r Programming Life Sciences Aug 2009

8/10/2019 r Programming Life Sciences Aug 2009

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R Programming for Life Scientists

Version 2.0Raymond R. Balise, Ph.D.

Health Research and PolicySpectrum


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Roadmap

What makes R different for the rest? Setting up R

Types of data Working with collections of data Importing and exporting data

Writing functions Graphics


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When to Use R

Shoestring budget Cutting edge statistics

Developing your own or fine-tuning existingmethods Local expertise


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Programming Languages

Procedural languages C, Fortran, Cobol, Basic use a model where the logic flows from the top of

the page to the bottom with calls to gotosubroutines as needed

It is hard to encapsulate the code.

Object oriented languages C++, Visual Basic, JAVA involves creating objects and then operating on them


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R is Object Oriented (OO)

You create objects vector of numbers, a graphic, etc.

You call methods/functions to operate on the

objects. Working with an OO language requires you to

learn about special methods to create, access,modify, or destroy objects and their properties. R hides these processes. It helps a lot if you want to write new statistics and

methods and is required for making new packages.


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OO Example

With R you write code in the editor which I willshow you in a minute.

You can create an object which holds a bunch of

numbers (a vector, if you remember math) You can then use (aka call ) a function (aka

method ) to operate on the object. The summary() function

Create and display a numeric summary object

The plot() function Create and display a graphic summary object


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Make theages object

Call thesummaryfunction.

Call theplot

function.


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But wait theres more!

There is a lot of functionality built into R. Itships with libraries that do many differenttasks. And you can download more.

Map most of theUSA.

Activate the mapdatasets and

functions.


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But hold on. There is MORE! You can add options to the function calls to

make them do fancy things like color. Or you can have one function act on the

output of another function.

And you can save output as objects!


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Important Objects

Vectors are lists of numbers. Dataframes are like database or spreadsheets.


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Where to Get R

R has two main websites. One describes the project:http://www.r-project.org/

The other has most of the stuff you want to

download:http://cran.r-project.org/

Because the R project has people working all overthe globe, the software download site is mirrored

everywhere. The closest mirror is USA CA1 (aka UCBerkeley).
http://www.r-project.org/http://cran.r-project.org/http://cran.r-project.org/http://cran.r-project.org/http://cran.r-project.org/http://www.r-project.org/http://www.r-project.org/http://www.r-project.org/


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http://cran.cnr.berkeley.edu/

There is an R installer for all the commonoperating systems:

cran.cnr.berkeley.edu/bin/windows/base/ cran.cnr.berkeley.edu/bin/macosx/ cran.cnr.berkeley.edu/bin/linux/

Each is basically self explanatory.
http://cran.cnr.berkeley.edu/bin/windows/base/http://cran.cnr.berkeley.edu/bin/macosx/http://cran.cnr.berkeley.edu/bin/linux/http://cran.cnr.berkeley.edu/bin/linux/http://cran.cnr.berkeley.edu/bin/linux/http://cran.cnr.berkeley.edu/bin/linux/http://cran.cnr.berkeley.edu/bin/macosx/http://cran.cnr.berkeley.edu/bin/macosx/http://cran.cnr.berkeley.edu/bin/macosx/http://cran.cnr.berkeley.edu/bin/windows/base/


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Installing on Windows

Double click the installer and just push nextuntil you get to this screen.

Specify that youwant to docustomized startup.

This will let you setup R to work withother programsnicely.


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Customize

Use these options, then hit Next> a bunch.


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help.start() and push enter to start the help. q() and push enter to quit but dont yet.


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GUIUse the built in

editor.

Save or restore allthe objects in use.

Save or reload thecode from theconsole.

Keep all the text inthe console for the

session.

Set the working

directory to saveobjects.


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GUIEdit existing data.

Tweak theappearance of the

console.


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Rprofile.site

If you have instructions that you always wantrun when R starts up, you can include them inthe Rprofile.site file:


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GUI

Commoncommands.

Show the add onpackages currently

accessible.


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Packages in R

User-supplied packages are typically found atone of three places: CRAN for all kinds of stuff

Omegahat for web-based statistics Bioconductor for genomic analysis

R packages update often.

