Programming to
School of Computer Engineering,
KIIT University
26.6.07

Overview
 Introduction to R
 Why use it?
 Setting up R Environment
 Data Types
 File Handling,
 Plotting and Graphic features
 Packages

What is ?
 “R is a freely available language and
environment for statistical computing and
graphics”
 Much like & , but bette !

What R is and what it is not
 R is
 a programming language
 a statistical package
 an interpreter
 Open Source
 R is not
 a database
 a collection of “black boxes”
 a spreadsheet software package
 commercially supported

Why use ?
 SPSS and Excel users are limited in their
ability to change their environment. The way
they approach a problem is constrained by
how Excel & SPSS were programmed to
approach it
 The users have to pay money to use the
software
 R users can rely on functions that have been
developed for them by statistical researchers
or create their own
 They don’t have to pay money to use them
 Once experienced enough they are almost
unlimited in their ability to change their
environment

Installing
 Go to R homepage:
http://www.r-project.org/
Choose a server
And just follow the installation instructions…

Getting started
 To obtain and install R on your computer
 Go to http://cran.r-project.org/mirrors.html to choose a
mirror near you
 Click on your favorite operating system (Linux, Mac,
or Windows)
 Download and install the “base”
 To install additional packages
 Start R on your computer
 Choose the appropriate item from the “Packages”
menu

Installing RStudio
 “RStudio is a new integrated development
environment (IDE) for R”
 Install the “desktop edition” from this link:
http://www.rstudio.org/download/

Using RStudio
Script
editor
View help,
plots & files;
manage
packages
View variables in
workspace and
history file
R console

Naming Convention
 must start with a letter (A-Z or a-z)
 can contain letters, digits (0-9), and/or periods “.”
 case-sensitive
mydata different from MyData
 do not use use underscore “_”
 To quit R, use >q()

Assignment
 “<-” used to indicate assignment
x<-c(1,2,3,4,5,6,7)
x<-c(1:7)
x<-1:4
 note: as of version 1.4 “=“ is also a valid
assignment operator

R as a calculator
> 5 + (6 + 7) * pi^2
[1] 133.3049
> log(exp(1))
[1] 1
> log(1000, 10)
[1] 3
> sin(pi/3)^2 + cos(pi/3)^2
[1] 1
> Sin(pi/3)^2 + cos(pi/3)^2
Error: couldn't find function "Sin"

R as a calculator
> log2(32)
[1] 5
> sqrt(2)
[1] 1.414214
> seq(0, 5, length=6)
[1] 0 1 2 3 4 5
> plot(sin(seq(0, 2*pi, length=100)))
0 20 40 60 80 100
-1.0
-0.5
0.0
0.5
1.0
Index
sin(seq(0,
2
*
pi,
length
=
100))

 A variable is a symbolic name
given to stored information
 Variables are assigned using
either ”=” or ”<-”
> x<-12.6
> x
[1] 12.6
Variables

Missing values
 R is designed to handle statistical data
and therefore predestined to deal with
missing values
 Numbers that are “not available”
> x <- c(1, 2, 3, NA)
> x + 3
[1] 4 5 6 NA
 “Not a number”
> log(c(0, 1, 2))
[1] -Inf 0.0000000 0.6931472
> 0/0
[1] NaN

Data Types
 Vectors{1,2,3,,,,}
 Lists { 1,”msg”,2.5, 3. “magi”}
 Matrices { 1 2
 3 4 }
 Arrays
 Factors
 Data Frames

Basic (atomic) data types
 Logical
> x <- T; y <- F
> x; y
[1] TRUE
[1] FALSE
 Numerical
> a <- 5; b <-
sqrt(2)
> a; b
[1] 5
[1] 1.414214
 Character
> a <- "1"; b <- 1
> a; b
[1] "1"
[1] 1
> a <- "character"
> b <- "a"; c <- a
> a; b; c
[1] "character"
[1] "a"
[1] "character"

R Program to Take Input From User
 readline() function to take input from the user (terminal).
 This function will return a single element character vector.
Example
my.name <- readline(prompt="Enter name: ")
my.age <- readline(prompt="Enter age: ")
# convert character into integer
my.age <- as.integer(my.age)
print(paste("Hi,", my.name, "next year you will be",
my.age+1, "years old."))
character vector into integer using the function as.integer().
prompt argument is printed in front of the user input. It
usually ends on ": ".

