R Basics - staff.pubhealth.ku.dkstaff.pubhealth.ku.dk/~pd/mixed-jan.2006/Basics.pdf · Using R R Language Basics Dealing with the workspace Reading Data Data Manipulation Concatenation

Using R R Language Basics Dealing with the workspace Reading Data Data Manipulation

R Basics

Peter Dalgaard

Department of BiostatisticsUniversity of Copenhagen

Mixed Models in R, Copenhagen, January 2006

R Basics Department of Biostatistics University of Copenhagen


Outline

Using R

R Language Basics

Dealing with the workspace

Reading Data

Data Manipulation



Basics of R

I What is R?I Interacting with RI Extended user interfacesI Later: Dealing with R’s workspace



Key Points about R

I Environment built around the programming language R,(an Open Source dialect of the S language).

I R is Free Software, and runs on a variety of platforms (I’llbe using Linux. Computer labs run on Windows.)

I Command-line execution based on function callsI Extensible with user functionsI Workspace containing data and functionsI Graphics devices



R packages

I Collections of R functions, data, and compiled codeI Well-defined format that ensures easy installation, a basic

standard of documentation, and enhances portability andreliability,



Interacting with R

I Command line interface (CLI)I The basic mode of interaction is “read – evaluate – print”I User types an expression at the command line,I R evaluates itI . . . and prints the resultI Batch variation: read commands from a file



Extended Interfaces

I Windows, Macintosh GUI: Fairly simple extensions of CLI,mostly offloads some tasks to menu interface, and addscommand recall

I Script editing: The ability to work with multiple lines of Rcode, save them to a file for later use, etc. A simple scripteditor is built into the R GUI in recent versions.

I External editor interfaces: TINN-R, R-WinEdt adds syntaxhighlighting. Highly recommended.

I R embedded in a text editor (ESS – Emacs SpeaksStatistics). Popular on Unix/Linux systems.



Demo 1

2+2log(10)help(log)summary(airquality)demo(graphics) # pretty pictures...



Basic Vector Types

I R is a vector based language, data types includeI Numeric (integer/double) vectorsI Character (strings) vectorsI Logical vectorsI These types are combined and extended to form more

complex objects



Basic operations

I Standard arithmetic is vectorized: x + y adds eachelement of x to the corresponding element of y

I c — concatenationI seq or from:to — sequencesI rep, gl — replicationI sum, mean, range, math functions,. . .



Demo 2

x <- round(rnorm(10,mean=20,sd=5)) # simulate dataxmean(x)m <- mean(x)mx - m # notice recycling(x - m)^2sum((x - m)^2)sqrt(sum((x - m)^2)/9)sd(x)



Concatenation

I The c() function joins a number of vectors end to endI An important special case is where the vectors are just

numbers> c(7,9,13)[1] 7 9 13

I This is used all over the place where you need to pass ashort vector to a function call (e.g ylim=c(0,100) in aplot.



Sequences

I The seq() function generates regular sequencesI The arguments are from, to, byI If by is 1 (default), you can also use from:to

> seq(1,9,2)[1] 1 3 5 7 9> 1:5[1] 1 2 3 4 5



Replication

I The rep() function generates a vector by replicatingelements from another vector a given number of times

I The arguments are x, times, but there are alternateforms, notably the each argument> x <- c(7,9,13)> rep(x,2)[1] 7 9 13 7 9 13> rep(x,each=2)[1] 7 7 9 9 13 13> rep(x, 1:3)[1] 7 9 9 13 13 13



Generating regular designs

I The gl() function “generates levels” for a regular designI The arguments are

I Number of levelsI Block sizeI Total length

I Notice that this generates a factor (more about this later)> gl(2,3,12) # 2 levels, blocks of 3, total length 12[1] 1 1 1 2 2 2 1 1 1 2 2 2Levels: 1 2



Classed Objects

I In R objects can have classesI These are used as the basis for function dispatchI I.e. the same (generic) function can have different methods

for different classesI Print methods are a prototypical exampleI There are two object systems, based (roughly) on S

version 3 and version 4. I will not go into details.



Factors

I Factors are used to describe groupings (the termoriginates from factorial designs)

I Basically, these are just integer codes plus a set of namesfor the levels

I They have class "factor" making them (a) print nicelyand (b) maintain consistency

I A factor can also be ordered (class "ordered"),signifying that there is a natural sort order on the levels

I In model specifications, factors play a fundamental role byindicating that a variable should be treated as aclassification rather than as a quantitative variable (similarto a CLASS statement in SAS)



Creating factors

I Factors can be created during read (but not alwayscorrectly)

I The factor function is used when, e.g., groups havebeen read as numeric codes> sexnr <- c(0,0,1,1,0,1)> (sex <- factor(sexnr, levels=c(1,0),+ labels=c("male", "female")))[1] female female male male female maleLevels: male female

I Notice the slightly confusing use of levels and labelsarguments.

