Trading volume mapping R in recent environment

Trading volume mapping in recent environment

Global Tokyo. 1

@teramonagi

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

Libraries to be needed

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library(data.table) library(rMaps) library(dplyr) library(magrittr) library(countrycode) library(xts) library(pings)

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I forgot to use googleVis

Sorry!!!

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Before we get on the main subject

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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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)

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

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 You will be hooked on this sound

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Get back to the main subject

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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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

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

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

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

Visualize with rMaps

• Install and load rMaps

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library(devtools) install_github('ramnathv/rCharts@dev') install_github('ramnathv/rMaps') library(rMaps)

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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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

Visualize with rMaps

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All Codes in this presentation:

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github.com/teramonagi/GlobalTokyoR1

Enjoy!!! 22