Your colleagues will recommend task-specificpackages. Rcmdr is my favorite.


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GUIUse a previously

downloaded package.I type library(name)

instead.USA (CA1) is closest

to Stanford.

Choose which set ofpackages to look at.

See the HUGE list ofpackages.

Update often!


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GUI

This is useful.


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HTMLhelp

This is usefulbut not

Google.

This will not findinformation if youhave not installedthe packages.


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Rseek.org is Google-driven

I highly recommend it.


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Mac Quick HelpSearch help for the

word "map".Search for details

on a function if thepackage is loadedand you know thefunctions name.

h h l f h


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Windows Quick HelpSearch help for the

word "map".

Search help for thefunction named"map".

Load the package.


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Mac Install

Download and double click the dmg file.

Click customize andmake sure Tcl/Tk is

checked on.


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X11

Some packages for R on the Mac (like Rcmdr)require X11 to be installed. I think it is part of the standard Leopard

installation but was an option with Tiger. If youneed it, try to install it off of the DVD that camewith your machine because people have reportedusing the dmg files from Apple.com.


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X11 and Add-on Packages To get add onpackages, use thismenu.

You can click hereto make sure X11

works.


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Getting or Updating Packages

ClickGet List , click the package name, be sureinstall dependencies is checked on, then clickinstall .


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Instead of Point and Click

You can also run this code to have Mac orWindows R download a list of packages:

usefulPackages = c("car", "foreign", "hexbin", "gdata","ggplot2", "gmodels", "gplots", "Hmisc", "reshape","Rcmdr")

install.packages(usefulPackages, dependencies = TRUE)

Be sure to take note of any packages that do not install.

marray , affy, Biobase , Rgraphviz were not available


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I suggest you install the Rcmdr package firstthing. Use the Install packages option on the package

menu to download Rcmdr To make it available for your R session type:

library(Rcmdr) CAPITALIZATION MATTERS! The first time you run it, it will ask you if it can

download additional packages.

Your First Package


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If you are on Windows you

can directly import Excel.

On a Mac you cannot directly import

from Excel.


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Hate Typing?

Tab is your fiend. It will auto-complete if itcan or give you a list of functions that matchwhat you have typed. It woks very well on theMac. In Windows sometimes you need to typetab twice.

In Windows if you type tab after a ( it displaysoptions for the function or they just appear inthe Mac.


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Data Set Objects

Vectors A bunch of data in a single row or column All of the same type

Matrix

A row and column arrangement of data All of the same type Data frame

A row and column arrangement of data Columns are of different types

List Very free-form structure A grouping of different types of data

Like a good spreadsheetor relational database file


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Types of Data Vectors

Numeric Integer, real, and complex are different types but

you will not need to pay attention to the details

NA means missing NAN means not a number

String Characters of the alphabet

Logical TRUE, FALSE or NA


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Making Vectors With c()

c stands for concatenateages = c(9, 11, 40, 41) ; agesstooges = c("Larry", "Moe", "Curly", "Shemp"); stooges


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Getting Details

You can use is functions and length to getdetails on a vector.is.vector(ages)

is.numeric(ages)is.logical(ages)length(ages)


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You can add one to all four ages.ages + c(1,1,1,1)

If you provide the scalar integer, R willtemporarily vectorize the 1 by recycling thatvalue to match the length of the ages vector.ages + 1

It will recycle a series also.agesages + c(1,2)

Recycling and Vectorizing


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Naming Parts of a Vector

You can assign names to the elements of a vector.This allows later access to the elements using thenames instead of the position.names(ages) = stoogesages

To erase them:names(ages) = NULL; ages

Notice what happens when the lengths differ:stooges= c("Larry", "Moe", "Curly")names(ages) = stoogesages


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Attributes

When you add names to things (objects) theyacquire or change their names attribute. attributes(ages)

When you strip off the names, the vector isleft with no attributes.names(ages) = NULLattributes(ages)


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A data frame is an object with manyattributes.

