rBasics.pdf

Computational Finance and Risk Management

R Programmingfor Quantitative Finance

Guy YollinApplied Mathematics

University of Washington

Guy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 1 / 53

http://faculty.washington.edu/gyollin/

Outline

1 R language overview and history

2 R language references

3 Short R Tutorial

4 The R help system

5 Web resources for R

6 IDE editors for R



Lecture references

J. Adler.R in a Nutshell: A Desktop Quick Reference.O’Reilly Media, 2010.

Chapters 1-3

W. N. Venables and D. M. Smith.An Introduction to R.2013.

Sections 1-3



Outline



3 Short R Tutorial

4 The R help system


6 IDE editors for R



The R programming language

R is a language and environment for statistical computing andgraphics

R is based on the S language originally developed by John Chambersand colleagues at AT&T Bell Labs in the late 1970s and early 1980s

R (sometimes called “GNU S" ) is free open source software licensedunder the GNU general public license (GPL 2)

R development was initiated by Robert Gentleman and Ross Ihaka atthe University of Auckland, New Zealand in the 1990s

R is formally known as The R Project for Statistical Computingwww.r-project.org


www.r-project.org


Strengths of the R programming language

Data manipulation

Data analysis

Statistical modeling

Data visualization1.

01.

52.

02.

53.

03.

54.

0

HAM1

EDHEC LS EQ

SP500 TR

Cum

ulat

ive

Ret

urn

HAM1 Performance

−0.

10−

0.05

0.00

0.05

Mon

thly

Ret

urn

Jan 96 Jan 97 Jan 98 Jan 99 Jan 00 Jan 01 Jan 02 Jan 03 Jan 04 Jan 05 Jan 06 Dec 06

Date

−0.

4−

0.3

−0.

2−

0.1

0.0

Dra

wdo

wn

Plot from the PerformanceAnalytics package



S language implementations

R is the most recent and full-featuredimplementation of the S language

Original S - AT & T Bell Labs

S-PLUS (S plus a GUI)Statistical Sciences, Inc.†Mathsoft, Inc.Insightful, Inc.Tibco, Inc.

R - The R Project for StatisticalComputing Figure from The History of S and R, John Chambers, 2006

†Founded by UW Professor Doug Martin, CompFin Program DirectorGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 7 / 53


R timeline

1976 2011

Work on S

Version 1

Exponential

growth of

R Users and

R packages

1993

StatSci Licenses S

1999

John Chambers

1998 ACM Software Award

2010

R & R

Given ASA

Statistical Computing

and Graphics Award

1997

Modern Applied Statistics

with S-PLUS

2nd Edition

2004

R 2.0.0

1984

S: An Interactive Envirnoment for

Data Analysis and Graphics

(Brown Book)1988

The New S Language

Written in C

(Blue Book)

1993

R on Statlib

1991

Statistical Models in S

(white book)

S3 methods

1999


with S-PLUS

3rd Edition

(S+ 2000, 5.x)

(R complements)

1997

R on CRAN

GNU Project

2002


with S

4th Edition

(S+ 6.x, R 1.5.0)

1988

S-PLULS

developed by

Statistical Sciences, Inc.

2001

R 1.4.0

(S4)

1998

Programming with Data

(Green Book)

(S+ 5.x)

1994


with S-PLUS

2000

R 1.0.0

(S3)

S era S-PLUS era R era



Recognition of software excellence

Association for ComputingMachineryJohn Chambers received the 1998ACM Software System Award

Dr. Chambers’ workwill forever alter theway people analyze,visualize, andmanipulate data

American StatisticalAssociationRobert Gentleman and RossIhaka received the 2009 ASAStatistical Computing andGraphics Award

In recognition for theirwork in initiating the RProject for StatisticalComputing



Outline



3 Short R Tutorial

4 The R help system


6 IDE editors for R



Essential web resources

An Introduction to RW.N. Venables, D.M. SmithR Development Core Team

R Reference Card 2.0Baggott & Short

R Reference Cardby Tom Short, EPRI Solutions, Inc., [email protected] 2005-07-12Granted to the public domain. See www.Rpad.org for the source and latestversion. Includes material from R for Beginners by Emmanuel Paradis (withpermission).

