SlideShare a Scribd company logo
1 of 47
David Chiu
R Language Tutorial
14/23/2013 Confidential | Copyright 2013 Trend Micro Inc.
Background of R
4/23/2013 2Confidential | Copyright 2012 Trend Micro Inc.
What is R?
• GNU Project Developed by John Chambers @ Bell Lab
• Free software environment for statistical computing and graphics
• Functional programming language written primarily in C, Fortran
4/23/2013 3Confidential | Copyright 2012 Trend Micro Inc.
R Language
• R is functional programming language
• R is an interpreted language
• R is object oriented-language
Why Using R
• Statistic analysis on the fly
• Mathematical function and graphic module embedded
• FREE! & Open Source!
– http://cran.r-project.org/src/base/
Kaggle
http://www.kaggle.com/
R is the most widely language used by
kaggle participants
Data Scientist of these Companies Using R
What is your programming language of
choice, R, Python or something else?
“I use R, and occasionally matlab, for data analysis. There is
a large, active and extremely knowledgeable R community at
Google.”
http://simplystatistics.org/2013/02/15/interview-with-nick-chamandy-statistician-at-google/
4/23/2013 7Confidential | Copyright 2013 Trend Micro Inc.
“Expert knowledge of SAS (With Enterprise
Guide/Miner) required and candidates with
strong knowledge of R will be preferred”
http://www.kdnuggets.com/jobs/13/03-29-apple-sr-data-
scientist.html?utm_source=twitterfeed&utm_medium=facebook&utm_campaign=t
fb&utm_content=FaceBook&utm_term=analytics#.UVXibgXOpfc.facebook
Commercial support for R
• In 2007, Revolution Analytics providea commercial support for
Revolution R
– http://www.revolutionanalytics.com/products/revolution-r.php
– http://www.revolutionanalytics.com/why-revolution-r/which-r-is-right-for-me.php
• Big Data Appliance, which integrates R, Apache Hadoop, Oracle
Enterprise Linux, and a NoSQL database with the
Exadata hardware
– http://www.oracle.com/us/products/database/big-data-
appliance/overview/index.html
Revolotion R
• Free for Community Version
– http://www.revolutionanalytics.com/downloads/
– http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php
4/23/2013 9Confidential | Copyright 2013 Trend Micro Inc.
Base R 2.14.2
64
Revolution R
(1-core)
Revolution R
(4-core)
Speedup (4 core)
Matrix
Calculation
17.4 sec 2.9 sec 2.0 sec 7.9x
Matrix Functions 10.3 sec 2.0 sec 1.2 sec 7.8x
Program Control 2.7 sec 2.7 sec 2.7 sec Not Appreciable
IDE
R Studio
• http://www.rstudio.com/
4/23/2013 10Confidential | Copyright 2013 Trend Micro Inc.
RGUI
• http://www.r-project.org/
Web App Development
Shiny makes it super simple for R users like you to turn
analyses into interactive web applications that anyone
can use
http://www.rstudio.com/shiny/
4/23/2013 11Confidential | Copyright 2013 Trend Micro Inc.
Package Management
• CRAN (Comprehensive R Archive Network)
4/23/2013 12Confidential | Copyright 2013 Trend Micro Inc.
Repository URL
CRAN http://cran.r-project.org/web/packages/
Bioconductor http://www.bioconductor.org/packages/release/Software.html
R-Forge http://r-forge.r-project.org/
R Basic
4/23/2013 13Confidential | Copyright 2012 Trend Micro Inc.
Basic Command
• help()
– help(demo)
• demo()
– demo(is.things)
• q()
• ls()
• rm()
– rm(x)
4/23/2013 14Confidential | Copyright 2013 Trend Micro Inc.
Basic Object
• Vector
• List
• Factor
• Array
• Matrix
• Data Frame
4/23/2013 15Confidential | Copyright 2013 Trend Micro Inc.
Objects & Arithmetic
• Scalar
– x=3; y<-5; x+y
• Vectors
– x = c(1,2,3, 7); y= c(2,3,5,1); x+y; x*y; x – y; x/y;
– x =seq(1,10); y= 2:11; x+y
– x =seq(1,10,by=2); y =seq(1,10,length=2)
– rep(c(5,8), 3)
– x= c(1,2,3); length(x)
4/23/2013 16Confidential | Copyright 2013 Trend Micro Inc.
Summaries and Subscripting
• Summary
– X = c(1,2,3,4,5,6,7,8,9,10)
– mean(x), min(x), median(x), max(x), var(x)
– summary(x)
• Subscripting
– x = c(1,2,3,4,5,6,7,8,9,10)
– x[1:3]; x[c(1,3,5)];
– x[c(1,3,5)] * 2 + x[c(2,2,2)]
– x[-(1:6)]
4/23/2013 17Confidential | Copyright 2013 Trend Micro Inc.
Lists
• Contain a heterogeneous selection of objects
– e <- list(thing="hat", size="8.25"); e
– l <- list(a=1,b=2,c=3,d=4,e=5,f=6,g=7,h=8,i=9,j=10)
– l$j
– man = list(name="Qoo", height=183); man$name
Factor
• Ordered collection of items to present categorical value
• Different values that the factor can take are called levels
• Factors
– phone =
factor(c('iphone', 'htc', 'iphone', 'samsung', 'iphone', 'samsung'))
– levels(phone)
4/23/2013 19Confidential | Copyright 2013 Trend Micro Inc.
Matrices & Array
• Array
– An extension of a vector to more than two dimensions
– a <- array(c(1,2,3,4,5,6,7,8,9,10,11,12),dim=c(3,4))
• Matrices
– A vector to two dimensions – 2d-array
– x = c(1,2,3); y = c(4,5,6); rbind(x,y);cbind(x,y)
– x = rbind(c(1,2,3),c(4,5,6)); dim(x)
– x<-matrix(c(1,2,3,4,5,6),nr=3);
– x<-matrix(c(1,2,3,4,5,6),nrow=3, ,byrow=T)
– x<-matrix(c(1,2,3,4),nr=2);y<-matrix(c(5,6),nr=2); x%*%y
– t(matrix(c(1,2,3,4),nr=2))
– solve(matrix(c(1,2,3,4),nr=2))
Data Frame
• Useful way to represent tabular data
• essentially a matrix with named columns may also
include non-numerical variables
• Example
– df = data.frame(a=c(1,2,3,4,5),b=c(2,3,4,5,6));df
Function
• Function
– `%myop%` <- function(a, b) {2*a + 2*b}; 1 %myop% 1
– f <- function(x) {return(x^2 + 3)}
create.vector.of.ones <- function(n) {