R ships with a lot of datasets if you want one help.start()

Click packages then datasets.esoph?esophattributes(esoph)

Complex Objects


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Getting at Parts of a Vector

Specify the element number.heyMoe = ages[2] ; heyMoe

Specify to drop everything except the elementnumber.ages[c(-1, -3, -4)]

Specify a list with TRUE and FALSEages[c(FALSE, TRUE, FALSE, FALSE)]


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Getting Parts with Names

ages = c(9, 11, 40, 41) ; agesnames(ages) = c("Larry", "Moe", "Curly", "Shemp")ages

Specify the name.heyMoe = ages["Moe"]


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Duplicate Names

That code only returns the first one if thereare duplicates.names(ages)[4] = "Moe"

agesheyMoe = ages["Moe"]heyMoe

Gives all if duplicates names(ages) %in% "Moe"ages[names(ages) %in% "Moe"]


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Parts of a Data Frame

You can select columns of a data frame justlike you selected elements from a vector.booze = esoph["alcgp"]

is.data.frame(booze)esoph[2]esoph[c(4,5)]


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Choosing Records

If you put a single item or series inside of thesquare brackets, R thinks you are requestingcolumns.

If you want to get access to specific rows, youinclude a comma after the rows. blah[rows, columns]

esoph[ 1 , ]esoph[ 1:3 , ]


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Smarter Access to a Vector

You can use logic checks to find the recordnumbers in a vector which meet your criteria.ages < 21

which(ages < 21)

You can then subset down your data to therecords of interest using the [ ] subset

operator.ages[which(ages < 21)]ages[ages < 21]


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Subset a Data Frame

Recall that you can select rows withframeName[rows,columns] and if you do notinclude a comma, all records are chosen.

which(esoph$ncases > 0) gives you a list ofrecords which adhere to that rule. Therefore,the code below gives you a subset

esoph[ which(esoph$ncases > 0) , ]oresoph[esoph$ncases > 0 , ]


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Choosing Values

If you need specific values, you can use the & (and) or the | (or) operators to get theordered set of TRUE and FALSE values.ages > 21 & ages < 41

! means not!(ages > 21 & ages < 41)

Notice that it is applying the one logic checkto the vector of ages. How does it do that?


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Math on Data Frame Columns

You have seen how to do scalar and vectoralgebra. Algebra on a data frame is easy.names(esoph)esoph$total=esoph$ncases + esoph$ncontrols

To see the end of the data frame, use tail()tail(esoph)


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Comparing Against Vectors

This one uses

recycling andgives wronganswers.

What happens when you try to compare a vectorto a set of things?gender = c(NA, "Male", "Female", "Blue", "Female")gender == "Male" | gender == "Female"gender == c("Male", "Female")

R recycles the shorter vector to be the longerlength, then does the comparison. Use the %in%

operator if you want to compare as if you wrote aseries of or statements.gender %in% c("Male", "Female")


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Categorical Variables

R makes a distinction between variables holdinga bunch of characters from the alphabet andvariables holding categorical information. If

you have a classification/categorical variable,you want R to treat it as a factor or an orderedfactor. Typical factors are treatment or gender.dose = c("low", "placebo", "high", "low")

dosetypeof(dose)


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Factors

To convert a character variable to a factor, use theas.factor function.doseF = as.factor(dose)

typeof(doseF)class(doseF)

Behind the scenes, the character variable isconverted into numbers and the numbers are

given character strings to display. In modern R the levels of the factor are ordered

alphabetically and the first one is representedwith the digit 1, the second is 2, etc.

There are is. or as. predicate functionsto check object types or convert

between types of objects.


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Comparing Factors

Notice wrong answer thanks to recycling.

You can compare a factor vs. a constant value.doseF == "high"as.integer(doseF) == 1

Or you can compare vs. vectors (CAREFULLY).doseF == c("high", "low")doseF %in% c("high", "low")

R will stop you from comparing factors that havedifferent categories.doseF2 = as.factor(c("blah", "placebo", "high", "low"))doseF == doseF2


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Recoding Factors

Often you will want to regroup factor levels.amount=as.factor(c("placebo", "10mg", "5mg", "10mg"))levels(amount)

regroup = list(none="placebo", some=c("5mg", "10mg"))levels(amount) = regroupamount

noneplacebo

some5mg

10mg


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Numeric Factors

If you have numeric factors, be carefulconverting from factors back to numbers.ID = c(1000, 1000, 1001, 2)

