Introduction

The US Census compiles data for exports, imports, and balances of goods for countries and groups of countries globally. This dataset has data back to 2009. Select years and countries are chosen for analysis below.

Load packages.

library(knitr)
## Warning: package 'knitr' was built under R version 3.2.4
library(stringr)
## Warning: package 'stringr' was built under R version 3.2.4
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.2.4
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.2.4
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.4
library(RCurl)
## Loading required package: bitops
## 
## Attaching package: 'RCurl'
## The following object is masked from 'package:tidyr':
## 
##     complete

Import CSV, column header = True.

Source: https://www.census.gov/foreign-trade/statistics/country/index.html

rawgoods<-data.frame(read.csv(file="ctyseasonal.csv", header=T, stringsAsFactors = FALSE))

Take subset of countries and years.

goods<-subset(rawgoods, CTYNAME== "China" | CTYNAME=="Germany" | CTYNAME=="Canada" | CTYNAME=="Mexico" | CTYNAME == "Japan")

goods<-subset(goods, year==2014 | year==2015)

goods<-subset(goods[,c(1:3,20:31,36:47)] )

kable(head(goods,10))
year CTY_CODE CTYNAME IJAN IFEB IMAR IAPR IMAY IJUN IJUL IAUG ISEP IOCT INOV IDEC EJAN EFEB EMAR EAPR EMAY EJUN EJUL EAUG ESEP EOCT ENOV EDEC
92 2014 1220 Canada 27,401 27,957 28,639 28,184 29,388 29,451 30,394 29,542 29,952 29,297 28,433 29,160 24,386 25,310 25,455 25,777 26,142 26,239 27,168 26,598 26,342 26,501 26,399 26,103
93 2014 5700 China 37,564 37,969 37,515 38,060 38,220 38,932 37,919 39,000 40,223 40,306 40,388 40,659 10,850 10,132 10,277 9,922 10,074 10,171 10,180 10,352 10,118 10,690 10,429 10,482
95 2014 4280 Germany 10,275 9,966 10,022 10,595 10,745 10,196 10,120 10,960 10,244 10,080 10,211 9,846 4,115 4,421 4,234 4,078 3,956 4,367 4,245 4,059 4,153 3,852 3,931 3,952
98 2014 5880 Japan 11,334 11,157 11,397 11,055 11,349 11,345 11,192 10,971 11,163 11,133 10,869 11,039 5,673 5,652 5,694 5,573 5,245 5,584 5,631 6,059 5,468 5,460 5,026 5,764
100 2014 2010 Mexico 23,414 24,035 23,890 24,192 24,313 24,766 24,911 24,839 24,513 25,504 24,593 25,104 19,443 19,890 19,589 19,788 20,709 20,257 20,452 20,295 19,957 20,061 20,123 19,687
110 2015 1220 Canada 25,729 25,017 24,229 23,798 23,990 26,161 25,709 25,356 24,506 23,175 23,538 23,982 24,870 23,596 23,421 23,684 24,151 23,062 23,733 23,191 22,661 22,788 22,561 22,609
111 2015 5700 China 40,225 36,119 48,222 37,694 40,142 39,410 39,092 42,696 40,932 39,750 39,298 38,301 10,013 8,996 9,297 10,217 9,530 10,475 10,356 9,791 10,208 9,551 9,136 8,618
113 2015 4280 Germany 10,423 10,541 10,007 10,287 10,221 10,862 10,298 10,652 9,897 10,748 9,711 10,493 3,979 4,151 4,286 4,354 3,861 4,035 4,316 3,866 4,195 4,580 4,183 4,140
116 2015 5880 Japan 11,762 9,746 11,815 12,518 11,621 10,349 10,696 10,471 10,134 10,337 10,647 11,023 5,250 5,353 5,542 5,836 5,262 5,172 5,314 5,275 4,635 5,063 5,011 4,759
118 2015 2010 Mexico 23,883 24,152 23,983 24,215 24,043 25,487 24,553 24,881 25,024 26,001 24,221 24,298 20,050 19,556 18,992 20,012 19,930 20,045 20,716 19,555 19,554 19,668 18,811 19,488

Reshape data, separate column, convert month abbreviations to numbers.

