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

library(knitr)
opts_knit$set(root.dir="../../..") # file paths are relative to the root of the project directory
opts_chunk$set(echo=FALSE)
library(tradeflows)
library(dplyr)
library(ggplot2)
library(reshape2)

There seemed to be a difference in partner flow information. Because reporterquantity was the estimated quantity by the cleaning process, while partnerquantity was the raw quantity from comtrade. This could been changed in the cleaning code. So that the function addpartnerflow is run a second time at the end of the cleaning process. reporterquantity and partnerquantity would then be consistent. The partner information wouldn't be lossed as it could be recomputed from the raw_data.

# Remove this chunk when this file is used as a template
# dtf in this chunk 
# will have to be provided to the report generating function
# load("data-raw/comtrade/440799.RData")
# load("data-raw/comtrade/4407.RData")
# dtfall <- rbind(dtfall, dtf)

### Investigate issues with partner flow information ###
dtf0 <- readdbproduct(440799, "validated_flow_yearly") %>% 
    filterworldeu28 %>%
    # Keep this partner information
    rename(quantitypartner0 = quantitypartner,
           quantityreporter0 = quantityreporter) %>%
    arrange(year, reporter, partner, flow)

dtf <- dtf0 %>%
    # Add the partner quantity again to check why there is a difference
    addpartnerflow %>%
    mutate(quantitypartnerdiff = quantitypartner0 - quantitypartner,
           quantityreporterdiff = quantityreporter0 - quantityreporter) %>%
    calculatediscrepancies %>%
    arrange(year, reporter, partner, flow)
# What is happening with some of the quantities?
summary(dtf0$quantitypartner0 - dtf0$quantitypartner)
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#       0       0       0       0       0       0 
summary(dtf0$quantityreporter0 - dtf0$quantityreporter)
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#       0       0       0       0       0       0 
summary(dtf$quantitypartner0 - dtf$quantitypartner)
#      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
#    -68940         0         0     81390        66 693600000      8351 
summary(dtf$quantityreporter0 - dtf$quantityreporter)
#      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
#    -68940         0         0     50920        15 693600000       637 
# This is the main reason why we have differences
summary(dtf0$quantity - dtf0$quantityreporter0)
#       Min.    1st Qu.     Median       Mean    3rd Qu.       Max.       NA's 
# -693600000        -15          0     -50920          0      68940        637 
summary(dtf$quantity - dtf$quantityreporter)
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#       0       0       0       0       0       0     637 

# Now let's look at the differences along the flags
dtf %>% group_by(flag) %>%
    summarise(quantityreporterdiff = sum(abs(quantityreporterdiff), na.rm=TRUE), 
              quantitypartnerdiff = sum(abs(quantitypartnerdiff),na.rm=TRUE))
# It appears that quantity reporter are similar where the quantity has beenleft unmodified by the cleaning process. 
# 
# Source: local data frame [25 x 3]
# 
#    flag quantityreporterdiff quantitypartnerdiff
# 1     0                    0            31577614
# 2     2                    0             9841168
# 3     4                    0            45691533
# 4     6                    0             4339060
# 5    10                    0              571966
# 6    14                    0               28008
# 7    20                    0              189759
# 8   300            142075647            19304452
# 9   302              1233230             7134147
# 10  304            730404392            18657010
# ..  ...                  ...                 ...
dtfstrange <- dtf %>% filter(quantitypartner0 != quantitypartner)
# For example Albania Brazil 2009 is different

dtf %>% filter(reporter == "Albania" & partner == "Brazil" & year == 2009) %>%
    select(year, reporter, partner, flow, flag,  quantityreporter, quantitypartner, 
           quantityreporter0, quantitypartner, quantity)
dtf0 %>% filter(reporter == "Albania" & partner == "Brazil" & year == 2009) %>%
    select(year, reporter, partner, flow, flag, quantityreporter0, quantitypartner0, quantity)
# Brazil didn'treport any export to Albania, that's consistentwith the above NA value under quantityreporter
dtf0 %>% filter(reporter == "Brazil" & partner == "Albania" & year == 2009) %>%
    select(year, reporter, partner, flow, quantityreporter0, quantitypartner0)
# Why did the quantity change from 2 to 5
# Flag 300 tels us that the quantity was shaved because the unit price was too high or too low.

# Check ivory coast 
dtfiv <- readdbproduct(440799, "validated_flow_yearly", convcountrynames = TRUE) %>%
    filter(reportercode == 384) %>% head
dtfivraw <- readdbproduct(440799, "raw_flow_yearly", convcountrynames = TRUE) %>%
    filter(reportercode == 384) %>% head

Thailand

Major export partners in weight in r max(dtf$year)

reporterinreport <- 764
flowselected <- "Export"
dtf2 <- dtf %>%
    filter(reportercode == reporterinreport)

for (p in unique(dtf$productcode)){
    exp <- dtf2 %>% 
        filter(flow == flowselected & year == max(year) & productcode==p) %>%
        select(partner, weight, discrv) %>%
        arrange(-weight) %>%
        rename(discrepancy = discrv) %>%
        head(10)
    partnerssorted <- unique(exp$partner)
    kable(exp)

        # Order the contries by trade volume
    exp <- exp %>%
        mutate(partner = ordered(partner, levels= partnerssorted)) %>%
        melt(id=c("partner"))

    chart <- ggplot(data=exp)+
        aes(partner, value, fill=variable) +
        geom_bar(position="dodge", stat="identity") +
        scale_fill_manual(values = c("chocolate3","black"))+
        theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
        ggtitle(paste(flowselected, p))
    print(chart)
    cat("\n\n")
}

# write.csv(exp, file="data-raw/export440799_Thailand_mainpartners2013.csv", row.names=FALSE)

Data can be exported to csv, Excel or other formats.

