R Basics

Introduction to the R language
http://www.allsoftsolutions.in

Acknowledgments
Workshop materials
developed with
• Robert Gentleman, Harvard
• Wolfgang Huber, German
Cancer Research Center
• Sandrine Dudoit, UC
Berkeley
Bioconductor core
developers include
• Vince Carey, Harvard
• Yongchao Ge, Mount Sinai
School of Medicine
• Robert Gentleman, Harvard
• Jeff Gentry, Dana-Farber
Cancer Institute
• Rafael Irizarry, Johns Hopkins
• Yee Hwa (Jean) Yang, UCSF
• Jianhua (John) Zhang, Dana-
Farber Cancer Institute
• Sandrine Dudoit, UC
Berkeley

Websites
• Bioconductor www.bioconductor.org
– software, data, and documentation;
– training materials from short courses;
www.bioconductor.org/workshops/UCSC03/ucsc03.html
– mailing list.
• R www.r-project.org
– software;
– documentation;
– RNews.

R language: Overview
• Open source and open development.
• Design and deployment of portable, extensible,
and scalable software.
• Interoperability with other languages: C, XML.
• Variety of statistical and numerical methods.
• High quality visualization and graphics tools.
• Effective, extensible user interface.
• Innovative tools for producing documentation
and training materials: vignettes.
• Supports the creation, testing, and distribution of
software and data modules: packages.http://www.allsoftsolutions.in

Object Oriented Programming (OOP)
Class
• software abstraction of a
real world object.
• reflects how we think of
objects and what
information they contain.
• defined in terms of slots.
• an object is an instance
of a class.
• defines the structure,
inheritance, and
initialization of objects.
Method
• function that performs an
action on data (objects).
• defines how a particular
function should behave
depending on the class of
its arguments.
• allows computations to be
adapted to particular
classes.
• A generic function is a
dispatcher.
J. M. Chambers (1998). Programming with Data.

R user interface
• Batch or command line processing
bash$ R to start
R> q() to quit
• Graphics windows
> X11()
> postscript()
> dev.off()
• File path is relative to working directory
> getwd()
> setwd()
• Load a package library with library()
• GUIs, tcltk

Getting Help
o Details about a specific command whose name
you know (input arguments, options, algorithm):
> ? t.test
> help(t.test)
o See an example of usage:
> demo(graphics)
> example(mean)
mean> x <- c(0:10, 50)
mean> xm <- mean(x)
mean> c(xm, mean(x, trim = 0.1))
[1] 8.75 5.50

Getting Help
o HTML search engine lets you search for topics
with regular expressions:
> help.search
o Find commands containing a regular expression
or object name:
> apropos("var")
[1] "var.na" ".__M__varLabels:Biobase"
[3] "varLabels" "var.test"
[5] "varimax" "all.vars"
[7] "var" "variable.names"
[9] "variable.names.default" "variable.names.lm"

Getting Help
o Vignettes contain text and executable code:
> library(tkWidgets)
> vExplorer()
> openVignette()
Created using the Sweave() function.
.Rnw files produce a PDF file and a vignette.
o To see code for a function, type the name
with no parentheses or arguments:
> plot

R as a Calculator
> log2(32)
[1] 5
> print(sqrt(2))
[1] 1.414214
> pi
[1] 3.141593
> seq(0, 5, length=6)
[1] 0 1 2 3 4 5
> 1+1:10
[1] 2 3 4 5 6 7 8 9 10 11

R as a Graphics Tool
> plot(sin(seq(0, 2*pi, length=100)))
0 20 40 60 80 100
-1.0-0.50.00.51.0
Index
sin(seq(0,2*pi,length=100))

> a <- 49
> sqrt(a)
[1] 7
> b <- "The dog ate my homework"
> sub("dog","cat",b)
[1] "The cat ate my homework"
> c <- (1+1==3)
> c
[1] FALSE
> as.character(b)
[1] "FALSE"
numeric
character
string
logical
Variables

