Download World Trade Database (R)

download_world_trade_db.R

# Download the World Trade Database from http://cid.econ.ucdavis.edu/ucdstats.html
# From documentation: value is in thousands of current USD.
library(reshape2)
library(dplyr)
library(readr)

process_file <- function(f_name) {
  # returns unzipped file.
  # create tempfile to remove later.
  tf <- tempfile(pattern="wtdb",fileext="zip")
  
  # download file at url `f_name` to tempfile `tf`
  download.file(url=f_name, destfile=tf, method='curl')
  
  # file is zipped; unzip it. there's two files inside, take the first one (second one is a readme)
  file <- unzip(tf)[[1]]
  
  # remove created tempfile
  file.remove(tf)
  
  # return the filename of the downloaded file
  return(file)
}

# downloads the data, returns a dataframe with years 80-99 and all SITC together.
main <- function() {

  c <- process_file("http://cid.econ.ucdavis.edu/data/ucdstatcan/COUNTRY.TXT.zip")
  
  # save country names and codes.
  names <- read_table(c,skip=7,col_names=c("code","name"),col_types=cols(code=col_character(),name=col_character()))
  
  # function to download/unzip the actual data
  # args = list(year=,index=)
  # year=80,...,97; index=1,2,3
  load_wtdb <- function(args) {
    year <- args[[1]]
    index <- args[[2]]

    f_name <- paste("http://cid.econ.ucdavis.edu/data/ucdstatcan/Wtdb",year,"_",index,".asc.txt.zip",sep="")
    x <- process_file(f_name)

    z <- read_table(x,col_names=c("imp_code","sitc","exp_code","value"),col_types=cols(.default='c', value='d')) #imp_code=col_character(),sitc=col_character(),exp_code=col_character(),value=col_double()))
    z <- z %>% mutate(year = 1900 + year) # make sure the year observation exists
    
    return(z)
  }
  
  # need to create the list of year/index pairs to download the full dataset
  x <- expand.grid(i=80:97,j=1:3)
  
  # split the data frame to get a list of argument pairs
  y <- split(x, 1:dim(x)[1])
  
  # apply the load_wtdb function to each pair in the list
  z <- lapply(y,FUN=load_wtdb)
  
  # z is a list of data frames; 
  w <- Reduce(rbind,z) # 6.3 million rows.
  
  # merge on the country names using the country code list
  w <- w %>% 
    left_join(names,by=c("imp_code" = "code")) %>% 
    rename(imp_name = name) # make sure we keep track of which name is the importer

  # merge on the country names using the country code list; 
  w <- w %>% 
    left_join(names,by=c("exp_code" = "code")) %>% 
    rename(exp_name = name) # make sure we keep track of which name is the exporter
  w <- w %>% select(-imp_code,-exp_code)
  return(w)
}

# actually run it.
w <- main()

# and save the data.
write_csv(w,"wtdb_clean.csv")

graph_exports.R

library(dplyr)
library(readr)
# load ggplot2
library(ggplot2)
# and cowplot---required to switch the x-axis to the top so you can read it where it matters.
library(cowplot)

# if you've already run main() and saved the data, do this instead
# so you don't need to re-download the data
w <- read_csv("wtdb_clean.csv")

# harder if you have data that includes internal trade.
exports <- w %>% 
  filter(year==1997) %>% 
  group_by(exp_name) %>% 
  mutate(value = value / 10^6) %>% # switch to billions
  summarize(value = sum(value)) 

p <- ggplot(exports,
            aes(reorder(exp_name, value),value)) + # re-order country names so USA is on the "right" side of 
                                                   # the x-axis (to be flipped later)
  geom_point() + 
  coord_flip() +
  labs(y="Value (Billons of current USD)", 
       x="Country", title="Total exports, 1997")

ggdraw(switch_axis_position(p +
                              theme_gray() + 
                              theme(axis.text.y = element_text(size=4)), 
                            axis = 'x')) # switch x-axis

ggsave("exports-1997.png",width=5,height=16)