R language tutorial

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

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/
“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
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/
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/

Package Management
• CRAN (Comprehensive R Archive Network)
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

Basic Command
• help()
– help(demo)
• demo()
– demo(is.things)
• q()
• ls()
• rm()
– rm(x)

Basic Object
• Vector
• List
• Factor
• Array
• Matrix
• Data Frame

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)

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)]

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)

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

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

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.

File IO
• Save and Load
– x = USPersonalExpenditure
– save(x, file="~/test.RData")
– rm(x)
– load("~/test.RData")
– x

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
• 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
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

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

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

Reference & Resource

Study Material
• R in a nutshell

Online Reference

Community Resources for R help

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

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/

Thank You!

R language tutorial

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