 A vector is a list of values. A numeric
vector is composed of numbers
 It may be created:
 Using the c() function (concatenate) :
x = c(3,7,9,11)
> x
[1] 3 7 9 11
 Using the rep(what,how_many_times) function
(replicate):
x = rep(10,3)
 Using the “:” operator, signifiying a series
of integers
x=4:15
Variables - Numeric Vectors

 Character strings are always double
quoted
 Vectors made of character strings:
> x=c("I","want","to","go","home")
> x
[1] "I" "want" "to" "go" "home"
 Using rep():
> rep("bye",2)
[1] "bye" "bye"
 Notice the difference using paste()
(1 element):
> paste("I","want","to","go","home")
[1] "I want to go home"
Variables - Character Vectors

 Logical; either FALSE or TRUE
 > 5>3
[1] TRUE
 > x=1:5
> x
[1] 1 2 3 4 5
> x<3
[1] TRUE TRUE FALSE FALSE FALSE
Variables - Boolean Vectors

 Our vector: x=c(100,101,102,103)
 [] are used to access elements in x
 Extract 2nd element in x
> x[2]
[1] 101
 Extract 3rd and 4th elements in x
> x[3:4] # or x[c(3,4)]
[1] 102 103
Manipulation of Vectors

 > x
[1] 100 101 102 103
 Add 1 to all elements in x:
> x+1
[1] 101 102 103 104
 Multiply all elements in x by 2:
> x*2
[1] 200 202 204 206
Manipulation of Vectors –
Cont.

Manipulation of Vectors –
Cont.
> x <- c(5.2, 1.7, 6.3)
> log(x)
[1] 1.6486586 0.5306283 1.8405496
> y <- 1:5
> z <- seq(1, 1.4, by = 0.1)
> y + z
[1] 2.0 3.1 4.2 5.3 6.4
> length(y)
[1] 5
> mean(y + z)
[1] 4.2

Mydata <- c(2,3.5,-0.2)
Vector c=“concatenate”)
Colors <- c("Red","Green","Red")
Character vector
x1 <- 25:30
> x1
[1] 25 26 27 28 29 30 Number sequences
> Colors[2]
[1] "Green" One element
> x1[3:5]
[1] 27 28 29 Various elements
Manipulation of Vectors –
Cont.

Manipulation of Vectors –
Cont.
Test on the elements
Extract the positive
elements
Remove elements
> Mydata
[1] 2 3.5 -0.2
> Mydata > 0
[1] TRUE TRUE FALSE
> Mydata[Mydata>0]
[1] 2 3.5
> Mydata[-c(1,3)]
[1] 3.5

More Operators
 Comparison operators:
Equal ==
Not equal !=
Less / greater than < / >
Less / greater than or equal <= /
>=
 Boolean (either FALSE or TRUE)
And &
Or |
Not !

 Our vector: x=100:150
 Elements of x higher than 145
> x[x>145]
[1] 146 147 148 149 150
 Elements of x higher than 135 and
lower than 140
> x[ x>135 & x<140 ]
[1] 136 137 138 139
Manipulation of Vectors –
Cont.