return.vector <- NA;
for (i in 1:n) {
return.vector[i] <- 1;
} return.vector;
}
– create.vector.of.ones(3)
• Control Structures
– If …else…
– Repeat, for, while
• Catch error – trycatch
Anonymous Function
• Functional language Characteristic
– apply.to.three <- function(f) {f(3)}
– apply.to.three(function(x) {x * 7})
Objects and Classes
• All R code manipulates objects.
• Every object in R has a type
• In assignment statements, R will copy the object, not
just the reference to the object Attributes
S3 & S4 Object
• Many R functions were implemented using S3 methods
• In S version 4 (hence S4), formal classes and methods
were introduced that allowed
– Multiple arguments
– Abstract types
– inheritance.
OOP of S4
• S4 OOP Example
– setClass("Student", representation(name =
"character", score="numeric"))
– studenta = new ("Student", name="david", score=80 )
– studentb = new ("Student", name="andy", score=90 )
setMethod("show", signature("Student"),
function(object) {
cat(object@score+100)
})
– setGeneric("getscore", function(object)
standardGeneric("getscore"))
– Studenta
Packages
• A package is a related set of functions, help files, and
data files that have been bundled together.
• Basic Command
– library(rpart)
– CRAN
– Install
– (.packages())
Package used in Machine Learning for
Hackers
4/23/2013 28Confidential | Copyright 2013 Trend Micro Inc.
Apply
• Apply
– Returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix.
– data <- cbind(c(1,2),c(3,4))
– data.rowsum <- apply(data,1,sum)
– data.colsum <- apply(data,2,sum)
– data
4/23/2013 29Confidential | Copyright 2013 Trend Micro Inc.
Apply
• lapply
– returns a list of the same length as X, each element of which is
the result of applying FUN to the corresponding element of X.
• sapply
– is a user-friendly version and wrapper of lapply by default
returning a vector, matrix or
• vapply
– is similar to sapply, but has a pre-specified type of return
value, so it can be safer (and sometimes faster) to use.
4/23/2013 30Confidential | Copyright 2013 Trend Micro Inc.
File IO
• Save and Load
– x = USPersonalExpenditure
– save(x, file="~/test.RData")
– rm(x)
– load("~/test.RData")
– x
Charts and Graphics
Plotting Example
– xrange = range(as.numeric(colnames(USPersonalExpenditure)));
– yrange= range(USPersonalExpenditure);
– plot(xrange, yrange, type="n", xlab="Year",ylab="Category" )
– for(i in 1:5) {
lines(as.numeric(colnames(USPersonalExpenditure)),USPersonalExpenditur
e[i,], type="b", lwd=1.5)
}
IRIS Dataset
• data()
IRIS Dataset
• The Iris flower data set or Fisher's Iris data set is
a multivariate data set introduced by Sir Ronald
Fisher (1936) as an example ofdiscriminant analysis.[1] It
is sometimes called Anderson's Iris data set
– http://en.wikipedia.org/wiki/Iris_flower_data_set
4/23/2013 35Confidential | Copyright 2013 Trend Micro Inc.
Iris setosa Iris versicolor Iris virginica
Classification of IRIS
• Classification Example
– install.packages("e1071")
– pairs(iris[1:4],main="Iris Data
(red=setosa,green=versicolor,blue=virginica)", pch=21,
bg=c("red","green3","blue")[unclass(iris$Species)])
– classifier<-naiveBayes(iris[,1:4], iris[,5])
– table(predict(classifier, iris[,-5]), iris[,5])
– classifier<-svm(iris[,1:4], iris[,5]) > table(predict(classifier, iris[,-
5]), iris[,5] + )
– prediction = predict(classifier, iris[,1:4])
• http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/Na%C3%A
Fve_Bayes
4/23/2013 36Confidential | Copyright 2013 Trend Micro Inc.
Performance Tips
• Use Built-in Math Functions
• Use Environments for Lookup Tables
• Use a Database to Query Large Data Sets
• Preallocate Memory
• Monitor How Much Memory You Are Using
• Cleaning Up Objects
• Functions for Big Data Sets
• Parallel Computation with R
R for Machine Learning
4/23/2013 38Confidential | Copyright 2012 Trend Micro Inc.
Helps of the Topic
• ?read.delim
– # Access a function's help file
• ??base::delim
– # Search for 'delim' in all help files for functions in 'base'
• help.search("delimited")
– # Search for 'delimited' in all help files
• RSiteSearch("parsing text")
– # Search for the term 'parsing text' on the R site.
Sample Code of Chapter 1
• https://github.com/johnmyleswhite/ML_for_Hackers.git
4/23/2013 40Confidential | Copyright 2013 Trend Micro Inc.
Reference & Resource
4/23/2013 41Confidential | Copyright 2012 Trend Micro Inc.
Study Material
• R in a nutshell
4/23/2013 42Confidential | Copyright 2013 Trend Micro Inc.
Online Reference
4/23/2013 43Confidential | Copyright 2013 Trend Micro Inc.
Community Resources for R help
4/23/2013 44Confidential | Copyright 2013 Trend Micro Inc.
Resource
• Websites
– Stackoverflow
– Cross Validated
– R-help
– R-devel
– R-sig-*
– Package-specific mailing list
• Blog
– R-bloggers
• Twitter
– https://twitter.com/#rstats
• Quora
– http://www.quora.com/R-software
4/23/2013 45Confidential | Copyright 2013 Trend Micro Inc.
Resource (Con’d)
• Conference
– useR!
– R in Finance
– R in Insurance
– Others
– Joint Statistical Meetings
– Royal Statistical Society Conference
• Local User Group
– http://blog.revolutionanalytics.com/local-r-groups.html
• Taiwan R User Group
– http://www.facebook.com/Tw.R.User
– http://www.meetup.com/Taiwan-R/
4/23/2013 46Confidential | Copyright 2013 Trend Micro Inc.
Thank You!
4/23/2013 47Confidential | Copyright 2012 Trend Micro Inc.

