Syllabus (COMPUTER APPLICATION)

Course Type: ME-5

Semester: 5

Course Code: BBCAMEA35C

Course Title: Introduction to R Programming

(L-P-Tu): 2-2-0

Credit: 4

Practical/Theory: Combined

Course Objective: Course Objectives: • Learn basic fundamentals of R Programming. • Understand R Functions, R Packages, R Loops etc. • Understand basic Statistical analysis using R. • Understand the technique of Data Visualization in R.

Learning Outcome: Course Outcomes: After completion of this course, students will be able to: • Implement Statistical functions using R Programming Language. • Analysis data using R. • Manipulate and summarize data sets using R. • Create visualization with R.

Syllabus:

Unit I: Theory Credit: 2 (L 30)

Introduction: Introduction to R, Advantages, Handling Packages in R, R Console, R studio, R Data Structures (Vectors, Lists, Arrays, Data Frames, Factors), Basic Statistical Functions, R operators (Arithmetic, Relational, Logical, Assignment, Miscellaneous). [L5]

Decision Making, Loops, and Strings: If, if-else, if-else if, switch, repeat loop, while loop, for loop, break, next, Manipulating text in Data. [L5]

Function and Matrices: Function Definition, Built-in Function, User-defined Function, Calling a function with/ without an argument values, Matrix Computations (Addition, Subtraction, Multiplication, Division, Transpose), Matrix functions like dim(), ncol(), nrow() etc. [L5]

Data Handling: getwd(), setwd(), Import CSV File, Import Excel File, Reading a CSV File, Reading a Excel File, Writing into a CSV File, Writing into a Excel File, File Analyzing. [L5]

Data Modeling and Visualization: Performing Queries, RSQLDF package, Classification, Regression, Pie Charts, Histograms, Bar Charts. [L10]

Unit II: Practical Credit: 2 (L 60)

Programming Lab (Using R): Practical will be based upon the topics covered in the theory part.

Reading References:

1. Sandip Rakshit, R Programming for Beginners, McGraw Hill Education.
2. Seema Acharya, Data Analytics using R, McGrawHill Education.
3. Jared P. Lander, R for Everyone: Advanced Analytics and Graphics, Addison-Wesley.
4. Norman Matoff, The Art of R Programming: A Tour of Statistical Software Design, No Starch Press.