element == element)))
    print(paste("###", element))
    p = ggplot(data=subset(dtf, Element == element)) +
        aes(x=Period, y=Volume, colour=Product, label = Product) +
        ggtitle(paste(country,"-",element)) +
        geom_line() + 
        # Subset last period to print country labels on the curves
        geom_text(data=subset(dtf, Element == element & Period==max(dtf$Period))) +
        xlim(1,5.5)
    print(p)
    #            theme(legend.position = "none") 
}

All on one graph

ggplot(data=subset(dtf, Element!="DPrice")) +
    aes(x=Period, y=Volume, colour=Product, label = Product) +
    ggtitle(paste(country)) +
    geom_line() + 
    # Subset last period to print country labels on the curves
    geom_text(data=subset(dtf, Element!="DPrice" & Period==max(dtf$Period))) +
    facet_wrap(~Element) 

Plot by product

Except for Prices

# Import
for (product in unique(dtf$Product)){
#     print(head(subset(dtf, Element == element)))
    cat(paste("###", product))
    dtf_product <- subset(dtf, Product == product & Element!="DPrice")
    p = ggplot(data=dtf_product) +
        aes(x=Period, y=Volume, colour=Element, label = Element) +
        ggtitle(paste(country,"-",product)) +
        geom_line() + 
        # Subset last period to print country labels on the curves
        geom_text(data=subset(dtf_product, Period==max(dtf_product$Period))) +
        xlim(1,5.5)
    print(p)
    #            theme(legend.position = "none") 
}

All on one graph

ggplot(data=subset(dtf, Element!="DPrice")) +
    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(dtf, Element!="DPrice" & Period==max(dtf$Period))) +
    facet_wrap(~Product) +
    xlim(1,6)

Plot by product type

Primary secondary product.

Data summaries

What products are demanded, supplied, produced or traded in the country?

whatproducts = function(dtf){unique(dtf$Product)}
a <- group_by(dtf, Element)
per_element <- summarize(a, p = unique(a$Product))
# dlply(dtf, .(Element), whatproducts)[-2]
do(dtf, prod = 1)
# unique(trainingScenarios$entity$Country)
# unique(trainingScenarios$entity$Element)

Explore GFPM scenarios by continents

Plots

Base scenario

plotProdByReg(trainingScenarios, "Sawnwood", "Base")

plotProdByReg(trainingScenarios, "IndRound", "Base")

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