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Statistical Software R

Statistical Software R. See http://www.statsci.org. More data sets …. What is R ?. A new(?) standard to interchange the ideas of statistics. - Public SW by GNU, under GPL ( It’s free ). - S language + Math/Stat Lib + Graphical tools. - 1 st version was published in early 90’s.

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Statistical Software R

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  1. Statistical Software R

  2. See http://www.statsci.org More data sets ….

  3. What is R ? A new(?) standard to interchange the ideas of statistics. - Public SW by GNU, under GPL ( It’s free ). - S language + Math/Stat Lib + Graphical tools - 1st version was published in early 90’s • More information: http://www.cran.r-project.org

  4. Time vs Time Develop for 1 month, run in 1 second. Or, develop for 1 day, run in 10 min. Run time Excel R C, FORTRAN Dev. time

  5. Applicability, range of Convenience C, FORTRAN Excel R Excel Calculator R C, FORTRAN Applicability

  6. R, Excel and C - Excel is a SW for general purpose - R is a professional SW - C is a developing tool having wide range of applicability

  7. GUI ? GUI is a good feature , especially for novice! • Clicking is slower and hard than typing !! • Clicking is not good for iterative job at company • Clicking is easy to generate garbage !!

  8. R is ~ • R = S lang. + Math & Stat Lib. + Graphic tools • Easy & efficient handling of data • Rich modern statistical routines • Free under GPL of GNU - To turn ideas into SW, quickly and faithfully. - R is at the center of statistical development. - R is a tool for saving & exchanging statistical data

  9. Very good book, but a little difficult to novice.

  10. Easieralternatives

  11. There are many easy books (try to find in amazon) and free tutorial guides in internet. Official free introductory guide: http://cran.r-project.org/doc/manuals/R-intro.pdf

  12. A free self study guide sites: http://tryr.codeschool.com/ http://www.sr.bham.ac.uk/~ajrs/R/index.html

  13. Download R ver. 2.10.1, base package, executable binary file : http://www.cran.r-project.org/bin/windows/base/R-2.10.1-win32.exe By clicking the install icon, you can install R easily. Contributed packages: downloading inside of R

  14. ENIAC programming, 1946

  15. FORTRAN Algol60 COBOL C APL Pascal Smalltalk C++ Lisp S-plus Scheme S A journey for easy scientific computing ENIAC OO Syntax Sense Semantics

  16. Features of R 1. Vector Arithmetic (APL, S-plus) 2. Object Oriented property (Smalltalk, S-plus) 3. Lazy evaluation (S-plus) 4. (Nested) lexical scoping (Scheme, PASCAL)

  17. 1. Vector Arithmetic x <- c(10,20,30) + c(5,5,5) y <- c(10,20,30) + c(1,2,3)

  18. 2. Object oriented property Smalltalk (1970, A. Kay, Xerox) Everything is an object, and every object has a class. Object is everything ? Integrated concept : Variable, Data, Function, ….. Unified framework to work on. (user) Class has the info of the object. (types of var)

  19. 거시기 갑옷을 거시기하자 (갑옷을 입자, 갑옷을 벗자) class:갑옷 method:거시기 object: 실제 개개의 갑옷

  20. Concept of OO Clicking the mouse button ! ( open a file, execute a pgm, delete a file, ….) Let the function work properly according to the characteristics of objects ! Make human command easier and make computer work harder to understand the command.

  21. OO in R • - diag(3), diag(c(1,2,3)), diag(diag(3)) • plot(sunspots) , plot(Titanic), plot(USJudgeRatings) • attributes(sunspots) , • attributes(Titanic), • attributes (USJudgeRatings)

  22. How to use R 1) Help : by menu, help(plot), ?title 2) demo(); demo(nlm); demo(image) 3) x <- matrix(1:4,2,); ls(); attributes(x) 4) #Install & Upload package tseries; search() 5) save.image("C:/temp/a.RData"); q()

  23. Memory & HDD Peripheral device Computer CPU Memory HDD

  24. How R works Memory .GlobalEnv Environment Namespace & Loaded Value … Frame for computing new objects Input Output HDD loaded package library …. …. > search() > searchpaths() > ls() # shows objects inside of libraries

  25. R data sets R has its own data sets for testing - data(); - Titanic; ?Titanic - plot(Titanic)

  26. Data sets of SVV http://www.aw.com/sharpe Get text file and excel file in your computer, and decompress. Make copies of text files under “C:\temp\text”

  27. SDV data : see p 188 # 32 , Economic Analysis data

  28. You can draw by yourself very simply ! data.svv<-dir("c:/temp/text") dfile.svv<-paste("c:/temp/text/",data.svv,sep="") dsv<- read.table(dfile.svv[37],head=TRUE, sep="\t") y<-dsv[,3] x<-dsv[,4] plot(x,y, pch=16, col="purple", xlab="Sogang Stat" ) points(20000,40, pch=1, cex=10, col="blue") title("Economic Analysis")

