A Tutorial on R Programming

1
A Tutorial on R Programming

• Courtesy of Ping Mas class notes 

2
Introduction

• GNU S-Plus
• A flexible programming language for statistical
  computing.
• Multitude of packages exist for computational
  biology analyses.
• BioConductor Project.
• Some Programming Gems
• Graphics
• Extensibility ports to perl, Python, Java,
  HTML, etc.
• Support active user community, especially in
  computation biology.
• Open source in design and nature.
• http//www.r-project.org
• http//cran.r-project.org

3
R Projects

• The BioConductor Project
• www.bioconductor.org
• A suite of statistical and graphical methods for
  analyzing genomic data.
• For example, software available for DNA
  microarray analysis normalization, CGH data, GO
  analysis, tiling arrays, plus some proteomics
  analyses.
• CRAN Comprehensive R Archive Network
• All areas of mathematical and statistical
  software applications.
• Finance modeling, time series, spatial modeling,
  high performance parallel computing,

4
Outline

• Data Structures
• Functionality
• Input/Output
• Workspace Management

5
Getting Started
Installation (usually) a snap download file,
unzip and run wizard Start up via icon or
inside a shell gt R
6
R Basics
gt x lt- 1 5 gt x 1 6 gt y lt- c(1,2,3,4) gt y
1 1 2 3 4 gt z lt- 14 gt z 1 1 2 3 4 gt
z1 1 1

• Note everything in R is case sensitive.
• Assignments can also made using .
• Variable names may be delimited by a .
• gt a.meaningful.name lt- 6
• Indices always begin with 1.
• Comments

7
Mathematical Operators
R as a calculator gt 2 3 1 5 gt 34/6 2(1
9) 1 22 gt AB matrix multiplication
8
Built-In R Functions
R comes with a suite a built-in mathematical and
statistical functions. gt sqrt(54) 1
7.348469 gt mean(15) 1 3 gt lm(yx) simple
linear regression For more specialized
functions, look at CRAN or BioConductor.
9
Matrices
Matrices are 2 dimensional vectors. gt A lt-
matrix(19, nrow3, ncol3, byrowT) gt A
,1 ,2 ,3 1, 1 2 3 2, 4 5
6 3, 7 8 9 gt row.names(A) lt- c(a,
b, c) gt colnames(A) lt- c(f, g, h) gt A
f g h a 1 2 3 b 4 5 6 c 7 8 9
10
Extracting and Extending Matrices
Extract information from the matrix using
indices. gt A,1 a b c 1 4 7 Extend the matrix
by adding rows or columns. gt B lt- cbind(A,
c(-10,-20,-30)) gt B f g h a 1 2 3 -10 b 4 5
6 -20 c 7 8 9 -30
gt A1, f g h 1 2 3 gt C lt- rbind(A,
c(-10,-20,-30)) gt C f g h a 1 2 3 b
4 5 6 c 7 8 9 -10 -20 -30
A matrix can only consist of the one data type
e.g. numeric, character.
11
Interrogating a Matrix Object
Useful functions are gt dim(A) 1 3 3 gt
ncol(A) 1 3 gt nrow(A) 1 3 gt length(A) 1 9
Similarly for a vector object gt length(x)
12
Operating on Matrices
A really useful function for matrices is the
apply function. This allows us to apply a
specific function to row-wise or column-wise. gt
apply(A, 1, mean) 1 2 5 8 the 1 means
row-wise, use 2 for column-wise.
13
Data Frame
A data frame is a collection of column vectors.
Gpdh Sod Xdh AvRate Myr Drosophila 1.50 25.
7 30.4 22.4 55 Fungi 40.0 24.9 13.7 21.4 300 A
nimal Phyla 13.2 19.2 19.2 17.5 600
A useful way to store table-like information. gt
molclock lt- data.frame(Gpdhc(1.50, 40, 13.2),
Sodc(25.7, 24.9, 19.2), Xdhc(30.4, 13.7, 19.2),
AvRatec(22.4, 21.4, 17.5), Myrc(55, 300,
600), row.namesc(Drosophila, Fungi,
Animal Phyla))
14
Working with Data Frame
Extracting data from a data frame object by
column, we can use indices or names gt
molclock,1 1 1.5 40.0 13.2 gt
molclock,Gpdh 1 1.5 40.0 13.2 For rows we
must use row indices. gt molclock2, Gpdh Sod
Xdh AvRate Myr Fungi 40 24.9 13.7 21.4 300 gt
class(molclock,1) 1 numeric gt
class(molclock2,) 1 data.frame
Recall a data.frame object is a collection of
column vectors.
15
List Structures