Help and basicsMost R functions have online documentation.help(topic) documentation on topic?topic id.help.search("topic") search the help systemapropos("topic") the names of all objects in the search list matching

the regular expression ”topic”help.start() start the HTML version of helpstr(a) display the internal *str*ucture of an R objectsummary(a) gives a “summary” of a, usually a statistical summary but it is

generic meaning it has different operations for different classes of als() show objects in the search path; specify pat="pat" to search on a

patternls.str() str() for each variable in the search pathdir() show files in the current directorymethods(a) shows S3 methods of amethods(class=class(a)) lists all the methods to handle objects of

class aoptions(...) set or examine many global options; common ones: width,

digits, errorlibrary(x) load add-on packages; library(help=x) lists datasets and

functions in package x.attach(x) database x to the R search path; x can be a list, data frame, or R

data file created with save. Use search() to show the search path.detach(x) x from the R search path; x can be a name or character string

of an object previously attached or a package.

Input and outputload() load the datasets written with savedata(x) loads specified data setsread.table(file) reads a file in table format and creates a data

frame from it; the default separator sep="" is any whitespace; useheader=TRUE to read the first line as a header of column names; useas.is=TRUE to prevent character vectors from being converted to fac-tors; use comment.char="" to prevent "#" from being interpreted asa comment; use skip=n to skip n lines before reading data; see thehelp for options on row naming, NA treatment, and others

read.csv("filename",header=TRUE) id. but with defaults set forreading comma-delimited files

read.delim("filename",header=TRUE) id. but with defaults setfor reading tab-delimited files

read.fwf(file,widths,header=FALSE,sep="�",as.is=FALSE)read a table of f ixed width f ormatted data into a ’data.frame’; widthsis an integer vector, giving the widths of the fixed-width fields

save(file,...) saves the specified objects (...) in the XDR platform-independent binary format

save.image(file) saves all objects

cat(..., file="", sep=" ") prints the arguments after coercing tocharacter; sep is the character separator between arguments

print(a, ...) prints its arguments; generic, meaning it can have differ-ent methods for different objects

format(x,...) format an R object for pretty printingwrite.table(x,file="",row.names=TRUE,col.names=TRUE,

sep=" ") prints x after converting to a data frame; if quote is TRUE,character or factor columns are surrounded by quotes ("); sep is thefield separator; eol is the end-of-line separator; na is the string formissing values; use col.names=NA to add a blank column header toget the column headers aligned correctly for spreadsheet input

sink(file) output to file, until sink()Most of the I/O functions have a file argument. This can often be a charac-ter string naming a file or a connection. file="" means the standard input oroutput. Connections can include files, pipes, zipped files, and R variables.On windows, the file connection can also be used with description ="clipboard". To read a table copied from Excel, usex <- read.delim("clipboard")To write a table to the clipboard for Excel, usewrite.table(x,"clipboard",sep="\t",col.names=NA)For database interaction, see packages RODBC, DBI, RMySQL, RPgSQL, andROracle. See packages XML, hdf5, netCDF for reading other file formats.

Data creationc(...) generic function to combine arguments with the default forming a

vector; with recursive=TRUE descends through lists combining allelements into one vector

from:to generates a sequence; “:” has operator priority; 1:4 + 1 is “2,3,4,5”seq(from,to) generates a sequence by= specifies increment; length=

specifies desired lengthseq(along=x) generates 1, 2, ..., length(x); useful for for loopsrep(x,times) replicate x times; use each= to repeat “each” el-

ement of x each times; rep(c(1,2,3),2) is 1 2 3 1 2 3;rep(c(1,2,3),each=2) is 1 1 2 2 3 3

data.frame(...) create a data frame of the named or unnamedarguments; data.frame(v=1:4,ch=c("a","B","c","d"),n=10);shorter vectors are recycled to the length of the longest

list(...) create a list of the named or unnamed arguments;list(a=c(1,2),b="hi",c=3i);

array(x,dim=) array with data x; specify dimensions likedim=c(3,4,2); elements of x recycle if x is not long enough