goods<- gather(goods,"Month","Volume", 4:27)
str(goods) #check structure
## 'data.frame':    240 obs. of  5 variables:
##  $ year    : int  2014 2014 2014 2014 2014 2015 2015 2015 2015 2015 ...
##  $ CTY_CODE: int  1220 5700 4280 5880 2010 1220 5700 4280 5880 2010 ...
##  $ CTYNAME : chr  "Canada" "China" "Germany" "Japan" ...
##  $ Month   : chr  "IJAN" "IJAN" "IJAN" "IJAN" ...
##  $ Volume  : chr  "27,401" "37,564" "10,275" "11,334" ...
goods<-separate(goods,Month, into = c("Type", "Month"), sep = 1)

goods$Volume<-as.numeric(gsub(",", "",goods$Volume))

goods$Month<-gsub("JAN","01",goods$Month)
goods$Month<-gsub("FEB","02",goods$Month)
goods$Month<-gsub("MAR","03",goods$Month)
goods$Month<-gsub("APR","04",goods$Month)
goods$Month<-gsub("MAY","05",goods$Month)
goods$Month<-gsub("JUN","06",goods$Month)
goods$Month<-gsub("JUL","07",goods$Month)
goods$Month<-gsub("AUG","08",goods$Month)
goods$Month<-gsub("SEP","09",goods$Month)
goods$Month<-gsub("OCT","10",goods$Month)
goods$Month<-gsub("NOV","11",goods$Month)
goods$Month<-gsub("DEC","12",goods$Month)
str(goods) #check structur
## 'data.frame':    240 obs. of  6 variables:
##  $ year    : int  2014 2014 2014 2014 2014 2015 2015 2015 2015 2015 ...
##  $ CTY_CODE: int  1220 5700 4280 5880 2010 1220 5700 4280 5880 2010 ...
##  $ CTYNAME : chr  "Canada" "China" "Germany" "Japan" ...
##  $ Type    : chr  "I" "I" "I" "I" ...
##  $ Month   : chr  "01" "01" "01" "01" ...
##  $ Volume  : num  27401 37564 10275 11334 23414 ...
kable(head(goods))
year CTY_CODE CTYNAME Type Month Volume
2014 1220 Canada I 01 27401
2014 5700 China I 01 37564
2014 4280 Germany I 01 10275
2014 5880 Japan I 01 11334
2014 2010 Mexico I 01 23414
2015 1220 Canada I 01 25729

Reshape data to create columns for Exports/Imports.

goods<-spread(goods,Type,Volume)

kable(head(goods))
year CTY_CODE CTYNAME Month E I
2014 1220 Canada 01 24386 27401
2014 1220 Canada 02 25310 27957
2014 1220 Canada 03 25455 28639
2014 1220 Canada 04 25777 28184
2014 1220 Canada 05 26142 29388
2014 1220 Canada 06 26239 29451

Add column for YYYY-MM

goods$ymdate<-paste(goods$year, goods$Month, sep="-")
goods<-mutate(goods,ymdate)

kable(head(goods))
year CTY_CODE CTYNAME Month E I ymdate
2014 1220 Canada 01 24386 27401 2014-01
2014 1220 Canada 02 25310 27957 2014-02
2014 1220 Canada 03 25455 28639 2014-03
2014 1220 Canada 04 25777 28184 2014-04
2014 1220 Canada 05 26142 29388 2014-05
2014 1220 Canada 06 26239 29451 2014-06

Show plots.

ggplot(
  goods, aes(x = ymdate, y = E)) + 
  geom_bar(stat = "identity",fill="#00ffbf",color="black", width = 0.8) +
  ggtitle("Exports by Month in 2014 and 2015")+ 
  facet_wrap(~ CTYNAME) +  
  theme(panel.grid.minor.x=element_blank(), panel.grid.major.x=element_blank(),axis.text=element_text(angle=90)) +
  scale_y_continuous(breaks=seq(0,40000,20000))+
  labs(x="Year and Month",y="Millions of Dollars") 

ggplot(
  goods, aes(x = ymdate, y = I)) + 
  geom_bar(stat = "identity",fill="blue",color="black", width = 0.8) +
  ggtitle("Imports by Month in 2014 and 2015") + 
  facet_wrap(~ CTYNAME) +  
  theme(panel.grid.minor.x=element_blank(), panel.grid.major.x=element_blank(),axis.text=element_text(angle=90)) +
  scale_y_continuous(breaks=seq(0,40000,20000))+
  labs(x="Year and Month",y="Millions of Dollars")

Conclusion

China had the most imports between January 2014 and December 2015. Canada had the most exports for this time period, more than twice China’s exports. Canada and China showed a similar dip in exports in early 2015. However, China had an outlier month for imports in March 2015 which was not mirrored by the other countries chosen. Othewrise, exports and imports for any chosen country showed parallel trends.