• Up until now, all our data structure objects have
  needed a uniform data type.
• List structures are powerful because we can store
  multiple data types in the same object.
• gt miscObjs lt- list("actin"c(1.3, 99.6, 2.45),
• "gapdh"matrix(rnorm(100), nrow10),
  "atp"molclock)
• We extract data from a list using names or
  indices.
• gt names(miscObjs)
• 1 "actin" "gapdh" "atp"
• gt miscObjsactin
• 1 1.30 99.60 2.45
• gt miscObjs1
• 1 1.30 99.60 2.45

16
Visualizing Data Plot Function
A simple scatter plot gt x.dat lt- rnorm(100)
100 N(0,1) rvs gt plot(x.dat, xlab"Index",
ylab"Normal RVS", main"Figure 1 Scatter
Plot")
17
Exporting Graphics

• In Windows
• right mouse click to copy to clipboard.
• For most operating systems
• gt bitmap("file.bmp")
• gt plot(x.dat) lt- insert code for making plot
  here
• gt dev.off()
• You can create export graphics to many file
  formats bitmap, jpeg, gif, postscript, etc.

18
Classes

• A class describes the way an object in R is
  stored.
• Strings Homo sapiens
• Numeric 3.141593
• Boolean TRUE, FALSE
• We can interrogate an object to find out its
  class
• gt a lt- FALSE
• gt class(a)
• 1 "logical"
• gt is.numeric(a)
• 1 FALSE
• Classes also reflect their data structure, eg.
  matrix, data.frame, function.

19
Working with Strings

• While Perl or Python are more competent languages
  for text parsing, R does have capabilities for
  manipulating and creating strings.
• Pasting Strings Together
• gt paste(c("Cat", "Dog"), sep"")
• 1 "CatDog"
• Splitting Strings
• gt strsplit("Seuss", "")
• 1
• 1 "S" "e" "u" "s" "s"
• Searching for Patterns
• gt grep("and", "Brown eggs and ham")
• 1 1 grep also lets you search with regexp
  patterns

20
Booleans Algebra

• In R, to test for equality use ""
• gt 1 3
• 1 FALSE
• gt 1 3
• 1 TRUE inequality
• Another powerful tip we can test for inclusion
  in a vector by asking with "in"
• gt x lt- 110 even.numbers lt- seq(from2, to10,
  by2)
• gt x
• 1 1 2 3 4 5 6 7 8 9 10
• gt even.numbers
• 1 2 4 6 8 10
• gt x in even.numbers
• 1 FALSE TRUE FALSE TRUE FALSE TRUE FALSE
  TRUE FALSE
• TRUE
• We can subset vectors with TRUE/FALSE flags
• gt xx in even.numbers
• 1 2 4 6 8 10

21
Missing Values

• NA is the all-inclusive symbol for a missing
  value in R.
• gt mean(c(1, 4, NA))
• 1 NA
• gt mean(c(1, 4, NA), na.rmT)
• 1 2.5
• We can test whether an object is a missing value.
  
• gt NA NA
• 1 NA this doesn't work!
• gt is.na(NA)
• 1 TRUE
• gt na.omit(c(1, 4, NA))
• 1 1 4
• Other objects NaN, Inf.

22
For Loops

• For loops are very simple in R.
• gt for( m in 13 )
• print(m)
• 1 1
• gt for( m in c("actin", "myosin", "gapdh") )
• print(m)
• 1 "actin"
• Note R does not process for loops very quickly,
  try to avoid them for large data if you can (eg.
  Use apply)

23
Conditional Statements

• We can use conditional statements to automate
  tasks and functions.
• If..Else Block
• If( condition 1 holds ) then do task 1. Else, do
  task 2.
• gt if( x gt 0 ) print("positive")
• else print("negative")
• While Block
• While( condition 1 holds) then do task 1. If
  condition 1 no longer holds, stop.
• gt while( x gt 0 ) x lt- x rnorm(1)
• You can put the break command inside an if( )
  to break out of the conditional loop.

