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R Basics Xudong Zou Prof. Yundong Wu Dr. Zhiqiang Ye 18 th Dec. 2013 1

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

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3 History of R language

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5 Robert Gentleman Ross Ihaka

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6 History of R language

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10 History of R language

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11 History of R language

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12 History of R language

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13 History of R language

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14 History of R language

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15 History of R language

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16 History of R language

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

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18 History of R language

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19 History of R language

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20 History of R language

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21 History of R language

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23 History of R language 5088

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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

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控制台 从这里输 入命令 How to use R

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？用来获 取帮助 新建或打 开 R 脚本 点这里添 加 R 包 How to use R

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Data type and Data structure numeric : integer, single float, double float character complex logical Data structure in R: Data type in R : ObjectsClass Mixed-class permitted ？ Vectornumeric, char, complex, logicalno Factornumeric, charno Arraynumeric, char, complex, logicalno Matrixnumeric, char, complex, logicalno Data framenumeric, char, complex, logicalyes listnumeric, char, complex, logical, func, exp…yes

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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. # Create two vectors: # Check the attributes: # basic operation on vector: 注意这个向 左的箭头

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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

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30 array and matrix > 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 An array can be considered as a multiply subscripted collection of data entries.

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

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

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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

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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

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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

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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 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) From other software load package

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37 Operators

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

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39 Function R Programming 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); }

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

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41 Case study Residue based Protein-Protein Interaction potential analysis ： Lu et al. (2003) Development of Unified Statistical Potentials Describing Protein-Protein Interactions, Biophysical Journal 84(3), p1895-1901

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

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