More Related Content

What's hot

How to get started with R programming
How to get started with R programmingHow to get started with R programming
How to get started with R programmingRamon Salazar
 
R programming slides
R  programming slidesR  programming slides
R programming slidesPankaj Saini
 
R Programming Language
R Programming LanguageR Programming Language
R Programming LanguageNareshKarela1
 
An Interactive Introduction To R (Programming Language For Statistics)
An Interactive Introduction To R (Programming Language For Statistics)An Interactive Introduction To R (Programming Language For Statistics)
An Interactive Introduction To R (Programming Language For Statistics)Dataspora
 
2 R Tutorial Programming
2 R Tutorial Programming2 R Tutorial Programming
2 R Tutorial ProgrammingSakthi Dasans
 
R programming presentation
R programming presentationR programming presentation
R programming presentationAkshat Sharma
 
R Programming: Introduction to Matrices
R Programming: Introduction to MatricesR Programming: Introduction to Matrices
R Programming: Introduction to MatricesRsquared Academy
 
Introduction to R
Introduction to RIntroduction to R
Introduction to RAjay Ohri
 
Introduction to R programming
Introduction to R programmingIntroduction to R programming
Introduction to R programmingVictor Ordu
 
Introduction to Database
Introduction to DatabaseIntroduction to Database
Introduction to DatabaseSiti Ismail
 
1 R Tutorial Introduction
1 R Tutorial Introduction1 R Tutorial Introduction
1 R Tutorial IntroductionSakthi Dasans
 
Relational database
Relational database Relational database
Relational database Megha Sharma
 
Data visualization using R
Data visualization using RData visualization using R
Data visualization using RUmmiya Mohammedi
 