matrix(x,nrow=,ncol=) matrix; elements of x recyclefactor(x,levels=) encodes a vector x as a factorgl(n,k,length=n*k,labels=1:n) generate levels (factors) by spec-

ifying the pattern of their levels; k is the number of levels, and n isthe number of replications

expand.grid() a data frame from all combinations of the supplied vec-tors or factors

rbind(...) combine arguments by rows for matrices, data frames, andothers

cbind(...) id. by columns

Slicing and extracting dataIndexing listsx[n] list with elements nx[[n]] nth element of the listx[["name"]] element of the list named "name"x$name id.Indexing vectorsx[n] nth elementx[-n] all but the nth elementx[1:n] first n elementsx[-(1:n)] elements from n+1 to the endx[c(1,4,2)] specific elementsx["name"] element named "name"x[x > 3] all elements greater than 3x[x > 3 & x < 5] all elements between 3 and 5x[x %in% c("a","and","the")] elements in the given setIndexing matricesx[i,j] element at row i, column jx[i,] row ix[,j] column jx[,c(1,3)] columns 1 and 3x["name",] row named "name"Indexing data frames (matrix indexing plus the following)x[["name"]] column named "name"x$name id.

Variable conversionas.array(x), as.data.frame(x), as.numeric(x),

as.logical(x), as.complex(x), as.character(x),... convert type; for a complete list, use methods(as)

Variable informationis.na(x), is.null(x), is.array(x), is.data.frame(x),

is.numeric(x), is.complex(x), is.character(x),... test for type; for a complete list, use methods(is)

length(x) number of elements in xdim(x) Retrieve or set the dimension of an object; dim(x) <- c(3,2)dimnames(x) Retrieve or set the dimension names of an objectnrow(x) number of rows; NROW(x) is the same but treats a vector as a one-

row matrixncol(x) and NCOL(x) id. for columnsclass(x) get or set the class of x; class(x) <- "myclass"unclass(x) remove the class attribute of xattr(x,which) get or set the attribute which of xattributes(obj) get or set the list of attributes of obj

Data selection and manipulationwhich.max(x) returns the index of the greatest element of xwhich.min(x) returns the index of the smallest element of xrev(x) reverses the elements of xsort(x) sorts the elements of x in increasing order; to sort in decreasing

order: rev(sort(x))cut(x,breaks) divides x into intervals (factors); breaks is the number

of cut intervals or a vector of cut points

Definitely obtain these PDF files from the R homepage or a CRAN mirrorGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 11 / 53


Introductory texts

R in a Nutshell: A DesktopQuick Reference

Joseph AdlerO’Reilly Media, 2009

A Beginner’s Guide to RZuur, Ieno, MeestersSpringer, 2009

Both texts available online through UW libraryGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 12 / 53


Introductory texts

R in ActionRobert KabacoffManning Publications, 2011

The Art of R ProgrammingNorman MatloffNo Starch Press, 2011

A T O U R O F S T A T I S T I C A L S O F T W A R E D E S I G N

N O R M A N M A T L O F F

T H E

A R T O F R

P R O G R A M M I N G

T H E

A R T O F R

P R O G R A M M I N G



Statistics with R

Introductory Statistics with R2nd Edition

P. DalgaardSpringer, 2008

Modern Applied Statisticswith S, 4th Edition

Venables and RipleySpringer, 2002



Experience with other statistical computing languages

For those with experience in MATLAB, David Hiebeler has created aMATLAB/R cross reference document:

http://www.math.umaine.edu/~hiebeler/comp/matlabR.pdf

For those with experience in SAS, SPSS, or Stata, Robert Muenchen haswritten R books for this audience:

http://r4stats.com


http://www.math.umaine.edu/~hiebeler/comp/matlabR.pdf

http://r4stats.com


Outline



3 Short R Tutorial

4 The R help system


6 IDE editors for R



Interacting with R

R is an interpreted language†

An R interpreter must be running in order to evaluate R commands orexecute R scripts

RGui which includes an R Console windowRStudio which includes an R Console window