IDf = factor(ID)as.integer(IDf)levels(IDf)

numbersAgain = as.numeric(levels(IDf))[IDf]


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Easier Recoding

Other packages like car have functions to recode:library(car)newAge2=recode(ages, ' 1:21="Young"; else= "Old" ')

newAge2detach("package:car")


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Attaching Data Frames

People who really hate typing attach dataframes so they can refer to them with shortnames.bmi = women$weight / women$height ^2 * 703

Instead you can attach the women data frameand an easier formula write:

attach(women)search()bmi = weight / height ^2 * 703; bmi

detach(women)


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Keeping Track

You can see what datasets are in each of thework environments/packages with the liststuff function ls().rm(list=ls(all=TRUE))ls()search()

attach(women)ls()search()

head(women); head(height)


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datasets

women

women

heightweight

.GlobalEnv

Look 1 st Look 2 nd Look 3 rd


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Adding a Variable & Making a DF

women$bmi = weight / height ^2 * 703; bmihead(women)ls()

datasets

women

women

heightweight

.GlobalEnv

women

Look 1 st Look 2 nd Look 3 rd

The data frame

with bmiand the data

frame withoutbmi


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Making a Data Frame

Frequently you will want to make data frames foranalysis with Rcmdr. Use the data.frame()command:

attach(sleep)pair = data.frame(extra[group=="A"], extra[group=="B"])


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Using Rcmdr for a paired t-testClick here.


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Loading Text Data into R

Reading text files:fakeAlleles=read.table("c:\\blah\\fakeAlleles.txt",

header=TRUE) See if it worked:

fakeAllelesnames(fakeAlleles)summary(fakeAlleles)fakeAlleles$dude = as.character(fakeAlleles$dude)

fakeAlleles A better option:

fakeAlleles = read.table("c:\\blah\\fakeAlleles.txt", header =TRUE, colClasses = c("character", "factor","factor"))


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Other Text Formats

Other text reading methods:read.csv = coma separated values read.csv2 = semicolon delimited files read.delim = read tab delimited files

read.fwf = read fixed width format files Use same options as read.table If the data has bad or no column headings you may

also want to include:read.table ( stuff, col.names = c("name1", "name2") )

To prevent characters from coming in as factors:options(stringsAsFactors = FALSE)


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Data Frames

The data imported into a data frame.class(fakeAlleles)

A data frame really is a list of vectors where thevectors are all the same length.as.list(fakeAlleles)

To select a column you specify the data frame $ variable name.theDudes = fakeAlleles$dude

All the stuff you saw for logic checks on vectorscan be used on the parts of a data frame.fakeAlleles$allele1 == "A"


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Subsetting Vectors (again)

Recall that you can subset using the [ ] operator:ages = c(9, 11, 40, 41)

heyMoe = ages[2]ages


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Subsetting Data Frames

Parts (subsets) of data frames are referencedby "column numbers comma row numbers": The first record: fakeAlleles[1, ]

The 2 nd and 3 rd columns: fakeAlleles[ , c(2,3)] The genotype for record 6: fakeAlleles[6, c(2,3)]

or by names:fakeAlleles[, c("allele1", "allele2")]


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Getting Counts with Rcmdr


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Subsetting Using Logic

You can use logic checks to subset:fakeAlleles$allele1 == "A" & fakeAlleles$allele2 =="A"fakeAlleles[ fakeAlleles$allele1 == "A" &

fakeAlleles$allele2 =="A", ]


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Importing From Excel

If you have PERL on your machine, you canuse the read.xls() function in the gdata libraryto easily get data out of Excel and into a data

frame. Mac has PERL Windows

http://www.activestate.com/activeperl/

d l
http://www.activestate.com/activeperl/http://www.activestate.com/activeperl/


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Using read.xls

Windows:library(gdata)sleepy = read.xls("c:\\blah\\sleep.xls")

Mac:library(gdata)read.xls("/users/balise/desktop/sleep.xls")

Its that easy Behind the scenes it is convertingthe xls file into a csv so you can use the textimporting options.

Do summary() on the data frame and notice whathappens to the missing value.