What's hot (20)

How to get started with R programming
How to get started with R programmingHow to get started with R programming
How to get started with R programming
 
R programming slides
R  programming slidesR  programming slides
R programming slides
 
R Programming Language
R Programming LanguageR Programming Language
R Programming Language
 
An Interactive Introduction To R (Programming Language For Statistics)
An Interactive Introduction To R (Programming Language For Statistics)An Interactive Introduction To R (Programming Language For Statistics)
An Interactive Introduction To R (Programming Language For Statistics)
 
Class ppt intro to r
Class ppt intro to rClass ppt intro to r
Class ppt intro to r
 
Unit 1 - R Programming (Part 2).pptx
Unit 1 - R Programming (Part 2).pptxUnit 1 - R Programming (Part 2).pptx
Unit 1 - R Programming (Part 2).pptx
 
R Programming
R ProgrammingR Programming
R Programming
 
2 R Tutorial Programming
2 R Tutorial Programming2 R Tutorial Programming
2 R Tutorial Programming
 
R programming presentation
R programming presentationR programming presentation
R programming presentation
 
R Programming: Introduction to Matrices
R Programming: Introduction to MatricesR Programming: Introduction to Matrices
R Programming: Introduction to Matrices
 
Introduction to R
Introduction to RIntroduction to R
Introduction to R
 
Introduction to R programming
Introduction to R programmingIntroduction to R programming
Introduction to R programming
 
R programming
R programmingR programming
R programming
 
Introduction to statistical software R
Introduction to statistical software RIntroduction to statistical software R
Introduction to statistical software R
 
Introduction to Database
Introduction to DatabaseIntroduction to Database
Introduction to Database
 
1 R Tutorial Introduction
1 R Tutorial Introduction1 R Tutorial Introduction
1 R Tutorial Introduction
 
11 Database Concepts
11 Database Concepts11 Database Concepts
11 Database Concepts
 
Relational database
Relational database Relational database
Relational database
 
Data visualization using R
Data visualization using RData visualization using R
Data visualization using R
 
software engineering
software engineeringsoftware engineering
software engineering
 

Similar to R Language Tutorial Overview

India software developers conference 2013 Bangalore
India software developers conference 2013 BangaloreIndia software developers conference 2013 Bangalore
India software developers conference 2013 BangaloreSatnam Singh
 
Machine Learning in R
Machine Learning in RMachine Learning in R
Machine Learning in RSujaAldrin
 
A Hands-on Intro to Data Science and R Presentation.ppt
A Hands-on Intro to Data Science and R Presentation.pptA Hands-on Intro to Data Science and R Presentation.ppt
A Hands-on Intro to Data Science and R Presentation.pptSanket Shikhar
 
STAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdf
STAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdfSTAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdf
STAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdfSOUMIQUE AHAMED
 
Intro to Apache Spark by Marco Vasquez
Intro to Apache Spark by Marco VasquezIntro to Apache Spark by Marco Vasquez
Intro to Apache Spark by Marco VasquezMapR Technologies
 
Introduction to Mahout
Introduction to MahoutIntroduction to Mahout
Introduction to MahoutTed Dunning
 
Introduction to Mahout given at Twin Cities HUG
Introduction to Mahout given at Twin Cities HUGIntroduction to Mahout given at Twin Cities HUG
Introduction to Mahout given at Twin Cities HUGMapR Technologies
 
Reproducibility with R
Reproducibility with RReproducibility with R
Reproducibility with RMartin Jung
 
2014-10-10-SBC361-Reproducible research
2014-10-10-SBC361-Reproducible research2014-10-10-SBC361-Reproducible research
2014-10-10-SBC361-Reproducible researchYannick Wurm
 
IRJET- A Survey on Predictive Analytics and Parallel Algorithms for Knowl...
IRJET-  	  A Survey on Predictive Analytics and Parallel Algorithms for Knowl...IRJET-  	  A Survey on Predictive Analytics and Parallel Algorithms for Knowl...
IRJET- A Survey on Predictive Analytics and Parallel Algorithms for Knowl...IRJET Journal
 
A Workshop on R
A Workshop on RA Workshop on R
A Workshop on RAjay Ohri
 
Introduction to R for data science
Introduction to R for data scienceIntroduction to R for data science
Introduction to R for data scienceLong Nguyen
 
Unit 2 - Data Manipulation with R.pptx
Unit 2 - Data Manipulation with R.pptxUnit 2 - Data Manipulation with R.pptx
Unit 2 - Data Manipulation with R.pptxMalla Reddy University
 
RDataMining slides-r-programming
RDataMining slides-r-programmingRDataMining slides-r-programming
RDataMining slides-r-programmingYanchang Zhao
 