†http://en.wikipedia.org/wiki/Interpreted_languageGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 17 / 53

http://en.wikipedia.org/wiki/Interpreted_language


R expression evaluation

R expressions are processed via R’s Read-eval-print loop †:

†http://en.wikipedia.org/wiki/Read-eval-print_loopGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 18 / 53

http://en.wikipedia.org/wiki/Read-eval-print_loop


Interacting with the RGui

The RGui is an interactive commanddriven environment:

Type R commands (expressions)into the R Console

Copy/Paste multiple Rcommands into the R ConsoleSource an R script

An R script is simply a textfile of multiple R commands

Commands entered interactively into the R console



Interacting with RStudio

The RStudio is an IntegratedDevelopment Environment (IDE) R:

Embedded R ConsoleRStudio runs an R interpreterautomatically

Program editor for R

Plot window

File browser

Integrated version control

R debugger

RStudio includes an embedded R Console



Calling functions

R makes extensive use of functions†

Functions can be defined to takezero or more arguments

Functions typically return a valuea return value is not required

Functions are called by name withany arguments enclosed inparentheses

even if the function has noarguments the parentheses arerequired

sin(pi/2)

## [1] 1

print("Hello, world")

## [1] "Hello, world"

abs(-8)

## [1] 8

cos(2*sqrt(2))

## [1] -0.95136313

date()

## [1] "Sun Aug 31 17:08:42 2014"†http://en.wikipedia.org/wiki/Functional_programming


http://en.wikipedia.org/wiki/Functional_programming


Assigning values to variables

Like other programming languages,values can be stored in variables

Variables are typically assignedin 1 of 3 ways:

assignment operator: <-

assignment function: assignequal sign: =

must be used to assignarguments in a function call

y <- 5y

## [1] 5

assign("e",2.7183)e

## [1] 2.7183

s = sqrt(2)s

## [1] 1.4142136

r <- rnorm(n=2)r

## [1] -1.0067110533 -0.0020828847

s*e+y

## [1] 8.8442567



Object orientation in R

Everything in R is an Object†

Use functions ls and objects to list all objects in the currentworkspace

x <- c(3.1416,2.7183)m <- matrix(rnorm(9),nrow=3)tab <- data.frame(store=c("downtown","eastside","airport"),sales=c(32,17,24))cities <- c("Seattle","Portland","San Francisco")ls()

## [1] "cities" "e" "filename" "m" "r" "s"## [7] "tab" "x" "y"

†http://en.wikipedia.org/wiki/Object-oriented_programmingGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 23 / 53

http://en.wikipedia.org/wiki/Object-oriented_programming


Object classes

All R objects have a class

The class of an object determineswhat it can do and what you cando with it

Use function class todisplay an object’s class

There are many R classes;basic classes are:

numericcharacterdata.framematrix

m

## [,1] [,2] [,3]## [1,] 0.374352397 0.586864810 -0.73778598## [2,] -0.071532765 -0.262264339 -0.19904931## [3,] 0.790144078 0.012603635 1.96472235

class(m)

## [1] "matrix"

tab

## store sales## 1 downtown 32## 2 eastside 17## 3 airport 24

class(tab)

## [1] "data.frame"



VectorsR is a vector/matrix programming language (also know as an arrayprogramming language†)

vectors can easily be created with c, the combine functionmost places where single value can be supplied, a vector can besupplied and R will perform a vectorized operation

my.vector <- c(2, 4, 3, 7, 10)my.vector

## [1] 2 4 3 7 10

my.vector^2

## [1] 4 16 9 49 100

sqrt(my.vector)

## [1] 1.4142136 2.0000000 1.7320508 2.6457513 3.1622777

†http://en.wikipedia.org/wiki/Array_programmingGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 25 / 53

http://en.wikipedia.org/wiki/Array_programming


Creating vectors with the c function

constants <- c(3.1416,2.7183,1.4142,1.6180)constants

## [1] 3.1416 2.7183 1.4142 1.6180

my.labels <- c("pi","euler","sqrt2","golden")my.labels

## [1] "pi" "euler" "sqrt2" "golden"

names(constants) <- my.labelsconstants

## pi euler sqrt2 golden## 3.1416 2.7183 1.4142 1.6180

The [1] in the above output is labeling the first element of the vectorThe c function can be used to create character vectors, numericvectors, as well as other types of vectors



