Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
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!!!
• Need...
Libraries to be needed
3
library(data.table)
library(rMaps)
library(dplyr)
library(magrittr)
library(countrycode)
library(...
4
I forgot to use googleVis
Sorry!!!
5
Before we get on
the main subject
Fantastic collaboration -dplyr, magrittr, pings-
• You can chain commands with
forward-pipe operator %>%
(magrittr)
• Data...
Fantastic collaboration -dplyr, magrittr, pings-
• Install and load packages
7
library(devtools)
install_github(“dichika/p...
You can write like this(dplyr+magritter):
8
iris %>%
#add new column “width”
mutate(Width=Sepal.Width+Petal.Width) %>%
#gr...
You can write like this(dplyr+magritter+pings):
9
pings(iris %>%
#add new column “width”
mutate(Width=Sepal.Width+Petal.Wi...
10
You will be hooked on this sound
11
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 t...
Data for analysis…
13
> str(x)
Classes ‘data.table’ and 'data.frame':
21245 obs. of 5 variables:
$ Date : Date, format: "2...
Data processing by dplyr
14
> xs <- x %>%
+ mutate(YearMonth=as.yearmon(Date)) %>%
+ group_by(YearMonth, ISO3C) %>%
+ summ...
Data processing by dplyr
15
> xs <- xs %>%
+ tally %>%
+ select(YearMonth) %>%
+ mutate(Counter=row_number(YearMonth)) %>%...
Data processing by dplyr
• Define some variables we use later
16
> min.date <- xs %>%
+ use_series(YearMonth) %>%
+ as.Dat...
Visualize with rMaps
• Install and load rMaps
17
library(devtools)
install_github('ramnathv/rCharts@dev')
install_github('...
Visualize with rMaps
• Easy to visualize yearly data
• But, we have monthly data
• We need to customize template
HTML and ...
Visualize with rMaps
19
d <- ichoropleth(log(Amount) ~ ISO3C, data=as.data.frame(xs), animate="Counter", map="world")
d$se...
Visualize with rMaps
20
All Codes in this presentation:
21
github.com/teramonagi/GlobalTokyoR1
Enjoy!!!
22
Upcoming SlideShare
Loading in …5
×

Trading volume mapping R in recent environment

1,849 views

Published on

My talk document for Global Tokyo.R 1

Published in: Technology, Education
  • Be the first to comment

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

×