Research @ RELEASeD (presented at SATTOSE2013)
Research @ RELEASeD (presented at SATTOSE2013)Research @ RELEASeD (presented at SATTOSE2013)
Research @ RELEASeD (presented at SATTOSE2013)kim.mens
 

Similar to R Language Tutorial Overview (20)

R tutorial
R tutorialR tutorial
R tutorial
 
India software developers conference 2013 Bangalore
India software developers conference 2013 BangaloreIndia software developers conference 2013 Bangalore
India software developers conference 2013 Bangalore
 
Machine Learning in R
Machine Learning in RMachine Learning in R
Machine Learning in R
 
R- Introduction
R- IntroductionR- Introduction
R- Introduction
 
A Hands-on Intro to Data Science and R Presentation.ppt
A Hands-on Intro to Data Science and R Presentation.pptA Hands-on Intro to Data Science and R Presentation.ppt
A Hands-on Intro to Data Science and R Presentation.ppt
 
STAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdf
STAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdfSTAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdf
STAT-522 (Data Analysis Using R) by SOUMIQUE AHAMED.pdf
 
Intro to Apache Spark by Marco Vasquez
Intro to Apache Spark by Marco VasquezIntro to Apache Spark by Marco Vasquez
Intro to Apache Spark by Marco Vasquez
 
Introduction to Mahout
Introduction to MahoutIntroduction to Mahout
Introduction to Mahout
 
Introduction to Mahout given at Twin Cities HUG
Introduction to Mahout given at Twin Cities HUGIntroduction to Mahout given at Twin Cities HUG
Introduction to Mahout given at Twin Cities HUG
 
Step By Step Guide to Learn R
Step By Step Guide to Learn RStep By Step Guide to Learn R
Step By Step Guide to Learn R
 
Reproducibility with R
Reproducibility with RReproducibility with R
Reproducibility with R
 
2014-10-10-SBC361-Reproducible research
2014-10-10-SBC361-Reproducible research2014-10-10-SBC361-Reproducible research
2014-10-10-SBC361-Reproducible research
 
IRJET- A Survey on Predictive Analytics and Parallel Algorithms for Knowl...
IRJET-  	  A Survey on Predictive Analytics and Parallel Algorithms for Knowl...IRJET-  	  A Survey on Predictive Analytics and Parallel Algorithms for Knowl...
IRJET- A Survey on Predictive Analytics and Parallel Algorithms for Knowl...
 
A Workshop on R
A Workshop on RA Workshop on R
A Workshop on R
 
Introduction to R for data science
Introduction to R for data scienceIntroduction to R for data science
Introduction to R for data science
 
Unit 2 - Data Manipulation with R.pptx
Unit 2 - Data Manipulation with R.pptxUnit 2 - Data Manipulation with R.pptx
Unit 2 - Data Manipulation with R.pptx
 
RDataMining slides-r-programming
RDataMining slides-r-programmingRDataMining slides-r-programming
RDataMining slides-r-programming
 
Research @ RELEASeD (presented at SATTOSE2013)
Research @ RELEASeD (presented at SATTOSE2013)Research @ RELEASeD (presented at SATTOSE2013)
Research @ RELEASeD (presented at SATTOSE2013)
 
User 2013-oracle-big-data-analytics-1971985
User 2013-oracle-big-data-analytics-1971985User 2013-oracle-big-data-analytics-1971985
User 2013-oracle-big-data-analytics-1971985
 
Basics of R
Basics of RBasics of R
Basics of R
 

More from David Chiu

無中生有 - 利用外部數據打造新商業模式
無中生有 - 利用外部數據打造新商業模式無中生有 - 利用外部數據打造新商業模式
無中生有 - 利用外部數據打造新商業模式David Chiu
 
洞見未來,用python 與 r 結合深度學習技術預測趨勢
洞見未來,用python 與 r 結合深度學習技術預測趨勢洞見未來,用python 與 r 結合深度學習技術預測趨勢
洞見未來,用python 與 r 結合深度學習技術預測趨勢David Chiu
 
python 實戰資料科學工作坊
python 實戰資料科學工作坊python 實戰資料科學工作坊
python 實戰資料科學工作坊David Chiu
 
新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)
新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)
新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)David Chiu
 
Data Analysis - Making Big Data Work
Data Analysis - Making Big Data WorkData Analysis - Making Big Data Work
Data Analysis - Making Big Data WorkDavid Chiu
 
PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)David Chiu
 
Big Data Analysis With RHadoop
Big Data Analysis With RHadoopBig Data Analysis With RHadoop
Big Data Analysis With RHadoopDavid Chiu
 
Social Network Analysis With R
Social Network Analysis With RSocial Network Analysis With R
Social Network Analysis With RDavid Chiu
 
Machine Learning With R
Machine Learning With RMachine Learning With R
Machine Learning With RDavid Chiu
 
