Basics.ppt

Basic of R language Jarno Tuimala

Learning aims • Basic use of R and R help • How to give R commands • R data structures • Reading and writing data • Some more R commands (exercises)

R project • ”R is a free software environment for statistical computing and graphics” (http://www.r-project.org) • ”Bioconductor is a software project for the analysis of genomic data” (http://www.bioconductor.org) – Currently works as an expansion to R

Packages • R consists of a core and packages. • Packages contain functions that are not available in the core. • For example, Bioconductor code is distributed as several dozen of packages for R. – Software packages – Metadata (annotation) packages

Anatomy of a help file 1/2 Function {package} General description Command and it’s argument Detailed description of arguments

Anatomy of a help file 2/2 Description of how function actually works What function returns Related functions Examples, can be run from R by: example(mas5)

Functions or commands in R 1/3 • To use a function in a package, the package needs to be loaded in memory. • Command for this is library( ), for example: library(affy) • There are three parts in a command: – the command – brackets – Arguments inside brackets (these are not always present)

Functions or commands in R 2/3 • R is case sensitive, so take care when typing in the commands! – library(affy) works, but Library(affy) does not. • Multiple commands can be written on the same line. Here we first remove missing values from the variable year, and then calculate it’s arithmetic average. – Writing: • na.omit(year) • mean(year) – Would be the same as • mean(na.omit(year))

Functions or commands in R 3/3 • Command can have many arguments. These are always giving inside the brackets. • Numeric (1, 2, 3…) or logic (T/F) values and names of existing objects are given for the arguments without quotes, but string values, such as file names, are always put inside quotes. For example: • mas5(dat3, normalize=T, analysis=”absolute”)

Data structures 1/6 • Vector – A list of numbers, such as (1,2,3,4,5) – R: a<-c(1,2,3,4,5) • Command c creates a vector that is assigned to object a • Factor – A list of levels, either numeric or string – R: b<-as.factor(a) • Vector a is converted into a factor

Data structures 2/6 • Data frame – A table where columns can contain numeric and string values – R: d<-data.frame(a, b) • Matrix – All columns must contain either numeric or string values, but these can not be combined – R: e<-as.matrix(d) • Data frame d is converted into a matrix e – R: f<-as.data.frame(e) • Matrix e is converted into a dataframe f

Data structures 3/6 • List – Contains a list of objects of possibly different types. – R: g<-as.list(d) • Converts a data frame d into a list g • Class structures – Many of the Bioconductor functions create a formal class structure, such as an AffyBatch object. – They contain data in slots – Slots can be accessed using the @-operator: • dat2@cdfName

Data structures 4/6 • Some command need to get, for example, a matrix, and do not accept a data frame. Data frame would give an error message. • To check the object type: – R: class(d) • To check what fields there are in the object: – R: d – R: str(d) • To check the size of the table/matrix: – R: dim(d) • To check the length of a factor of vector: – R: length(a)

Data structures 5/6 • Some data frame related commands: – R: names(d) • Reports column names – R: row.names(d) • Reports row names • These can also be used for giving the names for the data frame. For example: – R: row.names(d)<-c("a","b","c","d","e") • Letters from a to e are used as the row names for data frame d • Note the quotes around the string values! – R: row.names(d)

Data structures 5/6 • Naming objects: – Never use command names as object names! – If your unsure whether something is a command name, type to the comman line first. If it gives an error message, you’re safe to use it. – Object names can’t start with a number – Never use special characters, such as å, ä, or ö in object names. – Underscore (_) is not usable, use dot (.) instead: • Not acceptable: good_data • Better way: good.data – Object names are case sensitive, just like commands

Reading data 1/2 • Command for reading in text files is: read.table(”suomi.txt”, header=T, sep=”t”) • This examples has one command with three arguments: file name (in quotes), header that tells whether columns have titles, and sep that tells that the file is tab-delimited.

