My talk document for Global Tokyo.R 1

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
2

3. Libraries to be needed
3
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
7
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):
8
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):
9
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…
13
> 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
14
> 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
15
> 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
16
> 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
17
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…
18

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
20

21. All Codes in this presentation:
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github.com/teramonagi/GlobalTokyoR1

22. Enjoy!!!
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