In paul4forest/tradeflows: Process forest products trade flow data from COMTRADE and COMEXT

library(dplyr)
library(tradeflows)
library(knitr)
library(ggplot2)
options(digits = 4) #  Number of digits in knitr tables
opts_chunk$set(echo=TRUE, message=FALSE)

classificationitto2 <- classificationitto %>%
    select(product, productcode = productcodecomtrade) %>% unique

Sawnwood

swdcodes <- filter(classificationitto2, 
                   product== "SAWNWOOD")$productcode
classificationitto %>% select(product, productcodecomtrade, description)%>%
    filter(product == "SAWNWOOD") %>% unique

swd <- readdbtbl("raw_flow_yearly") %>%
    filter(reporter == "China" & partnercode == 0 &
               productcode %in% swdcodes) %>%
    group_by(productcode, year, flow) %>%
    summarise(tradevalue = sum(tradevalue)) %>%
    collect

# Coniferous
ggplot(filter(swd, productcode==440710),
       aes(x = year, y = tradevalue/1e6)) +
    ylab("Trade value in million US dollars") +
    geom_point() + facet_grid(flow~productcode, scales="free")
# Non coniferous
ggplot(filter(swd, productcode!=440710),
       aes(x = year, y = tradevalue/1e6)) +
    ylab("Trade value in million US dollars") +
    geom_point() + facet_grid(flow~productcode, scales="free")

Largest sawnwood trade flows reported by China

With Africa

africacodes <- filter(reportercomtrade, 
                      region == "Africa")$reportercode
swdChina <- readdbtbl("raw_flow_yearly") %>%
    filter(reporter == "China" & 
               partnercode %in% africacodes &
               productcode %in% swdcodes) %>%
    arrange(desc(tradevalue))  %>%
    select(year, reporter, partner, flow, productcode, 
           tradevalue, quantity) %>%
    head(100) %>%
    collect %>%         # Convert country names to utf-8
    mutate(reporter = iconv(reporter, "latin1", "utf-8"),
           partner = iconv(partner, "latin1", "utf-8"))

ggplot(swdChina, aes(x = year, y = tradevalue/1e6,
                     color = as.factor(productcode),
                     shape = as.factor(flow))) +
    geom_point() +
    ylab("Trade value in million US dollars") +
    ggtitle("100 Largest Sawnwood trade flows reported by China in Africa") +
    facet_wrap(flow ~ partner, scales="free")

ggplot(swdChina, aes(x = year, y = quantity/1e6,
                     color = as.factor(productcode),
                     shape = as.factor(flow))) +
    geom_point() +
    ylab("Quantity in million cubic meter ") +
    ggtitle("100 Largest trade flows reported by China in Africa") +
    facet_wrap(flow ~ partner, scales="free")

Compare with the EU

africacodes <- filter(reportercomtrade, 
                      region == "Africa")$reportercode
swdeuchina <- readdbtbl("raw_flow_yearly") %>%
    filter((reporter == "China" | reporter =="EU-28") & 
               partnercode %in% africacodes & flow =="Import" &
               productcode %in% swdcodes) %>%
    arrange(desc(tradevalue))  %>%
    select(year, reporter, partner, flow, productcode, 
           tradevalue, quantity) %>%
    head(100) %>%
    collect %>%         # Convert country names to utf-8
    mutate(reporter = iconv(reporter, "latin1", "utf-8"),
           partner = iconv(partner, "latin1", "utf-8"))

ggplot(swdeuchina, aes(x = as.numeric(year), y = tradevalue/1e6,
                       fill = as.factor(productcode))) +
    geom_bar(stat="identity") +
    ylab("Trade value in million US dollars") +
    theme(legend.position= "bottom") +
    scale_x_continuous(breaks = c(2010,2013)) +
    ggtitle("100 Largest Sawnwood import flows reported by China and the EU in Africa") +
    facet_grid(flow + reporter ~ partner) 

