Trading volume mapping R in recent environment

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My talk document for Global Tokyo.R 1

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Trading volume mapping R in recent environment

  1. 1. Trading volume mapping in recent environment Global Tokyo. 1 @teramonagi
  2. 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. 3. Libraries to be needed 3 library(data.table) library(rMaps) library(dplyr) library(magrittr) library(countrycode) library(xts) library(pings)
  4. 4. 4 I forgot to use googleVis Sorry!!!
  5. 5. 5 Before we get on the main subject
  6. 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) 6
  7. 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. 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. 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. 10 You will be hooked on this sound
  11. 11. 11 Get back to the main subject
  12. 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) 12
  13. 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. 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. 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. 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. 17. Visualize with rMaps • Install and load rMaps 17 library(devtools) install_github('ramnathv/rCharts@dev') install_github('ramnathv/rMaps') library(rMaps)
  18. 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. 19. Visualize with rMaps 19 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. 20. Visualize with rMaps 20
  21. 21. All Codes in this presentation: 21 github.com/teramonagi/GlobalTokyoR1
  22. 22. Enjoy!!! 22

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