24
Writing Your Own Functions

• Imagine you need to write a simple function that
  returns both the mean and the standard deviation
  of a vector in a list structure.
• gt mean.and.sd lt- function(x)
• res.mean lt- mean(x) res.sd lt- sd(x)
• res list(meanres.mean, sdres.sd)
• return(res)
• gt mean.and.sd(rpois(10,5))
• mean
• 1 4.4
• sd
• 1 0.9660918
• You can use the args function to find out what
  arguments a function needs.
• gt args(mean.and.sd)
• 1 function (x)
• NULL

25
Inputting Data into R

• R has capabilities for reading in data files of
  many different formats.
• For simple ASCII text files we can use the
  read.table function.
• The arguments specified by read.table are
• gt args(read.table)
• 1 function (file, header FALSE, sep "",
• quote "\"'", dec ".", row.names,
  col.names,
• as.is FALSE, na.strings "NA", colClasses
  NA,
• nrows -1, skip 0, check.names TRUE,
• fill !blank.lines.skip, strip.white FALSE,
  
• blank.lines.skip TRUE, comment.char "",
• allowEscapes FALSE)
• Other read-in functions read.csv, scan, readLines

26
Outputting Data from R

• To output data to a simple table text file, we
  can use write.table.
• gt args(write.table)
• function (x, file "", append FALSE, quote
  TRUE,
• sep " ", eol "\n", na "NA", dec ".",
• row.names TRUE, col.names TRUE,
• qmethod c("escape", "double"))
• Other write functions write, cat.

27
Porting to Other Languages

• A port is a piece of software that provides a
  means to get one programming language to
  communicate with another.
• The Omega Project for Statistical Computing
• An umbrella project to link different programming
  languages seamlessly.
• Some packages available RSPython, RSPerl,
  RMatlab.
• (Plus a variety of others).
• Example RSPython
• To call Python from R load RSPython, call py
  commands using .Python(func, args1, args2, )
• To call R from Python load RS module,
  RS.call("plot", x, y).

28
Workspace Management

• Where am I?
• gt getwd() returns the working directory
• gt setwd("C//Jess") sets the working directory
• gt dir() lists files in working directory
• gt list.files()
• How can I tell what objects I have?
• gt ls()
• To remove individual objects use rm()
• gt rm("name.of.object")
• To save specific objects use save()
• gt save(x, file"fileName.Rdata")
• At a later date, you can load this into your
  workspace
• gt load("fileName.RData")

29
Libraries

• Libraries are a collection of R functions that
  together perform a specialized analysis or task.
• For example genetics package.
• CRAN Description
• Classes and methods for handling genetic data.
  Includes classes to represent genotypes and
  haplotypes at single markers up to multiple
  markers on multiple chromosomes. Function include
  allele frequencies, flagging homo/heterozygotes,
  flagging carriers of certain alleles, estimating
  and testing for Hardy-Weinberg disequilibrium,
  estimating and testing for linkage
  disequilibrium, ...
• Consult CRAN for more http//cran.us.r-project.or
  g/

30
Helpful Functions

• To boot up HTML help files
• gt help.start()
• To pop up a help file on an individual function.
• gt help(function)
• To seach for help on something around a topic or
  function
• gt help.search("plot")
• To search on a string for something
• gt apropos("string")

31
More Info Resources

• For R tutorials and simple documents to learn
  more about R, consult the R website for lots of
  resources www.r-project.org/
• (go to Documentation gt Other gt Contributed
  Documentation
• Really Great HTML Tutorial Kickstarting R by Jim
  Lemon
• http//cran.r-project.org/doc/contrib/Lemon-kicks
  tart/index.html
• "R for Beginners" by Emmanuel Paradis short pdf
• There are also reference cards that contain the
  most important R functions (and their
  descriptions) you need to know (like a cheat
  sheet).
• "R Reference Card" by Jonathon Baron 1 page
  list