1. Introduction to the R language
Ahmed Rebai
Bioinformatics and Comparative Genome Analysis

2. Websites R www.r-project.org software; documentation; RNews.
Bioconductor
software, data, and documentation;
training materials from short courses;
mailing list.

3. 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.

4. R user interface Batch or command line processing Graphics windows
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

5. Getting Help
Details about a specific command whose name you know (input arguments, options, algorithm):
> ? t.test
> help(t.test)
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]

6. Getting Help
HTML search engine lets you search for topics with regular expressions:
> help.search
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"

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

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

9. R as a Graphics Tool
> plot(sin(seq(0, 2*pi, length=100)))

10. Variables > a <- 49 numeric > 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

11. 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
> max(c(NA, 4, 7))
> max(c(NA, 4, 7), na.rm=T)
[1] 7
> NA | TRUE
[1] TRUE
> NA & TRUE
[1] NA

12. 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 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

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

14. 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

15. 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
XX proximal FALSE
XX distal TRUE
XX proximal FALSE

16. What type is my data? 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.

17. Subsetting
Individual elements of a vector, matrix, array or data frame are accessed with “[ ]” by specifying their index, or their name
> a
localization tumorsize progress
XX proximal
XX distal
XX proximal
> a[3, 2]
[1] 10
> a["XX987", "tumorsize"]
> a["XX987",]
XX proximal

18. Example: subset rows by a vector of indices
localization tumorsize progress
XX proximal
XX distal
XX proximal
> 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

19. 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.

20. 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

21. Frequently used functions
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

22. 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

23. 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

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

25. 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) {
i<-i+sqrt(i)
Also: repeat, break, next

26. 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"

27. 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”)

28. 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

29. 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.

30. 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.

31. 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