 Our vector:
> x=c("I","want","to","go","home")
 Elements of x that do not equal “want”:
> x[x != "want"]
[1] "I" "to" "go" "home"
 Elements of x that equal “want” and “home”:
> x[x %in% c("want","home")]
[1] "want" "home"
Manipulation of Vectors –
Cont.
Note: use “==” for 1 element and “%in%” for several elements

Bar plot
marks = c(70, 95, 80, 74)
barplot(marks, main = "Comparing marks of 5
subjects",
xlab = "Marks",
ylab = "Subject",
names.arg = c("English", "Science", "Math.", "Hist."),
col = "darkred", horiz = FALSE)

1. Write a R program to take input from the user
(name and age) and display the values.
2. Write an R-script to initialize your rollno., name
and branch then display all the details.
3. Write an R-script to initialize two variables, then
find out the sum, multiplication, subtraction and
division of them.
4. Write an R-script to enter a 3-digits number
from the keyboard, then find out sum of all the
3-digits.
5. Write an R-script to enter the radius of a circle,
then calculate the area and circumference of
the circle.

6. Write a R program to create a sequence of numbers
from 20 to 50 and find the mean of numbers from 20 to
60 and sum of numbers from 51 to 91.
7. Write a R program to create a vector which contains 10
random integer values between -50 and +50.
8. Write a R program to find the maximum and the
minimum value of a given vector
9. Write a R program to create three vectors numeric data,
character data and logical data. Display the content of
the vectors and their type.
10. Write a R program to compute sum, mean and product
of a given vector elements.

41
Matrices
Matrix: A two dimensional rectangular
data set. It can be created using a
vector input to a matrix function.
The basic syntax for creating a matrix in R is:
matrix(data, nrow, ncol, byrow, dimnames)
C= { 1,2,3,4,5,6,7,8,9,10} matrix of 5X2 , byrow
M1= Matrix (C, 5,2,byrow) M2 =matrix (C,5,2,bycol)
data is the input vector which becomes the data elements of the matrix.
 nrow is the number of rows to be created.
 ncol is the number of columns to be created.
 byrow is a logical clue. If TRUE then the input vector elements are
arranged by row.
 dimname is the names assigned to the rows and columns.

42
Matrices
# generates 5 x 4 numeric matrix
y<-matrix(1:20, nrow=5,ncol=4)
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20

43
Matrices
# another example
cells <- c(1,26,24,68)
rnames <- c("R1", "R2")
cnames <- c("C1", "C2")
mymatrix <- matrix(cells, nrow=2,
ncol=2, byrow=TRUE,
dimnames=list(rnames, cnames))
C1 C2
R1 1 26
R2 24 68

Matrices Operations
> x <- c(3,-1,2,0,-3,6)
> x.mat <- matrix(x,ncol=2) Matrix with 2 cols
> x.mat
[,1] [,2]
[1,] 3 0
[2,] -1 -3
[3,] 2 6
> x.mat <- matrix(x,ncol=2,byrow=T)By row creation
> x.mat
[,1] [,2]
[1,] 3 -1
[2,] 2 0
[3,] -3 6

Matrices Operations
> x.mat[,2] 2nd col
[1] -1 0 6
> x.mat[c(1,3),] 1st and 3rd lines
[,1] [,2]
[1,] 3 -1
[2,] -3 6
> x.mat[-2,] No 2nd line
[,1] [,2]
[1,] 3 -1
[2,] -3 6

Matrices Operations
> dim(x.mat) Dimension
[1] 3 2
> t(x.mat) Transpose
[,1] [,2] [,3]
[1,] 3 2 -3
[2,] -1 0 6
> x.mat %*% t(x.mat) Multiplication
[,1] [,2] [,3]
[1,] 10 6 -15
[2,] 6 4 -6
[3,] -15 -6 45
> solve() Inverse of a square matrix
> eigen() Eigenvectors and eigenvalues

Matrices Operations
> m <- matrix(1:12, 4, byrow = T); m
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
[4,] 10 11 12
> y <- -1:2
> m.new <- m + y
> t(m.new)
[,1] [,2] [,3] [,4]
[1,] 0 4 8 12
[2,] 1 5 9 13
[3,] 2 6 10 14
> dim(m)
[1] 4 3
> dim(t(m.new))
[1] 3 4