Thailand Export of 440799 to China in recent years

reporterinreport <- 764
# Report could choosedisplay those time series for main partners  only
partnerinreport <- 156 
flowselected <- "Export"
productcodeinreport <- 440799
dtf2 <- dtf %>%
    filter(reportercode == reporterinreport & 
               partnercode==partnerinreport &
               productcode == productcodeinreport&
               flow == flowselected)
kable(select(dtf2, classification, quantity, weight, tradevalue, 
             discrq, discrq, discrv))
dtf2 <- dtf2 %>%
    select(year, weight, discrq, quantity, discrq, tradevalue, discrv) %>%
    melt(id=c("year"))
weight <- dtf2 %>% filter(variable %in% c("weight", "discrv"))
dtf2$v <- dtf2$variable
dtf2$v[dtf2$variable == "discrv"] <- "weight"
dtf2$v[dtf2$variable == "discrq"] <- "quantity"
dtf2$v[dtf2$variable == "discrv"] <- "tradevalue"
# for (v1 in unique(dtf2$v))
ggplot(data=dtf2)+#filter(dtf2,v=="weight"))+
    aes(year, value, fill=variable) +
    geom_bar(position="dodge", stat="identity") +
    scale_fill_manual(values = rep(c("chocolate3","black"),2))+
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle(paste(flowselected, p)) +
    facet_grid(v~., scales="free_y")


# csv file for Sergey
# write.csv(dtf2, file="data-raw/export440799_Thailand_to_China.csv", row.names=FALSE) 

Spain

Major import partners in weight in 2012

reporterinreport <- 724
flowselected <- "Import"
# yearselected <-   max(dtf$year)
yearselected <- 2012
dtf2 <- dtf %>%
    filter(reportercode == reporterinreport)

for (p in unique(dtf$productcode)){
    exp <- dtf2 %>% 
        filter(flow == flowselected & year == yearselected & productcode==p) %>%
        select(partner, weight, discrv, tradevalue, discrv, quantity, discrq) %>%
        arrange(-weight) %>%
        head(10)
    partnerssorted <- unique(exp$partner)
    cat("\n\n")
    print(kable(rename(exp, discrepancy_weight = discrv)))
    cat("\n\n")


    exp <- exp %>%
        select(partner, weight, discrv) %>%
        # Order the contries by trade volume
        mutate(partner = ordered(partner, levels= partnerssorted)) %>%
        melt(id=c("partner"))

    chart <- ggplot(data=exp)+
        aes(partner, value, fill=variable) +
        geom_bar(position="dodge", stat="identity") +
        scale_fill_manual(values = c("chocolate3","black"))+
        theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
        ggtitle(paste(flowselected, p))
    cat("\n\n")
    print(chart)
    cat("\n\n")
}
# write.csv(exp, file="data-raw/export440799_Spain_mainpartners2012", 
#           row.names=FALSE)

Cameroon

Cameroon Export of 440799 to Spain in recent years

reporterinreport <- 120 # Cameroon
# Report could choosedisplay those time series for main partners  only
partnerinreport <- 724 # Spain 
flowselected <- "Export"
productcodeinreport <- 440799
dtf2 <- dtf %>%
    filter(reportercode == reporterinreport & 
               partnercode==partnerinreport &
               productcode == productcodeinreport&
               flow == flowselected)
kable(select(dtf2, classification, quantity, weight, tradevalue, 
             discrq, discrv, discrv))
dtf2 <- dtf2 %>%
    select(year, weight, discrv, quantity, discrq, tradevalue, discrv) %>%
    melt(id=c("year"))
weight <- dtf2 %>% filter(variable %in% c("weight", "discrv"))
dtf2$v <- dtf2$variable
dtf2$v[dtf2$variable == "discrv"] <- "weight"
dtf2$v[dtf2$variable == "discrq"] <- "quantity"
dtf2$v[dtf2$variable == "discrv"] <- "tradevalue"
# for (v1 in unique(dtf2$v))
ggplot(data=dtf2)+#filter(dtf2,v=="weight"))+
    aes(year, value, fill=variable) +
    geom_bar(position="dodge", stat="identity") +
    scale_fill_manual(values = rep(c("chocolate3","black"),3))+
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle(paste(flowselected, p)) +
    facet_grid(v~., scales="free_y")
# write.csv(dtf2, file="data-raw/export440799_Cameroon_to_Spain.csv", 
#           row.names=FALSE)

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