Introduction to Data Science With R Lab Record

INTRODUCTION TO DATA SCIENCE WITH R
INDEX
Sno Date Program Page Number
1 24/08/2021 Basic programs of R and Sample Programs on
Arithmetic Operations on vectors-I
1. Addition
2. Subtraction
3. Multiplication
4. Division
5. Modulous
1
2 31/08/2021 Basic programs of R and Sample Programs on
Arithmetic Operations on vectors-II
1. Floor Division
2. Exponent
2
3 07/09/2021 Operations on Matrix
1. Matrix Addition
2. Matrix Subtraction
3. Matrix Multiplication
4. Matrix Division
3-5
4 14/09/2021 Operations on Matrix
1. Row Concatenation
2. Column Concatenation
2. Deleting a Column
3. Updating a Row
6-8
5 21/09/2021 Graphs
1. Line Graph
2. Bell Curve
9-11
6 28/09/2021 Graphs
1. Bar plot
2. Pie Chart
12-13
7 29/09/2021 Loops in R
1. repeat
2. while
3. for
14-16
8 05/10/2021 Functions in R 17

1 | P a g e
Program 1:
Basic Operations of R and sample programs on Arithmetic Operations
Program:
var1=c(4,5)
var2=c(2,4)
print("addition of two vectors")
print(var1+var2)
print("subtraction of two vectors")
print(var1-var2)
print("multiplication of two vectors")
print(var1*var2)
print("division of two vectors")
print(var1/var2)
print("modulous of two vectors")
print(var1%%var2)
Output:
[1] "addition of two vectors"
[1] 6 9
[1] "subtraction of two vectors"
[1] 2 1
[1] "multiplication of two vectors"
[1] 8 20
[1] "division of two vectors"
[1] 2.00 1.25
[1] "modulous of two vectors"
[1] 0 1

2 | P a g e
Program 2:
Basic programs of R and Sample Programs on Arithmetic Operations on vectors-II
1. Floor Division
2. Exponent
Program:
print("Floor Division of two vectors")
print(var1%/%var2)
print("Exponent")
print(var1^var2)
Output
[1] "Floor Division of two vectors"
[1] 2 1
[1] "Exponent"
[1] 16 625

3 | P a g e
Program 3
Operation on Matrix: Addition
Program:
A=matrix(c(3,5,4,6,7,8,9,3,5),nrow=3, ncol=3, byrow=TRUE)
print("First 3x3 matrix")
print(A)
B=matrix(c(5,6,7,2,4,3,6,9,7),nrow=3, ncol=3, byrow=TRUE)
print("Second 3x3 matrix")
print(B)
print("result of addition of two matrices")
print(A+B)
Output:
[1] "3x3 matrix"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[1] "3x3 matrix"
[,1] [,2] [,3]
[1,] 5 6 7
[2,] 2 4 3
[3,] 6 9 7
[1] "result of addition"
[,1] [,2] [,3]
[1,] 8 11 11
[2,] 8 11 11
[3,] 15 12 12

4 | P a g e
Subtraction of Two matrices
print("result of subtraction of two matrices")
print(A-B)
Output:
[1] "First 3x3 matrix"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[1] "Second 3x3 matrix"
[,1] [,2] [,3]
[1,] 5 6 7
[2,] 2 4 3
[3,] 6 9 7
[1] "result of subtraction of two matrices"
[,1] [,2] [,3]
[1,] -2 -1 -3
[2,] 4 3 5
[3,] 3 -6 -2
Multiplication of Two matrices
print("result of multiplication of two matrices")
print(A*B)
Output:
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5

5 | P a g e
[,1] [,2] [,3]
[1,] 5 6 7
[2,] 2 4 3
[3,] 6 9 7
[1] "result of multiplication of two matrices"
[,1] [,2] [,3]
[1,] 15 30 28
[2,] 12 28 24
[3,] 54 27 35
Division of Two matrices
print("result of division of two matrices")
print(A/B)
Output:
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[,1] [,2] [,3]
[1,] 5 6 7
[2,] 2 4 3
[3,] 6 9 7
[1] "result of division of two matrices"
[,1] [,2] [,3]
[1,] 0.6 0.8333333 0.5714286
[2,] 3.0 1.7500000 2.6666667
[3,] 1.5 0.3333333 0.7142857

6 | P a g e
Program 4:
Row Concatenation of two matrices
print("result of row concatenation of two matrices")
print(rbind(A,B))
Output:
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[,1] [,2] [,3]
[1,] 5 6 7
[2,] 2 4 3
[3,] 6 9 7
[1] "result of row concatenation of two matrices"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[4,] 5 6 7
[5,] 2 4 3
[6,] 6 9 7

7 | P a g e
Deleting Second Column
print("Before deleting Second column")
print(A)
A=A[,-2]
print("After deleting Second column")
print(A)
Output:
[1] "Before deleting Second column"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[1] "After deleting Second column"
[,1] [,2]
[1,] 3 4
[2,] 6 8
[3,] 9 5
Deleting Second Row
print("Before deleting Second Row")
print(A)
A=A[-2,]
print("After deleting Second Row")
print(A)
Output:
[1] "Before deleting Second Row"
[,1] [,2] [,3]
[1,] 3 5 4

8 | P a g e
[2,] 6 7 8
[3,] 9 3 5
[1] "After deleting Second Row"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 9 3 5
Updating Second Row
print("Before updating Second Row")
print(A)
A[2,]=c(11,12,15)
print("After updating Second Row")
print(A)
Output:
[1] "Before updating Second Row"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 6 7 8
[3,] 9 3 5
[1] "After updating Second Row"
[,1] [,2] [,3]
[1,] 3 5 4
[2,] 11 12 15
[3,] 9 3 5

