Syllabus

CSE2018- Introduction to R Programming (LTPC-3045) ( B.Tech (CSE))

CAP4004-Introduction to R (LTPC-3024)  (MCA)

Course Content

 Unit 1    Introduction to R                                                                                                                     5 Lectures

Introduction, R installation and basic syntax, Data Types – Vectors, Lists, Matrices, Arrays, Factors, Data Frames, Variables – Variable assignment, Data Type of a variable, Finding variables, Deleting variables , Operators in R, Creating and manipulating objects, Importing/Exporting data, Data Distribution, Data manipulation and extracting components, Data Shaping and Transformation

Unit – II              Programming Structures and Functions                                                                  6 Lectures

R Control Structures – if, if - else, if – else if – else, switch statement, Loop Statements – for, while, repeat, Loop Control Statements - break, next, Functions – Definition, Components, Built – in Function, User – defined Function, Calling a Function, Lazy Evaluation of Function Arguments, Default Values for Argument, Return Values, Functions are Objective, No Pointers in R, Recursion, A Quicksort Implementation-Extended Example: A Binary Search Tree

Unit – III             Math and Simulation in R                                                                                                  7 Lectures

Doing Math and Simulation in R, Math Function, Calculating Probability- Cumulative Sums and Products, Minima and Maxima, Functions for Statistical Distribution, Sorting, Linear Algebra Operations on Vectors and Matrices, Vector cross Product, Finding Stationary Distribution of Markov Chains, Set Operation, Input /output, Accessing the Keyboard and Monitor, Reading and writer Files

Unit – IV            Data Visualization                                                                                                                  6 Lectures

Introduction to R Graphics, Creating Graphs, The Workhorse of R Base Graphics, the plot () Function, Customizing Graphs, Saving Graphs to Files, ggplot2 package

Unit – V               Statistics in R                                                                                                                            8 Lectures

Probability Distributions, Normal Distribution, Binomial Distribution, Poisson Distribution, Basic Statistics, Correlation and Covariance, Hypothesis Testing, T-Tests, chi –square goodness of fit, Linear Model -  ANOVA, Simple Linear Regression, Multiple Regression, Logistic Regression, Poisson Regression, Generalized Linear Models-Survival Analysis, Nonlinear Models, Decision Tree, Random Forests

Text Books:

1. Jared P. Lander, “R for Everyone”, 2nd Edition

2. Garnett Grolemund, “ Hands – on Programming with R, 2014

References:

1. Norman Matloff, “The Art of R Programming”, 2011.

2. Paul Teetor, “R Cookbook”, Oreilly, 1st Edition.

3. Rob Kabacoff, “R in Action”, 2011.

4. Trevor Hastie, Robert Tibshirani, “Introduction to Statistical Learning”, 2013

Examination Scheme:

Mode of Evaluation

Quizzes, Assignments, Seminar/Presentation, Written Examinations

Examination Scheme

MSE  – Mid Semester Examination

ESE – End Semester Examination

For Lab Evaluation

 ESE- End Semester Examination