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RODBC

ODBC is a language/convention for accessingdatabases. R allows you to use ODBCconnections to burrow directly into databases

and other data containers like Excel.library(RODBC)channel


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SQL

If you have to learn one programminglanguage, learn SQL. With it you can manipulate data stored in nearly

every commercial database. You can aggregate, subset and modify data. It is well implemented inside of both R and SAS.

SQL with R is nicely documented in Spector's(2008) Data Manipulation with R . It is a mustown for people who want to learn R.

E i T Fil
http://lmldb.stanford.edu/cgi-bin/Pwebrecon.cgi?DB=local&Search_Arg=0359+L254617&Search_Code=CMD*&CNT=10&v1=1http://lmldb.stanford.edu/cgi-bin/Pwebrecon.cgi?DB=local&Search_Arg=0359+L254617&Search_Code=CMD*&CNT=10&v1=1


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Exporting Text Files

R can write objects full of data, including dataframes, into text files. By default, it will quote the character string and fill

in the letters NA where there were originallymissing values.

This code exports back to the original

appearance.write.table(sleepy, file = "c:\\blah\\exported.tab",sep ="\t", quote = FALSE, na ="")


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Office 2007 Excel

ODBC connection1. Control Pannels,2. Double click Adminstrative Tools3. Double click Data Sources (ODBC)

4. On the USER DNS tab choose ADD5. Click Microsoft Excel Driver (*.xls, *.xlsx, *.xlsm, *.xlsb)6. Give the connection a name and browse to the file.

Jot down the name of the connection for the R code.

U i ODBC i


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Using an ODBC connection

Once the ODBC connection is set-up use code like this:library(RODBC)connection = odbcConnect("sleepODBC")dataFromODBC= sqlFetch(connection, "Sheet1")odbcClose(connection)

C i P


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Creating Programs

You can write line-by-line instructions in the Rconsole, use the editors built into R, or use a thirdparty editor (like Tinn-R for windows or JGR).

Console Type history() to see the lines you have submitted

recently and then save to a file and re-run it later ifneeded.

Built-in Editor

Mac: Click the blank page at top of the console Windows: File > New Script

Wi d Ti R Edi


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Windows Tinn-R Editor

http://www.sciviews.org/Tinn-R/index.html

OO P i i R
http://www.sciviews.org/Tinn-R/index.htmlhttp://www.sciviews.org/Tinn-R/index.htmlhttp://www.sciviews.org/Tinn-R/index.htmlhttp://www.sciviews.org/Tinn-R/index.html


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OO Programming in R

OO programming requires objects classes

describe specific properties for groups of objects inheritance

classes related to eachother (derived from other classes) have relatedproperties

polymorphism the same function name applied to different classes does different things

R vs. JAVA: R typically has separate classes for actions instead ofbundling them with the data structures

JAVA Animal -> domesticated -> dog (walks) R

Animal -> domesticated -> dog Movement -> Walks

P l hi i F


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Polymorphism is Fun

plot does different things depending on thefunction arguments:plot(sleepy)

plot(sleepy$extra)plot(sleepy$baseline, sleepy$extra, sleepy$group)

Take a look at how it works:isS4(plot)methods(plot)getAnywhere(plot.factor)


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W iti g F ti


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Writing Functions

You can easily write functions, but notice that the lastthing calculated is returned:MandM = function(x){mean(x); median(x)}MandM(sleepy$extra) # returns only the median

Store the values you want into a list:MandM = function(x) {blah = list(theMean=0, theMedian=0)blah$theMean = mean(x)blah$theMedian = median(x)return (blah)

}MandM(sleepy$extra)

Oth A g t


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Other Arguments

MandM(sleepy$baseline) It points out that we need to deal with missing

values. Look up mean and median and you will

see they allow the na.rm parameter to determineif missing values are dropped. Using MandM(sleepy$baseline, na.rm=TRUE)

does not work because the parameter list doesnot allow it. We want to allow that parameter tobe passed along. So rewrite the function.

