R: Statistics? Programme? and Who are You?

1. R: Statistics? Programme?and Who are You? -- An ABC introduction to R Presented by Guohui Ding R&D, SIBS, CAS For Fudan University

2. Main Topics Today • What is R? • How to administrate R? • How does R work? • How to apply R for statistical problem? • How to program your R function? • ………

3. What is R? A brief history of R

4. The legend of R • R started in the early 1990’s as a project by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, intended to provide a statistical environment in their teaching lab. The lab had Macintosh computers, for which no suitable commercial environment was available. Ross Ihaka Robert Gentleman

5. R’s Parents(1) • The S language • S: an interactive environment for data analysis developed at Bell Laboratories since 1976 • Exclusively licensed by AT&T/Lucent to Insightful Corporation, Seattle WA. Product name: “S-plus”. My father is S, mother is Scheme, but why my name is “R”? You can learn more from: http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html

6. -- Ihaka R. & Gentleman R., 1996 R’s Parents(2) • The Scheme language Scheme is a statically scoped and properly tail-recursive dialect of the Lisp programming language invented by Guy Lewis Steele Jr. and Gerald Jay Sussman. Learn more: http://swiss.csail.mit.edu/projects/scheme/ • Scheme’s underlying semantics + S’syntax = R • “ We have named our language R –in part to acknowledge the • influence of S and in part to celebrate our own efforts.” • -- R. Ihaka • R. Gentleman

7. R Now • Since mid-1997 there has been a core group who can modify the R source code CVS archive. • The R package system CRAN (the Comprehensive R Archive Network ) http://www.r-project.org

8. The characters of R • R is “GNU S” — A language and environment for data manipula-tion, calculation and graphical display. • That is R is a Free Software(or Open source software). (Here, Free refers to freedom, not price, although R is free in that sense as well.) • The core of R is an interpreted computer language. • A mosaic of procedure-based programming and object-oriented programming • Good interface to procedures written in C, C++, FORTRAN and other languages • A flexible data exchange mechanism accessing relational databases -ODBC, PostgreSQL, MySQL and so on. ——小偷与强盗的谈判

9. R and Statistics • Most packages deal with statistics and data analysis. • Powerful statistical graphics. • Well crosstalking with other statistical softwares. • Most R user are statistical experts. You can learn more modern analysis method from they by email. • You can do it when you come across a thing no body do it before.

10. Install and administrate R Focus on Windows(MS)

11. How do I get R? • The informational web site http://www.r-project.org/ • CRAN - the Comprehensive R Archive Network. • The primary site is http://cran.r-project.org/ .Mirror sites are available for many countries. • CRAN sites have binary distributions for Windows 95, 98, ME, NT4, 2000 and XP on Intel, for the Macintosh (System 8.6 to 9.1 and MacOS X), and for several Linux distributions. • New releases occur frequently • about every 3 months. Be prepared to re-install frequently. • Also you can get it from your friends, teachers, etc. Down it! It is about 20.6M in size. Using Precompiled Binary Distributions

12. Installing R • Double click “rw1091.exe” using your mouse. That is OK. You can install it as all other standard MS softwares.

13. R Console/RGui in Windows(MS) Graphics box Menu Icons Command box

14. -- Ihaka R. & Gentleman R., 1996 Several concepts in Administrating R • Workspace • xxx.RData • History • xxx.Rhistory • Package • Object • Session • Console Run your R codes Load/save workspace Load/save History Change your working directory

15. Add a new package • Commands: • library() add a package in the library • detach(package : xxx) detach a package • All can do in the GUI (except detach()) Load a local package Install packages from internet or local Update the local package from internet

16. Packages in R Environment • Basic packages • "package:methods" "package:stats" "package:graphics“ "package:utils" "package:base" • Recommanded packages • grid; lattice;e1071… • Contributed packages (more than 366 packages nowadays) • …… You can see what packages loaded now by the command search().

17. Don’t lose your way! • Three useful system command • getwd()Get Working Directory • setwd() Set Working Directory • list.files()List the Files in a Directory/Folder

18. Show the Demonstrations of the Packages/Functions • Commands • demo() Demonstrations of R Functionality • example() Run an Examples Section from the Online Help

19. Getting Helps • Several commands • help.start() • help() or ?() • help.search() • apropos() • Internet searching • I like it very much. It seems omnipotence.

