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R programming lab sample, Schemes and Mind Maps of Relational Database Management Systems (RDBMS)

Bharathiar UniversityRelational Database Management Systems (RDBMS)

Prof. Rajkumar RAJKUMAR

R programming lab sample source code

Typology: Schemes and Mind Maps

2023/2024

Uploaded on 07/03/2025

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SRI RAMAKRISHNA MISSION VIDYALAYA
COLLEGE OF ARTS AND SCIENCE
[AUTONOMOUS]
Accredited by NAAC with A Grade
Coimbatore – 641 020
APR– 2025
DEPARTMENT OF COMPUTER APPLICATIONS
NAME :
REG. NO :
SEMESTER : IV
SUBJECT :NME: Data Science and Big data Analytics
SUBJECT CODE :20UCA4NM2
SRI RAMAKRISHNA MISSION VIDYALAYA
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Download R programming lab sample and more Schemes and Mind Maps Relational Database Management Systems (RDBMS) in PDF only on Docsity!

SRI RAMAKRISHNA MISSION VIDYALAYA

COLLEGE OF ARTS AND SCIENCE

[AUTONOMOUS]

Accredited by NAAC with A Grade

Coimbatore – 641 020

APR– 2025

DEPARTMENT OF COMPUTER APPLICATIONS

NAME :

REG. NO :

SEMESTER : IV

SUBJECT :NME: Data Science and Big data Analytics

SUBJECT CODE :20UCA4NM

SRI RAMAKRISHNA MISSION VIDYALAYA

COLLEGE OF ARTS AND SCIENCE

[AUTONOMOUS]

Accredited by NAAC with A Grade

Coimbatore – 641 020

DEPARTMENT OF COMPUTER APPLICATIONS

BONAFIDE CERTIFICATE

REGISTER NO:______________

This is to certify that it is a bonafide record work done by

_______________________ in “NME: Data Science and Big data AnalyticsLab”(20UCA4NM2) for the IV Semester during the Academic year

2021 - 2022. Submitted for the Semester Practical Examinations held on

______________.

Head of the Department Staff in Charge

Internal Examiner External Examiner

1. Adding two matrixes using array Aim To Perform a R Program on adding two matrixes using array Algorithm Step1: Start the Process Step2: Initialize two matrixes, and assign values in two different variables. Step3: print the addition of two matrixes as a result. Step 4: As in the same way of addition, we can subtract, multiply, and division process can be carried out. Step 5: print the result of subtracted, multiplied and divided values of two matrix. Step 6: Stop the process

R Program

  1. Create two 2x3 matrixes.

  2. m1 =matrix(c( 1 , 2 , 3 , 4 , 5 , 6 ),nrow= 2 )
  3. print("Matrix-1:")
  4. print(m1)
  5. m2 =matrix(c( 0 , 1 , 2 , 3 , 0 , 2 ),nrow= 2 )
  6. print("Matrix-2:")
  7. print(m2)
  8. result = m1 + m
  9. print("Result of addition")
  10. print(result)
  11. result = m1 - m
  12. print("Result of subtraction")
  13. print(result)
  14. result = m1 * m
  15. print("Result of multiplication")
  16. print(result)
  17. result = m1 / m
  18. print("Result of division:")
  19. print(result)

Output Screen

[1] "Matrix-1:"

[,1] [,2] [,3]

[1,] 1 3 5

[2,] 2 4 6

[1] "Matrix-2:"

[,1] [,2] [,3]

[1,] 0 2 0

[2,] 1 3 2

[1] "Result of addition"

[,1] [,2] [,3]

[1,] 1 5 5

2.Simple Calculator

Aim To Perform a R Program on arithmetic operations as a simple calculator. Algorithm Step1: Start the Process Step2: Initialize add, subtract, multiply and division function to perform the addition, subtraction, multiplication and division operations Step3: Getting a input of two numbers for the arithmetic operation as a integer value. Step4: Execute the program to perform the arithmetic operation and we can get the output. Step5: Stop the process

R Program

  1. add <- function(x, y) {
  2. return(x + y)
  3. }
  4. subtract <- function(x, y) {
  5. return(x - y)
  6. }
  7. multiply <- function(x, y) {
  8. return(x * y)
  9. }
  10. divide <- function(x, y) {
  11. return(x / y)
  12. }