M and M Again


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M and M Again

Recall that an in the argument list means"other stuff".MandM = function(x, ...) {

blah = list(theMean=0, theMedian=0)blah$theMean = mean(x , ...)blah$theMedian = median(x, ...)return (blah)

}MandM(sleepy$extra)MandM(sleepy$baseline, na.rm=TRUE)

Appling Your Function


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Appling Your Function

R does allow you to write loops to iterate overrecords or variables but if you are not writingnovel math functions, they can generally be

avoided. R will try to vectorize and process:

MandM(c(sleepy$baseline,sleepy$extra))

Use sapply to apply a function to a data frame:sapply(sleepy, MandM, rm.na=TRUE)

Better M and M


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Better M and M

MandM = function(x, ...) {blah = list(theMean=0, theMedian=0)if(is.numeric(x)== TRUE) {

blah$theMean = mean(x , ...)blah$theMedian = median(x, ...)}return (blah)

}sapply(sleepy, MandM, na.rm=TRUE)

Yummy M and M


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Yummy M and M

MandM = function(x, ...) {blah = list(theMean=NaN, theMedian=NaN)if(is.numeric(x)== TRUE) {blah$theMean = mean(x , ...)blah$theMedian = median(x, ...)}return (blah)

}MandM(sleepy$extra)MandM(sleepy$baseline, na.rm=TRUE)

sapply(sleepy, MandM, na.rm=TRUE)

Writing Novel Functions


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Writing Novel Functions

Look hard on rseek.org before you reinventthe wheel.

R syntax is very similar to C.

Select/Case logic is different (R short-circuits) . The R Book by Crawley is too big to buy for

just this topic but it is good for syntax. Get itfrom the library and read the early chapters.

The final chapter of Spector has a fewwonderful pages.


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Destroying Efficiency


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Destroying Efficiency

A matrix of data is really a vector with row andcolumn attributes added to it. This has profoundspeed issues if you add to the size of a matrix

because the data has to be shifted all over theplace. If you plan on writing your own functions to

manipulate matrices, build an empty matrix ofthe maximum size (or guess bigger) rather thanusing the functions to add rows or columns.

Writing Efficient Code


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Writing Efficient Code

R has decent tools for profiling code. The Rprof and summaryRprof functions will

help you figure out what is bogging down your

code.Rprof()MandM(rnorm(1000000))

Rprof(NULL)summaryRprof()


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Debugging in R


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Debugging in R

See Chapter 9 in Gentleman's book. The browser() function can be put inside a function to

pause execution and see what is going on. The codetools package is great for tweaking big

functions:findLocals(), findGlobals(),

shows you if variables and functions originate inside of afunction

checkUsage(), and checkUsagePackage() shows you what variables are modified or not touched in a

function

Creating Graphs


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Creating Graphs

Basic plots are easy but tweaking them forpublications can be rough because thedocumentation on the function arguments is

appalling. Data Analysis and Graphics Using R by John

Maindonald and John Braun is extremely useful. There are myriad graphics built into the core of R

plus more in the packages.addictedtor.free.fr/graphiques/thumbs.php

Test Scores
http://addictedtor.free.fr/graphiques/thumbs.phphttp://addictedtor.free.fr/graphiques/thumbs.phphttp://addictedtor.free.fr/graphiques/thumbs.phphttp://addictedtor.free.fr/graphiques/thumbs.php


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Test Scores

scores = read.table("c:\\blah\\walkerScores.txt", header = TRUE)rapply(scores, class)scores$CENTER = as.factor(scores$CENTER)scores$PAT = as.character(scores$PAT)

rapply(scores, class)scores$isSick = ifelse(scores$SCORE > 0, 1, 0);library(car)(scores$SEV = with(scores, recode(SCORE, '0 = "None" ;1:30 =

"Mild"; 31:69 = "Moderate"; 70:100 = "Severe"; else = "BADDATA"')))

(scores$SEV = factor(scores$SEV, levels = c("None", "Mild","Moderate", "Severe"), ordered = TRUE));

Common Plots are Easy


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Common Plots are Easy

attach(scores) #to avoid typing scores$plot(SEV, main = "MainTitle", xlab = "xlab", ylab =

"ylab")

plot(SCORE)hist (SCORE)boxplot(SCORE)boxplot(SCORE ~ SEX, ylim = c(0,100))detach(scores)

Graphics Tweaks


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Graphics Tweaks

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0.0 0.2 0.4 0.6 0.8 1.0

- 2

- 1

0

1

2

Quantiles = qnorm

p

Q u a n

t i l e ( Z )