20. Quit R • Command • q() Terminate an R Session

21. How does R work? Basic R Structure and data manipulation

22. Basic R working flow(Object orientation) package -- R for Beginners. Emmanuel Paradis

23. Object orientation • Object: a collection of atomic variables and/or other objects that belong together • Parlance: • class: the “abstract” definition of it • object: a concrete instance • method: other word for ‘function’ • slot: a component of an object

24. Types of Data in R • The basic data object is a vector of elements of type: • numeric numbers - either floating point or integer • character each element is a character string • logical each element is TRUE or FALSE • list elements can be any type of object, including other lists • Components of the S language, such as functions, are also vectors. • Any vector can include the missing data marker NA as an element. • All vectors have a length and a mode. The functions length and mode return this information as does the str function. • A structure consists of a data object plus additional information. Matrices (or arrays, in general) and time series are examples of structures.

25. Operators

26. Vectors, Matrices and Arrays • Command: • array(data = NA, dim = length(data), dimnames = NULL) • matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL)

27. Lists • List vs. Vector • list: an ordered collection of data of arbitrary types. • vector: an ordered collection of data of the same type. • Typically, vector elements are accessed by their index (an integer), list elements by their name (a character string). But both types support both access methods.

28. Factors • Factors: classification variables • If the levels of a factor are numeric (e.g. the treatments are labelled“1”, “2”, and “3”) it is important to ensure that the data are ctually stored as a factor and not as numeric data. Always check this by using summary.

29. Data frames • data frame: is supposed to represent the typical data table that researchers come up with – like a spreadsheet. • It is a rectangular table with rows and columns; data within each column has the same type (e.g. number, text, logical), but different columns may have different types. ( A list actually)

30. Subsetting Individual elements of a vector, matrix, array or data frame are accessed with “[ ]” by specifying their index, or their name

31. Using R on Windows(MS) Basic statistical analysis by R

32. Data Input • From the keyboard one by one • c( ); scan( ) • From the file • read.table(); read.csv();read.csv2(); read.dta(); read.spss(); … • By a spreadsheet • data.entry() • edit() • fix() • ……

33. Data Edit • Commands • edit() • fix() Tips: edit() can invoke an notepad in the RGui!

34. Data Discription • Commands • summary() • mean() • sd() • hist() • boxplot() • ……

35. Probability Distribution

36. Three useful prefix in Probability Distribution Function • dxxx for the density • pxxx for the CDF • qxxx for the quantile function • rxxx for the simulation(random deviates) They are different! The seed is set by the system. You can set seed yourself by set.seed().

37. Statistical Inference • Commands • qxxx () for the quantile function • t.test() • wilcox.test(stats) • kruskal.test(stats) • var.test(); shapiro.test(); qqnorm(); qqline() --……

38. Analysis of variance and Regression Analysis • Commands • anova() • lm() • ……

39. Experiment Design • Commands • sample() • power.t.test() • ……

40. Save Object/Data • Every R object can be stored into and restored from a file with the commands “save” and “load”. > save(x, file=“x.Rdata”) > load(“x.Rdata”) • Importing and exporting datawith rectangular tables in the form of tab-delimited text files. > write.table(x, file=“x.txt”, sep=“\t”)

41. Graphics with R

42. A Friendly R Environment -- Rcmdr If you don’t like a command line environment, package Rcmdr may be a good choice!

43. R programming (.R) Program your R code own

44. Control Flow • if(cond) expr • if(cond) cons.expr else alt.expr • for(var in seq) expr • while(cond) expr • repeat expr • break • next

45. Loops • The main loop construct in R is for. The commonest use, as in C and other languages, is to count from 1 to n. • for (i in 1:n) { ## do something }

46. Leaving loops • The breakand nextcommands allow the flow of a loop to be altered • break jumps out the loop • next jumps to the next iteration of the loop

47. Avoiding Iteration • The canonical bad R program looks like this • ## multiply two vectors • for(i in 1:n) { d[i] <- a[i] * b[i] • } • ##compute the inner product • s <- 0 • for (i in 1:n){ • s <- s + d[i] • } • The right way to do this is • s<-sum(a*b) • apply(); lapply(); sapply()

48. Write R function A function definition looks like median <- function(x, na.rm = FALSE) { …lots of code... ## a return value }

49. More • Packages • Objects and methods • Debugging and optimisation • Connecting to other packages • Interface to other programme language or DataBase R++? ++R!

50. Some Resources • A Course (The ppt is showed with R Development Core Group) • http://faculty.washington.edu/tlumley/Rcourse/ • A Paper (citing R in a publication) • Ihaka R. & Gentleman R. 1996. R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics 5: 299–314. • Two URL • http://www.r-project.org • http://www.ats.ucla.edu/stat/ • Several Books • Using R for Data Analysis and Graphics—An Introduction. J.H. Maindonald • An Introduction to R. The R Development Core Team • simpleR –Using R for Introductory Statistics. John Verzani • R for Beginners. Emmanuel Paradis • The R Reference Manual Base Package. The R Development Core Team