short introduction to r - cimmytgenomics.cimmyt.org/slu/slides paulino/0. r-intro/presenta.pdf ·...

Short Introduction to R

Paulino Pérez 1

José Crossa 2

1ColPos-México 2CIMMyT-México

September, 2014.

SLU,Sweden Short Introduction to R 1/51

Contents

1 Introduction

2 Simple objects

3 User defined functions

4 Graphs

5 Importing data

6 Installing packages

7 Questions


Introduction ¿R?

Contents1 Introduction

¿R?R and other sofwaresManuals and helpSample R session

2 Simple objectsVectorsMatricesdata.frame

3 User defined functions4 Graphs

Plotting user defined functionsPlot functionMore functions for graphs

5 Importing data6 Installing packages7 Questions


Introduction ¿R?

¿R?

R is a software for statistical analysis and graphics. It was developed by RossIhaka y Robert Gentleman. R was specially designed to perform dataanalysis, but can be used also as a programming language.

R is distributed freely under the GNU license(General Public Licence). Thedevelopment is done by the R Development Team.

R is available as source code and binary files compiled for windows and Mac.

The R language allows the user to use loops to perform complex analysis.


Introduction R and other sofwares









R and other softwares

Why R ?R is a free software, it can be executed in Windows, Linux and Mac.Excellent documentation and graphical capabilities.Most of the programs written in S plus can be run in R.It is powerful and easy to learn.It can be extended through the use of packages.



Disadvantages

Graphical user interface not as good as in other softwares.Lack of commercial support (partially true).



Installation in a Windows environmentGo to http://www.r-project.org and download the windows binary,

Figure 1: R web site.


http://www.r-project.org


Continue...

Go to the Downloads section and select CRAN,

Figure 2: CRAN mirrors



Continue...

Select the software for you OS,

Figure 3: Executables for various platforms.



Continue...

Download the base software,

Figure 4: Download R-base.



Continue...

Double click in the installer,

Figure 5: Installing R.


Introduction Manuals and help









Manuals

Once that you install R, you will have access to the following manuals in PDFformat:

An Introduction to RR Reference ManualR Data Import/ExportR Language DefinitionWriting R ExtensionsR InternalsR Installation and Administration



Continue...

Furthermore:

Contributed Docs(http://cran.r-project.org/other-docs.html).R-help mailing list archives(http://cran.r-project.org/search.html). Mailing list.Reference card. Summary of most useful R commands(http://www.rpad.org/Rpad/Rpad-refcard.pdf)S Programming, W. Venables and B. Ripley. Seehttp://www.stats.ox.ac.uk/pub/MASS3/Sprog.


http://cran.r-project.org/other-docs.html

http://cran.r-project.org/search.html

http://www.rpad.org/Rpad/Rpad-refcard.pdf

http://www.stats.ox.ac.uk/pub/MASS3/Sprog

Introduction Sample R session









Sample R session

Go to a Start->Programs->R->R-3.x.y, the working environment is as thatshown in the next Figure.

Figure 6: R ready to process commands.



The symbol > is the command prompt. We can write command there, forexample to show some help about matrices,

?matrix

then Enter

The help system can be accessed through the command line using thefollowing functions:

?texthelp.start()help.search("text to search")apropos("search for some thing similar to...")



Code editors

A set of R commands is usually known as “script". There are several texteditors. The one included by default in Windows installations is not as fancyas others that have syntax highlighting, for example Tinn-R, or R-studio.The standard text editor in R can be accessed from the File menu, File->NewScript

Figure 7: Code editor in R



Example (performing basic calculations):

7+42*3*(1+2)

The commands are written in the text editor, then the commands are selectedand the pop-up menu is activated, one of the entries in the menu has anoption to execute the code. The result will appear in the R console.

Figure 8: Executing R commands from the text editor.



Commands written in the text editor can be saved and restored for editinglater.

There exists a lot of text editors for writing R scripts, for example:

WinEdit (shareware)SciViews (freeware)Tinn-R (freeware)Emacs (free)Rstudio (free)



Continue...

Figure 9: Rstudio.


Simple objects

R works manipulating “objects”. The objects are manipulated usingfunctions and operators.

The most basic objects are:

vectors (type numeric or character)Matrixdata.frameListsFunctions

Some useful functions...

General pourpouse: sqrt(),log(),exp(),sin(),cos(), etc.Related to statistics: mean(), sd(), var(), quantile(), etc.