I levels are the value codes on inputI labels are the value codes on output (and become the

levels of the resulting factor)



Indexing

I R has several useful indexing mechanisms:I a[5] single elementI a[5:7] several elementsI a[-6] all except the 6thI a[b>200] logical index



Lists

I A vector where the elements can have different typesI Functions often return listsI lst <- list(A=rnorm(5), B="hello")

I Special indexing:I lst$A

I lst[[1]] first elementI (lst[1] list containing the first element)



Matrices/Tables/Arrays

I Used in matrix calculus and as input to, e.g.,chisq.test(). Results of tabulation.

I Matrices: Generate with matrix

I Indexing methods are like [i,j], [i,], [,j]I Leaving out the row index gives all rows, etc.



Data frames

I Like data set in other packagesI Technically: Lists of vectors/factors of same lengthI Indexed like matrices (Beware, though: Data frames are

not matrices) or as listsI Generate from read operation or with data.frame

I Many sample data frames are avalilable using data()



Demo 3

data(airquality)airquality[1:10,]airquality$Monthairquality[airquality$Month==5,]oz <- airquality[airquality$Month==5,]$Ozonemean(oz)mean(oz, na.rm=TRUE)



The workspace

I The global environment contains R objects created on thecommand line.

I There is an additional search path of loaded packages andattached data frames.

I When you request an object by name, R looks first in theglobal environment, and if it doesn’t find it there, itcontinues along the search path.

I The search path is maintained by library(), attach(),and detach()

I Notice that objects in the global environment may maskobjects in packages and attached data frames



Demo 4

attach(airquality)mean(Ozone, na.rm=TRUE)tapply(Ozone, Month, mean, na.rm=TRUE)detach()search()library(ISwR)data(intake) # From ISwRls()attach(intake)search()ls("intake") # show variables in data framepost - prerm(intake) # remove data framedetach() # remove from search path



A Common Mistake

attach(mydata)sex <- factor(sex)tapply(height, sex, mean)detach()attach(subset(mydata, age > 25))sex <- factor(sex)tapply(height, sex, mean)

You get an error saying that height and tanner are ofdifferent length. What went wrong?Second time around, sex was found in the global environmentbefore the attached data frame.



Getting Organized

Several possibilities:I Save/restore entire workspace (objects only)I Save selected objects and load themI source() script filesI Batch processing (R CMD BATCH file.R)



Reading Data, Overview

I Simple data vectors can be read using scan()

I Data frames can be read from most reasonably structuredtext file formats (space separated columns, tab- andcomma-delimited files) using read.table() orread.delim().

I The foreign package can read files from Stata, SASexport libraries, SPSS, and Epi-Info, Minitab, and someS-PLUS versions.

I For spreadsheets and databases, the quick and easy wayis to export to a delimited file, but you can work via ODBCconnections and database access packages



The Simplest Way to Read Data

I This is what you’d normally want to do:I Have data in a plain text fileI Columns separated by whitespaceI Missing values coded as the string "NA"

I Preferably have a row of variable names at the topI Use d <- read.table("myfile", header=TRUE)



Demo 5

dir <- system.file("data", package="ISwR")fname <- file.path(dir, "thuesen.txt")fname

file.show(fname)read.table(fname, header=TRUE)

(Notice the use of portable constructs to find the data directoryinside a package and the construction of the full pathname.)



Options and Details

I read.table has quite a few options and detailsI Different codings of missing values (na.strings)I Different decimal separators (dec argument)I Text strings can be quoted if embedded blanksI You may skip lines, read a limited number of lines, and

more. Please consult the manual page for details.



Data Manipulation Functions

I Single-column modificationsI Modifying and subsetting data frames



Constructors

I R deals with many kinds of objects besides data setsI Need to have ways of constructing them from the

command lineI We have seen the c and list functionsI Extracting and setting names with names(x)

I For matrices and arrays, use the (surprise) matrix andarray functions. data.frame for data frames.

I It is also fairly common to construct a matrix from itscolumns using cbind



The cut Function

I The cut function converts a numerical variable into groupsaccording to a set of break points

I Notice that the number of breaks is one more than thenumber of intervals

I Notice also that the intervals are left-open, right-closed bydefault (right=FALSE changes that)

I . . . and that the lowest endpoint is not included by default(set include.lowest=TRUE if it bothers you)



Modifying and Subsetting Data Frames

I The syntax for indexing data frames gets awkward:airquality[airquality$Month == 5 &airquality$Ozone > 50,]

I The subset function allows you to saysubset(airquality, Month == 5 & Ozone >50). I.e., it evaluates the second argument within the dataframe.

I The transform function is similar. It allows you to definenew variables or modify old ones using code likejuulnew <- transform(juul,

sex=factor(sex, labels=c("M","F")),tanner=factor(tanner))


Documents

R Basics - staff.pubhealth.ku.dkstaff.pubhealth.ku.dk/~pd/mixed-jan.2006/Basics.pdf · Using R R Language Basics Dealing with the workspace Reading Data Data Manipulation Concatenation