Hidden Markov Model & Stock Prediction
Hidden Markov Model & Stock PredictionHidden Markov Model & Stock Prediction
Hidden Markov Model & Stock PredictionDavid Chiu
 

More from David Chiu (10)

無中生有 - 利用外部數據打造新商業模式
無中生有 - 利用外部數據打造新商業模式無中生有 - 利用外部數據打造新商業模式
無中生有 - 利用外部數據打造新商業模式
 
洞見未來,用python 與 r 結合深度學習技術預測趨勢
洞見未來,用python 與 r 結合深度學習技術預測趨勢洞見未來,用python 與 r 結合深度學習技術預測趨勢
洞見未來,用python 與 r 結合深度學習技術預測趨勢
 
python 實戰資料科學工作坊
python 實戰資料科學工作坊python 實戰資料科學工作坊
python 實戰資料科學工作坊
 
新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)
新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)
新聞 X 謊言 用文字探勘挖掘財經新聞沒告訴你的真相(丘祐瑋)
 
Data Analysis - Making Big Data Work
Data Analysis - Making Big Data WorkData Analysis - Making Big Data Work
Data Analysis - Making Big Data Work
 
PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
PyCon APAC 2014 - Social Network Analysis Using Python (David Chiu)
 
Big Data Analysis With RHadoop
Big Data Analysis With RHadoopBig Data Analysis With RHadoop
Big Data Analysis With RHadoop
 
Social Network Analysis With R
Social Network Analysis With RSocial Network Analysis With R
Social Network Analysis With R
 
Machine Learning With R
Machine Learning With RMachine Learning With R
Machine Learning With R
 
Hidden Markov Model & Stock Prediction
Hidden Markov Model & Stock PredictionHidden Markov Model & Stock Prediction
Hidden Markov Model & Stock Prediction
 

Recently uploaded

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 

Recently uploaded (20)