The assignment operator is = with R>=1.4.0 or <- in any R version.


Simple objects

Notes:

R distinguish between upper and lowercase lettersThe symbol "#" is used to comment the codeObject’s names can contain any combination of characters, exceptspaces and special symbols, for example "$","%","#", etc.Missing data can be represented with the special symbol "NA" (NotAvailable), and errors in computations for example dividing by 0 with thespecial symbol "NaN" (Not a Number) or "Inf"


Simple objects Vectors









Vectors

Vectors are created using the functions c(),seq(),:, rep().

Examples:

a=c(1,2,3,4,5)

ab=c("a","b","c")bd=1:10

de=seq(1,10,by=0.5)e f=seq(1,10,length.out=20)fg=rep(10,3)gh=c(e,f)h



Vector operations

We can perform most common operations using vectors with the same length.Operations are performed element wise.

Examples:

a=c(1,2,3)b=c(2,3,5)a+b #sum a and ba-b #a-ba*b #element wise producta^b #power function3*a+2*b #product and suma/b #element wise quotienta^2 #takes the square of each element

It is also possible to apply a function to a vector, for example:

exp(a) #Exponential functionlog(a) #logarithm functiond=sqrt(a)+log(b) #square root and logarithmd #shows d



To extract some elements from the vectors we can use the [] operator, forexample:

a=c(1,2,5,7,9)b=a[1:3] #first 3 elements in a, assign the result to bb #shows bc=a[-1] #take all elements in b except the first one

#and crete a new objecta[c(1,5)] #first and last component in a

There exists a lot of operations that can be performed using vectors, forexample:

w=c(1,2,3,NA,-1,2)which(is.na(w)) #Missing valueswhich.max(w) #Position of the maximumwhich.min(w) #Position of the minumumw>2 #numbers that are bigger than 2?which(w>2) #which numbers are bigger than 2?sort(w) #sort in ascending ordersort(w,decreasing=T) #sorts in decreasing order


Simple objects Matrices









Creating matricesMatrices can contain numbers or characters. The creation of matrices isshown in the examples below.

#a)Identity matrix, nxn#Identity matrix of order 4x4Identity=diag(c(1,1,1,1))Identity

#Alternatively...Identity=diag(rep(1,4))Identity

#b)J matrix#J matrix, order 3x3J=matrix(1,nrow=3,ncol=3)J

#c)In general#matrix(data = NA, nrow = 1, ncol = 1,byrow = FALSE, dimnames = NULL)

A=matrix(nrow=3,ncol=3)A[1,]=c(1,2,3)A[2,]=c(4,5,6)A[3,]=c(7,8,9)



#Alternatively ...A=matrix(c(1:9),nrow=3,ncol=3,byrow=TRUE)A

#Alternatively...A=matrix(c(1,4,7,2,5,8,3,4,8),nrow=3,ncol=3,byrow=FALSE)A



Matrix operations

#Sum and substractionC=A+JCD=A-JD

#Matrix product (%*%)Dsq=D%*%DDsqDsqA=D%*%D%*%(A)DsqA

#Transpose, use t()t(D)

#Determinant, det()det(D)

#Inverse, use the function solve()InvI=solve(Identidad)InvI



#Rangk, use the QR decompositionqr(Identidad)qr(Identidad)$rank

Noninvertible=matrix(c(1,2,3,1,2,1,2,4,6),nrow=3,ncol=3,byrow=TRUE)det(Noninvertible)qr(Noninvertible)$rank



We can also extract some elements of the matrix, for example:

A #Shows AA[1,1] #Element in row 1, column 1 in AA[1,] #First row of AA[c(1,2),] #First and second row of AA[-3,] #All rows except the third oneA[,1] #First column of AA[,c(1,3)] #Columns one and third in AA[,-3] #All columns except third one

Note: When we extract a row or column it is automatically converted to avector.


Simple objects data.frame








Simple objects data.frame

data.frame

Tables are created using the function data.frame(v1,...,vn), where v1 is thevector 1 and vn is the vector n. The rows usually represents individuals andthe columns covariates.

Examples

ID=c("genO","genB","genZ")subj1=c(10,25,33)subj2=c(NA,34,15)oncogen=c(TRUE,TRUE,FALSE)loc=c(1,30,125)data1=data.frame(ID,subj1,subj2,oncogen,loc)data1

#If you want to display the column names in a#data.frame, use the function namesnames(data1)

#To show or extract a column use the operator $data1$subj2data1$subj2data1$oncogen


User defined functions


R implements a lot of statistical methodologies using functions. The functionsare organized in libraries. The base library contains all the functions that wehave been using so far. The libraries can be downloaded freely from theinternet.

