First we load the csv data into R from Github. We also load the required packages.

library(RCurl)
## Loading required package: bitops
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.2.3
## 
## Attaching package: 'tidyr'
## 
## The following object is masked from 'package:RCurl':
## 
##     complete
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.2.2
## 
## 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
url = 'https://raw.githubusercontent.com/cyadusha/countries/master/ctyseasonal.csv'
x = getURL(url)
countries = read.csv(file = textConnection(x), header = TRUE)

Because we are only interested in the imports and exports for India and Canada, we subset the entire dataset where the country names are either India or Canada. We also select the columns which are titled “year” and the columns which are titled from BJAN to BDEC. These are the months of the year. We do not need the code for each country. Therefore we do not select the column that has country codes.

countries = subset(countries, countries$CTYNAME == 'India'| countries$CTYNAME == 'Canada', select = c(CTYNAME, year, BJAN:BDEC))

We name the columns as follows.

colnames(countries) = c("Country", "Year", "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")

Now, we gather all of the months into one column. All of the numeric values below the month names are the amount imported by the country. Negative values indicate export.

countries = countries %>%
  gather(Country, Year, January:December, na.rm = F)
## Warning: attributes are not identical across measure variables; they will
## be dropped

We name the columns of our new dataset as follows.

colnames(countries) = c("Country", "Year", "Month", "Import")

Now, the imports are arranged by country and year. There is no need to arrange it by month. Otherwise each month will be ordered in alphabetical order.