Random numbers = rnorm

z

f r e q u e n c y

-4 -2 0 2

0

5 0

1 0 0

1 5 0

2 0 0

mfrow is used to setnumber of rows and

columns of graphics ona page

Strip Charts for Small Datasets


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Strip Charts for Small Datasets

par(cex = 1.5) # big font with(Gad, stripchart(HAMA ~ DOSEGRP, xlab =

"HAMA", pch = 16))

20 25 30 35

H I

L O

P B

HAMA

3 Languages for the Price of 1


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3 Languages for the Price of 1

The graphics I have shown use the classicgraphic methods.

There are trellis plots from the lattice package

that split the data into multiple panesautomatically.

ggplot2 uses a "grammar of graphics"

approach (like SPSS).

Dont play with pie!


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Don t play with pie!

library(lattice) trellis.par.set(list(fontsize=list(points=20))) trellis.par.set(list(fontsize=list(text=25))) dotplot(table(Gad$DOSEGRP), xlim = c(-1, 21))

Freq

HI

LO

PB

0 5 10 15 20

HI

LO

PB

DOSEGRP

The lattice package makestrellis graphics (I didnt makeup these names!).

EE

8 1216

EE EE

8 1216

EE EE

8 1216

EE EE

8 1216

EE EE


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Compression Ratio

N O x

( m i c r o g r a m s

/ J )

1

2

3

4

8 1216

EE EE

8 1216

EE EE

8 1216

EE EE

8 1216

EE EE

8 1216

EE

Typical lattice plot with

banding to showsubsets


114/120


115/120

Basic plot + geometricdetails + adding details+ adding more details +

yet more details

qplot(carat, price, data = diamonds, geom= c("point", "smooth"))

Use Rcmdr (R Commander)


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Rcmdr has A LOT of great graphics built intothe point and click interface.

library(Rcmdr)

Look up my short course (5 talks) coveringbasic statistics to see how to code manygraphics.

www.stanford.edu/~balise/HowToDoBiostatistics.htm

Use Rcmdr (R Commander)

You are Going to Need More Help
http://www.stanford.edu/~balise/HowToDoBiostatistics.htmhttp://www.stanford.edu/~balise/HowToDoBiostatistics.htm


117/120

Data Manipulation with R by Spector. A must-have book on how to read and write data with or without SQL,

manipulate data with R, aggregate data, and reshape datasets easily.

R Programming For Bioinformatics by Gentleman. A very good intermediate level book on how R object-oriented

programming really works.

The R Book or Statistical Computing by Crawley. These have nicely written intermediate level statistics. But they are highly redundant across the two books.

Redundant
http://lmldb.stanford.edu/cgi-bin/Pwebrecon.cgi?DB=local&Search_Arg=0359+L252108&Search_Code=CMD*&CNT=10&v1=1http://lmldb.stanford.edu/cgi-bin/Pwebrecon.cgi?DB=local&Search_Arg=0359+L252112&Search_Code=CMD*&CNT=10&v1=1http://lmldb.stanford.edu/cgi-bin/Pwebrecon.cgi?DB=local&Search_Arg=0359+L260334&Search_Code=CMD*&CNT=10&v1=1http://lmldb.stanford.edu/cgi-bin/Pwebrecon.cgi?DB=local&Search_Arg=0359+L254617&Search_Code=CMD*&CNT=10&v1=1


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Biostatistics


119/120

Biostatistics

John Fox, the guy who made Rcmdr, is anexcellent author and he provides an R basedsupplement for his superb statitics book.

Spectrum


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Spectrum

If you are doing biomedical research and havequestions we are here to help. Study design

Analysis plan Power and sample size calculation (Limited availability help with SAS and R code)

med.stanford.edu/spctrm/biostatistician.html
http://med.stanford.edu/spctrm/biostatistician.htmlhttp://med.stanford.edu/spctrm/biostatistician.htmlhttp://med.stanford.edu/spctrm/biostatistician.htmlhttp://med.stanford.edu/spctrm/biostatistician.htmlhttp://med.stanford.edu/spctrm/biostatistician.html

Documents

r Programming Life Sciences Aug 2009