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 

R Language Tutorial Overview

  • 1. David Chiu R Language Tutorial 14/23/2013 Confidential | Copyright 2013 Trend Micro Inc.
  • 2. Background of R 4/23/2013 2Confidential | Copyright 2012 Trend Micro Inc.
  • 3. What is R? • GNU Project Developed by John Chambers @ Bell Lab • Free software environment for statistical computing and graphics • Functional programming language written primarily in C, Fortran 4/23/2013 3Confidential | Copyright 2012 Trend Micro Inc.
  • 4. R Language • R is functional programming language • R is an interpreted language • R is object oriented-language
  • 5. Why Using R • Statistic analysis on the fly • Mathematical function and graphic module embedded • FREE! & Open Source! – http://cran.r-project.org/src/base/
  • 6. Kaggle http://www.kaggle.com/ R is the most widely language used by kaggle participants
  • 7. Data Scientist of these Companies Using R What is your programming language of choice, R, Python or something else? “I use R, and occasionally matlab, for data analysis. There is a large, active and extremely knowledgeable R community at Google.” http://simplystatistics.org/2013/02/15/interview-with-nick-chamandy-statistician-at-google/ 4/23/2013 7Confidential | Copyright 2013 Trend Micro Inc. “Expert knowledge of SAS (With Enterprise Guide/Miner) required and candidates with strong knowledge of R will be preferred” http://www.kdnuggets.com/jobs/13/03-29-apple-sr-data- scientist.html?utm_source=twitterfeed&utm_medium=facebook&utm_campaign=t fb&utm_content=FaceBook&utm_term=analytics#.UVXibgXOpfc.facebook
  • 8. Commercial support for R • In 2007, Revolution Analytics providea commercial support for Revolution R – http://www.revolutionanalytics.com/products/revolution-r.php – http://www.revolutionanalytics.com/why-revolution-r/which-r-is-right-for-me.php • Big Data Appliance, which integrates R, Apache Hadoop, Oracle Enterprise Linux, and a NoSQL database with the Exadata hardware – http://www.oracle.com/us/products/database/big-data- appliance/overview/index.html
  • 9. Revolotion R • Free for Community Version – http://www.revolutionanalytics.com/downloads/ – http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php 4/23/2013 9Confidential | Copyright 2013 Trend Micro Inc. Base R 2.14.2 64 Revolution R (1-core) Revolution R (4-core) Speedup (4 core) Matrix Calculation 17.4 sec 2.9 sec 2.0 sec 7.9x Matrix Functions 10.3 sec 2.0 sec 1.2 sec 7.8x Program Control 2.7 sec 2.7 sec 2.7 sec Not Appreciable
  • 10. IDE R Studio • http://www.rstudio.com/ 4/23/2013 10Confidential | Copyright 2013 Trend Micro Inc. RGUI • http://www.r-project.org/
  • 11. Web App Development Shiny makes it super simple for R users like you to turn analyses into interactive web applications that anyone can use http://www.rstudio.com/shiny/ 4/23/2013 11Confidential | Copyright 2013 Trend Micro Inc.
  • 12. Package Management • CRAN (Comprehensive R Archive Network) 4/23/2013 12Confidential | Copyright 2013 Trend Micro Inc. Repository URL CRAN http://cran.r-project.org/web/packages/ Bioconductor http://www.bioconductor.org/packages/release/Software.html R-Forge http://r-forge.r-project.org/
  • 13. R Basic 4/23/2013 13Confidential | Copyright 2012 Trend Micro Inc.
  • 14. Basic Command • help() – help(demo) • demo() – demo(is.things) • q() • ls() • rm() – rm(x) 4/23/2013 14Confidential | Copyright 2013 Trend Micro Inc.
  • 15. Basic Object • Vector • List • Factor • Array • Matrix • Data Frame 4/23/2013 15Confidential | Copyright 2013 Trend Micro Inc.
  • 16. Objects & Arithmetic • Scalar – x=3; y<-5; x+y • Vectors – x = c(1,2,3, 7); y= c(2,3,5,1); x+y; x*y; x – y; x/y; – x =seq(1,10); y= 2:11; x+y – x =seq(1,10,by=2); y =seq(1,10,length=2) – rep(c(5,8), 3) – x= c(1,2,3); length(x) 4/23/2013 16Confidential | Copyright 2013 Trend Micro Inc.
  • 17. Summaries and Subscripting • Summary – X = c(1,2,3,4,5,6,7,8,9,10) – mean(x), min(x), median(x), max(x), var(x) – summary(x) • Subscripting – x = c(1,2,3,4,5,6,7,8,9,10) – x[1:3]; x[c(1,3,5)]; – x[c(1,3,5)] * 2 + x[c(2,2,2)] – x[-(1:6)] 4/23/2013 17Confidential | Copyright 2013 Trend Micro Inc.
  • 18. Lists • Contain a heterogeneous selection of objects – e <- list(thing="hat", size="8.25"); e – l <- list(a=1,b=2,c=3,d=4,e=5,f=6,g=7,h=8,i=9,j=10) – l$j – man = list(name="Qoo", height=183); man$name
  • 19. Factor • Ordered collection of items to present categorical value • Different values that the factor can take are called levels • Factors – phone = factor(c('iphone', 'htc', 'iphone', 'samsung', 'iphone', 'samsung')) – levels(phone) 4/23/2013 19Confidential | Copyright 2013 Trend Micro Inc.
  • 20. Matrices & Array • Array – An extension of a vector to more than two dimensions – a <- array(c(1,2,3,4,5,6,7,8,9,10,11,12),dim=c(3,4)) • Matrices – A vector to two dimensions – 2d-array – x = c(1,2,3); y = c(4,5,6); rbind(x,y);cbind(x,y) – x = rbind(c(1,2,3),c(4,5,6)); dim(x) – x<-matrix(c(1,2,3,4,5,6),nr=3); – x<-matrix(c(1,2,3,4,5,6),nrow=3, ,byrow=T) – x<-matrix(c(1,2,3,4),nr=2);y<-matrix(c(5,6),nr=2); x%*%y – t(matrix(c(1,2,3,4),nr=2)) – solve(matrix(c(1,2,3,4),nr=2))
  • 21. Data Frame • Useful way to represent tabular data • essentially a matrix with named columns may also include non-numerical variables • Example – df = data.frame(a=c(1,2,3,4,5),b=c(2,3,4,5,6));df
