R language tutorial

David Chiu

R Language Tutorial

04/11/2023

Background of R

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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

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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/

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“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

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

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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

R Studio

• http://www.rstudio.com/

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• 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/

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Package Management

• CRAN (Comprehensive R Archive Network)

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Repository URLCRAN http://cran.r-project.org/web/packages/Bioconductor http://www.bioconductor.org/packages/release/Software.htmlR-Forge http://r-forge.r-project.org/

R Basic

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Basic Command

• help()– help(demo)

• demo()– demo(is.things)

• q()

• ls()

• rm()– rm(x)

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Basic Object

• Vector

• List

• Factor

• Array

• Matrix

• Data Frame

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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)

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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)]

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• 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)

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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

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• 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

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• 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.

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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)),USPersonalExpenditure[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

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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%AFve_Bayes

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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

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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

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Reference & Resource

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Study Material

• R in a nutshell

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Online Reference

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Community Resources for R help

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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

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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/

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Thank You!

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