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

library(dplyr)
library(tradeflows)
library(knitr)
library(ggplot2)

Global trade in value for major forest products

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

global1 <- readdbtbl("raw_flow_yearly") %>%
    # Filter only trade flows with the world
    filter(partnercode ==0) %>%
    group_by(productcode) %>%
    summarise(tradevalue = sum(tradevalue)) %>%
    merge(classificationitto2) %>%
    arrange(desc(tradevalue)) 

# Keep this table for future use, to get the 10 largest products
global2 <- global1 %>%
    group_by(product) %>%
    summarise(tradevalue = sum(tradevalue)) %>%
    arrange(desc(tradevalue))

global2 %>% kable

Time series of the 10 major products

global3 <- readdbtbl("raw_flow_yearly") %>%
    # Filter only trade flows with the world
    filter(partnercode == 0) %>%
    # First grouping before the merge
    group_by(productcode, year, flow) %>% 
    summarise(tradevalue = sum(tradevalue)) %>%
    # Merge here and not before, otherwise, the merge takes too long
    # There may be a way to do a join in the database instead?
    merge(classificationitto2) %>%
    # Second grouping after the merge
    group_by(product, year, flow) %>%
    summarise(tradevalue = sum(tradevalue)) %>%
    arrange(desc(tradevalue))


biggest <- head(global2,10)$product
ggplot(filter(global3, product %in% biggest),
       aes(x = year, y = tradevalue / 1e9)) + 
    geom_point() + 
    facet_grid(flow ~ product, scales="free") +
    ylab("Trade value in billion usd")

Sawnwood

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

swd <- readdbtbl("raw_flow_yearly") %>%
    filter(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)) +
    geom_point() + facet_grid(flow~productcode)
# Non coniferous
ggplot(filter(swd, productcode!=440710),
       aes(x = year, y = tradevalue)) +
    geom_point() + facet_grid(flow~productcode)

Largest sawnwood trade flows

swdlarge <-  readdbtbl("raw_flow_yearly") %>%
    filter(partnercode != 0 & productcode %in% swdcodes) %>%
    arrange(desc(tradevalue)) %>% 
    select(year, reporter, partner, flow, productcode, tradevalue) %>%
    head(100) %>%
    collect
ggplot(swdlarge, aes(x = year, y = tradevalue, color=flow)) +
    geom_point() + facet_grid(reporter ~ partner)

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
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 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")

Roundwood

Largest roundwood trade flows

rwdcodes <- filter(classificationitto2, product== "LOGS")$productcode

rwdlarge <-  readdbtbl("raw_flow_yearly") %>%
    filter(partnercode != 0 & productcode %in% rwdcodes) %>%
    arrange(desc(tradevalue)) %>% 
    select(year, reporter, partner, flow, productcode, tradevalue) %>%
    head(100) %>%
    collect
ggplot(rwdlarge, aes(x = year, y = tradevalue, color=flow)) +
    geom_point() + facet_grid(reporter ~ partner)

Largest trade flows overall

large <-  readdbtbl("raw_flow_yearly") %>%
    filter(partnercode != 0) %>%
    arrange(desc(tradevalue)) %>% 
    select(year, reporter, partner, flow, flowcode, 
           productcode, tradevalue) %>%
    head(100) %>%
    collect
large <- large %>% merge(classificationitto2)
ggplot(large, aes(x = year, y = tradevalue, 
                     color=product, shape=as.factor(flow))) +
    geom_point() +
    facet_grid(reporter ~ partner)

Largest trade flows in volume

Largest trade flows in weight

large <-  readdbtbl("raw_flow_yearly") %>%
    filter(partnercode != 0) %>%
    arrange(desc(weight)) %>% 
    select(year, reporter, partner, flow, flowcode, 
           productcode, weight) %>%
    head(100) %>%
    collect
large <- large %>% merge(classificationitto2)
ggplot(large, aes(x = year, y = weight, 
                     color=product, shape=as.factor(flow))) +
    geom_point() +
    facet_grid(reporter ~ partner)

Global export value by product

European Union

Largest trade partners reported by the EU-28

largeeu <-  readdbtbl("raw_flow_yearly") %>%
    filter(reporter == "EU-28" & partnercode!=0) %>%
    arrange(desc(tradevalue)) %>% 
    select(year, reporter, partner, flow, flowcode, 
           productcode, tradevalue) %>%
    head(100) %>%
    collect
largeeu <- largeeu %>% merge(classificationitto2)
ggplot(largeeu, aes(x = year, y = tradevalue, 
                     color=product, shape=as.factor(flow))) +
    geom_point() +
    facet_wrap( partner ~ flow)


ggplot(largeeu, aes(x = year, y = tradevalue/1e6, 
                     color = flow)) +
    geom_point() + ylab("Trade value in million USD") +
    facet_grid(product ~ partner, scales="free_y")

Most important products traded by the EU-28

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