This document discusses visualizing trading volume data using R. It includes: 1. Loading and manipulating trading volume data from 2012 using data.table and dplyr packages to summarize the monthly total trading amount by country. 2. Creating an interactive choropleth map visualization of the monthly trading data over time using the rMaps package. Custom HTML and JavaScript are used to animate the map updating each month. 3. Providing the full codes in a GitHub repository for others to reproduce the analysis and visualization.

1. Trading volume mapping
in recent environment
Global Tokyo. 1
@teramonagi

2. Motivation of this Talk
• is rapidly evolving in recent years.
• Greate packages are appearing one after
another!!!
• Needless to say, googleVis too
• Let me show how to use these for data
analysis in this presentation
1. Data manipulation by dplyr
2. Visualization by rMaps
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3. Libraries to be needed
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library(data.table)
library(rMaps)
library(dplyr)
library(magrittr)
library(countrycode)
library(xts)
library(pings)

4. 4
I forgot to use googleVis
Sorry!!!

5. 5
Before we get on
the main subject

6. Fantastic collaboration -dplyr, magrittr, pings-
• You can chain commands with
forward-pipe operator %>%
(magrittr)
• Data manipulation like “mutate”,
“group_by”, “summarize” (dplyr)
• Soundlize(?) data manipulation
(pings)
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7. Fantastic collaboration -dplyr, magrittr, pings-
• Install and load packages
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library(devtools)
install_github(“dichika/pings”)
install.packages(c(“dplyr”, “magrittr”))
library(pings)
library(dplyr)
library(magrittr)

8. You can write like this(dplyr+magritter):
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iris %>%
#add new column “width”
mutate(Width=Sepal.Width+Petal.Width) %>%
#grouping data by species
group_by(Species) %>%
#calculate mean value of Width column
summarize(AverageWidth=mean(Width)) %>%
#Extract only the column “AverageWidth”
use_series(AverageWidth) %>%
#Dvide AverageWidth value by 3
divide_by(3) %>%
#Get the maximum value of AverageWidth/3
max

9. You can write like this(dplyr+magritter+pings):
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pings(iris %>%
#add new column “width”
mutate(Width=Sepal.Width+Petal.Width) %>%
#grouping data by species
group_by(Species) %>%
#calculate mean value of Width column
summarize(AverageWidth=mean(Width)) %>%
#Extract only the column “AverageWidth”
use_series(AverageWidth) %>%
#Dvide AverageWidth value by 3
divide_by(3) %>%
#Get the maximum value of AverageWidth/3
max)

10. 10
You will be hooked on this sound

11. 11
Get back to
the main subject

12. Data for analysis….
• I prepared trading volume data
• It is already formed
• I used “data.table” package which
gives us the function to fast speed
data loading(fread function)
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13. Data for analysis…
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> str(x)
Classes ‘data.table’ and 'data.frame':
21245 obs. of 5 variables:
$ Date : Date, format: "2012-11-01" "2012-11-
01" ...
$ User_Country: chr "AR" "AT" "AU" "BD" ...
$ Amount : num 775582 931593 565871 566 7986 ...
$ ISO3C : chr "ARG" "AUT" "AUS" "BGD" ...
$ Date2 : num 15645 15645 15645 15645 15645 ...
- attr(*, ".internal.selfref")=
> head(x)
Date User_Country Amount ISO3C Date2
1 2012-11-01 AR 775581.543 ARG 15645
2 2012-11-01 AT 931592.986 AUT 15645
3 2012-11-01 AU 565870.994 AUS 15645
4 2012-11-01 BD 565.863 BGD 15645
5 2012-11-01 BE 7985.860 BEL 15645
6 2012-11-01 BG 56863.958 BGR 15645

14. Data processing by dplyr
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> xs <- x %>%
+ mutate(YearMonth=as.yearmon(Date)) %>%
+ group_by(YearMonth, ISO3C) %>%
+ summarize(Amount=floor(sum(Amount)/10^4))
> head(xs)
YearMonth ISO3C Amount
1 11 2012 ALB 0
2 11 2012 ARE 7
3 11 2012 ARG 647
4 11 2012 AUS 2153
5 11 2012 AUT 503
6 11 2012 BEL 41

15. Data processing by dplyr
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> xs <- xs %>%
+ tally %>%
+ select(YearMonth) %>%
+ mutate(Counter=row_number(YearMonth)) %>%
+ inner_join(y=xs)
> head(xs)
YearMonth Counter ISO3C Amount
1 11 2012 1 ALB 0
2 11 2012 1 ARE 7
3 11 2012 1 ARG 647
4 11 2012 1 AUS 2153

16. Data processing by dplyr
• Define some variables we use later
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> min.date <- xs %>%
+ use_series(YearMonth) %>%
+ as.Date %>% min %>% as.character
> max.counter <- xs %>%
+ use_series(Counter) %>% max
> min.date
[1] "2012-11-01"
> max.counter
[1] 12

17. Visualize with rMaps
• Install and load rMaps
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library(devtools)
install_github('ramnathv/rCharts@dev')
install_github('ramnathv/rMaps')
library(rMaps)

18. Visualize with rMaps
• Easy to visualize yearly data
• But, we have monthly data
• We need to customize template
HTML and javascript code
• Little bit long code…
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19. Visualize with rMaps
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d <- ichoropleth(log(Amount) ~ ISO3C, data=as.data.frame(xs), animate="Counter", map="world")
d$setTemplate(chartDiv = sprintf("
<div class='container'>
<button ng-click='animateMap()'>Play</button>
<span ng-bind='date_show'></span>
<div id='{{chartId}}' class='rChart datamaps'></div>
</div>
<script src='http://ajax.googleapis.com/ajax/libs/jquery/1/jquery.min.js'></script>
<script src='http://ajax.googleapis.com/ajax/libs/jqueryui/1/jquery-ui.min.js'></script>
<script>
function rChartsCtrl($scope, $timeout){
$scope.counter = 1;
$scope.date = new Date('%s');
$scope.date_show = $.datepicker.formatDate('yy-mm', $scope.date);
$scope.animateMap = function(){
if ($scope.counter > %s){
return;
}
map{{chartId}}.updateChoropleth(chartParams.newData[$scope.counter]);
$scope.counter += 1;
$scope.date.setMonth($scope.date.getMonth()+1);
$scope.date_show = $.datepicker.formatDate('yy-mm', $scope.date);
$timeout($scope.animateMap, 1000)
}
}
</script>", min.date, max.counter)
)
d

20. Visualize with rMaps
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21. All Codes in this presentation:
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github.com/teramonagi/GlobalTokyoR1

22. Enjoy!!!
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