R Basics

1. R Basics XudongZou Prof. Yundong Wu Dr. Zhiqiang Ye 18thDec. 2013

2. R Basics • History of R language • How to use R • Data type and Data Structure • Data input • R programming • Summary • Case study

3. History of R language

5. Robert Gentleman Ross Ihaka

6. History of R language

7. History of R language

8. History of R language

9. History of R language

10. History of R language

11. History of R language

12. History of R language

13. History of R language

14. History of R language

15. History of R language

16. History of R language

17. 2013-09-25: Version: R-3.0.2

18. History of R language

19. History of R language

20. History of R language

21. History of R language

22. History of R language

23. History of R language 5088

24. What is R? • R is a programming language, and also a environment for statistics analysis and graphics • Why use R • R is open and free. Currently contains 5088 packages that makes R a powerful tool for financial analysis, bioinformatics, social network analysis and natural language process and so on. • More and more people in science tend to learn and use R • # BioConduct: bioinformatics analysis(microarray) • # survival: Survival analysis

25. How to use R 从这里输入命令 控制台

26. How to use R 新建或打开R脚本 点这里添加R包 ？用来获取帮助

27. Data type and Data structure Data type in R : numeric : integer, single float, double float character complex logical Data structure in R:

28. Vector and vector operation Vector is the simplest data structure in R, which is a single entity containing a collection of numbers, characters, complexes or logical. 注意这个向左的箭头 # Create two vectors: # Check the attributes: # basic operation on vector:

29. Vector and vector operation # basic operation on vector: > max( vec1) > min (vec1) > mean( vec1) > median(vec1) > sum(vec1) > summary(vec1) > vec1 > vec1[1] > x <- vec1[-1] ; x [1] > vec1[7] <- 15;vec1

30. array and matrix An array can be considered as a multiply subscripted collection of data entries. > x <- 1:24 > dim( x ) <- c( 4,6) # create a 2D array with 4 rows and 6 columns > dim( x ) <- c(2,3,4) # create a 3D array

31. array and matrix array() > x <- 1:24 > array( data=x, dim=c(4,6)) > array( x , dim= c(2,3,4) ) array indexing > x <- 1:24 > y <- array( data=x, dim=c(2,3,4)) > y[1,1,1] > y[,,2] > y[,,1:2]

32. array and matrix Matrix is a specific array that its dimension is 2 > class(potentials) # “matrix” > dim(potentials) # 20 20 > rownames(potentials) # GLY ALA SER … > colnames(potentials) # GLY ALA SER … > min(potentials) # -4.4

33. list List is an object that containing other objects as its component which can be a numeric vector, a logical value, a character or another list, and so on. And the components of a list do not need to be one type, they can be mixed type. >Lst <- list(drugName="warfarin",no.target=3,price=500, + symb.target=c("geneA","geneB","geneC") >length(Lst) # 4 >attributes(Lst) >names(Lst) >Lst[[1]] >Lst[[“drugName”]] >Lst$drugName

34. Data Frame A data frame is a list with some restricts: ① the components must be vectors, factors, numeric matrices, lists or other data frame ② Numeric vectors, logicals and factors are included as is, and by default character vectors are coerced to be factors, whose levels are the unique values appearing in the vector ③ Vector structures appearing as variables of the data frame must all have the same length, and matrix structures must all have the same row size Names of components

35. Data Frame > names(cars) [1] "Plant" "Type" "Treatment" "conc" "uptake“ > length(cars) # 2 > cars[[1]] > cars$speed# recommended > attach(cars) # ?what’s this > detach(cars) > summary(cars$conc) # do what we can do for a vector

36. Data Input scan(file, what=double(), sep=“”, …) # scan will return a vector with data type the same as the what give. read.table(file, header=FALSE, sep= “ ”, row.names, col.names, …) # read.table will return a data.frame object # my_data.frame<- read.table("MULTIPOT_lu.txt",row.names=1,header=TRUE) From other software # from SPSS and SAS library(Hmisc) mydata <- spss.get(“test.file”,use.value.labels=TRUE) mydata <- sasxport.get(“test.file”) #from Stata and systat library(foreign) mydata<- read.dta(“test.file”) mydata<-read.systat(“test.file”) # from excel library(RODBC) channel <- odbcConnectExcel(“D:/myexcel.xls”) mydata <- sqlFetch(channel, “mysheet”) odbcclose(channel) load package

37. Operators

38. R Programming Control Statements # repeat {…} # switch( statement, list)

39. R Programming Function Definition： Example： matrix.axes <- function(data) { x <- (1:dim(data)[1] - 1) / (dim(data)[1] - 1); axis(side=1, at=x, labels=rownames(data), las=2); x <- (1:dim(data)[2] - 1) / (dim(data)[2] - 1); axis(side=2, at=x, labels=colnames(data), las=2); }

40. Summary numeric, character, complex, logical Data type and Data Structure vector, array/matrix, list, data frame scan, read.table Data Input load from other software: SPSS, SAS, excel Operators : <- R Programming:

41. Case study Residue based Protein-Protein Interaction potential analysis： Lu et al. (2003) Development of Unified Statistical Potentials Describing Protein-Protein Interactions, Biophysical Journal84(3), p1895-1901

42. Reference CRAN-Manual：http://cran.r-project.org/ Quick-R：http://www.statmethods.net/index.html R tutorial：http://www.r-tutor.com/ MOAC：http://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/r/matrix_contour/

43. Thanks for your attention