Indexing vectors

Vectors indices are placed withsquare brackets: []

Vectors can be indexed in any of thefollowing ways:

vector of positive integersvector of negative integersvector of named itemslogical vector

constants[c(1,3,4)]

## pi sqrt2 golden## 3.1416 1.4142 1.6180

constants[c(-1,-2)]

## sqrt2 golden## 1.4142 1.6180

constants[c("pi","golden")]

## pi golden## 3.1416 1.6180

constants > 2

## pi euler sqrt2 golden## TRUE TRUE FALSE FALSE

constants[constants > 2]

## pi euler## 3.1416 2.7183



Creating integer sequences with the a:b operatorThe sequence operator will generate a vector of integers between a and bSequences of this type are particularly useful for indexing vectors, matrices,data.frames etc.1:5

## [1] 1 2 3 4 5

-(1:4)

## [1] -1 -2 -3 -4

letters[1:15]

## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"

letters[16:26]

## [1] "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

letters[-(1:15)]

## [1] "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"



Comparing vector and non-vector computing

# vectorized operation# taking the log of each element in a vectorx <- c(97.87,96.18,95,86.39,88.18,90.8,86.06,82.27,83.32,85.3,83.25,82.13,78.54)log(x)

## [1] 4.5836401 4.5662214 4.5538769 4.4588719 4.4793802 4.5086593 4.4550447## [8] 4.4100065 4.4226886 4.4461745 4.4218481 4.4083034 4.3636080

# non-vectorized computation# taking the log of each element in a vectorn <- length(x)y <- rep(0,n)for( i in 1:n )

y[i] <- log(x[i])y

## [1] 4.5836401 4.5662214 4.5538769 4.4588719 4.4793802 4.5086593 4.4550447## [8] 4.4100065 4.4226886 4.4461745 4.4218481 4.4083034 4.3636080



Comparing vector and non-vector computing

# vectorized operation# taking the log of each element in a matrixx <- matrix(c(2,9,4,7,5,3,6,1,8),nrow=3)x^2

## [,1] [,2] [,3]## [1,] 4 49 36## [2,] 81 25 1## [3,] 16 9 64

# non-vectorized computation# taking the log of each element in a matrixy <- xfor( i in 1:nrow(x) )

for( j in 1:ncol(x) )y[i,j] <- x[i,j]^2

y

## [,1] [,2] [,3]## [1,] 4 49 36## [2,] 81 25 1## [3,] 16 9 64



Outline



3 Short R Tutorial

4 The R help system


6 IDE editors for R



The HTML help system

R has a comprehensive Html helpfacility

Run the help.start functionR GUI menu itemHelp|Html help

help.start()

## If nothing happens, you should open## 'http://127.0.0.1:28913/doc/html/index.html' yourself



The help function

Obtain help on a particular topic viathe help function

help(topic)

?topic

help(read.table)



The help.search function

Search help for a particular topic viathe help.search function

help.search(topic)

??topic

??predict



Help tab in RStudio

RStudio incorporates a dedicated help tab which facilitates accessing the RHtml help system



Outline



3 Short R Tutorial

4 The R help system


6 IDE editors for R



R Homepage

http://www.r-project.org

List of CRAN mirror sitesManualsFAQsSite seachMailing listsLinks


http://www.r-project.org


CRAN - Comprehensive R Archive Network

http://cran.fhcrc.org

CRAN MirrorsAbout 45 countriesAbout 100 sites worldwideAbout 15 sites in US

R BinariesR Packages

5800+ packagesR SourcesTask Views

use your closest CRAN mirror siteGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 38 / 53

http://cran.fhcrc.org


CRAN Task Views

Organizes the 5800+ R packages byapplication

FinanceTime SeriesEconometricsOptimizationMachine Learning



Stackoverflow

Stackoverflow has become the primary resource for help with R

http://stackoverflow.com/


http://stackoverflow.com/


R-SIG-FINANCE

Nerve center of the R financecommunity

Daily must read

Exclusively for Finance-specificquestions, not general Rquestions

https://stat.ethz.ch/mailman/listinfo/r-sig-finance


https://stat.ethz.ch/mailman/listinfo/r-sig-finance


Google’s R Style Guide

Naming conventionCoding SyntaxProgram Organization

http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html


http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html


Quick R

http://www.statmethods.net

Introductory R LessonsR InterfaceData InputData ManagementBasic StatisticsAdvanced StatisticsBasic GraphsAdvanced Graphs