  29. Install & load packages Memory Server Load Internet HDD Install

  30. Stock price data from finance.yahoo.com ghq<-get.hist.quote # upload the package “tseries” time<- "1996-01-01" kospi <- ghq(ins = "^ks11", start =time, quote = "Close") dscon <- ghq(ins = "011160.ks", start = time, quote ="Close") tm <- ghq(ins = "tm", start =time, quote = "Close") plot(tm,xlab="Toyata Motors") plot(kospi,dscon,type="l", xlab="종합주가지수", ylab="두산건설" )

  31. Hanoi Tower By simple programming, graphical implementation of Hanoi tower is possible in R . The code & program were loaded to cyber campus. - hanoi(4) - hanoi(14)

  32. Business Statistics, Sogang Business School # This is comment line. # download R from cran.r-project.org # explain menu first q() # Stop R session; Do not save the workspace # .First<-function() cat("Helo everyone ?\n") # .Last<-function() { cat(“Bye, SBS Students !")} # ls() # ls(all=TRUE) q() # Save the workspace

  33. # Now, we know the first and the last of R # That is, we know everything of R q help help(q)

  34. data() help(data) sunspots help(sunspots) hist(sunspots) help(hist) args(hist) # arguments of the function hist() hist(sunspots, nclass=10) # with more intervals

  35. par(mfrow=c(1,2)) # set graphic layout hist(sunspots) # in different layout hist(sunspots, nclass=20) # two in a picture hist(sunspots, nclass=20,plot=F) # without plot

  36. ?co2 # co2 and sunspots in Jan 59 - Dec 83 ? co2x<- co2[1:(12*(83-58))] sunpt<-sunspots[-(1:(12*(1958-1748)))] par(mfrow=c(2,1)) plot(co2x) plot(sunpt)

  37. x <- rnorm(100,0,1) # random number generator y<-rnorm(100,0,1) # each has 100 elements x # show x y # show y xy<- x + y ( z<-rnorm(100,0,1) ) # assign and show ls() # show objects in …

  38. # tuning for graphic layout help(par) # Text and Symbols: cex, pch, type, xlab, ylab, .... # The Plot Area: bty, pty, xlim, ylim, .... # Figure and Page Areas: mfrow, .... # Miscellaneous: lty, ....

  39. plot(x,y) plot(xy, y) # set the graphic parameters par(mfrow=c(2,2), pty="s") plot(x, y, pch=0, cex=0.7 ) # pch and cex plot(xy, y, pch=16,cex=0.7) plot(x,y, pch=0, cex=1.2 ) plot(xy,y, pch=16, cex=1.2 )

  40. par(mfrow=c(1,1)) # mfrow plot(xy,y, pch=16, cex=1.2 ) plot(xy,y, type="n") # prepare axis only points(xy,y, pch=16, cex=1.2 ) lines(xy,y) # plot only points, but not axis plot(xy,y, axes=FALSE, xlab="x+y", ylab="y")

  41. cbind(x, y, xy) # column binding y[y>0] xy[y>0] cbind(x, y, xy) [y>0] plot(xy,y, type="n", xlab="x+y", ylab="y" ) # axis only points(xy[y>0],y[y>0], pch=16, cex=0.6 ) # for y>0 points(xy[y<=0],y[y<=0], pch=1, cex=0.8 ) # y <= 0

  42. # pch plot(c(-1,8),c(-1,8), type="n") for(i in 0:7) for(j in 0:7) points(i, j, pch=i+8*j, cex=1.2) points(-0.5, -0.5, pch="9", cex=1.2) points(7.5, 7.5, pch="한", cex=1.2)

  43. identify( xy, y, x) # to pick the points, using (left) mouse button identify( xy, y, round(x,2), cex=0.6) # to stop, use (right) mouse button pts<-locator(5) polygon(pts) help(polygon)

  44. par() # all graphic parameters par()$usr # usr uc <- par()$usr # to simplify lines( c(uc[1], uc[2]), c(0,0), lty=2) # center line lines( c(0,0), c(uc[3], uc[4]), lty=2) # lty # diagonal line lines( c(uc[1], uc[2]), c(uc[3], uc[4]) , lty=1) text( 1.0, -1.2, " positive y-values ! ") title(" (x+y) and y from N(0,1) ", cex=0.6 )

  45. help(USJudgeRatings) USJudgeRatings pairs(USJudgeRatings) pairs(USJudgeRatings[1:5])

  46. ## put histograms on the diagonal panel.hist <- function(x, ...) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col="cyan", ...) } pairs(USJudgeRatings[1:5], panel=panel.smooth, cex = 1.5, pch = 24, bg="light blue", diag.panel=panel.hist, cex.labels = 2, font.labels=2)

  47. # You can fix and modify the picture in power point # Class Assignment. # draw the picture of (2x+y, 2y) # for different pch parameters # in a plot and put a legend.

  48. # Important functions to understand R # ls(); search(); searchpaths() # attributes() # c(); data.frame() ; factor(); ordered() # apply()

  49. Thank you !!

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