9 | P a g e
Program 5:
Line Graph
A line chart is a graph that connects a series of points by drawing line segments between
them. These points are ordered in one of their coordinate (usually the x-coordinate) value.
Line charts are usually used in identifying the trends in data.
The plot() function in R is used to create the line graph.
Syntax
The basic syntax to create a line chart in R is –
plot(v,type,col,xlab,ylab)
Following is the description of the parameters used −
• v is a vector containing the numeric values.
• type takes the value "p" to draw only the points, "l" to draw only the lines and "o" to
draw both points and lines.
• xlab is the label for x axis.
• ylab is the label for y axis.
• main is the Title of the chart.
• col is used to give colors to both the points and lines.
Program:
marks=c(15,22,35,55,45,65)
plot(marks, type="l", col="Blue")
Output:
Program2:

10 | P a g e
marks=c(15,22,35,55,45,65)
plot(marks,type = "o", col = "red", xlab = "Roll Number", ylab = "Marks in Statistics",
main = "Marks Obtained by Data Science Students")
Output

11 | P a g e
Bell Curves
1. First we generate normal distributed data using rnorm function
Syntax of rnorm function in R:
rnorm(n, mean, sd)
n: It is the number of observations(sample size).
mean: It is the mean value of the sample data. Its default value is zero.
sd: It is the standard deviation. Its default value is 1.
Program
n=floor(rnorm(10000,500,100))
t=table(n)
plot(t)
Output:

12 | P a g e
Program 6:
Bar Chart
Program:
marks=c(92,50,45,73)
barplot(marks, main="Comparing marks of 5 subjects", xlab="marks", ylab="subjects",
names.arg = c("eng","comp","math"," r program"), col="blue",horiz=FALSE)
Output:

13 | P a g e
Pie Chart
Syntax:
The basic syntax for creating a pie-chart using the R is −
pie(x, labels, radius, main, col, clockwise)
Following is the description of the parameters used −
x is a vector containing the numeric values used in the pie chart.
labels is used to give description to the slices.
radius indicates the radius of the circle of the pie chart.(value between −1 and +1).
main indicates the title of the chart.
col indicates the color palette.
clockwise is a logical value indicating if the slices are drawn clockwise or anti clockwise.
Program
vtr=c(43,31,64,40,29)
names=c("london","paris","los angeles", "mexico","new york")
pie(vtr,labels=names,main="Favourable Places", col= rainbow(length(vtr)))
Output:

14 | P a g e
Program 7:
There are three types of loop in R programming:
1. for
2. while
3. repeat
1. for loop
Syntax:
for (value in sequence)
{
statement
}
Flow Chart:
Program to print first five natural numbers:
for (i in 1:5)
{
# statement
print(i)
}
Output:
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
Program to display days of week using for loop
week = c('Sunday', 'Monday','Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday')
for (day in week)

15 | P a g e
{
print(day)
}
Output:
[1] "Sunday"
[1] "Monday"
[1] "Tuesday"
[1] "Wednesday"
[1] "Thursday"
[1] "Friday"
[1] "Saturday"
While loop
Syntax:
while( condition)
{
statement
}
Flow chart:
Program to calculate factorial of 5.
n=5
factorial = 1
i = 1
while (i <= n)
{
factorial = factorial * i
i = i + 1
}
print(factorial)
Output:
[1] 120

16 | P a g e
Repeat Loop
Repeat loop does not have any condition to terminate the loop, a programmer must
specifically place a condition within the loop’s body and use the declaration of a break
statement to terminate this loop. If no condition is present in the body of the repeat loop then
it will iterate infinitely.
Syntax:
repeat
{
statement
if( condition )
{
break
}
}
Flow chart:
Program:
Program to display numbers from 1 to 5 using repeat loop in R.
val = 1
repeat
{
print(val)
val = val + 1
if(val > 5)
{
break
}
}
Output:
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

17 | P a g e
Program 8: Functions in R
Function Components
The different parts of a function are −
1. Function Name − This is the actual name of the function. It is stored in R environment
as an object with this name.
2. Arguments − An argument is a placeholder. When a function is invoked, you pass a
value to the argument. Arguments are optional; that is, a function may contain no
arguments. Also arguments can have default values.
3. Function Body − The function body contains a collection of statements that defines
what the function does.
4. Return Value − The return value of a function is the last expression in the function
body to be evaluated.
R has many in-built functions which can be directly called in the program without defining
them first. We can also create and use our own functions referred as user defined functions.
Built-in Function
Simple examples of in-built functions are seq(), mean(), max(), sum(x) and paste(...) etc.
They are directly called by user written programs. You can refer most widely used R
functions.
# Create a sequence of numbers from 32 to 44.
print(seq(32,44))
# Find mean of numbers from 25 to 82.
print(mean(25:82))
# Find sum of numbers frm 41 to 68.
print(sum(41:68))
User-defined Function
We can create user-defined functions in R. They are specific to what a user wants and once
created they can be used like the built-in functions. Below is an example of how a function is
created and used.
Program to create a function to print squares of first n natural numbers
new = function(n)
{
for(i in 1:n)
{
b =i^2
print(b)
}
}
new(5)
Output:
[1] 1
[1] 4
[1] 9
[1] 16
[1] 25

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