countries %>% arrange(Country, Year)
##     Country Year     Month Import
## 1    Canada 2009   January -1,791
## 2    Canada 2009  February -1,893
## 3    Canada 2009     March -1,384
## 4    Canada 2009     April -1,092
## 5    Canada 2009       May   -816
## 6    Canada 2009      June -1,854
## 7    Canada 2009      July -2,323
## 8    Canada 2009    August -1,516
## 9    Canada 2009 September -1,787
## 10   Canada 2009   October -2,647
## 11   Canada 2009  November -2,023
## 12   Canada 2009  December -2,465
## 13   Canada 2010   January -3,128
## 14   Canada 2010  February -2,830
## 15   Canada 2010     March -2,536
## 16   Canada 2010     April -2,653
## 17   Canada 2010       May -2,885
## 18   Canada 2010      June -2,644
## 19   Canada 2010      July -1,625
## 20   Canada 2010    August -2,318
## 21   Canada 2010 September -1,468
## 22   Canada 2010   October -1,543
## 23   Canada 2010  November -2,139
## 24   Canada 2010  December -2,610
## 25   Canada 2011   January -2,420
## 26   Canada 2011  February -2,830
## 27   Canada 2011     March -3,046
## 28   Canada 2011     April -1,971
## 29   Canada 2011       May -3,109
## 30   Canada 2011      June -2,976
## 31   Canada 2011      July -3,327
## 32   Canada 2011    August -2,387
## 33   Canada 2011 September -3,499
## 34   Canada 2011   October -2,474
## 35   Canada 2011  November -3,290
## 36   Canada 2011  December -2,705
## 37   Canada 2012   January -2,761
## 38   Canada 2012  February -2,684
## 39   Canada 2012     March -3,088
## 40   Canada 2012     April -3,183
## 41   Canada 2012       May -2,275
## 42   Canada 2012      June -2,030
## 43   Canada 2012      July -2,393
## 44   Canada 2012    August -2,549
## 45   Canada 2012 September -1,741
## 46   Canada 2012   October -2,533
## 47   Canada 2012  November -3,765
## 48   Canada 2012  December -2,610
## 49   Canada 2013   January -2,791
## 50   Canada 2013  February -2,720
## 51   Canada 2013     March -1,986
## 52   Canada 2013     April -2,825
## 53   Canada 2013       May -2,594
## 54   Canada 2013      June -2,341
## 55   Canada 2013      July -2,913
## 56   Canada 2013    August -2,771
## 57   Canada 2013 September -3,034
## 58   Canada 2013   October -3,179
## 59   Canada 2013  November -2,326
## 60   Canada 2013  December -2,322
## 61   Canada 2014   January -3,015
## 62   Canada 2014  February -2,646
## 63   Canada 2014     March -3,185
## 64   Canada 2014     April -2,407
## 65   Canada 2014       May -3,246
## 66   Canada 2014      June -3,212
## 67   Canada 2014      July -3,225
## 68   Canada 2014    August -2,944
## 69   Canada 2014 September -3,610
## 70   Canada 2014   October -2,796
## 71   Canada 2014  November -2,034
## 72   Canada 2014  December -3,057
## 73   Canada 2015   January   -859
## 74   Canada 2015  February -1,422
## 75   Canada 2015     March   -808
## 76   Canada 2015     April   -114
## 77   Canada 2015       May    161
## 78   Canada 2015      June -3,098
## 79   Canada 2015      July -1,976
## 80   Canada 2015    August -2,165
## 81   Canada 2015 September -1,845
## 82   Canada 2015   October   -387
## 83   Canada 2015  November   -977
## 84   Canada 2015  December -1,374
## 85    India 2009   January   -527
## 86    India 2009  February   -537
## 87    India 2009     March   -463
## 88    India 2009     April   -338
## 89    India 2009       May   -244
## 90    India 2009      June   -252
## 91    India 2009      July   -257
## 92    India 2009    August    -22
## 93    India 2009 September   -417
## 94    India 2009   October   -430
## 95    India 2009  November   -613
## 96    India 2009  December   -624
## 97    India 2010   January   -686
## 98    India 2010  February   -797
## 99    India 2010     March   -788
## 100   India 2010     April   -711
## 101   India 2010       May   -901
## 102   India 2010      June   -952
## 103   India 2010      July   -898
## 104   India 2010    August   -966
## 105   India 2010 September   -867
## 106   India 2010   October   -966
## 107   India 2010  November   -861
## 108   India 2010  December   -892
## 109   India 2011   January -1,035
## 110   India 2011  February   -882
## 111   India 2011     March -1,142
## 112   India 2011     April -1,245
## 113   India 2011       May -1,297
## 114   India 2011      June -1,190
## 115   India 2011      July -1,392
## 116   India 2011    August -1,400
## 117   India 2011 September -1,332
## 118   India 2011   October -1,475
## 119   India 2011  November   -948
## 120   India 2011  December -1,273
## 121   India 2012   January -1,540
## 122   India 2012  February -1,753
## 123   India 2012     March -1,441
## 124   India 2012     April -1,127
## 125   India 2012       May -1,468
## 126   India 2012      June -1,940
## 127   India 2012      July -2,161
## 128   India 2012    August -1,703
## 129   India 2012 September -1,242
## 130   India 2012   October -1,014
## 131   India 2012  November -1,812
## 132   India 2012  December -1,205
## 133   India 2013   January -1,152
## 134   India 2013  February -1,524
## 135   India 2013     March -1,720
## 136   India 2013     April -1,887
## 137   India 2013       May -1,852
## 138   India 2013      June -1,386
## 139   India 2013      July -1,856
## 140   India 2013    August -1,571
## 141   India 2013 September -1,702
## 142   India 2013   October -1,744
## 143   India 2013  November -1,564
## 144   India 2013  December -2,039
## 145   India 2014   January -1,883
## 146   India 2014  February -1,989
## 147   India 2014     March -1,990
## 148   India 2014     April -2,486
## 149   India 2014       May -2,071
## 150   India 2014      June -1,447
## 151   India 2014      July -1,774
## 152   India 2014    August -2,047
## 153   India 2014 September -2,075
## 154   India 2014   October -1,999
## 155   India 2014  November -1,782
## 156   India 2014  December -2,092
## 157   India 2015   January -2,040
## 158   India 2015  February -1,986
## 159   India 2015     March -1,955
## 160   India 2015     April -1,529
## 161   India 2015       May -2,026
## 162   India 2015      June -1,630
## 163   India 2015      July -1,996
## 164   India 2015    August -1,938
## 165   India 2015 September -1,984
## 166   India 2015   October -2,030
## 167   India 2015  November -2,127
## 168   India 2015  December -1,970

Now, we subset our new data set two times. One subset is for performing monthly analysis only for Canada. The other subset is for performing monthly analysis only for India. We arrange the rows in each subset by year.

cCanada = filter(countries, Country == 'Canada') %>% arrange(Year) 
cIndia = filter(countries, Country == 'India') %>% arrange(Year)

Now, we load the ggplot2 package and generate a scatterplot for each country. This scatterplot plots the amount imported over each month.

library(ggplot2)
yearmonth = seq(1,84,1)
qplot(yearmonth, cIndia$Import, xlab = "Month", ylab = "Import", main = "India's Imports")

qplot(yearmonth, cCanada$Import, xlab = "Month", ylab = "Import", main = "Canada's Imports")

According to the scatterplot for India, from January 2009 to about October 2010, India seems to import more goods and export less. Then, from November 2010 onwards, India seems to export more goods and import almost no goods.

The scatterplot for Canada shows an almost uniform distribution. The export seems to fluctuate between high and low over the time period examined.

However, for both cases, the amount of imports is low. Therefore a favorable balance of trade is maintained for both countries. Import less and export more.