Site maintained by Robert Kabacoff, author of R in ActionGuy Yollin (Copyright © 2014) R Programming for Quantitative Finance R Basics 43 / 53

http://www.statmethods.net


R graphics details, colors, and other tech notes

Site of Earl Glynn of Stowers Institute for Medical Research

R Graphics and other useful informationR Color ChartUsing Color in R (great presentation)Plot area, margins, multiple figuresMixture modelsDistance measures and clusteringUsing Windows Explorer to Start R with Specified Working Directory(under tech notes)

http://research.stowers-institute.org/efg/R/index.htm


http://research.stowers-institute.org/efg/R/index.htm


Programming in R

Online R programming manual from UC Riverside:R Basics

Finding Help

Code Editors for R

Control Structures

Functions

Object Oriented Programming

Building R Packages

http://manuals.bioinformatics.ucr.edu/home/programming-in-r


http://manuals.bioinformatics.ucr.edu/home/programming-in-r


Other useful R sites

R Bloggers Aggregation of about 550 R blogshttp://www.r-bloggers.com

R Site Search Search R function help, vignettes, R-helphttp://finzi.psych.upenn.edu/search.html

R Seek R specific search sitehttp://www.rseek.org/

Revolution Blog Blog from David Smith of Revolutionhttp://blog.revolutionanalytics.com

Inside-R R community site by Revolution Analyticshttp://www.inside-r.org


http://www.r-bloggers.com

http://finzi.psych.upenn.edu/search.html

http://www.rseek.org/

http://blog.revolutionanalytics.com

http://www.inside-r.org


Outline



3 Short R Tutorial

4 The R help system


6 IDE editors for R



RStudio

RStudio is a fully-featured open-source IDEfor R

R language highlightingPaste/Source to R consoleobject explorertabbed graphics windowintegrated version control1-click kintr/Sweave compilation

RStudio also provides a server-based version (R running in the cloud)



Revolution R Enterprize Visual Development Environment

Revolution Analytics is a companythat sells a commercial distributionof R including a desktop IDE

Revolution R Enterprize is free toacademic users

R language highlightingPaste/Source code to RSource code debuggerobject explorerruns R in SDI mode

http://www.revolutionanalytics.com


http://www.revolutionanalytics.com


WinEdt and R-Sweave

Based on WinEdt, an excellentshareware editor with support forLATEX and Sweave development

R language highlightingPaste/Source code to R1-click Sweave compilationSupports R in MDI modePaste/Source code to S-PLUS

http://www.winedt.comhttp://www.winedt.org/Config/modes/R-Sweave.php


http://www.winedt.com

http://www.winedt.org/Config/modes/R-Sweave.php


StatET - An Eclipse Plug-In for R

StatET is a plug-in for theopen-source Eclipse developmentenvironment

R language highlightingPaste/Source code to RSource code debugger1-click Sweave compilationSupports R in SDI modeExcellent documentation byLonghow Lam

http://www.walware.de/goto/statet


http://www.walware.de/goto/statet


Notepad++ and NpptoR

NpptoR is an automation widget(based on AuotHotkey) which allowsthe very useful program editorNotepad++ to interact with R

R language highlightingPaste/Source code to RSupports R in SDI mode

http://notepad-plus-plus.orghttp://sourceforge.net/projects/npptor


http://notepad-plus-plus.org

http://sourceforge.net/projects/npptor


Computational Finance and Risk Management

http://depts.washington.edu/compfin


http://depts.washington.edu/compfin


rBasics.pdf

Documents

Transcript of rBasics.pdf