R language

1. R Language
ian

2. Why ?

3. Background of R

4. 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

5.  R is functional programming language
 R is an interpreted language
 R is object oriented-language
R Language

6.  Statistic analysis on the fly
 Mathematical function and graphic module
embedded
 FREE! & Open Source!
 http://cran.r-project.org/src/base/
Why Using R

7. 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/
Data Scientist of these Companies
Using R
“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=tfb&utm
_content=FaceBook&utm_term=analytics#.UVXibgXOpfc.facebook

8.  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
Commercial support for R

9.  Free for Community Version
 http://www.revolutionanalytics.com/downloads/
 http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php
Revolotion R
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. R Studio
 http://www.rstudio.com/
IDE
RGUI
•http://www.r-project.org/

11. 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/
Web App Development

12.  CRAN (Comprehensive R Archive Network)
Package Management
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

14.  help()
 help(demo)
 demo()
 demo(is.things)
 q()
 ls()
 rm()
 rm(x)
Basic Command

15.  Vector
 List
 Factor
 Array
 Matrix
 Data Frame
Basic Object

16.  物件類型(type)主要是向量(vector),矩陣(matrix),陣列(array),因
素(factor),列表(list),資料框架(data frame),函式(function).
 物件基本元素之“模式” (basic mode)分成
 1."numeric",實數型,含"integer",整數型(有時需特別指定),與
"double",倍精確度型.
 2."logical",邏輯型(true or false),以TRUE(T)或FALSE(F)呈現, (也
可以是1 (T)與0 (F).
 3."complex",複數型
 4."character",文字型(或字串),通常輸入時,在文字或字串兩側加
上雙引號(").

17.  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)
Objects & Arithmetic

18.  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)]
Summaries and Subscripting

19.  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
Lists

20.  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)
Factor

21.  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))
Matrices & Array

22.  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
Data Frame

23.  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
Function

24.  Functional language Characteristic
 apply.to.three <- function(f) {f(3)}
 apply.to.three(function(x) {x * 7})
Anonymous Function

25.  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
Objects and Classes

26.  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.
S3 & S4 Object

27.  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
OOP of S4

28.  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())
Packages

29. 29
Package used in Machine Learning
for Hackers

30.  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

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

32. Charts and Graphics

33.  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)),USPersonalExpendi
ture[i,], type="b", lwd=1.5)
}
Plotting Example

34. Reference & Resource

35.  R in a nutshell
Study Material

36. Online Reference

37. 37
Community Resources for R help

38.  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

39.  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/
Resource (Con’d)

40. Thank You!
11/20/2015 40Confidential | Copyright 2012 Trend Micro Inc.