 A matrix is a table of a different class
 Each column must be of the same class
(e.g. numeric, character, etc.)
 The number of elements in each
row must be identical
Variables – Matrices
 Accessing elements in
matrices:
x[row,column]
The ‘Height’ column:
> x[,”Height”] # or:
> x[,2]
Note: you cannot use “$”
> x$Weight

 Another way of creating a matrix is by using
functions cbind() and rbind() as in column bind and row
bind.
cbind(c(1,2,3),c(4,5,6))
[,1] [,2]
[1,] 1 4
[2,] 2 5
[3,] 3 6
rbind(c(1,2,3),c(4,5,6))
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6

50
Useful Functions
 length(object) # number of elements or components
 str(object) # structure of an object
 class(object) # class or type of an object
 names(object) # names
 c(object,object,...) # combine objects into a
vector
 cbind(object, object, ...) # combine objects as
columns
 rbind(object, object, ...) # combine objects as rows
 ls() # list current objects
 rm(object) # delete an object
 newobject <- edit(object) # edit copy and save a
newobject
 fix(object) # edit in place

6. Create a matrix taking a vector of numbers as input:
c(3:14), nrows = 4
Perform the following operations:
i) Elements are arranged sequentially by row.
ii) Elements are arranged sequentially by column.
iii) Define the column and row names.
iv) Access the element at 3rd column and 1st row.
v) Access the element at 2nd column and 4th row.
vi) Access only the 2nd row.
vii) Access only the 3rd column.

7. Create two 2x3 matrices.
 Perform the following operations:
Add the matrices.
Subtract the matrices
Multiply the matrices.
Divide the matrices

Lists
vector: an ordered collection of data of the same
type.
> a = c(7,5,1)
> a[2]
[1] 5
list: an ordered collection of data of arbitrary
types.
> doe = list(name="john",age=28,married=F)
> doe$name
[1] "john“
> doe$age
[1] 28
Typically, vector elements are accessed by their index
(an integer), list elements by their name (a character
string). But both types support both access methods.

Lists 1
 A list is an object consisting of objects
called components.
 The components of a list don’t need to be
of the same mode or type and they can be a
numeric vector, a logical value and a
function and so on.
 A component of a list can be referred as
aa[[I]] or aa$times, where aa is the name
of the list and times is a name of a
component of aa.

Lists 2
 The names of components may be abbreviated
down to the minimum number of letters
needed to identify them uniquely.
 aa[[1]] is the first component of aa,
while aa[1] is the sublist consisting of
the first component of aa only.
 There are functions whose return value is
a List. We have seen some of them, eigen,
svd, …

Lists are very flexible
> my.list <- list(c(5,4,-1),c("X1","X2","X3"))
> my.list
[[1]]:
[1] 5 4 -1
[[2]]:
[1] "X1" "X2" "X3"
> my.list[[1]]
[1] 5 4 -1
> my.list <- list(c1=c(5,4,-1),c2=c("X1","X2","X3"))
> my.list$c2[2:3]
[1] "X2" "X3"

Lists: Session
Empl <- list(employee=“Anna”, spouse=“Fred”,
children=3, child.ages=c(4,7,9))
Empl[[4]]
Empl$child.a
Empl[4] # a sublist consisting of the 4th
component of Empl
names(Empl) <- letters[1:4]
Empl <- c(Empl, service=8)
unlist(Empl) # converts it to a vector.
Mixed types will be converted to character,
giving a character vector.

More lists
> x.mat
[,1] [,2]
[1,] 3 -1
[2,] 2 0
[3,] -3 6
> dimnames(x.mat) <- list(c("L1","L2","L3"),
c("R1","R2"))
> x.mat
R1 R2
L1 3 -1
L2 2 0
L3 -3 6