Reading data 2/2 • It is customary to save the data in an object in R. This is done with the assignment operator (<-): dat<-read.table(”suomi.txt”, header=T, sep=”t”) • Here, the data read from file suomi.txt is saved in an object dat in R memory. • The name of the object is on the left and what is assigned to the object is on the right. • Command read.table( ) creates a data frame.

Using data frames • Individual columns in the data frame can be accessed using one of the following ways: – Use its name: • dat$year • dat is the data frame, and year is the header of one of its columns. Dollar sign ($) is an opertaor that accesses that column. – Split the data frame into variables, and use the names directly: • attach(dat) • year – Use subscripts

Subscripts 1/2 • Subscripts are given inside square brackets after the object’s name: – dat[,1] • Gets the first column from the object dat – dat[,1] • Gets the first row from the object dat – dat[1,1] • Gets the first row and it’s first column from the object dat • Note that dat is now an object, not a command!

Subscripts 2/2 • Subscripts can be used for, e.g., extracting a subset of the data: – dat[which(dat$year>1900),] • Now, this takes a bit of pondering to work out… • First we have the object dat, and we are accessing a part of it, because it’s name is followed by the square brackets • Then we have one command (which) that makes an evaluation whether the column year in the object dat has a value higher than 1900. • Last the subscript ends with a comma, that tells us that we are accessing rows. • So this command takes all the rows that have a year higher 1900 from the object dat that is a data frame.

Writing tables • To write a table: – write.table(dat, ”dat.txt”, sep=”t”) – Here an object dat is written to a file called dat.txt. This file should be tab-delimited (argument sep). • To capture what is written on the screen: – sink(”output.txt”) – dat – sink( ) – Here, output written on the screen should be written to a file output.txt instead. Contents of the object dat are written to the named file. Last, the file is closed. – Note that if you accidentally omit the last command, you’ll not be able to see any output on the screen, because output is still redirected to a file!

Quitting R • Use command q() or menu choise File->Exit. • R asks whether to save workspace image. If you do, all the object currently in R memory are written to a file .Rdata, and all command will be written a file .Rhistory. • These can be loaded later, and you can continue your work from where you left it. • Loading can be done after starting R using the manu choises File->Load Workspace and File-> Load History.

In summary 1/2 • Commands can be recognized from the brackets ”( )” that follow them. If you calculate how many bracket pairs there are, you’ll be able to identify the number of commands. – pData(dat)<-pd • Assignment to an object is denoted by ”<-” or ”->” or ”=”. If you see a notation ”= =”, you’ll looking at a comparison operator. – Many other notations can be found from the documentation for the Base package or R. • Table-like objects are often followed by square brackets ”[ ]”. Square never associate with commands, only objects. – dat[,1] • Special characters $ and @ are used denoting individual columns in a data frame or an individual slot in a class type of an object, respectively. – dat$year – dat2@cdfName

In summary 2/2 • If you encounter a new command during the exercises, and you’d like to know what it does, please consult the documentation. All R commands are listed nowhere, and the only way to get to know new commands is to read the documentation files, so we’d like you to practise this youself. • You’ll probably see command and notations that were not introduced in this talk. This in intentional, because we thought that these things are best handled on a situational basis. In such cases, please ask for more clarifications if needed. • If you run into problems, please ask for help from the teachers. That’s why we are here!

Installing R for Windows • Execute the R-2.3.0-win32.exe with administrator privileges • Once the program is installed, run the R program by clicking on its icon • R 2.2.1 with Bioconductor 1.7.0 is installed on corona.csc.fi, also • R 2.3.1 is in works

Installing Bioconductor (the best way) • Alternatively, you can install Bioconductor using a script: source("http://www.bioconductor.org/biocLite.R") biocLite() biocLite(c(” "hgu133a", "hgu133acdf", "hgu133aprobe", "ygs98", "ygs98cdf", "ygs98probe")

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