We can create our own functions for data analysis. The syntax for creating anew function is as follows:

funcion_name=function(arg1,...,argn){function body;return the value;

}

Examples:



A function to compute f (x) = x2

f=function(x){x^2

}f(2)f(c(1,2,3))

A function to compute∑n

i=1 i

my_sum=function(n){

tmp=c(1:n)sum(tmp)

}#The result should be identical to n(n+1)/2n=100my_sum(n)n*(n+1)/2


Graphs

R includes many functions to produce high quality graphics ready forpublication. We can explore the graphical capabilities of the software includedin the demos. Type demo() in the command prompt and the software willdisplay a list of demos that we can execute, for example:

graphicsimageperspplotmath

demo()demo(graphics) #Some graphical capabilitiesdemo(image) #Working with imagesdemo(persp)demo(plotmath) #Mathematical symbols in graphs


Graphs Plotting user defined functions








Graphs Plotting user defined functions

Plotting user defined functions

To plot user defined functions we can use the functions curve(), or plot(). Wewill give more details about the later in the next slides.

Examples

#1: Plotting f(x)=x^2, -4<=x<=4curve(x^2,-4,4)

#2: Plotting f(x)=-x^3, -4<=x<=4curve(-x^2,-4,4)

#3:op=par(mfrow=c(2,2))curve(x^3-3*x, -2, 2)curve(x^2-2, add = TRUE, col = "violet")plot(cos, xlim =c(-pi,3*pi), n = 1001, col = "blue")chippy=function(x) sin(cos(x)*exp(-x/2))curve(chippy, -8, 7, n=2001)curve(chippy,-8, -5)

#4: Standard normalcurve(1/sqrt(2*pi)*exp(-1/2*x^2),-3,3)


Graphs Plot function








Graphs Plot function

Plot function

One of the most useful function for plotting is the plot function. With thisfunction we can plot points (scatterplot), lines (time series), or functions.

Examples

y<-c(1,2,3,4,5)x<-c(1,4,9,16,25)plot(x,y,main="",ylab="", xlab="")plot(x,y,type="l")plot(dnorm, -3,3,col = "blue")


Graphs More functions for graphs








Graphs More functions for graphs

More functions for graphs

barplotpiehistogramboxplot


Importing data

Importing data

There are several routines to import data into the R environment. For ASCIIfiles we can use:

1 read.table2 read.csv

The function setwd is useful for setting the working directory so that we donot have to write the entire PATH of a file each time that we want to read it.

R can save and load objects in a native binary format. The functions loadand save can be used to that end.


Importing data

Examples

This data set is from CIMMYT global Wheat breeding program and comprisesphenotypic, genotypic and pedigree information of n = 599 wheat lines. Thedata set was made publicly available by Crossa et al. (2010). Lines wereevaluated for grain yield at four different environments. Each of the lines weregenotyped for p = 1,279 Diversity Array Technology (DArT) markers. At eachmarker two homocygous genotypes were possible and these were coded as0/1. Marker genotypes are given in the object X. Finally a matrix A providesthe pedigree-relationships between lines computed from the pedigree.


Importing data

Continue...

rm(list=ls())

library(doBy)

setwd("~/0. R-Intro/examples")

#Load genotypic dataload("pedigree_markers.RData")

#Load phenotypic datapheno=read.table(file="599_yield_raw-1.prn",header=TRUE)

colnames(pheno)

pheno=pheno[,c(2,5,6)]

out=summaryBy(GY~env+gen1,data=pheno,FUN=mean)

Y=data.frame(yield=out$GY.mean,VAR=out$gen1,ENV=out$env)


Installing packages

Installing packages

Many users all over the world are creating software packages for R,

Figure 10: Packages.


Installing packages

Continue...

A package is just a collection of routines used to perform some calculations.Usually these routines are made available to the user through functions welldocumented.

The function install.packages() is used to install software from theCRAN website, for example:

install.packages("BLR")install.packages("BGLR")install.packagses("doBy")

Once that a package is installed, it should be loaded with the functionlibrary,

library(doBy)library(BGLR)


Questions

Questions?


short introduction to r - cimmytgenomics.cimmyt.org/slu/slides paulino/0. r-intro/presenta.pdf ·...

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