Missing Values
Variables of each data type (numeric, character, logical)
can also take the value NA: not available.
o NA is not the same as 0
o NA is not the same as “”
o NA is not the same as FALSE
o NA is not the same as NULL
Operations that involve NA may or may not produce NA:
> NA==1
[1] NA
> 1+NA
[1] NA
> max(c(NA, 4, 7))
[1] NA
> max(c(NA, 4, 7), na.rm=T)
[1] 7
> NA | TRUE
[1] TRUE
> NA & TRUE
[1] NA

Vectors
vector: an ordered collection of data of the
same type
> a <- c(1,2,3)
> a*2
[1] 2 4 6
Example: the mean spot intensities of all 15488
spots on a microarray is a numeric vector
In R, a single number is the special case of a
vector with 1 element.
Other vector types: character strings, logical

Matrices and Arrays
matrix: rectangular table of data of the same
type
Example: the expression values for 10000
genes for 30 tissue biopsies is a numeric
matrix with 10000 rows and 30 columns.
array: 3-,4-,..dimensional matrix
Example: the red and green foreground and
background values for 20000 spots on 120
arrays is a 4 x 20000 x 120 (3D) array.

Lists
list: ordered collection of data of arbitrary types.
Example:
> doe <- list(name="john",age=28,married=F)
> doe$name
[1] "john“
> doe$age
[1] 28
> doe[[3]]
[1] FALSE
Typically, vector elements are accessed by their
index (an integer) and list elements by $name (a
character string). But both types support both
access methods. Slots are accessed by @name.http://www.allsoftsolutions.in

Data Frames
data frame: rectangular table with rows and columns; data
within each column has the same type (e.g. number, text,
logical), but different columns may have different types.
Represents the typical data table that researchers come up
with – like a spreadsheet.
Example:
> a <-
data.frame(localization,tumorsize,progress,row
.names=patients)
> a
localization tumorsize progress
XX348 proximal 6.3 FALSE
XX234 distal 8.0 TRUE
XX987 proximal 10.0 FALSEhttp://www.allsoftsolutions.in

What type is my data?
class Class from which object inherits
(vector, matrix, function, logical, list, … )
mode Numeric, character, logical, …
storage.mode
typeof
Mode used by R to store object
(double, integer, character, logical, …)
is.function Logical (TRUE if function)
is.na Logical (TRUE if missing)
names Names associated with object
dimnames Names for each dim of array
slotNames Names of slots of BioC objects
attributes Names, class, etc.http://www.allsoftsolutions.in

Subsetting
Individual elements of a vector, matrix, array or data frame are
accessed with “[ ]” by specifying their index, or their name
> a
XX348 proximal 6.3 0
XX234 distal 8.0 1
> a[3, 2]
[1] 10
> a["XX987", "tumorsize"]
[1] 10
> a["XX987",]
XX987 proximal 10 0

>a
XX234 distal 8.0 1
> a[c(1,3),]
> a[-c(1,2),]
> a[c(T,F,T),]
> a$localization
[1] "proximal" "distal" "proximal"
> a$localization=="proximal"
[1] TRUE FALSE TRUE
> a[ a$localization=="proximal", ]
subset rows by a
vector of indices
subset rows by a
logical vector
subset columns
comparison resulting
in logical vector
subset the
selected rows
Example:

Functions and Operators
Functions do things with data
“Input”: function arguments (0,1,2,…)
“Output”: function result (exactly one)
Example:
add <- function(a,b) {
result <- a+b
return(result)
}
Operators: Short-cut writing for frequently
used functions of one or two arguments.