# Make the same plot with exports reported from those countries

Roundwood

Largest roundwood trade flows reported by China with Africa

filter(classificationitto, product== "LOGS" & nomenclature== "HS07") %>% kable
rwdcodes <- filter(classificationitto2, 
                   product== "LOGS")$productcode

rwdeuchina <- readdbtbl("raw_flow_yearly") %>%
    filter((reporter == "China" | reporter =="EU-28") & 
               partnercode %in% africacodes & flow =="Import" &
               productcode %in% rwdcodes) %>%
    arrange(desc(tradevalue))  %>%
    select(year, reporter, partner, flow, productcode, 
           tradevalue, quantity) %>%
    head(100) %>%
    mutate(reporter = iconv(reporter, "latin1", "utf-8"),
           partner = iconv(partner, "latin1", "utf-8")) 


ggplot(rwdeuchina, aes(x = as.numeric(year), y = tradevalue/1e6,
                       fill = as.factor(productcode))) +
    geom_bar(stat="identity") +
    ylab("Trade value in million US dollars") +
    theme(legend.position= "bottom") +
    scale_x_continuous(breaks = c(2010,2012)) +
    ggtitle("100 Largest roundwood import flows reported by China and the EU in Africa") +
    facet_grid(flow + reporter ~ partner) + 
    guides(fill=guide_legend(nrow=2,byrow=TRUE))

# Make the same plot with exports reported from those countries

All products

africacodes <- filter(reportercomtrade, 
                      region == "Africa")$reportercode
alleuchina <- readdbtbl("raw_flow_yearly") %>%
    filter((reporter == "China" | reporter =="EU-28") & 
               partnercode %in% africacodes & flow =="Import") %>%
    arrange(desc(tradevalue))  %>%
    select(year, reporter, partner, flow, productcode, 
           tradevalue, quantity) %>%
    head(100) %>%
    merge(classificationitto2) %>%
    mutate(reporter = iconv(reporter, "latin1", "utf-8"),
           partner = iconv(partner, "latin1", "utf-8")) %>%    
    group_by(year, reporter, partner, flow, product) %>%
    summarise(tradevalue = sum(tradevalue)) 


ggplot(alleuchina, aes(x = as.numeric(year), y = tradevalue/1e6,
                       fill = as.factor(product))) +
    geom_bar(stat="identity") +
    ylab("Trade value in million US dollars") +
    theme(legend.position= "bottom") +
    scale_x_continuous(breaks = c(2010,2012)) +
    ggtitle("100 Largest wood products import flows reported by China and the EU in Africa") +
    facet_grid(flow + reporter ~ partner) + 
    guides(fill=guide_legend(nrow=2,byrow=TRUE))

# Make the same plot with exports reported from those countries

China EU trade

alleuchina <- readdbtbl("raw_flow_yearly") %>%
    filter(partner == "China" & reporter =="EU-28") %>%
    arrange(desc(tradevalue))  %>%
    select(year, reporter, partner, flow, productcode, 
           tradevalue, quantity) %>% 
    merge(classificationitto2) %>%
    mutate(reporter = iconv(reporter, "latin1", "utf-8"),
           partner = iconv(partner, "latin1", "utf-8")) %>%    
    group_by(year, reporter, partner, flow, product) %>%
    summarise(tradevalue = sum(tradevalue)) 


ggplot(alleuchina, aes(x = as.numeric(year), y = tradevalue/1e6,
                       fill = as.factor(product))) +
    geom_bar(stat="identity") +
    scale_x_continuous(breaks = c(2010,2012)) +
    ggtitle("100 Largest wood products import flows reported by the EU with China") +
    facet_grid(flow + reporter ~ partner) + 
    ylab("Trade value in million US dollars") +
    theme(legend.position= "bottom") +
    guides(fill=guide_legend(nrow=4,byrow=TRUE))

China France trade

allfrancechina <- readdbtbl("raw_flow_yearly") %>%
    filter(partner == "China" & reporter =="France") %>%
    arrange(desc(tradevalue))  %>%
    select(year, reporter, partner, flow, productcode, 
           tradevalue, quantity) %>% 
    merge(classificationitto2) %>%
    mutate(reporter = iconv(reporter, "latin1", "utf-8"),
           partner = iconv(partner, "latin1", "utf-8")) %>%    
    group_by(year, reporter, partner, flow, product) %>%
    summarise(tradevalue = sum(tradevalue)) 


ggplot(allfrancechina, aes(x = as.numeric(year), y = tradevalue/1e6,
                       fill = as.factor(product))) +
    geom_bar(stat="identity") +
    scale_x_continuous(breaks = c(2010,2012)) +
    ggtitle("100 Largest wood products import flows reported by the EU with China") +
    facet_grid(flow + reporter ~ partner) + 
    ylab("Trade value in million US dollars") +
    theme(legend.position= "bottom") +
    guides(fill=guide_legend(nrow=4,byrow=TRUE))

paul4forest/tradeflows documentation built on Oct. 8, 2019, 10:35 a.m.