  13. take input from the user

  14. print("Select operation.")
  15. print("1.Add")
  16. print("2.Subtract")
  17. print("3.Multiply")
  18. print("4.Divide")
  19. choice = as.integer(readline(prompt="Enter choice[1/2/3/4]: "))
  20. num1 = as.integer(readline(prompt="Enter first number: "))
  21. num2 = as.integer(readline(prompt="Enter second number: "))
  22. operator <- switch(choice,"+","-","*","/")
  23. result <- switch(choice, add(num1, num2), subtract(num1, num2), multiply(num1, num2), divide(num1, num2))
  24. print(paste(num1, operator, num2, "=", result))

3.Multiplication Table

Aim To Perform a R Program on print the Multiplication table from the given number. Algorithm Step1: Start the Process Step2: Initialize the variable, for print the multiplication table. Step3: To print the multiplication table, we can use the for-loop statement. Step4: Using with the for-loop statement increment the variable value. Step5: Print the given value and also print the given number multiplication table. Step6. Stop the Process.

R Program

R Program to find the multiplicationtable (from 1 to 10)

take input from the user

num = as.integer(readline(prompt = "Enter a number: "))

use for loop to iterate 10 times

for(i in 1:10) { print(paste(num,'x', i, '=', num*i)) }

Output:

Enter a number: 7 [1] "7 x 1 = 7" [1] "7 x 2 = 14" [1] "7 x 3 = 21" [1] "7 x 4 = 28" [1] "7 x 5 = 35" [1] "7 x 6 = 42" [1] "7 x 7 = 49" [1] "7 x 8 = 56" [1] "7 x 9 = 63" [1] "7 x 10 = 70" Result Thus, the program was successfully verified and Executed

R Program

x =c(10,20,30)

print("Sum:")

print(sum(x))

print("Mean:")

print(mean(x))

print("Product:")

print(prod(x))

Output:

[1] "Sum:"

[1] 60

[1] "Mean:"

[1] 20

[1] "Product:"

[1] 6000

Result Thus, the program was successfully verified and Executed

5. To Convert Decimal into Binary using Recursive Aim To Perform a R Program on Decimal into Binary using Recursive Algorithm STEP 1: Call function convert_to_binary() STEP 2: Pass the decimal number which needs to be converted to binary as decnum to the function. STEP 3: inside the function check if the given number is greater than 1, if yes call function convert_to_binary() again with decnum/2 as the argument STEP 4: divide the number decnum successively by 2 and print the remainder

6. K-Means clustering technique.

Aim To Perform a R Program on K-means clustering technique. Algorithm Step 1: Installing the relevant packages and calling their libraries Step 2: Loading and making sense of the dataset. Step 3: Eliminating the target variable Step 4: The elbow point technique Step 5: Implementing K-means Step 6: Plotting our data-points in clusters Step 7: Kmeans with K = 3 Step 8: Plotting the new clustered graph

R Program

Installing Packages

install.packages("ClusterR") install.packages("cluster")

Loading package

library(ClusterR) library(cluster)

Removing initial label of

Species from original dataset

iris_1 < - iris[, - 5 ]

Fitting K-Means clustering Model

to training dataset

set.seed( 240 ) # Setting seed kmeans.re < - kmeans(iris_1, centers = 3 , nstart = 20 ) kmeans.re

Cluster identification for

each observation

kmeans.re$cluster

Confusion Matrix

cm < - table(iris$Species, kmeans.re$cluster) cm

Model Evaluation and visualization

plot(iris_1[c("Sepal.Length", "Sepal.Width")]) plot(iris_1[c("Sepal.Length", "Sepal.Width")], col = kmeans.re$cluster) plot(iris_1[c("Sepal.Length", "Sepal.Width")], col = kmeans.re$cluster, main = "K-means with 3 clusters")

Plotiing cluster centers

kmeans.re$centers kmeans.re$centers[, c("Sepal.Length", "Sepal.Width")]

cex is font size, pch is symbol

points(kmeans.re$centers[, c("Sepal.Length", "Sepal.Width")], col = 1 : 3 , pch = 8 , cex = 3 )

Visualizing clusters

y_kmeans< - kmeans.re$cluster clusplot(iris_1[, c("Sepal.Length", "Sepal.Width")], y_kmeans, lines = 0 , shade = TRUE,

Output:

● Model kmeans_re:

The 3 clusters are made which are of 50, 62, and 38 sizes respectively.

Within the cluster, the sum of squares is 88.4%.

● Cluster identification:

● Confusion Matrix:

● K-means with 3 clusters plot:

● Plotting cluster centers:

● Plot of clusters:

Result Thus, the program was successfully verified and Executed