  • 22. Function • Function – `%myop%` <- function(a, b) {2*a + 2*b}; 1 %myop% 1 – f <- function(x) {return(x^2 + 3)} create.vector.of.ones <- function(n) { return.vector <- NA; for (i in 1:n) { return.vector[i] <- 1; } return.vector; } – create.vector.of.ones(3) • Control Structures – If …else… – Repeat, for, while • Catch error – trycatch
  • 23. Anonymous Function • Functional language Characteristic – apply.to.three <- function(f) {f(3)} – apply.to.three(function(x) {x * 7})
  • 24. Objects and Classes • All R code manipulates objects. • Every object in R has a type • In assignment statements, R will copy the object, not just the reference to the object Attributes
  • 25. S3 & S4 Object • Many R functions were implemented using S3 methods • In S version 4 (hence S4), formal classes and methods were introduced that allowed – Multiple arguments – Abstract types – inheritance.
  • 26. OOP of S4 • S4 OOP Example – setClass("Student", representation(name = "character", score="numeric")) – studenta = new ("Student", name="david", score=80 ) – studentb = new ("Student", name="andy", score=90 ) setMethod("show", signature("Student"), function(object) { cat(object@score+100) }) – setGeneric("getscore", function(object) standardGeneric("getscore")) – Studenta
  • 27. Packages • A package is a related set of functions, help files, and data files that have been bundled together. • Basic Command – library(rpart) – CRAN – Install – (.packages())
  • 28. Package used in Machine Learning for Hackers 4/23/2013 28Confidential | Copyright 2013 Trend Micro Inc.
  • 29. Apply • Apply – Returns a vector or array or list of values obtained by applying a function to margins of an array or matrix. – data <- cbind(c(1,2),c(3,4)) – data.rowsum <- apply(data,1,sum) – data.colsum <- apply(data,2,sum) – data 4/23/2013 29Confidential | Copyright 2013 Trend Micro Inc.
  • 30. Apply • lapply – returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X. • sapply – is a user-friendly version and wrapper of lapply by default returning a vector, matrix or • vapply – is similar to sapply, but has a pre-specified type of return value, so it can be safer (and sometimes faster) to use. 4/23/2013 30Confidential | Copyright 2013 Trend Micro Inc.
  • 31. File IO • Save and Load – x = USPersonalExpenditure – save(x, file="~/test.RData") – rm(x) – load("~/test.RData") – x
  • 33. Plotting Example – xrange = range(as.numeric(colnames(USPersonalExpenditure))); – yrange= range(USPersonalExpenditure); – plot(xrange, yrange, type="n", xlab="Year",ylab="Category" ) – for(i in 1:5) { lines(as.numeric(colnames(USPersonalExpenditure)),USPersonalExpenditur e[i,], type="b", lwd=1.5) }
  • 35. IRIS Dataset • The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Fisher (1936) as an example ofdiscriminant analysis.[1] It is sometimes called Anderson's Iris data set – http://en.wikipedia.org/wiki/Iris_flower_data_set 4/23/2013 35Confidential | Copyright 2013 Trend Micro Inc. Iris setosa Iris versicolor Iris virginica
  • 36. Classification of IRIS • Classification Example – install.packages("e1071") – pairs(iris[1:4],main="Iris Data (red=setosa,green=versicolor,blue=virginica)", pch=21, bg=c("red","green3","blue")[unclass(iris$Species)]) – classifier<-naiveBayes(iris[,1:4], iris[,5]) – table(predict(classifier, iris[,-5]), iris[,5]) – classifier<-svm(iris[,1:4], iris[,5]) > table(predict(classifier, iris[,- 5]), iris[,5] + ) – prediction = predict(classifier, iris[,1:4]) • http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/Na%C3%A Fve_Bayes 4/23/2013 36Confidential | Copyright 2013 Trend Micro Inc.
  • 37. Performance Tips • Use Built-in Math Functions • Use Environments for Lookup Tables • Use a Database to Query Large Data Sets • Preallocate Memory • Monitor How Much Memory You Are Using • Cleaning Up Objects • Functions for Big Data Sets • Parallel Computation with R
  • 38. R for Machine Learning 4/23/2013 38Confidential | Copyright 2012 Trend Micro Inc.
  • 39. Helps of the Topic • ?read.delim – # Access a function's help file • ??base::delim – # Search for 'delim' in all help files for functions in 'base' • help.search("delimited") – # Search for 'delimited' in all help files • RSiteSearch("parsing text") – # Search for the term 'parsing text' on the R site.
  • 40. Sample Code of Chapter 1 • https://github.com/johnmyleswhite/ML_for_Hackers.git 4/23/2013 40Confidential | Copyright 2013 Trend Micro Inc.
  • 41. Reference & Resource 4/23/2013 41Confidential | Copyright 2012 Trend Micro Inc.
  • 42. Study Material • R in a nutshell 4/23/2013 42Confidential | Copyright 2013 Trend Micro Inc.
  • 43. Online Reference 4/23/2013 43Confidential | Copyright 2013 Trend Micro Inc.
  • 44. Community Resources for R help 4/23/2013 44Confidential | Copyright 2013 Trend Micro Inc.
  • 45. Resource • Websites – Stackoverflow – Cross Validated – R-help – R-devel – R-sig-* – Package-specific mailing list • Blog – R-bloggers • Twitter – https://twitter.com/#rstats • Quora – http://www.quora.com/R-software 4/23/2013 45Confidential | Copyright 2013 Trend Micro Inc.
  • 46. Resource (Con’d) • Conference – useR! – R in Finance – R in Insurance – Others – Joint Statistical Meetings – Royal Statistical Society Conference • Local User Group – http://blog.revolutionanalytics.com/local-r-groups.html • Taiwan R User Group – http://www.facebook.com/Tw.R.User – http://www.meetup.com/Taiwan-R/ 4/23/2013 46Confidential | Copyright 2013 Trend Micro Inc.
  • 47. Thank You! 4/23/2013 47Confidential | Copyright 2012 Trend Micro Inc.