library(dplyr) library(tradeflows) library(knitr) library(ggplot2) options(digits = 4) # Number of digits in knitr tables opts_chunk$set(echo=TRUE, message=FALSE)
classificationitto2 <- classificationitto %>% select(product, productcode = productcodecomtrade) %>% unique
swdcodes <- filter(classificationitto2, product== "SAWNWOOD")$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")
With Africa
africacodes <- filter(reportercomtrade, region == "Africa")$reportercode swdChina <- readdbtbl("raw_flow_yearly") %>% filter(reporter == "China" & partnercode %in% africacodes & productcode %in% swdcodes) %>% arrange(desc(tradevalue)) %>% select(year, reporter, partner, flow, productcode, tradevalue, quantity) %>% head(100) %>% collect %>% # Convert country names to utf-8 mutate(reporter = iconv(reporter, "latin1", "utf-8"), partner = iconv(partner, "latin1", "utf-8")) ggplot(swdChina, aes(x = year, y = tradevalue/1e6, color = as.factor(productcode), shape = as.factor(flow))) + geom_point() + ylab("Trade value in million US dollars") + ggtitle("100 Largest Sawnwood trade flows reported by China in Africa") + facet_wrap(flow ~ partner, scales="free") ggplot(swdChina, aes(x = year, y = quantity/1e6, color = as.factor(productcode), shape = as.factor(flow))) + geom_point() + ylab("Quantity in million cubic meter ") + ggtitle("100 Largest trade flows reported by China in Africa") + facet_wrap(flow ~ partner, scales="free")
africacodes <- filter(reportercomtrade, region == "Africa")$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
Largest roundwood trade flows reported by China with Africa
filter(classificationitto, product== "LOGS" & nomenclature== "HS07") %>% kable rwdcodes <- filter(classificationitto2, product== "LOGS")$productcode rwdeuchina <- readdbtbl("raw_flow_yearly") %>% filter((reporter == "China" | reporter =="EU-28") & partnercode %in% africacodes & flow =="Import" & productcode %in% rwdcodes) %>% arrange(desc(tradevalue)) %>% select(year, reporter, partner, flow, productcode, tradevalue, quantity) %>% head(100) %>% mutate(reporter = iconv(reporter, "latin1", "utf-8"), partner = iconv(partner, "latin1", "utf-8")) ggplot(rwdeuchina, aes(x = as.numeric(year), y = tradevalue/1e6, fill = as.factor(productcode))) + geom_bar(stat="identity") + ylab("Trade value in million US dollars") + theme(legend.position= "bottom") + scale_x_continuous(breaks = c(2010,2012)) + ggtitle("100 Largest roundwood import flows reported by China and the EU in Africa") + facet_grid(flow + reporter ~ partner) + guides(fill=guide_legend(nrow=2,byrow=TRUE)) # Make the same plot with exports reported from those countries
africacodes <- filter(reportercomtrade, region == "Africa")$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
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))
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))
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