Frequently used operators
<- Assign
+ Sum
- Difference
* Multiplication
/ Division
^ Exponent
%% Mod
%*% Dot product
%/% Integer division
%in% Subset
| Or
& And
< Less
> Greater
<= Less or =
>= Greater or =
! Not
!= Not equal
== Is equal

Frequently used functions
c Concatenate
cbind,
rbind
Concatenate
vectors
min Minimum
max Maximum
length # values
dim # rows, cols
floor Max integer in
which TRUE indices
table Counts
summary Generic stats
Sort,
order,
rank
Sort, order,
rank a vector
print Show value
cat Print as char
paste c() as char
round Round
apply Repeat over
rows, cols

Statistical functions
rnorm, dnorm,
pnorm, qnorm
Normal distribution random sample,
density, cdf and quantiles
lm, glm, anova Model fitting
loess, lowess Smooth curve fitting
sample Resampling (bootstrap, permutation)
.Random.seed Random number generation
mean, median Location statistics
var, cor, cov,
mad, range
Scale statistics
svd, qr, chol,
eigen
Linear algebra

Graphical functions
plot Generic plot eg: scatter
points Add points
lines, abline Add lines
text, mtext Add text
legend Add a legend
axis Add axes
box Add box around all axes
par Plotting parameters (lots!)
colors, palette Use colors

Branching
if (logical expression) {
statements
}
else {
alternative statements
}
else branch is optional
{ } are optional with one statement
ifelse (logical expression, yes
statement, no statement)

Loops
When the same or similar tasks need to be
performed multiple times; for all elements of a
list; for all columns of an array; etc.
for(i in 1:10) {
print(i*i)
}
i<-1
while(i<=10) {
print(i*i)
i<-i+sqrt(i)
}
Also: repeat, break, nexthttp://www.allsoftsolutions.in

Regular Expressions
Tools for text matching and replacement which are available in similar
forms in many programming languages (Perl, Unix shells, Java)
> a <- c("CENP-F","Ly-9", "MLN50", "ZNF191", "CLH-17")
> grep("L", a)
[1] 2 3 5
> grep("L", a, value=T)
[1] "Ly-9" "MLN50" "CLH-17"
> grep("^L", a, value=T)
[1] "Ly-9"
> grep("[0-9]", a, value=T)
[1] "Ly-9" "MLN50" "ZNF191" "CLH-17"
> gsub("[0-9]", "X", a)
[1] "CENP-F" "Ly-X" "MLNXX" "ZNFXXX" "CLH-XX"http://www.allsoftsolutions.in

Storing Data
Every R object can be stored into and restored
from a file with the commands
“save” and “load”.
This uses the XDR (external data
representation) standard of Sun Microsystems
and others, and is portable between MS-
Windows, Unix, Mac.
> save(x, file=“x.Rdata”)
> load(“x.Rdata”)

Importing and Exporting Data
There are many ways to get data in and out.
Most programs (e.g. Excel), as well as humans,
know how to deal with rectangular tables in the
form of tab-delimited text files.
> x <- read.delim(“filename.txt”)
Also: read.table, read.csv, scan
> write.table(x, file=“x.txt”, sep=“t”)
Also: write.matrix, write

Importing Data: caveats
Type conversions: by default, the read functions try to
guess and auto convert the data types of the different
columns (e.g. number, factor, character). There are
options as.is and colClasses to control this.
Special characters: the delimiter character (space,
comma, tabulator) and the end-of-line character cannot
be part of a data field. To circumvent this, text may be
“quoted”. However, if this option is used (the default),
then the quote characters themselves cannot be part of
a data field. Except if they themselves are within
quotes…
Understand the conventions your input files use and set
the quote options accordingly.

Bioconductor
• R software project for the analysis and
comprehension of biomedical and genomic data.
– Gene expression arrays (cDNA, Affymetrix)
– Pathway graphs
– Genome sequence data
• Started in 2001 by Robert Gentleman, Dana
Farber Cancer Institute.
• About 25 core developers, at various institutions
in the US and Europe.
• Tools for integrating biological metadata from
the web (annotation, literature) in the analysis of
experimental metadata.
• End-user and developer packages.

Example R/BioC Packages
methods Class/method tools
tools
tkWidgets
Sweave(),gui tools
marrayTools,
marrayPlots
Spotted cDNA array analysis
affy Affymetrix array analysis
annotate Link microarray data to metadata
on the web
mva, cluster,
clust, class
Clustering and classification
t.test, prop.test,
wilcox.test
Statistical tests

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